From c0a3394f808e313b1b8e8f90d144cc6ed69fd7f2 Mon Sep 17 00:00:00 2001
From: mudabbir-ahmad <mudabbira.uk@gmail.com>
Date: Sun, 26 Apr 2026 15:57:09 +0100
Subject: [PATCH] Refined Answers!

---
 Q1.ipynb | 2506 +++++++++++++++++++++++++++---------------------------
 Q2.ipynb | 1849 ++++++++++++++++++++--------------------
 2 files changed, 2161 insertions(+), 2194 deletions(-)

diff --git a/Q1.ipynb b/Q1.ipynb
index 87222d1..f9127f1 100644
--- a/Q1.ipynb
+++ b/Q1.ipynb
@@ -12,57 +12,52 @@
    "source": [
     "## This is the Q1 Notebook!\n",
     "\n",
-    "It's tracked via GitHub! hence the need for this line for the init commit"
+    "It's tracked via GitHub! hence the need for this line for the init commit\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
    "id": "edaea0c939a83b79",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:27.822717300Z",
-     "start_time": "2026-04-26T14:22:27.814871100Z"
+     "end_time": "2026-04-26T14:56:42.154757400Z",
+     "start_time": "2026-04-26T14:56:42.149519800Z"
     }
    },
-   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 71
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
    "id": "15479f82",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:27.850316700Z",
-     "start_time": "2026-04-26T14:22:27.825227800Z"
+     "end_time": "2026-04-26T14:56:42.183724400Z",
+     "start_time": "2026-04-26T14:56:42.158794100Z"
     }
    },
+   "source": "seed = 101",
    "outputs": [],
-   "source": [
-    "# Keep one fixed seed so the same split/results appear each run.\n",
-    "seed = 101"
-   ]
+   "execution_count": 72
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
    "id": "e657e9baacc13e6b",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:27.897899900Z",
-     "start_time": "2026-04-26T14:22:27.870320Z"
+     "end_time": "2026-04-26T14:56:42.211091Z",
+     "start_time": "2026-04-26T14:56:42.184723400Z"
     }
    },
+   "source": "df = pd.read_csv('data/googleplaystore_new.csv')",
    "outputs": [],
-   "source": [
-    "df = pd.read_csv('data/googleplaystore_new.csv')"
-   ]
+   "execution_count": 73
   },
   {
    "cell_type": "markdown",
@@ -74,38 +69,63 @@
     "\n",
     "Q: Check for any missing values. If any, delete all rows that contain null values Then,\n",
     "check for duplicates (similar contents for every feature). If any, delete the\n",
-    "redundant rows. **[1 mark]**"
+    "redundant rows. **[1 mark]**\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
    "id": "756c92821453bbb3",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:27.920788500Z",
-     "start_time": "2026-04-26T14:22:27.898898200Z"
+     "end_time": "2026-04-26T14:56:42.260879500Z",
+     "start_time": "2026-04-26T14:56:42.233110900Z"
     }
    },
-   "outputs": [],
    "source": [
     "df = df.dropna()\n",
     "df = df.drop_duplicates()"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 74
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
    "id": "4e1be303d63f47a4",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:27.935089700Z",
-     "start_time": "2026-04-26T14:22:27.921790600Z"
+     "end_time": "2026-04-26T14:56:42.312882900Z",
+     "start_time": "2026-04-26T14:56:42.262878700Z"
     }
    },
+   "source": "df.head(10)",
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "                              App            Category  Rating  Reviews  Size  \\\n",
+       "0            Market Update Helper  LIBRARIES_AND_DEMO     4.1    20145   11k   \n",
+       "1                    SuperLivePro            BUSINESS     4.3    46353   21M   \n",
+       "2            Wifi Connect Library  LIBRARIES_AND_DEMO     3.9    58055   41k   \n",
+       "3                   Apk Installer  LIBRARIES_AND_DEMO     3.8     7750  292k   \n",
+       "4          English speaking texts           EDUCATION     4.4     1619  3.0M   \n",
+       "5                    Eternal life  LIBRARIES_AND_DEMO     5.0       26  2.5M   \n",
+       "6  Dresses Ideas & Fashions +3000              BEAUTY     4.5      473  8.2M   \n",
+       "7                     GO Notifier       COMMUNICATION     4.2   124346  695k   \n",
+       "8                      Prosperity              EVENTS     5.0       16  2.3M   \n",
+       "9              NSE Mobile Trading             FINANCE     4.1    13868  1.4M   \n",
+       "\n",
+       "      Installs  Type Price Content Rating            Genres Android Ver  \n",
+       "0   1,000,000+  Free     0       Everyone  Libraries & Demo  1.5 and up  \n",
+       "1   1,000,000+  Free     0       Everyone          Business  1.5 and up  \n",
+       "2   5,000,000+  Free     0       Everyone  Libraries & Demo  1.5 and up  \n",
+       "3   1,000,000+  Free     0       Everyone  Libraries & Demo  1.6 and up  \n",
+       "4   1,000,000+  Free     0       Everyone         Education  1.6 and up  \n",
+       "5       1,000+  Free     0       Everyone  Libraries & Demo  1.6 and up  \n",
+       "6     100,000+  Free     0     Mature 17+            Beauty  1.6 and up  \n",
+       "7  10,000,000+  Free     0       Everyone     Communication  2.0 and up  \n",
+       "8         100+  Free     0       Everyone            Events  2.0 and up  \n",
+       "9   1,000,000+  Free     0       Everyone           Finance  2.1 and up  "
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -282,41 +302,14 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                              App            Category  Rating  Reviews  Size  \\\n",
-       "0            Market Update Helper  LIBRARIES_AND_DEMO     4.1    20145   11k   \n",
-       "1                    SuperLivePro            BUSINESS     4.3    46353   21M   \n",
-       "2            Wifi Connect Library  LIBRARIES_AND_DEMO     3.9    58055   41k   \n",
-       "3                   Apk Installer  LIBRARIES_AND_DEMO     3.8     7750  292k   \n",
-       "4          English speaking texts           EDUCATION     4.4     1619  3.0M   \n",
-       "5                    Eternal life  LIBRARIES_AND_DEMO     5.0       26  2.5M   \n",
-       "6  Dresses Ideas & Fashions +3000              BEAUTY     4.5      473  8.2M   \n",
-       "7                     GO Notifier       COMMUNICATION     4.2   124346  695k   \n",
-       "8                      Prosperity              EVENTS     5.0       16  2.3M   \n",
-       "9              NSE Mobile Trading             FINANCE     4.1    13868  1.4M   \n",
-       "\n",
-       "      Installs  Type Price Content Rating            Genres Android Ver  \n",
-       "0   1,000,000+  Free     0       Everyone  Libraries & Demo  1.5 and up  \n",
-       "1   1,000,000+  Free     0       Everyone          Business  1.5 and up  \n",
-       "2   5,000,000+  Free     0       Everyone  Libraries & Demo  1.5 and up  \n",
-       "3   1,000,000+  Free     0       Everyone  Libraries & Demo  1.6 and up  \n",
-       "4   1,000,000+  Free     0       Everyone         Education  1.6 and up  \n",
-       "5       1,000+  Free     0       Everyone  Libraries & Demo  1.6 and up  \n",
-       "6     100,000+  Free     0     Mature 17+            Beauty  1.6 and up  \n",
-       "7  10,000,000+  Free     0       Everyone     Communication  2.0 and up  \n",
-       "8         100+  Free     0       Everyone            Events  2.0 and up  \n",
-       "9   1,000,000+  Free     0       Everyone           Finance  2.1 and up  "
       ]
      },
-     "execution_count": 26,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "df.head(10)"
-   ]
+   "execution_count": 75
   },
   {
    "cell_type": "markdown",
@@ -336,17 +329,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
    "id": "c15cb7f9831e0f81",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:27.956595900Z",
-     "start_time": "2026-04-26T14:22:27.936085900Z"
+     "end_time": "2026-04-26T14:56:42.366041900Z",
+     "start_time": "2026-04-26T14:56:42.339395200Z"
     }
    },
-   "outputs": [],
    "source": [
-    "# Getting the column for size, checking if it's ending with an M or a k and converting the Mb to kb with 1024* and then again from kb to just b by another 1024*\n",
     "def parse_size(size_str):\n",
     "    if isinstance(size_str, str):\n",
     "        if size_str.endswith('M'):\n",
@@ -354,36 +344,49 @@
     "        elif size_str.endswith('k'):\n",
     "            return float(size_str[:-1]) * 1024\n",
     "    return size_str"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 76
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
    "id": "c76da70de24ddc72",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:27.981893900Z",
-     "start_time": "2026-04-26T14:22:27.957595200Z"
+     "end_time": "2026-04-26T14:56:42.393115600Z",
+     "start_time": "2026-04-26T14:56:42.368041500Z"
     }
    },
+   "source": "df['Size in bytes'] = df['Size'].apply(parse_size)",
    "outputs": [],
-   "source": [
-    "df['Size in bytes'] = df['Size'].apply(parse_size)"
-   ]
+   "execution_count": 77
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
    "id": "73784ad66975e81f",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.029674900Z",
-     "start_time": "2026-04-26T14:22:27.983890Z"
+     "end_time": "2026-04-26T14:56:42.470291300Z",
+     "start_time": "2026-04-26T14:56:42.417130200Z"
     }
    },
+   "source": "df[['Size', 'Size in bytes']].head(10)",
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   Size  Size in bytes\n",
+       "0   11k        11264.0\n",
+       "1   21M     22020096.0\n",
+       "2   41k        41984.0\n",
+       "3  292k       299008.0\n",
+       "4  3.0M      3145728.0\n",
+       "5  2.5M      2621440.0\n",
+       "6  8.2M      8598323.2\n",
+       "7  695k       711680.0\n",
+       "8  2.3M      2411724.8\n",
+       "9  1.4M      1468006.4"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -461,43 +464,40 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Size  Size in bytes\n",
-       "0   11k        11264.0\n",
-       "1   21M     22020096.0\n",
-       "2   41k        41984.0\n",
-       "3  292k       299008.0\n",
-       "4  3.0M      3145728.0\n",
-       "5  2.5M      2621440.0\n",
-       "6  8.2M      8598323.2\n",
-       "7  695k       711680.0\n",
-       "8  2.3M      2411724.8\n",
-       "9  1.4M      1468006.4"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "df[['Size', 'Size in bytes']].head(10)"
-   ]
+   "execution_count": 78
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
    "id": "f16b316e8c3e58c0",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.082713600Z",
-     "start_time": "2026-04-26T14:22:28.033674500Z"
+     "end_time": "2026-04-26T14:56:42.545931300Z",
+     "start_time": "2026-04-26T14:56:42.494316800Z"
     }
    },
+   "source": [
+    "conversion_check = pd.DataFrame({\n",
+    "    'Example Input': ['11k', '21M', '1.4M'],\n",
+    "    'Expected Bytes': [11 * 1024, 21 * 1024 * 1024, 1.4 * 1024 * 1024]\n",
+    "})\n",
+    "conversion_check"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "  Example Input  Expected Bytes\n",
+       "0           11k         11264.0\n",
+       "1           21M      22020096.0\n",
+       "2          1.4M       1468006.4"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -540,41 +540,65 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "  Example Input  Expected Bytes\n",
-       "0           11k         11264.0\n",
-       "1           21M      22020096.0\n",
-       "2          1.4M       1468006.4"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# Quick conversion check table (cleaner than multiple prints)\n",
-    "conversion_check = pd.DataFrame({\n",
-    "    'Example Input': ['11k', '21M', '1.4M'],\n",
-    "    'Expected Bytes': [11 * 1024, 21 * 1024 * 1024, 1.4 * 1024 * 1024]\n",
-    "})\n",
-    "conversion_check"
-   ]
+   "execution_count": 79
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
    "id": "3e1112ae100d010c",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.146681Z",
-     "start_time": "2026-04-26T14:22:28.083717300Z"
+     "end_time": "2026-04-26T14:56:42.625051500Z",
+     "start_time": "2026-04-26T14:56:42.571993400Z"
     }
    },
+   "source": "df.head(10)",
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "                              App            Category  Rating  Reviews  Size  \\\n",
+       "0            Market Update Helper  LIBRARIES_AND_DEMO     4.1    20145   11k   \n",
+       "1                    SuperLivePro            BUSINESS     4.3    46353   21M   \n",
+       "2            Wifi Connect Library  LIBRARIES_AND_DEMO     3.9    58055   41k   \n",
+       "3                   Apk Installer  LIBRARIES_AND_DEMO     3.8     7750  292k   \n",
+       "4          English speaking texts           EDUCATION     4.4     1619  3.0M   \n",
+       "5                    Eternal life  LIBRARIES_AND_DEMO     5.0       26  2.5M   \n",
+       "6  Dresses Ideas & Fashions +3000              BEAUTY     4.5      473  8.2M   \n",
+       "7                     GO Notifier       COMMUNICATION     4.2   124346  695k   \n",
+       "8                      Prosperity              EVENTS     5.0       16  2.3M   \n",
+       "9              NSE Mobile Trading             FINANCE     4.1    13868  1.4M   \n",
+       "\n",
+       "      Installs  Type Price Content Rating            Genres Android Ver  \\\n",
+       "0   1,000,000+  Free     0       Everyone  Libraries & Demo  1.5 and up   \n",
+       "1   1,000,000+  Free     0       Everyone          Business  1.5 and up   \n",
+       "2   5,000,000+  Free     0       Everyone  Libraries & Demo  1.5 and up   \n",
+       "3   1,000,000+  Free     0       Everyone  Libraries & Demo  1.6 and up   \n",
+       "4   1,000,000+  Free     0       Everyone         Education  1.6 and up   \n",
+       "5       1,000+  Free     0       Everyone  Libraries & Demo  1.6 and up   \n",
+       "6     100,000+  Free     0     Mature 17+            Beauty  1.6 and up   \n",
+       "7  10,000,000+  Free     0       Everyone     Communication  2.0 and up   \n",
+       "8         100+  Free     0       Everyone            Events  2.0 and up   \n",
+       "9   1,000,000+  Free     0       Everyone           Finance  2.1 and up   \n",
+       "\n",
+       "   Size in bytes  \n",
+       "0        11264.0  \n",
+       "1     22020096.0  \n",
+       "2        41984.0  \n",
+       "3       299008.0  \n",
+       "4      3145728.0  \n",
+       "5      2621440.0  \n",
+       "6      8598323.2  \n",
+       "7       711680.0  \n",
+       "8      2411724.8  \n",
+       "9      1468006.4  "
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -762,7 +786,58 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 80
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9883e69704d0a69e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T13:28:40.682532800Z",
+     "start_time": "2026-04-25T13:28:40.678521Z"
+    }
+   },
+   "source": [
+    "## Part C\n",
+    "\n",
+    "Q: Create a new column called ‘Numeric_installs' (numeric) and convert the entries\n",
+    "from ‘Installs’ column (remove ‘+’ and ‘,’; for example, “5,000,000+” becomes\n",
+    "“5000000” as an Integer). **[1 mark]**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "c8d4f46526918c20",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-26T14:56:42.648934900Z",
+     "start_time": "2026-04-26T14:56:42.626051300Z"
+    }
+   },
+   "source": "df['Numeric Installs'] = df['Installs'].str.replace('+', '').str.replace(',', '').astype(int)",
+   "outputs": [],
+   "execution_count": 81
+  },
+  {
+   "cell_type": "code",
+   "id": "cffd24a82d242352",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-26T14:56:42.742232400Z",
+     "start_time": "2026-04-26T14:56:42.676459300Z"
+    }
+   },
+   "source": "df.head(10)",
+   "outputs": [
+    {
+     "data": {
       "text/plain": [
        "                              App            Category  Rating  Reviews  Size  \\\n",
        "0            Market Update Helper  LIBRARIES_AND_DEMO     4.1    20145   11k   \n",
@@ -788,73 +863,18 @@
        "8         100+  Free     0       Everyone            Events  2.0 and up   \n",
        "9   1,000,000+  Free     0       Everyone           Finance  2.1 and up   \n",
        "\n",
-       "   Size in bytes  \n",
-       "0        11264.0  \n",
-       "1     22020096.0  \n",
-       "2        41984.0  \n",
-       "3       299008.0  \n",
-       "4      3145728.0  \n",
-       "5      2621440.0  \n",
-       "6      8598323.2  \n",
-       "7       711680.0  \n",
-       "8      2411724.8  \n",
-       "9      1468006.4  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9883e69704d0a69e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-25T13:28:40.682532800Z",
-     "start_time": "2026-04-25T13:28:40.678521Z"
-    }
-   },
-   "source": [
-    "## Part C\n",
-    "\n",
-    "Q: Create a new column called ‘Numeric_installs' (numeric) and convert the entries\n",
-    "from ‘Installs’ column (remove ‘+’ and ‘,’; for example, “5,000,000+” becomes\n",
-    "“5000000” as an Integer). **[1 mark]**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "c8d4f46526918c20",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.169093Z",
-     "start_time": "2026-04-26T14:22:28.148687700Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df['Numeric Installs'] = df['Installs'].str.replace('+', '').str.replace(',', '').astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "cffd24a82d242352",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.211996600Z",
-     "start_time": "2026-04-26T14:22:28.170093500Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
+       "   Size in bytes  Numeric Installs  \n",
+       "0        11264.0           1000000  \n",
+       "1     22020096.0           1000000  \n",
+       "2        41984.0           5000000  \n",
+       "3       299008.0           1000000  \n",
+       "4      3145728.0           1000000  \n",
+       "5      2621440.0              1000  \n",
+       "6      8598323.2            100000  \n",
+       "7       711680.0          10000000  \n",
+       "8      2411724.8               100  \n",
+       "9      1468006.4           1000000  "
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1053,53 +1073,14 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                              App            Category  Rating  Reviews  Size  \\\n",
-       "0            Market Update Helper  LIBRARIES_AND_DEMO     4.1    20145   11k   \n",
-       "1                    SuperLivePro            BUSINESS     4.3    46353   21M   \n",
-       "2            Wifi Connect Library  LIBRARIES_AND_DEMO     3.9    58055   41k   \n",
-       "3                   Apk Installer  LIBRARIES_AND_DEMO     3.8     7750  292k   \n",
-       "4          English speaking texts           EDUCATION     4.4     1619  3.0M   \n",
-       "5                    Eternal life  LIBRARIES_AND_DEMO     5.0       26  2.5M   \n",
-       "6  Dresses Ideas & Fashions +3000              BEAUTY     4.5      473  8.2M   \n",
-       "7                     GO Notifier       COMMUNICATION     4.2   124346  695k   \n",
-       "8                      Prosperity              EVENTS     5.0       16  2.3M   \n",
-       "9              NSE Mobile Trading             FINANCE     4.1    13868  1.4M   \n",
-       "\n",
-       "      Installs  Type Price Content Rating            Genres Android Ver  \\\n",
-       "0   1,000,000+  Free     0       Everyone  Libraries & Demo  1.5 and up   \n",
-       "1   1,000,000+  Free     0       Everyone          Business  1.5 and up   \n",
-       "2   5,000,000+  Free     0       Everyone  Libraries & Demo  1.5 and up   \n",
-       "3   1,000,000+  Free     0       Everyone  Libraries & Demo  1.6 and up   \n",
-       "4   1,000,000+  Free     0       Everyone         Education  1.6 and up   \n",
-       "5       1,000+  Free     0       Everyone  Libraries & Demo  1.6 and up   \n",
-       "6     100,000+  Free     0     Mature 17+            Beauty  1.6 and up   \n",
-       "7  10,000,000+  Free     0       Everyone     Communication  2.0 and up   \n",
-       "8         100+  Free     0       Everyone            Events  2.0 and up   \n",
-       "9   1,000,000+  Free     0       Everyone           Finance  2.1 and up   \n",
-       "\n",
-       "   Size in bytes  Numeric Installs  \n",
-       "0        11264.0           1000000  \n",
-       "1     22020096.0           1000000  \n",
-       "2        41984.0           5000000  \n",
-       "3       299008.0           1000000  \n",
-       "4      3145728.0           1000000  \n",
-       "5      2621440.0              1000  \n",
-       "6      8598323.2            100000  \n",
-       "7       711680.0          10000000  \n",
-       "8      2411724.8               100  \n",
-       "9      1468006.4           1000000  "
       ]
      },
-     "execution_count": 33,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "df.head(10)"
-   ]
+   "execution_count": 82
   },
   {
    "cell_type": "markdown",
@@ -1113,18 +1094,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
    "id": "5a99edb9f8b29b12",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.261943100Z",
-     "start_time": "2026-04-26T14:22:28.237508200Z"
+     "end_time": "2026-04-26T14:56:42.795273800Z",
+     "start_time": "2026-04-26T14:56:42.770743700Z"
     }
    },
+   "source": "df.to_csv('data/googleplaystore_new_new.csv', index=False)",
    "outputs": [],
-   "source": [
-    "df.to_csv('data/googleplaystore_new_new.csv', index=False)"
-   ]
+   "execution_count": 83
   },
   {
    "cell_type": "markdown",
@@ -1140,33 +1119,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
    "id": "8cc741d5b19eaa62",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.289446500Z",
-     "start_time": "2026-04-26T14:22:28.262987100Z"
+     "end_time": "2026-04-26T14:56:42.818024400Z",
+     "start_time": "2026-04-26T14:56:42.797275500Z"
     }
    },
-   "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
     "from sklearn.preprocessing import PolynomialFeatures\n",
     "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 84
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
    "id": "8e417dd93534ef5f",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.336984400Z",
-     "start_time": "2026-04-26T14:22:28.290448200Z"
+     "end_time": "2026-04-26T14:56:42.828041900Z",
+     "start_time": "2026-04-26T14:56:42.819640800Z"
     }
    },
-   "outputs": [],
    "source": [
     "def split_overview(train_rows, test_rows, val_rows=None):\n",
     "    rows = [\n",
@@ -1180,7 +1157,6 @@
     "    out['Pct_of_Total'] = (out['Rows'] / out['Rows'].sum() * 100).round(1)\n",
     "    return out\n",
     "\n",
-    "\n",
     "def evaluate_model(model, X_train, X_test, y_train, y_test, name):\n",
     "    model.fit(X_train, y_train)\n",
     "    y_pred = model.predict(X_test)\n",
@@ -1204,52 +1180,97 @@
     "    sheet['Abs_Error'] = sheet['Residual'].abs()\n",
     "    sheet['Model'] = model_name\n",
     "    return sheet.head(head_n)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 85
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
    "id": "c2a7db56bb223aa2",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.366685700Z",
-     "start_time": "2026-04-26T14:22:28.338982400Z"
+     "end_time": "2026-04-26T14:56:42.837439700Z",
+     "start_time": "2026-04-26T14:56:42.828041900Z"
     }
    },
+   "source": "df_new = pd.read_csv('data/googleplaystore_new_new.csv')",
    "outputs": [],
-   "source": [
-    "df_new = pd.read_csv('data/googleplaystore_new_new.csv')"
-   ]
+   "execution_count": 86
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
    "id": "70f60382c2cb1c26",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.406155500Z",
-     "start_time": "2026-04-26T14:22:28.367686300Z"
+     "end_time": "2026-04-26T14:56:42.844200800Z",
+     "start_time": "2026-04-26T14:56:42.838444600Z"
     }
    },
-   "outputs": [],
    "source": [
     "columns_to_keep = ['Category', 'Reviews', 'Content Rating', 'Size in bytes', 'Numeric Installs', 'Rating']\n",
     "df_min = df_new[columns_to_keep]"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 87
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
    "id": "720d514df1483929",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.430514500Z",
-     "start_time": "2026-04-26T14:22:28.406155500Z"
+     "end_time": "2026-04-26T14:56:42.855711Z",
+     "start_time": "2026-04-26T14:56:42.844200800Z"
     }
    },
+   "source": "df_min.head(20)",
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "               Category  Reviews Content Rating  Size in bytes  \\\n",
+       "0    LIBRARIES_AND_DEMO    20145       Everyone        11264.0   \n",
+       "1              BUSINESS    46353       Everyone     22020096.0   \n",
+       "2    LIBRARIES_AND_DEMO    58055       Everyone        41984.0   \n",
+       "3    LIBRARIES_AND_DEMO     7750       Everyone       299008.0   \n",
+       "4             EDUCATION     1619       Everyone      3145728.0   \n",
+       "5    LIBRARIES_AND_DEMO       26       Everyone      2621440.0   \n",
+       "6                BEAUTY      473     Mature 17+      8598323.2   \n",
+       "7         COMMUNICATION   124346       Everyone       711680.0   \n",
+       "8                EVENTS       16       Everyone      2411724.8   \n",
+       "9               FINANCE    13868       Everyone      1468006.4   \n",
+       "10        COMMUNICATION    32254       Everyone      5767168.0   \n",
+       "11        COMMUNICATION   125232       Everyone      2831155.2   \n",
+       "12            EDUCATION      430       Everyone       538624.0   \n",
+       "13            EDUCATION      275       Everyone      2411724.8   \n",
+       "14  BOOKS_AND_REFERENCE     1778     Mature 17+      5138022.4   \n",
+       "15             BUSINESS     2287       Everyone      1572864.0   \n",
+       "16   LIBRARIES_AND_DEMO   126862       Everyone       638976.0   \n",
+       "17        COMMUNICATION      255       Everyone      1677721.6   \n",
+       "18            LIFESTYLE      360       Everyone      4823449.6   \n",
+       "19            EDUCATION      656       Everyone       569344.0   \n",
+       "\n",
+       "    Numeric Installs  Rating  \n",
+       "0            1000000     4.1  \n",
+       "1            1000000     4.3  \n",
+       "2            5000000     3.9  \n",
+       "3            1000000     3.8  \n",
+       "4            1000000     4.4  \n",
+       "5               1000     5.0  \n",
+       "6             100000     4.5  \n",
+       "7           10000000     4.2  \n",
+       "8                100     5.0  \n",
+       "9            1000000     4.1  \n",
+       "10           1000000     4.4  \n",
+       "11          10000000     4.2  \n",
+       "12             10000     4.0  \n",
+       "13             50000     4.0  \n",
+       "14            500000     3.9  \n",
+       "15           1000000     4.4  \n",
+       "16          10000000     3.5  \n",
+       "17             10000     4.1  \n",
+       "18             10000     4.1  \n",
+       "19             10000     4.3  "
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1461,121 +1482,75 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "               Category  Reviews Content Rating  Size in bytes  \\\n",
-       "0    LIBRARIES_AND_DEMO    20145       Everyone        11264.0   \n",
-       "1              BUSINESS    46353       Everyone     22020096.0   \n",
-       "2    LIBRARIES_AND_DEMO    58055       Everyone        41984.0   \n",
-       "3    LIBRARIES_AND_DEMO     7750       Everyone       299008.0   \n",
-       "4             EDUCATION     1619       Everyone      3145728.0   \n",
-       "5    LIBRARIES_AND_DEMO       26       Everyone      2621440.0   \n",
-       "6                BEAUTY      473     Mature 17+      8598323.2   \n",
-       "7         COMMUNICATION   124346       Everyone       711680.0   \n",
-       "8                EVENTS       16       Everyone      2411724.8   \n",
-       "9               FINANCE    13868       Everyone      1468006.4   \n",
-       "10        COMMUNICATION    32254       Everyone      5767168.0   \n",
-       "11        COMMUNICATION   125232       Everyone      2831155.2   \n",
-       "12            EDUCATION      430       Everyone       538624.0   \n",
-       "13            EDUCATION      275       Everyone      2411724.8   \n",
-       "14  BOOKS_AND_REFERENCE     1778     Mature 17+      5138022.4   \n",
-       "15             BUSINESS     2287       Everyone      1572864.0   \n",
-       "16   LIBRARIES_AND_DEMO   126862       Everyone       638976.0   \n",
-       "17        COMMUNICATION      255       Everyone      1677721.6   \n",
-       "18            LIFESTYLE      360       Everyone      4823449.6   \n",
-       "19            EDUCATION      656       Everyone       569344.0   \n",
-       "\n",
-       "    Numeric Installs  Rating  \n",
-       "0            1000000     4.1  \n",
-       "1            1000000     4.3  \n",
-       "2            5000000     3.9  \n",
-       "3            1000000     3.8  \n",
-       "4            1000000     4.4  \n",
-       "5               1000     5.0  \n",
-       "6             100000     4.5  \n",
-       "7           10000000     4.2  \n",
-       "8                100     5.0  \n",
-       "9            1000000     4.1  \n",
-       "10           1000000     4.4  \n",
-       "11          10000000     4.2  \n",
-       "12             10000     4.0  \n",
-       "13             50000     4.0  \n",
-       "14            500000     3.9  \n",
-       "15           1000000     4.4  \n",
-       "16          10000000     3.5  \n",
-       "17             10000     4.1  \n",
-       "18             10000     4.1  \n",
-       "19             10000     4.3  "
       ]
      },
-     "execution_count": 39,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "df_min.head(20)"
-   ]
+   "execution_count": 88
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
    "id": "90953b3b6403afd6",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.453593Z",
-     "start_time": "2026-04-26T14:22:28.432121200Z"
+     "end_time": "2026-04-26T14:56:42.878241400Z",
+     "start_time": "2026-04-26T14:56:42.855711Z"
     }
    },
+   "source": "df_encoded = pd.get_dummies(df_min, columns=['Category', 'Content Rating'])",
    "outputs": [],
-   "source": [
-    "df_encoded = pd.get_dummies(df_min, columns=['Category', 'Content Rating'])"
-   ]
+   "execution_count": 89
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
    "id": "518e961a8b35c05e",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.478579800Z",
-     "start_time": "2026-04-26T14:22:28.454594500Z"
+     "end_time": "2026-04-26T14:56:42.884619Z",
+     "start_time": "2026-04-26T14:56:42.878241400Z"
     }
    },
-   "outputs": [],
    "source": [
     "X = df_encoded.drop('Rating', axis=1)\n",
     "y = df_encoded['Rating']"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 90
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
    "id": "882619704ae0dce2",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.503829100Z",
-     "start_time": "2026-04-26T14:22:28.479581600Z"
+     "end_time": "2026-04-26T14:56:42.891670400Z",
+     "start_time": "2026-04-26T14:56:42.886618500Z"
     }
    },
+   "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=seed)",
    "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=seed)"
-   ]
+   "execution_count": 91
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
    "id": "102cda28a2a7c9cb",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.553238100Z",
-     "start_time": "2026-04-26T14:22:28.504830200Z"
+     "end_time": "2026-04-26T14:56:42.902464100Z",
+     "start_time": "2026-04-26T14:56:42.893675300Z"
     }
    },
+   "source": "split_overview(len(X_train), len(X_test))",
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   Split  Rows  Pct_of_Total\n",
+       "0  Train   774          69.9\n",
+       "1   Test   333          30.1"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1616,84 +1591,62 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Split  Rows  Pct_of_Total\n",
-       "0  Train   774          69.9\n",
-       "1   Test   333          30.1"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "split_overview(len(X_train), len(X_test))"
-   ]
+   "execution_count": 92
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
    "id": "93cd24d4523d5fda",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.630516300Z",
-     "start_time": "2026-04-26T14:22:28.555750100Z"
+     "end_time": "2026-04-26T14:56:42.939730600Z",
+     "start_time": "2026-04-26T14:56:42.921972Z"
     }
    },
+   "source": "results = []",
    "outputs": [],
-   "source": [
-    "results = []"
-   ]
+   "execution_count": 93
   },
   {
    "cell_type": "markdown",
    "id": "e52a65f09530a141",
    "metadata": {},
    "source": [
-    "## Trying Linear Regression"
+    "## Trying Linear Regression\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
    "id": "db88942671bdf26a",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.758831400Z",
-     "start_time": "2026-04-26T14:22:28.631518600Z"
+     "end_time": "2026-04-26T14:56:42.950203600Z",
+     "start_time": "2026-04-26T14:56:42.940731Z"
     }
    },
-   "outputs": [],
    "source": [
     "lin_model = LinearRegression()\n",
     "lin_res, y_pred_lin = evaluate_model(lin_model, X_train, X_test, y_train, y_test, \"Linear Regression\")\n",
     "results.append(lin_res)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 94
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
    "id": "e9dbb4ba0c9432ed",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:28.971416600Z",
-     "start_time": "2026-04-26T14:22:28.759831600Z"
+     "end_time": "2026-04-26T14:56:43.020608300Z",
+     "start_time": "2026-04-26T14:56:42.951203100Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "residual_linear = y_test - y_pred_lin\n",
     "\n",
@@ -1705,32 +1658,34 @@
     "plt.ylabel(\"Residual (y - y_hat)\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "35edc2f60d805d30",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.122438300Z",
-     "start_time": "2026-04-26T14:22:28.972416400Z"
-    }
-   },
+   ],
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
+       "<Figure size 800x500 with 1 Axes>"
+      ],
+      "image/png": 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"
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
+   "execution_count": 95
+  },
+  {
+   "cell_type": "code",
+   "id": "35edc2f60d805d30",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-26T14:56:43.097741300Z",
+     "start_time": "2026-04-26T14:56:43.022608700Z"
+    }
+   },
    "source": [
-    "# Quick visual check: points close to the diagonal mean prediction ~= true value.\n",
     "plt.figure(figsize=(6, 6))\n",
     "plt.scatter(y_test, y_pred_lin, alpha=0.5)\n",
     "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')\n",
@@ -1739,73 +1694,41 @@
     "plt.ylabel(\"Predicted Rating\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 96
   },
   {
    "cell_type": "markdown",
    "id": "28a6926de3a1a2e7",
    "metadata": {},
    "source": [
-    "## Trying Polynomial Regression"
+    "## Trying Polynomial Regression\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
    "id": "b867289f4b8dd0ae",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.485413400Z",
-     "start_time": "2026-04-26T14:22:29.145792800Z"
+     "end_time": "2026-04-26T14:56:43.347227300Z",
+     "start_time": "2026-04-26T14:56:43.098741Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Selection</th>\n",
-       "      <th>Best degree</th>\n",
-       "      <th>RMSE</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Polynomial best degree by RMSE</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.415806</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                        Selection  Best degree      RMSE\n",
-       "0  Polynomial best degree by RMSE            2  0.415806"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "best_degree = None\n",
     "best_poly_rmse = float('inf')\n",
@@ -1840,47 +1763,85 @@
     "X_train_p = poly_converter.fit_transform(X_train)\n",
     "X_test_p = poly_converter.transform(X_test)\n",
     "y_pred_poly = best_y_pred_poly"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                        Selection  Best degree      RMSE\n",
+       "0  Polynomial best degree by RMSE            2  0.415806"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Selection</th>\n",
+       "      <th>Best degree</th>\n",
+       "      <th>RMSE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Polynomial best degree by RMSE</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.415806</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 97
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
    "id": "61b135564e36f71d",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.546506800Z",
-     "start_time": "2026-04-26T14:22:29.487412300Z"
+     "end_time": "2026-04-26T14:56:43.379249Z",
+     "start_time": "2026-04-26T14:56:43.348227Z"
     }
    },
-   "outputs": [],
    "source": [
     "poly_model = LinearRegression()\n",
     "poly_res, y_pred_poly = evaluate_model(poly_model, X_train_p, X_test_p, y_train, y_test, \"Polynomial Regression\")\n",
     "results.append(poly_res)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 98
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
    "id": "5358eeb45dc985",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.665949600Z",
-     "start_time": "2026-04-26T14:22:29.572021400Z"
+     "end_time": "2026-04-26T14:56:43.491703300Z",
+     "start_time": "2026-04-26T14:56:43.380250Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "plt.figure(figsize=(6, 6))\n",
     "plt.scatter(y_test, y_pred_poly, alpha=0.5)\n",
@@ -1890,7 +1851,23 @@
     "plt.ylabel(\"Predicted Rating\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 99
   },
   {
    "cell_type": "markdown",
@@ -1902,22 +1879,54 @@
     }
    },
    "source": [
-    "## Trying Ridge Regression"
+    "## Trying Ridge Regression\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
    "id": "f7b09be17154ad47",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.694126100Z",
-     "start_time": "2026-04-26T14:22:29.666949800Z"
+     "end_time": "2026-04-26T14:56:43.588296500Z",
+     "start_time": "2026-04-26T14:56:43.520726500Z"
     }
    },
+   "source": [
+    "best_ridge_alpha = None\n",
+    "best_ridge_rmse = float('inf')\n",
+    "best_ridge_model = None\n",
+    "best_y_pred_ridge = None\n",
+    "best_ridge_res = None\n",
+    "\n",
+    "for alpha in [0.1, 1.0, 10.0, 50.0]:\n",
+    "    ridge_model_tmp = Ridge(alpha=alpha, solver='lsqr', random_state=seed)\n",
+    "    ridge_res_tmp, y_pred_ridge_tmp = evaluate_model(ridge_model_tmp, X_train, X_test, y_train, y_test, f\"Ridge Regression (alpha={alpha})\")\n",
+    "\n",
+    "    if ridge_res_tmp['RMSE'] < best_ridge_rmse:\n",
+    "        best_ridge_rmse = ridge_res_tmp['RMSE']\n",
+    "        best_ridge_alpha = alpha\n",
+    "        best_ridge_model = ridge_model_tmp\n",
+    "        best_y_pred_ridge = y_pred_ridge_tmp\n",
+    "        best_ridge_res = ridge_res_tmp\n",
+    "\n",
+    "display(pd.DataFrame([{\n",
+    "    'Selection': 'Ridge best alpha by RMSE',\n",
+    "    'Best alpha': best_ridge_alpha,\n",
+    "    'RMSE': best_ridge_rmse\n",
+    "}]))\n",
+    "\n",
+    "ridge_model = best_ridge_model\n",
+    "ridge_res = best_ridge_res\n",
+    "y_pred_ridge = best_y_pred_ridge\n",
+    "results.append(ridge_res)"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "                  Selection  Best alpha      RMSE\n",
+       "0  Ridge best alpha by RMSE        50.0  0.416581"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1952,68 +1961,26 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                  Selection  Best alpha      RMSE\n",
-       "0  Ridge best alpha by RMSE        50.0  0.416581"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "best_ridge_alpha = None\n",
-    "best_ridge_rmse = float('inf')\n",
-    "best_ridge_model = None\n",
-    "best_y_pred_ridge = None\n",
-    "best_ridge_res = None\n",
-    "\n",
-    "for alpha in [0.1, 1.0, 10.0, 50.0]:\n",
-    "    ridge_model_tmp = Ridge(alpha=alpha, solver='lsqr', random_state=seed)\n",
-    "    ridge_res_tmp, y_pred_ridge_tmp = evaluate_model(ridge_model_tmp, X_train, X_test, y_train, y_test, f\"Ridge Regression (alpha={alpha})\")\n",
-    "\n",
-    "    if ridge_res_tmp['RMSE'] < best_ridge_rmse:\n",
-    "        best_ridge_rmse = ridge_res_tmp['RMSE']\n",
-    "        best_ridge_alpha = alpha\n",
-    "        best_ridge_model = ridge_model_tmp\n",
-    "        best_y_pred_ridge = y_pred_ridge_tmp\n",
-    "        best_ridge_res = ridge_res_tmp\n",
-    "\n",
-    "display(pd.DataFrame([{\n",
-    "    'Selection': 'Ridge best alpha by RMSE',\n",
-    "    'Best alpha': best_ridge_alpha,\n",
-    "    'RMSE': best_ridge_rmse\n",
-    "}]))\n",
-    "\n",
-    "ridge_model = best_ridge_model\n",
-    "ridge_res = best_ridge_res\n",
-    "y_pred_ridge = best_y_pred_ridge\n",
-    "results.append(ridge_res)"
-   ]
+   "execution_count": 100
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
    "id": "751e4cca3fc3b4bb",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.849994300Z",
-     "start_time": "2026-04-26T14:22:29.697126100Z"
+     "end_time": "2026-04-26T14:56:43.765152600Z",
+     "start_time": "2026-04-26T14:56:43.590296800Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "plt.figure(figsize=(6, 6))\n",
     "plt.scatter(y_test, y_pred_ridge, alpha=0.5)\n",
@@ -2023,7 +1990,23 @@
     "plt.ylabel(\"Predicted Rating\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 101
   },
   {
    "cell_type": "markdown",
@@ -2035,22 +2018,54 @@
     }
    },
    "source": [
-    "## Trying Lasso Regression"
+    "## Trying Lasso Regression\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
    "id": "11024fb6674354a3",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.913020Z",
-     "start_time": "2026-04-26T14:22:29.851504500Z"
+     "end_time": "2026-04-26T14:56:43.868079700Z",
+     "start_time": "2026-04-26T14:56:43.773660600Z"
     }
    },
+   "source": [
+    "best_lasso_alpha = None\n",
+    "best_lasso_rmse = float('inf')\n",
+    "best_lasso_model = None\n",
+    "best_y_pred_lasso = None\n",
+    "best_lasso_res = None\n",
+    "\n",
+    "for alpha in [1e-4, 5e-4, 1e-3, 5e-3, 1e-2]:\n",
+    "    lasso_model_tmp = Lasso(alpha=alpha, max_iter=20000, random_state=seed)\n",
+    "    lasso_res_tmp, y_pred_lasso_tmp = evaluate_model(lasso_model_tmp, X_train, X_test, y_train, y_test, f\"Lasso Regression (alpha={alpha})\")\n",
+    "\n",
+    "    if lasso_res_tmp['RMSE'] < best_lasso_rmse:\n",
+    "        best_lasso_rmse = lasso_res_tmp['RMSE']\n",
+    "        best_lasso_alpha = alpha\n",
+    "        best_lasso_model = lasso_model_tmp\n",
+    "        best_y_pred_lasso = y_pred_lasso_tmp\n",
+    "        best_lasso_res = lasso_res_tmp\n",
+    "\n",
+    "display(pd.DataFrame([{\n",
+    "    'Selection': 'Lasso best alpha by RMSE',\n",
+    "    'Best alpha': best_lasso_alpha,\n",
+    "    'RMSE': best_lasso_rmse\n",
+    "}]))\n",
+    "\n",
+    "lasso_model = best_lasso_model\n",
+    "lasso_res = best_lasso_res\n",
+    "y_pred_lasso = best_y_pred_lasso\n",
+    "results.append(lasso_res)"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "                  Selection  Best alpha      RMSE\n",
+       "0  Lasso best alpha by RMSE      0.0005  0.386607"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -2085,68 +2100,26 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                  Selection  Best alpha      RMSE\n",
-       "0  Lasso best alpha by RMSE      0.0005  0.386607"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "best_lasso_alpha = None\n",
-    "best_lasso_rmse = float('inf')\n",
-    "best_lasso_model = None\n",
-    "best_y_pred_lasso = None\n",
-    "best_lasso_res = None\n",
-    "\n",
-    "for alpha in [1e-4, 5e-4, 1e-3, 5e-3, 1e-2]:\n",
-    "    lasso_model_tmp = Lasso(alpha=alpha, max_iter=20000, random_state=seed)\n",
-    "    lasso_res_tmp, y_pred_lasso_tmp = evaluate_model(lasso_model_tmp, X_train, X_test, y_train, y_test, f\"Lasso Regression (alpha={alpha})\")\n",
-    "\n",
-    "    if lasso_res_tmp['RMSE'] < best_lasso_rmse:\n",
-    "        best_lasso_rmse = lasso_res_tmp['RMSE']\n",
-    "        best_lasso_alpha = alpha\n",
-    "        best_lasso_model = lasso_model_tmp\n",
-    "        best_y_pred_lasso = y_pred_lasso_tmp\n",
-    "        best_lasso_res = lasso_res_tmp\n",
-    "\n",
-    "display(pd.DataFrame([{\n",
-    "    'Selection': 'Lasso best alpha by RMSE',\n",
-    "    'Best alpha': best_lasso_alpha,\n",
-    "    'RMSE': best_lasso_rmse\n",
-    "}]))\n",
-    "\n",
-    "lasso_model = best_lasso_model\n",
-    "lasso_res = best_lasso_res\n",
-    "y_pred_lasso = best_y_pred_lasso\n",
-    "results.append(lasso_res)"
-   ]
+   "execution_count": 102
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
    "id": "3f0658c719c2ecc6",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.047383500Z",
-     "start_time": "2026-04-26T14:22:29.914021400Z"
+     "end_time": "2026-04-26T14:56:43.964901Z",
+     "start_time": "2026-04-26T14:56:43.868079700Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "plt.figure(figsize=(6, 6))\n",
     "plt.scatter(y_test, y_pred_lasso, alpha=0.5)\n",
@@ -2156,21 +2129,47 @@
     "plt.ylabel(\"Predicted Rating\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "b6d9827d298e4fa6",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.087595400Z",
-     "start_time": "2026-04-26T14:22:30.048388300Z"
-    }
-   },
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 103
+  },
+  {
+   "cell_type": "code",
+   "id": "b6d9827d298e4fa6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-26T14:56:43.989302200Z",
+     "start_time": "2026-04-26T14:56:43.965900700Z"
+    }
+   },
+   "source": [
+    "results_df = pd.DataFrame(results)\n",
+    "results_df"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                             Model       MAE       MSE      RMSE        R2\n",
+       "0                Linear Regression  0.280613  0.149546  0.386712  0.146514\n",
+       "1            Polynomial Regression  0.295032  0.172895  0.415806  0.013258\n",
+       "2    Ridge Regression (alpha=50.0)  0.302947  0.173539  0.416581  0.009580\n",
+       "3  Lasso Regression (alpha=0.0005)  0.280977  0.149465  0.386607  0.146976"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -2233,38 +2232,69 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                             Model       MAE       MSE      RMSE        R2\n",
-       "0                Linear Regression  0.280613  0.149546  0.386712  0.146514\n",
-       "1            Polynomial Regression  0.295032  0.172895  0.415806  0.013258\n",
-       "2    Ridge Regression (alpha=50.0)  0.302947  0.173539  0.416581  0.009580\n",
-       "3  Lasso Regression (alpha=0.0005)  0.280977  0.149465  0.386607  0.146976"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "results_df = pd.DataFrame(results)\n",
-    "results_df"
-   ]
+   "execution_count": 104
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
    "id": "aa66d8d2df993766",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.135775600Z",
-     "start_time": "2026-04-26T14:22:30.088593300Z"
+     "end_time": "2026-04-26T14:56:44.022349500Z",
+     "start_time": "2026-04-26T14:56:43.990308500Z"
     }
    },
+   "source": [
+    "pred_sheet_part_e_linear = regression_prediction_sheet(y_test, y_pred_lin, 'Linear Regression')\n",
+    "pred_sheet_part_e_poly = regression_prediction_sheet(y_test, y_pred_poly, 'Polynomial Regression')\n",
+    "pred_sheet_part_e_ridge = regression_prediction_sheet(y_test, y_pred_ridge, f'Ridge Regression (alpha={best_ridge_alpha})')\n",
+    "pred_sheet_part_e_lasso = regression_prediction_sheet(y_test, y_pred_lasso, f'Lasso Regression (alpha={best_lasso_alpha})')\n",
+    "\n",
+    "best_model_part_e = results_df.sort_values('RMSE').iloc[0]['Model']\n",
+    "part_e_pred_sheet = {\n",
+    "    'Linear Regression': pred_sheet_part_e_linear,\n",
+    "    'Polynomial Regression': pred_sheet_part_e_poly,\n",
+    "}.get(best_model_part_e, pred_sheet_part_e_linear)\n",
+    "\n",
+    "if 'Ridge Regression' in best_model_part_e:\n",
+    "    part_e_pred_sheet = pred_sheet_part_e_ridge\n",
+    "if 'Lasso Regression' in best_model_part_e:\n",
+    "    part_e_pred_sheet = pred_sheet_part_e_lasso\n",
+    "\n",
+    "part_e_pred_sheet"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "    True  Predicted  Residual  Abs_Error                            Model\n",
+       "0    4.1   4.306335 -0.206335   0.206335  Lasso Regression (alpha=0.0005)\n",
+       "1    4.1   4.287329 -0.187329   0.187329  Lasso Regression (alpha=0.0005)\n",
+       "2    4.4   4.297757  0.102243   0.102243  Lasso Regression (alpha=0.0005)\n",
+       "3    4.6   4.197993  0.402007   0.402007  Lasso Regression (alpha=0.0005)\n",
+       "4    4.5   4.371379  0.128621   0.128621  Lasso Regression (alpha=0.0005)\n",
+       "5    3.2   4.304839 -1.104839   1.104839  Lasso Regression (alpha=0.0005)\n",
+       "6    4.2   4.324286 -0.124286   0.124286  Lasso Regression (alpha=0.0005)\n",
+       "7    3.9   4.005045 -0.105045   0.105045  Lasso Regression (alpha=0.0005)\n",
+       "8    3.7   4.138157 -0.438157   0.438157  Lasso Regression (alpha=0.0005)\n",
+       "9    4.6   4.312731  0.287269   0.287269  Lasso Regression (alpha=0.0005)\n",
+       "10   4.6   4.468202  0.131798   0.131798  Lasso Regression (alpha=0.0005)\n",
+       "11   4.5   4.364965  0.135035   0.135035  Lasso Regression (alpha=0.0005)\n",
+       "12   4.6   4.443139  0.156861   0.156861  Lasso Regression (alpha=0.0005)\n",
+       "13   4.4   4.289412  0.110588   0.110588  Lasso Regression (alpha=0.0005)\n",
+       "14   4.0   4.302132 -0.302132   0.302132  Lasso Regression (alpha=0.0005)\n",
+       "15   4.3   4.373179 -0.073179   0.073179  Lasso Regression (alpha=0.0005)\n",
+       "16   4.4   4.172033  0.227967   0.227967  Lasso Regression (alpha=0.0005)\n",
+       "17   3.8   4.161517 -0.361517   0.361517  Lasso Regression (alpha=0.0005)\n",
+       "18   4.5   4.134825  0.365175   0.365175  Lasso Regression (alpha=0.0005)\n",
+       "19   4.2   4.279803 -0.079803   0.079803  Lasso Regression (alpha=0.0005)"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -2455,70 +2485,72 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "    True  Predicted  Residual  Abs_Error                            Model\n",
-       "0    4.1   4.306335 -0.206335   0.206335  Lasso Regression (alpha=0.0005)\n",
-       "1    4.1   4.287329 -0.187329   0.187329  Lasso Regression (alpha=0.0005)\n",
-       "2    4.4   4.297757  0.102243   0.102243  Lasso Regression (alpha=0.0005)\n",
-       "3    4.6   4.197993  0.402007   0.402007  Lasso Regression (alpha=0.0005)\n",
-       "4    4.5   4.371379  0.128621   0.128621  Lasso Regression (alpha=0.0005)\n",
-       "5    3.2   4.304839 -1.104839   1.104839  Lasso Regression (alpha=0.0005)\n",
-       "6    4.2   4.324286 -0.124286   0.124286  Lasso Regression (alpha=0.0005)\n",
-       "7    3.9   4.005045 -0.105045   0.105045  Lasso Regression (alpha=0.0005)\n",
-       "8    3.7   4.138157 -0.438157   0.438157  Lasso Regression (alpha=0.0005)\n",
-       "9    4.6   4.312731  0.287269   0.287269  Lasso Regression (alpha=0.0005)\n",
-       "10   4.6   4.468202  0.131798   0.131798  Lasso Regression (alpha=0.0005)\n",
-       "11   4.5   4.364965  0.135035   0.135035  Lasso Regression (alpha=0.0005)\n",
-       "12   4.6   4.443139  0.156861   0.156861  Lasso Regression (alpha=0.0005)\n",
-       "13   4.4   4.289412  0.110588   0.110588  Lasso Regression (alpha=0.0005)\n",
-       "14   4.0   4.302132 -0.302132   0.302132  Lasso Regression (alpha=0.0005)\n",
-       "15   4.3   4.373179 -0.073179   0.073179  Lasso Regression (alpha=0.0005)\n",
-       "16   4.4   4.172033  0.227967   0.227967  Lasso Regression (alpha=0.0005)\n",
-       "17   3.8   4.161517 -0.361517   0.361517  Lasso Regression (alpha=0.0005)\n",
-       "18   4.5   4.134825  0.365175   0.365175  Lasso Regression (alpha=0.0005)\n",
-       "19   4.2   4.279803 -0.079803   0.079803  Lasso Regression (alpha=0.0005)"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "pred_sheet_part_e_linear = regression_prediction_sheet(y_test, y_pred_lin, 'Linear Regression')\n",
-    "pred_sheet_part_e_poly = regression_prediction_sheet(y_test, y_pred_poly, 'Polynomial Regression')\n",
-    "pred_sheet_part_e_ridge = regression_prediction_sheet(y_test, y_pred_ridge, f'Ridge Regression (alpha={best_ridge_alpha})')\n",
-    "pred_sheet_part_e_lasso = regression_prediction_sheet(y_test, y_pred_lasso, f'Lasso Regression (alpha={best_lasso_alpha})')\n",
-    "\n",
-    "# Predicted output sheet (shown for best RMSE model)\n",
-    "best_model_part_e = results_df.sort_values('RMSE').iloc[0]['Model']\n",
-    "part_e_pred_sheet = {\n",
-    "    'Linear Regression': pred_sheet_part_e_linear,\n",
-    "    'Polynomial Regression': pred_sheet_part_e_poly,\n",
-    "}.get(best_model_part_e, pred_sheet_part_e_linear)\n",
-    "\n",
-    "if 'Ridge Regression' in best_model_part_e:\n",
-    "    part_e_pred_sheet = pred_sheet_part_e_ridge\n",
-    "if 'Lasso Regression' in best_model_part_e:\n",
-    "    part_e_pred_sheet = pred_sheet_part_e_lasso\n",
-    "\n",
-    "part_e_pred_sheet"
-   ]
+   "execution_count": 105
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
    "id": "7e02905489f7769d",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.173423200Z",
-     "start_time": "2026-04-26T14:22:30.136777300Z"
+     "end_time": "2026-04-26T14:56:44.069476700Z",
+     "start_time": "2026-04-26T14:56:44.023349500Z"
     }
    },
+   "source": [
+    "part_e_prediction_compare = pd.concat([\n",
+    "    pred_sheet_part_e_linear.head(8),\n",
+    "    pred_sheet_part_e_poly.head(8),\n",
+    "    pred_sheet_part_e_ridge.head(8),\n",
+    "    pred_sheet_part_e_lasso.head(8),\n",
+    "], ignore_index=True)\n",
+    "\n",
+    "part_e_prediction_compare"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "    True  Predicted  Residual  Abs_Error                            Model\n",
+       "0    4.1   4.299298 -0.199298   0.199298                Linear Regression\n",
+       "1    4.1   4.277702 -0.177702   0.177702                Linear Regression\n",
+       "2    4.4   4.314717  0.085283   0.085283                Linear Regression\n",
+       "3    4.6   4.189314  0.410686   0.410686                Linear Regression\n",
+       "4    4.5   4.376513  0.123487   0.123487                Linear Regression\n",
+       "5    3.2   4.317265 -1.117265   1.117265                Linear Regression\n",
+       "6    4.2   4.345980 -0.145980   0.145980                Linear Regression\n",
+       "7    3.9   4.000260 -0.100260   0.100260                Linear Regression\n",
+       "8    4.1   4.279363 -0.179363   0.179363            Polynomial Regression\n",
+       "9    4.1   4.292264 -0.192264   0.192264            Polynomial Regression\n",
+       "10   4.4   4.275074  0.124926   0.124926            Polynomial Regression\n",
+       "11   4.6   4.230751  0.369249   0.369249            Polynomial Regression\n",
+       "12   4.5   4.287948  0.212052   0.212052            Polynomial Regression\n",
+       "13   3.2   4.233728 -1.033728   1.033728            Polynomial Regression\n",
+       "14   4.2   4.233425 -0.033425   0.033425            Polynomial Regression\n",
+       "15   3.9   4.081532 -0.181532   0.181532            Polynomial Regression\n",
+       "16   4.1   4.290152 -0.190152   0.190152    Ridge Regression (alpha=50.0)\n",
+       "17   4.1   4.261329 -0.161329   0.161329    Ridge Regression (alpha=50.0)\n",
+       "18   4.4   4.250952  0.149048   0.149048    Ridge Regression (alpha=50.0)\n",
+       "19   4.6   4.245164  0.354836   0.354836    Ridge Regression (alpha=50.0)\n",
+       "20   4.5   4.254136  0.245864   0.245864    Ridge Regression (alpha=50.0)\n",
+       "21   3.2   4.237515 -1.037515   1.037515    Ridge Regression (alpha=50.0)\n",
+       "22   4.2   4.237951 -0.037951   0.037951    Ridge Regression (alpha=50.0)\n",
+       "23   3.9   4.262971 -0.362971   0.362971    Ridge Regression (alpha=50.0)\n",
+       "24   4.1   4.306335 -0.206335   0.206335  Lasso Regression (alpha=0.0005)\n",
+       "25   4.1   4.287329 -0.187329   0.187329  Lasso Regression (alpha=0.0005)\n",
+       "26   4.4   4.297757  0.102243   0.102243  Lasso Regression (alpha=0.0005)\n",
+       "27   4.6   4.197993  0.402007   0.402007  Lasso Regression (alpha=0.0005)\n",
+       "28   4.5   4.371379  0.128621   0.128621  Lasso Regression (alpha=0.0005)\n",
+       "29   3.2   4.304839 -1.104839   1.104839  Lasso Regression (alpha=0.0005)\n",
+       "30   4.2   4.324286 -0.124286   0.124286  Lasso Regression (alpha=0.0005)\n",
+       "31   3.9   4.005045 -0.105045   0.105045  Lasso Regression (alpha=0.0005)"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -2805,82 +2837,24 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "    True  Predicted  Residual  Abs_Error                            Model\n",
-       "0    4.1   4.299298 -0.199298   0.199298                Linear Regression\n",
-       "1    4.1   4.277702 -0.177702   0.177702                Linear Regression\n",
-       "2    4.4   4.314717  0.085283   0.085283                Linear Regression\n",
-       "3    4.6   4.189314  0.410686   0.410686                Linear Regression\n",
-       "4    4.5   4.376513  0.123487   0.123487                Linear Regression\n",
-       "5    3.2   4.317265 -1.117265   1.117265                Linear Regression\n",
-       "6    4.2   4.345980 -0.145980   0.145980                Linear Regression\n",
-       "7    3.9   4.000260 -0.100260   0.100260                Linear Regression\n",
-       "8    4.1   4.279363 -0.179363   0.179363            Polynomial Regression\n",
-       "9    4.1   4.292264 -0.192264   0.192264            Polynomial Regression\n",
-       "10   4.4   4.275074  0.124926   0.124926            Polynomial Regression\n",
-       "11   4.6   4.230751  0.369249   0.369249            Polynomial Regression\n",
-       "12   4.5   4.287948  0.212052   0.212052            Polynomial Regression\n",
-       "13   3.2   4.233728 -1.033728   1.033728            Polynomial Regression\n",
-       "14   4.2   4.233425 -0.033425   0.033425            Polynomial Regression\n",
-       "15   3.9   4.081532 -0.181532   0.181532            Polynomial Regression\n",
-       "16   4.1   4.290152 -0.190152   0.190152    Ridge Regression (alpha=50.0)\n",
-       "17   4.1   4.261329 -0.161329   0.161329    Ridge Regression (alpha=50.0)\n",
-       "18   4.4   4.250952  0.149048   0.149048    Ridge Regression (alpha=50.0)\n",
-       "19   4.6   4.245164  0.354836   0.354836    Ridge Regression (alpha=50.0)\n",
-       "20   4.5   4.254136  0.245864   0.245864    Ridge Regression (alpha=50.0)\n",
-       "21   3.2   4.237515 -1.037515   1.037515    Ridge Regression (alpha=50.0)\n",
-       "22   4.2   4.237951 -0.037951   0.037951    Ridge Regression (alpha=50.0)\n",
-       "23   3.9   4.262971 -0.362971   0.362971    Ridge Regression (alpha=50.0)\n",
-       "24   4.1   4.306335 -0.206335   0.206335  Lasso Regression (alpha=0.0005)\n",
-       "25   4.1   4.287329 -0.187329   0.187329  Lasso Regression (alpha=0.0005)\n",
-       "26   4.4   4.297757  0.102243   0.102243  Lasso Regression (alpha=0.0005)\n",
-       "27   4.6   4.197993  0.402007   0.402007  Lasso Regression (alpha=0.0005)\n",
-       "28   4.5   4.371379  0.128621   0.128621  Lasso Regression (alpha=0.0005)\n",
-       "29   3.2   4.304839 -1.104839   1.104839  Lasso Regression (alpha=0.0005)\n",
-       "30   4.2   4.324286 -0.124286   0.124286  Lasso Regression (alpha=0.0005)\n",
-       "31   3.9   4.005045 -0.105045   0.105045  Lasso Regression (alpha=0.0005)"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# Show a short side-by-side sample so the marker can compare model outputs quickly.\n",
-    "part_e_prediction_compare = pd.concat([\n",
-    "    pred_sheet_part_e_linear.head(8),\n",
-    "    pred_sheet_part_e_poly.head(8),\n",
-    "    pred_sheet_part_e_ridge.head(8),\n",
-    "    pred_sheet_part_e_lasso.head(8),\n",
-    "], ignore_index=True)\n",
-    "\n",
-    "part_e_prediction_compare"
-   ]
+   "execution_count": 106
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
    "id": "24cd7fb5c2d8d4b2",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.341040400Z",
-     "start_time": "2026-04-26T14:22:30.174424100Z"
+     "end_time": "2026-04-26T14:56:44.232721200Z",
+     "start_time": "2026-04-26T14:56:44.070477300Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "results_melted = results_df.melt(\n",
     "    id_vars='Model',\n",
@@ -2895,7 +2869,23 @@
     "plt.xticks(rotation=15)\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 107
   },
   {
    "cell_type": "markdown",
@@ -2904,14 +2894,15 @@
    "source": [
     "# Model Performance Analysis\n",
     "\n",
-    "The plot above compares the regression models using **MAE**, **RMSE**, and **R²**.\n",
+    "The plot above compares the regression models using **MAE**, **RMSE**, and **R²**. \n",
+    "I'm looking at these metrics to figure out which model is actually the best at predicting the rating:\n",
+    "- **RMSE** (Root Mean Squared Error) and **MAE** (Mean Absolute Error): these basically tell us how far off our predictions are on average. Lower values mean the model is closer to the true ratings.\n",
+    "- **R²**: this tells us how much of the variation in the ratings our model actually explains. We want this to be as close to 1 as possible. If it's near 0 (or negative), the model is just guessing or doing worse than a simple average.\n",
     "\n",
-    "- **RMSE / MAE**: lower values mean the model’s predictions are closer to the true values (smaller average error).\n",
-    "- **R²**: closer to 1 means the model explains more of the variation in the target; values closer to 0 (or negative) mean weak predictive power.\n",
+    "Looking at the results, **Linear Regression** gives the best overall performance for this task since it has the lowest error (RMSE/MAE) and the highest R² out of all the models I tried. \n",
+    "The polynomial model actually performs a bit worse here, and Ridge/Lasso are super close to the linear model but don't really improve it for this specific dataset.\n",
     "\n",
-    "From the results shown, **Linear Regression** gives the best overall performance for this task (lowest error metrics and the highest R² among the tested models). The polynomial model performs worse here, and Ridge/Lasso are close to linear but do not improve on it for this dataset.\n",
-    "\n",
-    "So, based on these metrics, **Linear Regression is the best-performing regression model in this comparison**."
+    "So, based on the metrics, I'll go with **Linear Regression as the best-performing regression model** for predicting app ratings.\n"
    ]
   },
   {
@@ -2927,31 +2918,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
    "id": "ab09192a122e6e8f",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.365077800Z",
-     "start_time": "2026-04-26T14:22:30.342047500Z"
+     "end_time": "2026-04-26T14:56:44.258816700Z",
+     "start_time": "2026-04-26T14:56:44.233721200Z"
     }
    },
-   "outputs": [],
    "source": [
     "features_to_test = ['Reviews', 'Size in bytes', 'Numeric Installs']\n",
     "feature_results = []"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 108
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
    "id": "3f65e25b63cc8e93",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.416064900Z",
-     "start_time": "2026-04-26T14:22:30.366077Z"
+     "end_time": "2026-04-26T14:56:44.306356700Z",
+     "start_time": "2026-04-26T14:56:44.258816700Z"
     }
    },
-   "outputs": [],
    "source": [
     "for feature in features_to_test:\n",
     "    # Use a single input at a time to see how predictive it is for app rating\n",
@@ -2972,21 +2961,32 @@
     "        'RMSE': np.sqrt(mean_squared_error(y_test_s, y_pred_s)),\n",
     "        'R2': r2_score(y_test_s, y_pred_s)\n",
     "    })"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 109
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
    "id": "33cc0452de54f874",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.448560800Z",
-     "start_time": "2026-04-26T14:22:30.417061900Z"
+     "end_time": "2026-04-26T14:56:44.336457100Z",
+     "start_time": "2026-04-26T14:56:44.306356700Z"
     }
    },
+   "source": [
+    "feature_results_df = pd.DataFrame(feature_results).sort_values('RMSE')\n",
+    "feature_results_df"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "            Feature       MAE       MSE      RMSE        R2\n",
+       "1     Size in bytes  0.304495  0.173620  0.416677  0.009120\n",
+       "0           Reviews  0.304707  0.173865  0.416971  0.007722\n",
+       "2  Numeric Installs  0.307489  0.174643  0.417903  0.003281"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3041,35 +3041,24 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "            Feature       MAE       MSE      RMSE        R2\n",
-       "1     Size in bytes  0.304495  0.173620  0.416677  0.009120\n",
-       "0           Reviews  0.304707  0.173865  0.416971  0.007722\n",
-       "2  Numeric Installs  0.307489  0.174643  0.417903  0.003281"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "feature_results_df = pd.DataFrame(feature_results).sort_values('RMSE')\n",
-    "feature_results_df"
-   ]
+   "execution_count": 110
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
    "id": "4f73fdfeefd697e2",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.454588200Z",
-     "start_time": "2026-04-26T14:22:30.448560800Z"
+     "end_time": "2026-04-26T14:56:44.383004500Z",
+     "start_time": "2026-04-26T14:56:44.337457700Z"
     }
    },
-   "outputs": [],
    "source": [
     "feature_melted = feature_results_df.melt(\n",
     "    id_vars='Feature',\n",
@@ -3077,30 +3066,19 @@
     "    var_name='Metric',\n",
     "    value_name='Score'\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 111
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
    "id": "3b1a720808cf9b77",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.595029300Z",
-     "start_time": "2026-04-26T14:22:30.454588200Z"
+     "end_time": "2026-04-26T14:56:44.606132800Z",
+     "start_time": "2026-04-26T14:56:44.404689200Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "plt.figure(figsize=(9, 5))\n",
     "sns.barplot(data=feature_melted, x='Feature', y='Score', hue='Metric')\n",
@@ -3108,21 +3086,52 @@
     "plt.xticks(rotation=15)\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "593913702f875fa0",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.631246400Z",
-     "start_time": "2026-04-26T14:22:30.595029300Z"
-    }
-   },
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 112
+  },
+  {
+   "cell_type": "code",
+   "id": "593913702f875fa0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-26T14:56:44.772635300Z",
+     "start_time": "2026-04-26T14:56:44.637162200Z"
+    }
+   },
+   "source": [
+    "best_feature = feature_results_df.iloc[0]['Feature']\n",
+    "\n",
+    "best_row = feature_results_df.iloc[0]\n",
+    "display(pd.DataFrame([{\n",
+    "    'Best Feature': best_row['Feature'],\n",
+    "    'MAE': best_row['MAE'],\n",
+    "    'MSE': best_row['MSE'],\n",
+    "    'RMSE': best_row['RMSE'],\n",
+    "    'R2': best_row['R2']\n",
+    "}]))"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "    Best Feature       MAE      MSE      RMSE       R2\n",
+       "0  Size in bytes  0.304495  0.17362  0.416677  0.00912"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3161,77 +3170,91 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "    Best Feature       MAE      MSE      RMSE       R2\n",
-       "0  Size in bytes  0.304495  0.17362  0.416677  0.00912"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "best_feature = feature_results_df.iloc[0]['Feature']\n",
-    "\n",
-    "best_row = feature_results_df.iloc[0]\n",
-    "display(pd.DataFrame([{\n",
-    "    'Best Feature': best_row['Feature'],\n",
-    "    'MAE': best_row['MAE'],\n",
-    "    'MSE': best_row['MSE'],\n",
-    "    'RMSE': best_row['RMSE'],\n",
-    "    'R2': best_row['R2']\n",
-    "}]))"
-   ]
+   "execution_count": 113
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
    "id": "2be649a00dbb9fc7",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.685659Z",
-     "start_time": "2026-04-26T14:22:30.632246600Z"
+     "end_time": "2026-04-26T14:56:44.877796Z",
+     "start_time": "2026-04-26T14:56:44.774635400Z"
     }
    },
-   "outputs": [],
    "source": [
     "X_best = df_encoded[[best_feature]]\n",
     "X_train_b, X_test_b, y_train_b, y_test_b = train_test_split(\n",
     "    X_best, y, test_size=0.3, random_state=seed\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 114
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
    "id": "d36175ff718e84a1",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.712628200Z",
-     "start_time": "2026-04-26T14:22:30.686659800Z"
+     "end_time": "2026-04-26T14:56:44.907736300Z",
+     "start_time": "2026-04-26T14:56:44.880305300Z"
     }
    },
-   "outputs": [],
    "source": [
     "best_model = LinearRegression()\n",
     "best_model.fit(X_train_b, y_train_b)\n",
     "y_pred_b = best_model.predict(X_test_b)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 115
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
    "id": "ba0e16192124782b",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.746999400Z",
-     "start_time": "2026-04-26T14:22:30.713628100Z"
+     "end_time": "2026-04-26T14:56:45.031840400Z",
+     "start_time": "2026-04-26T14:56:44.941762Z"
     }
    },
+   "source": [
+    "pred_sheet_part_f = regression_prediction_sheet(y_test_b, y_pred_b, f'Linear Regression ({best_feature})')\n",
+    "pred_sheet_part_f"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "    True  Predicted  Residual  Abs_Error                              Model\n",
+       "0    4.1   4.307502 -0.207502   0.207502  Linear Regression (Size in bytes)\n",
+       "1    4.1   4.267637 -0.167637   0.167637  Linear Regression (Size in bytes)\n",
+       "2    4.4   4.251691  0.148309   0.148309  Linear Regression (Size in bytes)\n",
+       "3    4.6   4.243240  0.356760   0.356760  Linear Regression (Size in bytes)\n",
+       "4    4.5   4.256475  0.243525   0.243525  Linear Regression (Size in bytes)\n",
+       "5    3.2   4.232078 -1.032078   1.032078  Linear Regression (Size in bytes)\n",
+       "6    4.2   4.232716 -0.032716   0.032716  Linear Regression (Size in bytes)\n",
+       "7    3.9   4.269232 -0.369232   0.369232  Linear Regression (Size in bytes)\n",
+       "8    3.7   4.251691 -0.551691   0.551691  Linear Regression (Size in bytes)\n",
+       "9    4.6   4.336205  0.263795   0.263795  Linear Regression (Size in bytes)\n",
+       "10   4.6   4.376070  0.223930   0.223930  Linear Regression (Size in bytes)\n",
+       "11   4.5   4.231759  0.268241   0.268241  Linear Regression (Size in bytes)\n",
+       "12   4.6   4.289962  0.310038   0.310038  Linear Regression (Size in bytes)\n",
+       "13   4.4   4.245313  0.154687   0.154687  Linear Regression (Size in bytes)\n",
+       "14   4.0   4.294746 -0.294746   0.294746  Linear Regression (Size in bytes)\n",
+       "15   4.3   4.261259  0.038741   0.038741  Linear Regression (Size in bytes)\n",
+       "16   4.4   4.275610  0.124390   0.124390  Linear Regression (Size in bytes)\n",
+       "17   3.8   4.241167 -0.441167   0.441167  Linear Regression (Size in bytes)\n",
+       "18   4.5   4.239732  0.260268   0.260268  Linear Regression (Size in bytes)\n",
+       "19   4.2   4.232237 -0.032237   0.032237  Linear Regression (Size in bytes)"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3422,63 +3445,24 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "    True  Predicted  Residual  Abs_Error                              Model\n",
-       "0    4.1   4.307502 -0.207502   0.207502  Linear Regression (Size in bytes)\n",
-       "1    4.1   4.267637 -0.167637   0.167637  Linear Regression (Size in bytes)\n",
-       "2    4.4   4.251691  0.148309   0.148309  Linear Regression (Size in bytes)\n",
-       "3    4.6   4.243240  0.356760   0.356760  Linear Regression (Size in bytes)\n",
-       "4    4.5   4.256475  0.243525   0.243525  Linear Regression (Size in bytes)\n",
-       "5    3.2   4.232078 -1.032078   1.032078  Linear Regression (Size in bytes)\n",
-       "6    4.2   4.232716 -0.032716   0.032716  Linear Regression (Size in bytes)\n",
-       "7    3.9   4.269232 -0.369232   0.369232  Linear Regression (Size in bytes)\n",
-       "8    3.7   4.251691 -0.551691   0.551691  Linear Regression (Size in bytes)\n",
-       "9    4.6   4.336205  0.263795   0.263795  Linear Regression (Size in bytes)\n",
-       "10   4.6   4.376070  0.223930   0.223930  Linear Regression (Size in bytes)\n",
-       "11   4.5   4.231759  0.268241   0.268241  Linear Regression (Size in bytes)\n",
-       "12   4.6   4.289962  0.310038   0.310038  Linear Regression (Size in bytes)\n",
-       "13   4.4   4.245313  0.154687   0.154687  Linear Regression (Size in bytes)\n",
-       "14   4.0   4.294746 -0.294746   0.294746  Linear Regression (Size in bytes)\n",
-       "15   4.3   4.261259  0.038741   0.038741  Linear Regression (Size in bytes)\n",
-       "16   4.4   4.275610  0.124390   0.124390  Linear Regression (Size in bytes)\n",
-       "17   3.8   4.241167 -0.441167   0.441167  Linear Regression (Size in bytes)\n",
-       "18   4.5   4.239732  0.260268   0.260268  Linear Regression (Size in bytes)\n",
-       "19   4.2   4.232237 -0.032237   0.032237  Linear Regression (Size in bytes)"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "pred_sheet_part_f = regression_prediction_sheet(y_test_b, y_pred_b, f'Linear Regression ({best_feature})')\n",
-    "pred_sheet_part_f"
-   ]
+   "execution_count": 116
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
    "id": "9f485161f95e22d7",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.866624300Z",
-     "start_time": "2026-04-26T14:22:30.747995400Z"
+     "end_time": "2026-04-26T14:56:45.201888500Z",
+     "start_time": "2026-04-26T14:56:45.033844200Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "plt.figure(figsize=(6, 6))\n",
     "plt.scatter(y_test_b, y_pred_b, alpha=0.5)\n",
@@ -3488,7 +3472,23 @@
     "plt.ylabel(\"Predicted Rating\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 117
   },
   {
    "cell_type": "markdown",
@@ -3497,15 +3497,15 @@
    "source": [
     "### Part F Analysis\n",
     "\n",
-    "To identify the most predictive single input for app rating, I trained three separate **simple linear regression** models using **only one input at a time**:\n",
-    "\n",
+    "To find out which single input is the best at predicting the app rating, I trained three separate simple linear regression models using just one input at a time:\n",
     "- `Reviews` (number of reviews)\n",
     "- `Size in bytes` (app size)\n",
-    "- `Numeric Installs` (install count as a number)\n",
+    "- `Numeric Installs` (install count)\n",
     "\n",
-    "The table and bar chart above compare each single-input model using **MAE**, **RMSE**, and **R²**. The feature with the **lowest RMSE** is the most predictive single input (this is also printed in the code cell above as the \"Best single input\").\n",
+    "The bar chart above compares each model using **MAE**, **RMSE**, and **R²**. The feature with the lowest RMSE is our most predictive single input. \n",
+    "I printed out the best feature in the code cell above.\n",
     "\n",
-    "Finally, the \"Actual vs Predicted\" scatter plot for the best single input shows the typical spread you get when using only one piece of information to predict rating, which is why using multiple inputs together generally gives more accurate rating predictions than any single input alone."
+    "Looking at the \"Actual vs Predicted\" scatter plot for the best single feature, you can see the typical spread you get when trying to guess the rating from just one piece of info. This is why using multiple inputs together (like in the previous part) generally gives much better predictions than relying on just one.\n"
    ]
   },
   {
@@ -3527,98 +3527,93 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
    "id": "137267551f08dae5",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.890649300Z",
-     "start_time": "2026-04-26T14:22:30.867624800Z"
+     "end_time": "2026-04-26T14:56:45.276895Z",
+     "start_time": "2026-04-26T14:56:45.230572100Z"
     }
    },
+   "source": "from sklearn.model_selection import cross_val_score",
    "outputs": [],
-   "source": [
-    "from sklearn.model_selection import cross_val_score"
-   ]
+   "execution_count": 118
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
    "id": "725e85ef4abe3948",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.927351600Z",
-     "start_time": "2026-04-26T14:22:30.891653600Z"
+     "end_time": "2026-04-26T14:56:45.348577600Z",
+     "start_time": "2026-04-26T14:56:45.277893100Z"
     }
    },
-   "outputs": [],
    "source": [
     "columns_to_keep_g = ['Category', 'Reviews', 'Content Rating', 'Rating', 'Numeric Installs', 'Size in bytes']\n",
     "df_g = df_new[columns_to_keep_g]"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 119
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
    "id": "7a6b3f84b1265599",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.974128500Z",
-     "start_time": "2026-04-26T14:22:30.927351600Z"
+     "end_time": "2026-04-26T14:56:45.411376800Z",
+     "start_time": "2026-04-26T14:56:45.349577100Z"
     }
    },
+   "source": "df_g_encoded = pd.get_dummies(df_g, columns=['Category', 'Content Rating'])",
    "outputs": [],
-   "source": [
-    "df_g_encoded = pd.get_dummies(df_g, columns=['Category', 'Content Rating'])"
-   ]
+   "execution_count": 120
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
    "id": "679b52eb182f0e1c",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.007707400Z",
-     "start_time": "2026-04-26T14:22:30.976128500Z"
+     "end_time": "2026-04-26T14:56:45.442308400Z",
+     "start_time": "2026-04-26T14:56:45.413374400Z"
     }
    },
-   "outputs": [],
    "source": [
-    "# Inputs (Category + Reviews + Content Rating + Rating + Installs_Num)\n",
     "X_g = df_g_encoded.drop('Size in bytes', axis=1)\n",
-    "\n",
-    "# Target we want to predict (Size in Bytes)\n",
-    "# NOTE: use the target from the SAME filtered dataframe so X and y stay aligned\n",
     "y_g = df_g_encoded['Size in bytes']"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 121
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
    "id": "35f87078cd8053fa",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.038020300Z",
-     "start_time": "2026-04-26T14:22:31.008708Z"
+     "end_time": "2026-04-26T14:56:45.475799500Z",
+     "start_time": "2026-04-26T14:56:45.443307100Z"
     }
    },
+   "source": "X_train_g, X_test_g, y_train_g, y_test_g = train_test_split(X_g, y_g, test_size=0.3, random_state=seed)",
    "outputs": [],
-   "source": [
-    "X_train_g, X_test_g, y_train_g, y_test_g = train_test_split(X_g, y_g, test_size=0.3, random_state=seed)"
-   ]
+   "execution_count": 122
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
    "id": "862fb1943bf890b5",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.086062700Z",
-     "start_time": "2026-04-26T14:22:31.059022100Z"
+     "end_time": "2026-04-26T14:56:45.509051800Z",
+     "start_time": "2026-04-26T14:56:45.475799500Z"
     }
    },
+   "source": "split_overview(len(X_train_g), len(X_test_g))",
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   Split  Rows  Pct_of_Total\n",
+       "0  Train   774          69.9\n",
+       "1   Test   333          30.1"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3659,40 +3654,29 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Split  Rows  Pct_of_Total\n",
-       "0  Train   774          69.9\n",
-       "1   Test   333          30.1"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "split_overview(len(X_train_g), len(X_test_g))"
-   ]
+   "execution_count": 123
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
    "id": "c3c062e2eebf8675",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.167844300Z",
-     "start_time": "2026-04-26T14:22:31.087063800Z"
+     "end_time": "2026-04-26T14:56:45.550111300Z",
+     "start_time": "2026-04-26T14:56:45.510559200Z"
     }
    },
-   "outputs": [],
    "source": [
     "def evaluate_model_cv(model, X_train, X_test, y_train, y_test, name):\n",
-    "    \"\"\"Evaluate model with both train/test split AND cross-validation\"\"\"\n",
     "    model.fit(X_train, y_train)\n",
     "    y_pred = model.predict(X_test)\n",
     "\n",
-    "    # Cross-validation scores (5-fold)\n",
     "    cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='r2')\n",
     "\n",
     "    results = {\n",
@@ -3705,85 +3689,70 @@
     "        'CV R2 (std)': cv_scores.std()\n",
     "    }\n",
     "    return results, y_pred, cv_scores"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 124
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
    "id": "7bd72610ea9b4047",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.234779200Z",
-     "start_time": "2026-04-26T14:22:31.169844400Z"
+     "end_time": "2026-04-26T14:56:45.569279500Z",
+     "start_time": "2026-04-26T14:56:45.550111300Z"
     }
    },
+   "source": "results_g = []",
    "outputs": [],
-   "source": [
-    "results_g = []"
-   ]
+   "execution_count": 125
   },
   {
    "cell_type": "markdown",
    "id": "ec07cf5f77d93f3b",
    "metadata": {},
    "source": [
-    "## Trying Linear Regression for Part G"
+    "## Trying Linear Regression for Part G\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
    "id": "5077d997183c52b7",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.261165700Z",
-     "start_time": "2026-04-26T14:22:31.238779400Z"
+     "end_time": "2026-04-26T14:56:45.631563Z",
+     "start_time": "2026-04-26T14:56:45.571280400Z"
     }
    },
-   "outputs": [],
    "source": [
     "lin_model_g = LinearRegression()\n",
     "lin_res_g, y_pred_lin_g, cv_lin_g = evaluate_model_cv(lin_model_g, X_train_g, X_test_g, y_train_g, y_test_g, \"Linear Regression\")\n",
     "results_g.append(lin_res_g)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 126
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
    "id": "dcf6fd613a1d2d79",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.277760400Z",
-     "start_time": "2026-04-26T14:22:31.261165700Z"
+     "end_time": "2026-04-26T14:56:45.683902200Z",
+     "start_time": "2026-04-26T14:56:45.631563Z"
     }
    },
+   "source": "residual_lin_g = y_test_g - y_pred_lin_g",
    "outputs": [],
-   "source": [
-    "residual_lin_g = y_test_g - y_pred_lin_g"
-   ]
+   "execution_count": 127
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
    "id": "39fd78fe960acb67",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.356530200Z",
-     "start_time": "2026-04-26T14:22:31.279268400Z"
+     "end_time": "2026-04-26T14:56:45.769598400Z",
+     "start_time": "2026-04-26T14:56:45.683902200Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "plt.figure(figsize=(8, 5))\n",
     "plt.scatter(y_pred_lin_g, residual_lin_g, alpha=0.5)\n",
@@ -3793,72 +3762,75 @@
     "plt.ylabel(\"Residual (y - y_hat)\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 128
   },
   {
    "cell_type": "markdown",
    "id": "5566a6d1839e472f",
    "metadata": {},
    "source": [
-    "## Trying polynomial regression for Part G"
+    "## Trying polynomial regression for Part G\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
    "id": "b5e9cdb3ef42c22f",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.380401100Z",
-     "start_time": "2026-04-26T14:22:31.372014900Z"
+     "end_time": "2026-04-26T14:56:45.791146300Z",
+     "start_time": "2026-04-26T14:56:45.769598400Z"
     }
    },
-   "outputs": [],
    "source": [
     "poly_converter_g = PolynomialFeatures(degree=2, include_bias=False)\n",
     "X_train_p_g = poly_converter_g.fit_transform(X_train_g)\n",
     "X_test_p_g = poly_converter_g.transform(X_test_g)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 129
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
    "id": "8d5313ca848e4519",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.543772600Z",
-     "start_time": "2026-04-26T14:22:31.380401100Z"
+     "end_time": "2026-04-26T14:56:45.953011100Z",
+     "start_time": "2026-04-26T14:56:45.791146300Z"
     }
    },
-   "outputs": [],
    "source": [
     "poly_model_g = LinearRegression()\n",
     "poly_res_g, y_pred_poly_g, cv_poly_g = evaluate_model_cv(poly_model_g, X_train_p_g, X_test_p_g, y_train_g, y_test_g, \"Polynomial Regression\")\n",
     "results_g.append(poly_res_g)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 130
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
    "id": "8c81b3b6dab60fee",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.640034900Z",
-     "start_time": "2026-04-26T14:22:31.544770800Z"
+     "end_time": "2026-04-26T14:56:46.058071300Z",
+     "start_time": "2026-04-26T14:56:45.954005800Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "plt.figure(figsize=(6, 6))\n",
     "plt.scatter(y_test_g, y_pred_poly_g, alpha=0.5)\n",
@@ -3868,29 +3840,91 @@
     "plt.ylabel(\"Predicted Size in Bytes\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 131
   },
   {
    "cell_type": "markdown",
    "id": "636e6b9a053c8f8d",
    "metadata": {},
    "source": [
-    "# Trying Ridge Regression for Part G"
+    "# Trying Ridge Regression for Part G\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
    "id": "a31ad4ed704f4541",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.718195600Z",
-     "start_time": "2026-04-26T14:22:31.641035800Z"
+     "end_time": "2026-04-26T14:56:46.167539400Z",
+     "start_time": "2026-04-26T14:56:46.058071300Z"
     }
    },
+   "source": [
+    "best_ridge_alpha_g = None\n",
+    "best_ridge_cv_r2_g = float('-inf')\n",
+    "best_ridge_rmse_g = float('inf')\n",
+    "best_ridge_model_g = None\n",
+    "best_ridge_res_g = None\n",
+    "best_y_pred_ridge_g = None\n",
+    "best_cv_ridge_g = None\n",
+    "\n",
+    "for alpha in [0.1, 1.0, 10.0, 50.0, 100.0]:\n",
+    "    ridge_model_tmp = Ridge(alpha=alpha, solver='lsqr', random_state=seed)\n",
+    "    ridge_res_tmp, y_pred_tmp, cv_tmp = evaluate_model_cv(\n",
+    "        ridge_model_tmp, X_train_g, X_test_g, y_train_g, y_test_g, f\"Ridge Regression (alpha={alpha})\"\n",
+    "    )\n",
+    "\n",
+    "    if (ridge_res_tmp['CV R2 (mean)'] > best_ridge_cv_r2_g) or (\n",
+    "        ridge_res_tmp['CV R2 (mean)'] == best_ridge_cv_r2_g and ridge_res_tmp['RMSE (test)'] < best_ridge_rmse_g\n",
+    "    ):\n",
+    "        best_ridge_cv_r2_g = ridge_res_tmp['CV R2 (mean)']\n",
+    "        best_ridge_rmse_g = ridge_res_tmp['RMSE (test)']\n",
+    "        best_ridge_alpha_g = alpha\n",
+    "        best_ridge_model_g = ridge_model_tmp\n",
+    "        best_ridge_res_g = ridge_res_tmp\n",
+    "        best_y_pred_ridge_g = y_pred_tmp\n",
+    "        best_cv_ridge_g = cv_tmp\n",
+    "\n",
+    "display(pd.DataFrame([{\n",
+    "    'Selection': 'Part G Ridge best alpha by CV R2 mean then RMSE',\n",
+    "    'Best alpha': best_ridge_alpha_g,\n",
+    "    'CV R2 mean': best_ridge_cv_r2_g,\n",
+    "    'RMSE (test)': best_ridge_rmse_g\n",
+    "}]))\n",
+    "\n",
+    "ridge_model_g = best_ridge_model_g\n",
+    "ridge_res_g = best_ridge_res_g\n",
+    "y_pred_ridge_g = best_y_pred_ridge_g\n",
+    "cv_ridge_g = best_cv_ridge_g\n",
+    "\n",
+    "results_g.append(ridge_res_g)"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "                                         Selection  Best alpha  CV R2 mean  \\\n",
+       "0  Part G Ridge best alpha by CV R2 mean then RMSE         1.0    0.090846   \n",
+       "\n",
+       "    RMSE (test)  \n",
+       "0  2.162718e+07  "
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3927,85 +3961,26 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                                         Selection  Best alpha  CV R2 mean  \\\n",
-       "0  Part G Ridge best alpha by CV R2 mean then RMSE         1.0    0.090846   \n",
-       "\n",
-       "    RMSE (test)  \n",
-       "0  2.162718e+07  "
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "# Ridge (Part G): try a small range of alphas and keep the best by CV mean R²\n",
-    "\n",
-    "best_ridge_alpha_g = None\n",
-    "best_ridge_cv_r2_g = float('-inf')\n",
-    "best_ridge_rmse_g = float('inf')\n",
-    "best_ridge_model_g = None\n",
-    "best_ridge_res_g = None\n",
-    "best_y_pred_ridge_g = None\n",
-    "best_cv_ridge_g = None\n",
-    "\n",
-    "for alpha in [0.1, 1.0, 10.0, 50.0, 100.0]:\n",
-    "    ridge_model_tmp = Ridge(alpha=alpha, solver='lsqr', random_state=seed)\n",
-    "    ridge_res_tmp, y_pred_tmp, cv_tmp = evaluate_model_cv(\n",
-    "        ridge_model_tmp, X_train_g, X_test_g, y_train_g, y_test_g, f\"Ridge Regression (alpha={alpha})\"\n",
-    "    )\n",
-    "\n",
-    "    # rank by CV mean R² first, then test RMSE\n",
-    "    if (ridge_res_tmp['CV R2 (mean)'] > best_ridge_cv_r2_g) or (\n",
-    "        ridge_res_tmp['CV R2 (mean)'] == best_ridge_cv_r2_g and ridge_res_tmp['RMSE (test)'] < best_ridge_rmse_g\n",
-    "    ):\n",
-    "        best_ridge_cv_r2_g = ridge_res_tmp['CV R2 (mean)']\n",
-    "        best_ridge_rmse_g = ridge_res_tmp['RMSE (test)']\n",
-    "        best_ridge_alpha_g = alpha\n",
-    "        best_ridge_model_g = ridge_model_tmp\n",
-    "        best_ridge_res_g = ridge_res_tmp\n",
-    "        best_y_pred_ridge_g = y_pred_tmp\n",
-    "        best_cv_ridge_g = cv_tmp\n",
-    "\n",
-    "display(pd.DataFrame([{\n",
-    "    'Selection': 'Part G Ridge best alpha by CV R2 mean then RMSE',\n",
-    "    'Best alpha': best_ridge_alpha_g,\n",
-    "    'CV R2 mean': best_ridge_cv_r2_g,\n",
-    "    'RMSE (test)': best_ridge_rmse_g\n",
-    "}]))\n",
-    "\n",
-    "ridge_model_g = best_ridge_model_g\n",
-    "ridge_res_g = best_ridge_res_g\n",
-    "y_pred_ridge_g = best_y_pred_ridge_g\n",
-    "cv_ridge_g = best_cv_ridge_g\n",
-    "\n",
-    "results_g.append(ridge_res_g)"
-   ]
+   "execution_count": 132
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
    "id": "121ba346ffaea0e5",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.803996200Z",
-     "start_time": "2026-04-26T14:22:31.718195600Z"
+     "end_time": "2026-04-26T14:56:46.335645900Z",
+     "start_time": "2026-04-26T14:56:46.198104800Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "plt.figure(figsize=(6, 6))\n",
     "plt.scatter(y_test_g, y_pred_ridge_g, alpha=0.5)\n",
@@ -4015,92 +3990,48 @@
     "plt.ylabel(\"Predicted Size in Bytes\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 133
   },
   {
    "cell_type": "markdown",
    "id": "f238f4198de0c527",
    "metadata": {},
    "source": [
-    "# Regression Model Results"
+    "# Regression Model Results\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
    "id": "c7a5e958c9ab89e6",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.823840700Z",
-     "start_time": "2026-04-26T14:22:31.804997400Z"
+     "end_time": "2026-04-26T14:56:46.366328100Z",
+     "start_time": "2026-04-26T14:56:46.338645100Z"
     }
    },
+   "source": [
+    "results_df_g = pd.DataFrame(results_g)\n",
+    "results_df_g"
+   ],
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Model</th>\n",
-       "      <th>MAE (test)</th>\n",
-       "      <th>MSE (test)</th>\n",
-       "      <th>RMSE (test)</th>\n",
-       "      <th>R2 (test)</th>\n",
-       "      <th>CV R2 (mean)</th>\n",
-       "      <th>CV R2 (std)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Linear Regression</td>\n",
-       "      <td>1.458116e+07</td>\n",
-       "      <td>3.634642e+14</td>\n",
-       "      <td>1.906474e+07</td>\n",
-       "      <td>0.292880</td>\n",
-       "      <td>0.245829</td>\n",
-       "      <td>0.067041</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Polynomial Regression</td>\n",
-       "      <td>1.708302e+07</td>\n",
-       "      <td>7.093839e+14</td>\n",
-       "      <td>2.663426e+07</td>\n",
-       "      <td>-0.380108</td>\n",
-       "      <td>-0.452607</td>\n",
-       "      <td>0.900419</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ridge Regression (alpha=1.0)</td>\n",
-       "      <td>1.629031e+07</td>\n",
-       "      <td>4.677350e+14</td>\n",
-       "      <td>2.162718e+07</td>\n",
-       "      <td>0.090021</td>\n",
-       "      <td>0.090846</td>\n",
-       "      <td>0.056075</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
        "                          Model    MAE (test)    MSE (test)   RMSE (test)  \\\n",
        "0             Linear Regression  1.458116e+07  3.634642e+14  1.906474e+07   \n",
@@ -4111,29 +4042,87 @@
        "0   0.292880      0.245829     0.067041  \n",
        "1  -0.380108     -0.452607     0.900419  \n",
        "2   0.090021      0.090846     0.056075  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>MAE (test)</th>\n",
+       "      <th>MSE (test)</th>\n",
+       "      <th>RMSE (test)</th>\n",
+       "      <th>R2 (test)</th>\n",
+       "      <th>CV R2 (mean)</th>\n",
+       "      <th>CV R2 (std)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Linear Regression</td>\n",
+       "      <td>1.458116e+07</td>\n",
+       "      <td>3.634642e+14</td>\n",
+       "      <td>1.906474e+07</td>\n",
+       "      <td>0.292880</td>\n",
+       "      <td>0.245829</td>\n",
+       "      <td>0.067041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Polynomial Regression</td>\n",
+       "      <td>1.708302e+07</td>\n",
+       "      <td>7.093839e+14</td>\n",
+       "      <td>2.663426e+07</td>\n",
+       "      <td>-0.380108</td>\n",
+       "      <td>-0.452607</td>\n",
+       "      <td>0.900419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Ridge Regression (alpha=1.0)</td>\n",
+       "      <td>1.629031e+07</td>\n",
+       "      <td>4.677350e+14</td>\n",
+       "      <td>2.162718e+07</td>\n",
+       "      <td>0.090021</td>\n",
+       "      <td>0.090846</td>\n",
+       "      <td>0.056075</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "results_df_g = pd.DataFrame(results_g)\n",
-    "results_df_g"
-   ]
+   "execution_count": 134
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
    "id": "a8b2b05314122ac3",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.875504800Z",
-     "start_time": "2026-04-26T14:22:31.849372600Z"
+     "end_time": "2026-04-26T14:56:46.397666500Z",
+     "start_time": "2026-04-26T14:56:46.366832Z"
     }
    },
-   "outputs": [],
    "source": [
     "results_melted_g = results_df_g.melt(\n",
     "    id_vars='Model',\n",
@@ -4141,31 +4130,59 @@
     "    var_name='Metric',\n",
     "    value_name='Score'\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 135
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
    "id": "28a6da6deea25737",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.028098900Z",
-     "start_time": "2026-04-26T14:22:31.891520200Z"
+     "end_time": "2026-04-26T14:56:46.566613300Z",
+     "start_time": "2026-04-26T14:56:46.397666500Z"
     }
    },
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(data=results_melted_g, x='Model', y='Score', hue='Metric')\n",
+    "plt.title(\"Part G: Regression Model Comparison for Size in Bytes (with Cross-Validation)\")\n",
+    "plt.xticks(rotation=15)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "ranked_g = results_df_g.sort_values(by=['CV R2 (mean)', 'RMSE (test)'], ascending=[False, True]).reset_index(drop=True)\n",
+    "display(ranked_g)\n",
+    "\n",
+    "best_model_g = ranked_g.iloc[0]['Model']"
+   ],
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
-      ]
+      ],
+      "image/png": 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"
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
+      "text/plain": [
+       "                          Model    MAE (test)    MSE (test)   RMSE (test)  \\\n",
+       "0             Linear Regression  1.458116e+07  3.634642e+14  1.906474e+07   \n",
+       "1  Ridge Regression (alpha=1.0)  1.629031e+07  4.677350e+14  2.162718e+07   \n",
+       "2         Polynomial Regression  1.708302e+07  7.093839e+14  2.663426e+07   \n",
+       "\n",
+       "   R2 (test)  CV R2 (mean)  CV R2 (std)  \n",
+       "0   0.292880      0.245829     0.067041  \n",
+       "1   0.090021      0.090846     0.056075  \n",
+       "2  -0.380108     -0.452607     0.900419  "
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -4228,51 +4245,50 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                          Model    MAE (test)    MSE (test)   RMSE (test)  \\\n",
-       "0             Linear Regression  1.458116e+07  3.634642e+14  1.906474e+07   \n",
-       "1  Ridge Regression (alpha=1.0)  1.629031e+07  4.677350e+14  2.162718e+07   \n",
-       "2         Polynomial Regression  1.708302e+07  7.093839e+14  2.663426e+07   \n",
-       "\n",
-       "   R2 (test)  CV R2 (mean)  CV R2 (std)  \n",
-       "0   0.292880      0.245829     0.067041  \n",
-       "1   0.090021      0.090846     0.056075  \n",
-       "2  -0.380108     -0.452607     0.900419  "
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.barplot(data=results_melted_g, x='Model', y='Score', hue='Metric')\n",
-    "plt.title(\"Part G: Regression Model Comparison for Size in Bytes (with Cross-Validation)\")\n",
-    "plt.xticks(rotation=15)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# Rank models primarily by cross-validation mean R² (generalisation), then use test RMSE as a tie-breaker\n",
-    "ranked_g = results_df_g.sort_values(by=['CV R2 (mean)', 'RMSE (test)'], ascending=[False, True]).reset_index(drop=True)\n",
-    "display(ranked_g)\n",
-    "\n",
-    "best_model_g = ranked_g.iloc[0]['Model']"
-   ]
+   "execution_count": 136
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
    "id": "28a5dae231975e76",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.072309500Z",
-     "start_time": "2026-04-26T14:22:32.048607700Z"
+     "end_time": "2026-04-26T14:56:46.614873800Z",
+     "start_time": "2026-04-26T14:56:46.586136200Z"
     }
    },
+   "source": [
+    "display(pd.DataFrame([{'Part G Best Model': best_model_g}]))\n",
+    "\n",
+    "pred_sheet_part_g_linear = regression_prediction_sheet(y_test_g, y_pred_lin_g, 'Linear Regression')\n",
+    "pred_sheet_part_g_poly = regression_prediction_sheet(y_test_g, y_pred_poly_g, 'Polynomial Regression')\n",
+    "pred_sheet_part_g_ridge = regression_prediction_sheet(y_test_g, y_pred_ridge_g, f'Ridge Regression (alpha={best_ridge_alpha_g})')\n",
+    "\n",
+    "part_g_pred_sheet = {\n",
+    "    'Linear Regression': pred_sheet_part_g_linear,\n",
+    "    'Polynomial Regression': pred_sheet_part_g_poly,\n",
+    "}.get(best_model_g, pred_sheet_part_g_linear)\n",
+    "\n",
+    "if 'Ridge Regression' in best_model_g:\n",
+    "    part_g_pred_sheet = pred_sheet_part_g_ridge\n",
+    "\n",
+    "part_g_pred_sheet"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   Part G Best Model\n",
+       "0  Linear Regression"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -4303,17 +4319,39 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Part G Best Model\n",
-       "0  Linear Regression"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
+      "text/plain": [
+       "          True     Predicted      Residual     Abs_Error              Model\n",
+       "0   52428800.0  3.171894e+07  2.070986e+07  2.070986e+07  Linear Regression\n",
+       "1   26214400.0  1.364562e+07  1.256878e+07  1.256878e+07  Linear Regression\n",
+       "2   15728640.0  2.325830e+07 -7.529660e+06  7.529660e+06  Linear Regression\n",
+       "3   10171187.2  2.181280e+07 -1.164161e+07  1.164161e+07  Linear Regression\n",
+       "4   18874368.0  2.145150e+07 -2.577137e+06  2.577137e+06  Linear Regression\n",
+       "5    2831155.2  1.371277e+07 -1.088162e+07  1.088162e+07  Linear Regression\n",
+       "6    3250585.6  1.375252e+07 -1.050193e+07  1.050193e+07  Linear Regression\n",
+       "7   27262976.0  1.933636e+07  7.926614e+06  7.926614e+06  Linear Regression\n",
+       "8   15728640.0  1.726232e+07 -1.533682e+06  1.533682e+06  Linear Regression\n",
+       "9   71303168.0  3.203716e+07  3.926601e+07  3.926601e+07  Linear Regression\n",
+       "10  97517568.0  2.900348e+07  6.851409e+07  6.851409e+07  Linear Regression\n",
+       "11   2621440.0  2.145499e+07 -1.883355e+07  1.883355e+07  Linear Regression\n",
+       "12  40894464.0  2.853311e+07  1.236136e+07  1.236136e+07  Linear Regression\n",
+       "13  11534336.0  3.188308e+07 -2.034874e+07  2.034874e+07  Linear Regression\n",
+       "14  44040192.0  3.146167e+07  1.257852e+07  1.257852e+07  Linear Regression\n",
+       "15  22020096.0  2.135446e+07  6.656323e+05  6.656323e+05  Linear Regression\n",
+       "16  31457280.0  2.574583e+07  5.711451e+06  5.711451e+06  Linear Regression\n",
+       "17   8808038.4  2.481990e+07 -1.601186e+07  1.601186e+07  Linear Regression\n",
+       "18   7864320.0  1.801774e+07 -1.015342e+07  1.015342e+07  Linear Regression\n",
+       "19   2936012.8  1.400022e+07 -1.106421e+07  1.106421e+07  Linear Regression"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -4504,67 +4542,89 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "          True     Predicted      Residual     Abs_Error              Model\n",
-       "0   52428800.0  3.171894e+07  2.070986e+07  2.070986e+07  Linear Regression\n",
-       "1   26214400.0  1.364562e+07  1.256878e+07  1.256878e+07  Linear Regression\n",
-       "2   15728640.0  2.325830e+07 -7.529660e+06  7.529660e+06  Linear Regression\n",
-       "3   10171187.2  2.181280e+07 -1.164161e+07  1.164161e+07  Linear Regression\n",
-       "4   18874368.0  2.145150e+07 -2.577137e+06  2.577137e+06  Linear Regression\n",
-       "5    2831155.2  1.371277e+07 -1.088162e+07  1.088162e+07  Linear Regression\n",
-       "6    3250585.6  1.375252e+07 -1.050193e+07  1.050193e+07  Linear Regression\n",
-       "7   27262976.0  1.933636e+07  7.926614e+06  7.926614e+06  Linear Regression\n",
-       "8   15728640.0  1.726232e+07 -1.533682e+06  1.533682e+06  Linear Regression\n",
-       "9   71303168.0  3.203716e+07  3.926601e+07  3.926601e+07  Linear Regression\n",
-       "10  97517568.0  2.900348e+07  6.851409e+07  6.851409e+07  Linear Regression\n",
-       "11   2621440.0  2.145499e+07 -1.883355e+07  1.883355e+07  Linear Regression\n",
-       "12  40894464.0  2.853311e+07  1.236136e+07  1.236136e+07  Linear Regression\n",
-       "13  11534336.0  3.188308e+07 -2.034874e+07  2.034874e+07  Linear Regression\n",
-       "14  44040192.0  3.146167e+07  1.257852e+07  1.257852e+07  Linear Regression\n",
-       "15  22020096.0  2.135446e+07  6.656323e+05  6.656323e+05  Linear Regression\n",
-       "16  31457280.0  2.574583e+07  5.711451e+06  5.711451e+06  Linear Regression\n",
-       "17   8808038.4  2.481990e+07 -1.601186e+07  1.601186e+07  Linear Regression\n",
-       "18   7864320.0  1.801774e+07 -1.015342e+07  1.015342e+07  Linear Regression\n",
-       "19   2936012.8  1.400022e+07 -1.106421e+07  1.106421e+07  Linear Regression"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "display(pd.DataFrame([{'Part G Best Model': best_model_g}]))\n",
-    "\n",
-    "pred_sheet_part_g_linear = regression_prediction_sheet(y_test_g, y_pred_lin_g, 'Linear Regression')\n",
-    "pred_sheet_part_g_poly = regression_prediction_sheet(y_test_g, y_pred_poly_g, 'Polynomial Regression')\n",
-    "pred_sheet_part_g_ridge = regression_prediction_sheet(y_test_g, y_pred_ridge_g, f'Ridge Regression (alpha={best_ridge_alpha_g})')\n",
-    "\n",
-    "part_g_pred_sheet = {\n",
-    "    'Linear Regression': pred_sheet_part_g_linear,\n",
-    "    'Polynomial Regression': pred_sheet_part_g_poly,\n",
-    "}.get(best_model_g, pred_sheet_part_g_linear)\n",
-    "\n",
-    "if 'Ridge Regression' in best_model_g:\n",
-    "    part_g_pred_sheet = pred_sheet_part_g_ridge\n",
-    "\n",
-    "part_g_pred_sheet"
-   ]
+   "execution_count": 137
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
    "id": "fdf6287a5686175e",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.087583500Z",
-     "start_time": "2026-04-26T14:22:32.073314700Z"
+     "end_time": "2026-04-26T14:56:46.658180900Z",
+     "start_time": "2026-04-26T14:56:46.615876800Z"
     }
    },
+   "source": [
+    "part_g_prediction_compare = pd.concat([\n",
+    "    pred_sheet_part_g_linear.head(8),\n",
+    "    pred_sheet_part_g_poly.head(8),\n",
+    "    pred_sheet_part_g_ridge.head(8),\n",
+    "], ignore_index=True)\n",
+    "\n",
+    "part_g_prediction_compare"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "          True     Predicted      Residual     Abs_Error  \\\n",
+       "0   52428800.0  3.171894e+07  2.070986e+07  2.070986e+07   \n",
+       "1   26214400.0  1.364562e+07  1.256878e+07  1.256878e+07   \n",
+       "2   15728640.0  2.325830e+07 -7.529660e+06  7.529660e+06   \n",
+       "3   10171187.2  2.181280e+07 -1.164161e+07  1.164161e+07   \n",
+       "4   18874368.0  2.145150e+07 -2.577137e+06  2.577137e+06   \n",
+       "5    2831155.2  1.371277e+07 -1.088162e+07  1.088162e+07   \n",
+       "6    3250585.6  1.375252e+07 -1.050193e+07  1.050193e+07   \n",
+       "7   27262976.0  1.933636e+07  7.926614e+06  7.926614e+06   \n",
+       "8   52428800.0  2.267715e+07  2.975165e+07  2.975165e+07   \n",
+       "9   26214400.0  1.925624e+07  6.958158e+06  6.958158e+06   \n",
+       "10  15728640.0  2.101692e+07 -5.288280e+06  5.288280e+06   \n",
+       "11  10171187.2  2.107864e+07 -1.090745e+07  1.090745e+07   \n",
+       "12  18874368.0  2.302922e+07 -4.154853e+06  4.154853e+06   \n",
+       "13   2831155.2  2.101016e+07 -1.817901e+07  1.817901e+07   \n",
+       "14   3250585.6  2.098037e+07 -1.772979e+07  1.772979e+07   \n",
+       "15  27262976.0  2.102464e+07  6.238332e+06  6.238332e+06   \n",
+       "16  52428800.0  2.375635e+07  2.867245e+07  2.867245e+07   \n",
+       "17  26214400.0  2.357213e+07  2.642274e+06  2.642274e+06   \n",
+       "18  15728640.0  2.349521e+07 -7.766567e+06  7.766567e+06   \n",
+       "19  10171187.2  2.349771e+07 -1.332652e+07  1.332652e+07   \n",
+       "20  18874368.0  2.349279e+07 -4.618422e+06  4.618422e+06   \n",
+       "21   2831155.2  2.349545e+07 -2.066429e+07  2.066429e+07   \n",
+       "22   3250585.6  2.349522e+07 -2.024464e+07  2.024464e+07   \n",
+       "23  27262976.0  2.349620e+07  3.766777e+06  3.766777e+06   \n",
+       "\n",
+       "                           Model  \n",
+       "0              Linear Regression  \n",
+       "1              Linear Regression  \n",
+       "2              Linear Regression  \n",
+       "3              Linear Regression  \n",
+       "4              Linear Regression  \n",
+       "5              Linear Regression  \n",
+       "6              Linear Regression  \n",
+       "7              Linear Regression  \n",
+       "8          Polynomial Regression  \n",
+       "9          Polynomial Regression  \n",
+       "10         Polynomial Regression  \n",
+       "11         Polynomial Regression  \n",
+       "12         Polynomial Regression  \n",
+       "13         Polynomial Regression  \n",
+       "14         Polynomial Regression  \n",
+       "15         Polynomial Regression  \n",
+       "16  Ridge Regression (alpha=1.0)  \n",
+       "17  Ridge Regression (alpha=1.0)  \n",
+       "18  Ridge Regression (alpha=1.0)  \n",
+       "19  Ridge Regression (alpha=1.0)  \n",
+       "20  Ridge Regression (alpha=1.0)  \n",
+       "21  Ridge Regression (alpha=1.0)  \n",
+       "22  Ridge Regression (alpha=1.0)  \n",
+       "23  Ridge Regression (alpha=1.0)  "
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -4787,75 +4847,14 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "          True     Predicted      Residual     Abs_Error  \\\n",
-       "0   52428800.0  3.171894e+07  2.070986e+07  2.070986e+07   \n",
-       "1   26214400.0  1.364562e+07  1.256878e+07  1.256878e+07   \n",
-       "2   15728640.0  2.325830e+07 -7.529660e+06  7.529660e+06   \n",
-       "3   10171187.2  2.181280e+07 -1.164161e+07  1.164161e+07   \n",
-       "4   18874368.0  2.145150e+07 -2.577137e+06  2.577137e+06   \n",
-       "5    2831155.2  1.371277e+07 -1.088162e+07  1.088162e+07   \n",
-       "6    3250585.6  1.375252e+07 -1.050193e+07  1.050193e+07   \n",
-       "7   27262976.0  1.933636e+07  7.926614e+06  7.926614e+06   \n",
-       "8   52428800.0  2.267715e+07  2.975165e+07  2.975165e+07   \n",
-       "9   26214400.0  1.925624e+07  6.958158e+06  6.958158e+06   \n",
-       "10  15728640.0  2.101692e+07 -5.288280e+06  5.288280e+06   \n",
-       "11  10171187.2  2.107864e+07 -1.090745e+07  1.090745e+07   \n",
-       "12  18874368.0  2.302922e+07 -4.154853e+06  4.154853e+06   \n",
-       "13   2831155.2  2.101016e+07 -1.817901e+07  1.817901e+07   \n",
-       "14   3250585.6  2.098037e+07 -1.772979e+07  1.772979e+07   \n",
-       "15  27262976.0  2.102464e+07  6.238332e+06  6.238332e+06   \n",
-       "16  52428800.0  2.375635e+07  2.867245e+07  2.867245e+07   \n",
-       "17  26214400.0  2.357213e+07  2.642274e+06  2.642274e+06   \n",
-       "18  15728640.0  2.349521e+07 -7.766567e+06  7.766567e+06   \n",
-       "19  10171187.2  2.349771e+07 -1.332652e+07  1.332652e+07   \n",
-       "20  18874368.0  2.349279e+07 -4.618422e+06  4.618422e+06   \n",
-       "21   2831155.2  2.349545e+07 -2.066429e+07  2.066429e+07   \n",
-       "22   3250585.6  2.349522e+07 -2.024464e+07  2.024464e+07   \n",
-       "23  27262976.0  2.349620e+07  3.766777e+06  3.766777e+06   \n",
-       "\n",
-       "                           Model  \n",
-       "0              Linear Regression  \n",
-       "1              Linear Regression  \n",
-       "2              Linear Regression  \n",
-       "3              Linear Regression  \n",
-       "4              Linear Regression  \n",
-       "5              Linear Regression  \n",
-       "6              Linear Regression  \n",
-       "7              Linear Regression  \n",
-       "8          Polynomial Regression  \n",
-       "9          Polynomial Regression  \n",
-       "10         Polynomial Regression  \n",
-       "11         Polynomial Regression  \n",
-       "12         Polynomial Regression  \n",
-       "13         Polynomial Regression  \n",
-       "14         Polynomial Regression  \n",
-       "15         Polynomial Regression  \n",
-       "16  Ridge Regression (alpha=1.0)  \n",
-       "17  Ridge Regression (alpha=1.0)  \n",
-       "18  Ridge Regression (alpha=1.0)  \n",
-       "19  Ridge Regression (alpha=1.0)  \n",
-       "20  Ridge Regression (alpha=1.0)  \n",
-       "21  Ridge Regression (alpha=1.0)  \n",
-       "22  Ridge Regression (alpha=1.0)  \n",
-       "23  Ridge Regression (alpha=1.0)  "
       ]
      },
-     "execution_count": 89,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "part_g_prediction_compare = pd.concat([\n",
-    "    pred_sheet_part_g_linear.head(8),\n",
-    "    pred_sheet_part_g_poly.head(8),\n",
-    "    pred_sheet_part_g_ridge.head(8),\n",
-    "], ignore_index=True)\n",
-    "\n",
-    "part_g_prediction_compare"
-   ]
+   "execution_count": 138
   },
   {
    "cell_type": "markdown",
@@ -4864,28 +4863,27 @@
    "source": [
     "### Part G Answer (with Cross-Validation)\n",
     "\n",
-    "For Part G, the goal is to predict **`Size in bytes`** using the inputs:\n",
+    "For Part G, the goal is to predict **`Size in bytes`** using the other inputs:\n",
+    "- `Category` \n",
+    "- `Reviews` \n",
+    "- `Content Rating` \n",
+    "- `Rating` \n",
+    "- `Numeric Installs` \n",
     "\n",
-    "- `Category` (app category)\n",
-    "- `Reviews` (number of reviews)\n",
-    "- `Content Rating` (age/content classification)\n",
-    "- `Rating` (average rating)\n",
-    "- `Numeric Installs` (install count as a number)\n",
+    "I compared the allowed regression models using a standard train/test split, but this time I also added **5-fold cross-validation** on the training set.\n",
     "\n",
-    "I compared the allowed regression models using a **standard train/test split** plus **5-fold cross-validation** on the training set.\n",
-    "\n",
-    "**How I decide the “best” model:**\n",
-    "- I prioritise the model with the highest **Cross-Validation mean R²** (best generalisation across folds).\n",
-    "- I then use **Test RMSE** as a secondary check (lower is better).\n",
+    "**How I decided which model is best:**\n",
+    "- First, I looked for the model with the highest **Cross-Validation mean R²** since that tells me how well the model generalizes across different splits of the data.\n",
+    "- Then, I used **Test RMSE** as a secondary check (lower is better).\n",
     "\n",
     "**What the results show:**\n",
-    "- **Linear Regression** is the strongest overall here: it has the best (highest) mean CV R² and the best test R² among the tested models, with a lower test RMSE than Ridge and far better stability than the polynomial model.\n",
-    "- **Polynomial Regression** performs poorly for this target on this dataset (negative test R² and a very unstable CV R² with large standard deviation), which suggests it is not generalising well.\n",
-    "- **Ridge Regression** is more stable than the polynomial model, but it still underperforms Linear Regression on both CV R² and test RMSE.\n",
+    "- **Linear Regression** is the strongest overall here: it has the best mean CV R² and the best test R², along with a lower test RMSE than Ridge. It's also way more stable than the polynomial model.\n",
+    "- **Polynomial Regression** actually performs really poorly for this dataset (it got a negative test R² and a super unstable CV R²), meaning it's overcomplicating things and not generalizing well.\n",
+    "- **Ridge Regression** is more stable than the polynomial model, but it still underperforms compared to standard Linear Regression on both metrics.\n",
     "\n",
-    "Based on the cross-validation and test metrics above, **Linear Regression is the best regression model for predicting `Size in bytes` for Part G**.\n",
+    "So, based on the cross-validation and test metrics, **Linear Regression is the best model for predicting `Size in bytes`**.\n",
     "\n",
-    "I also use the residual and actual-vs-predicted plots to visually confirm whether errors are randomly spread (good) or show patterns (model mismatch)."
+    "I also use the residual and actual-vs-predicted plots to visually confirm if the errors are randomly spread out (which is what we want) or if they show patterns (which means the model is missing something).\n"
    ]
   }
  ],
diff --git a/Q2.ipynb b/Q2.ipynb
index 5a81874..fea82af 100644
--- a/Q2.ipynb
+++ b/Q2.ipynb
@@ -9,20 +9,18 @@
    "source": [
     "## This is the Q2 Notebook!\n",
     "\n",
-    "It's tracked via GitHub! hence the need for this line for the init commit"
+    "It's tracked via GitHub! hence the need for this line for the init commit\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
    "id": "76e70b2ed9af0b56",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.055434300Z",
-     "start_time": "2026-04-26T14:22:29.010947900Z"
+     "end_time": "2026-04-26T14:56:44.335458200Z",
+     "start_time": "2026-04-26T14:56:44.305355100Z"
     }
    },
-   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -31,39 +29,36 @@
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
-    "import matplotlib.pyplot as plt"
-   ]
+    "import matplotlib.pyplot as plt\n"
+   ],
+   "outputs": [],
+   "execution_count": 58
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
    "id": "4a05800d",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.119438800Z",
-     "start_time": "2026-04-26T14:22:29.079996400Z"
+     "end_time": "2026-04-26T14:56:44.383004500Z",
+     "start_time": "2026-04-26T14:56:44.336457100Z"
     }
    },
+   "source": "seed = 101",
    "outputs": [],
-   "source": [
-    "# Keep one fixed seed so your splits and metrics are stable each run.\n",
-    "seed = 101"
-   ]
+   "execution_count": 59
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
    "id": "f44380615d3aba25",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.148305400Z",
-     "start_time": "2026-04-26T14:22:29.122438300Z"
+     "end_time": "2026-04-26T14:56:44.406688400Z",
+     "start_time": "2026-04-26T14:56:44.385009400Z"
     }
    },
+   "source": "df = pd.read_csv('data/googleplaystore_new_new.csv')",
    "outputs": [],
-   "source": [
-    "df = pd.read_csv('data/googleplaystore_new_new.csv')"
-   ]
+   "execution_count": 60
   },
   {
    "cell_type": "markdown",
@@ -80,117 +75,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
    "id": "f6fd7137bf91f31e",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.200889200Z",
-     "start_time": "2026-04-26T14:22:29.170323700Z"
+     "end_time": "2026-04-26T14:56:44.467226100Z",
+     "start_time": "2026-04-26T14:56:44.409261600Z"
     }
    },
-   "outputs": [],
    "source": [
     "columns_to_keep = ['Rating', 'Reviews', 'Content Rating', 'Size in bytes', 'Numeric Installs', 'Category']\n",
     "df_part_a = df[columns_to_keep]"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 61
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
    "id": "b5daf475a5d5ca15",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.231293400Z",
-     "start_time": "2026-04-26T14:22:29.201895100Z"
+     "end_time": "2026-04-26T14:56:44.520866900Z",
+     "start_time": "2026-04-26T14:56:44.493245700Z"
     }
    },
-   "outputs": [],
    "source": [
     "X_raw = df_part_a.drop('Category', axis=1)\n",
     "y = df_part_a['Category']"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 62
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
    "id": "99a441c665dcc19b",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.285866900Z",
-     "start_time": "2026-04-26T14:22:29.232290500Z"
+     "end_time": "2026-04-26T14:56:44.551356Z",
+     "start_time": "2026-04-26T14:56:44.522872900Z"
     }
    },
+   "source": "X = pd.get_dummies(X_raw, columns=['Content Rating'])",
    "outputs": [],
-   "source": [
-    "X = pd.get_dummies(X_raw, columns=['Content Rating'])"
-   ]
+   "execution_count": 63
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
    "id": "c6eb622c0f7ec63c",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.328943200Z",
-     "start_time": "2026-04-26T14:22:29.288372900Z"
+     "end_time": "2026-04-26T14:56:44.636162Z",
+     "start_time": "2026-04-26T14:56:44.582107Z"
     }
    },
+   "source": "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)",
    "outputs": [],
-   "source": [
-    "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)"
-   ]
+   "execution_count": 64
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
    "id": "89e8f117f057e0e2",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.372324200Z",
-     "start_time": "2026-04-26T14:22:29.330948900Z"
+     "end_time": "2026-04-26T14:56:44.771635900Z",
+     "start_time": "2026-04-26T14:56:44.683619300Z"
     }
    },
+   "source": "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.25, random_state=seed)",
    "outputs": [],
-   "source": [
-    "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.25, random_state=seed)"
-   ]
+   "execution_count": 65
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
    "id": "b9c0f0dcdb1e5097",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.426224200Z",
-     "start_time": "2026-04-26T14:22:29.374321200Z"
+     "end_time": "2026-04-26T14:56:44.877796Z",
+     "start_time": "2026-04-26T14:56:44.774635400Z"
     }
    },
-   "outputs": [],
    "source": [
     "scaler = StandardScaler()\n",
     "scaled_X_train = scaler.fit_transform(X_train)\n",
     "scaled_X_val = scaler.transform(X_val)\n",
     "scaled_X_test = scaler.transform(X_test)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 66
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
    "id": "930d2242b3f0683d",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.456903100Z",
-     "start_time": "2026-04-26T14:22:29.427224100Z"
+     "end_time": "2026-04-26T14:56:44.938761200Z",
+     "start_time": "2026-04-26T14:56:44.880305300Z"
     }
    },
-   "outputs": [],
    "source": [
     "def eval_metrics(y_true, y_pred, label=\"Model\"):\n",
     "    acc = accuracy_score(y_true, y_pred)\n",
     "    f1_w = f1_score(y_true, y_pred, average='weighted')\n",
     "    return acc, f1_w\n",
     "\n",
-    "\n",
     "def split_overview(train_rows, test_rows, val_rows=None):\n",
     "    rows = [\n",
     "        {'Split': 'Train', 'Rows': train_rows},\n",
@@ -200,7 +186,6 @@
     "        rows.insert(1, {'Split': 'Validation', 'Rows': val_rows})\n",
     "    return pd.DataFrame(rows)\n",
     "\n",
-    "\n",
     "def prediction_sheet(y_true, y_pred, y_prob, prefix, head_n=20):\n",
     "    pred_idx = np.argmax(y_prob, axis=1)\n",
     "    pred_conf = y_prob[np.arange(len(y_prob)), pred_idx]\n",
@@ -211,21 +196,29 @@
     "    })\n",
     "    sheet['Correct'] = sheet[f'True_{prefix}'] == sheet[f'Predicted_{prefix}']\n",
     "    return sheet.head(head_n)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 67
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
    "id": "1a3a319e65a8a24b",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.544499600Z",
-     "start_time": "2026-04-26T14:22:29.484412800Z"
+     "end_time": "2026-04-26T14:56:45.030840800Z",
+     "start_time": "2026-04-26T14:56:44.943761600Z"
     }
    },
+   "source": "split_overview(len(X_train), len(X_test), len(X_val))",
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "        Split  Rows\n",
+       "0       Train   663\n",
+       "1  Validation   222\n",
+       "2        Test   222"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -268,57 +261,63 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "        Split  Rows\n",
-       "0       Train   663\n",
-       "1  Validation   222\n",
-       "2        Test   222"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "split_overview(len(X_train), len(X_test), len(X_val))"
-   ]
+   "execution_count": 68
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
    "id": "2f619e1e9117258e",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.601534800Z",
-     "start_time": "2026-04-26T14:22:29.546506800Z"
+     "end_time": "2026-04-26T14:56:45.078915300Z",
+     "start_time": "2026-04-26T14:56:45.032840900Z"
     }
    },
+   "source": [],
    "outputs": [],
-   "source": []
+   "execution_count": 68
   },
   {
    "cell_type": "markdown",
    "id": "66dc17cdf1a00a06",
    "metadata": {},
    "source": [
-    "## Logistic Regression"
+    "## Logistic Regression\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
    "id": "84128fa4e7823565",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.657949200Z",
-     "start_time": "2026-04-26T14:22:29.603533700Z"
+     "end_time": "2026-04-26T14:56:45.171623800Z",
+     "start_time": "2026-04-26T14:56:45.079913800Z"
     }
    },
+   "source": [
+    "# Logistic Regression (tuned a bit for better performance on imbalanced classes)\n",
+    "log_model = LogisticRegression(\n",
+    "    max_iter=5000,\n",
+    "    random_state=seed,\n",
+    "    class_weight='balanced',\n",
+    "    C=2.0,\n",
+    "    solver='lbfgs'\n",
+    ")\n",
+    "log_model.fit(scaled_X_train, y_train)"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "LogisticRegression(C=2.0, class_weight='balanced', max_iter=5000,\n",
+       "                   random_state=101)"
+      ],
       "text/html": [
        "<style>#sk-container-id-3 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -1221,74 +1220,82 @@
        "}\n",
        "\n",
        "forceTheme('sk-container-id-3');</script></body>"
-      ],
-      "text/plain": [
-       "LogisticRegression(C=2.0, class_weight='balanced', max_iter=5000,\n",
-       "                   random_state=101)"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# Logistic Regression (tuned a bit for better performance on imbalanced classes)\n",
-    "log_model = LogisticRegression(\n",
-    "    max_iter=5000,\n",
-    "    random_state=seed,\n",
-    "    class_weight='balanced',\n",
-    "    C=2.0,\n",
-    "    solver='lbfgs'\n",
-    ")\n",
-    "log_model.fit(scaled_X_train, y_train)"
-   ]
+   "execution_count": 69
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
    "id": "965e16ce48443c97",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.693126Z",
-     "start_time": "2026-04-26T14:22:29.664952800Z"
+     "end_time": "2026-04-26T14:56:45.231570600Z",
+     "start_time": "2026-04-26T14:56:45.175623300Z"
     }
    },
-   "outputs": [],
    "source": [
     "y_val_pred_log = log_model.predict(scaled_X_val)\n",
     "log_val_acc, log_val_f1 = eval_metrics(y_val, y_val_pred_log, \"LogReg (Validation)\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 70
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
    "id": "9be803af2f34cd6",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.738350400Z",
-     "start_time": "2026-04-26T14:22:29.694126100Z"
+     "end_time": "2026-04-26T14:56:45.276895Z",
+     "start_time": "2026-04-26T14:56:45.232570600Z"
     }
    },
-   "outputs": [],
    "source": [
     "y_test_pred_log = log_model.predict(scaled_X_test)\n",
     "log_test_acc, log_test_f1 = eval_metrics(y_test, y_test_pred_log, \"LogReg (Test)\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 71
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
    "id": "6a22b4c9c5a0876a",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:29.767862Z",
-     "start_time": "2026-04-26T14:22:29.739348Z"
+     "end_time": "2026-04-26T14:56:45.350577500Z",
+     "start_time": "2026-04-26T14:56:45.277893100Z"
     }
    },
+   "source": [
+    "log_probs = log_model.predict_proba(scaled_X_test)\n",
+    "log_pred_df = prediction_sheet(y_test, y_test_pred_log, log_probs, 'Category', head_n=15)\n",
+    "log_pred_df"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "         True_Category  Predicted_Category  Predicted_Confidence  Correct\n",
+       "0              FINANCE             FINANCE              0.144609     True\n",
+       "1        COMMUNICATION            BUSINESS              0.094501    False\n",
+       "2   LIBRARIES_AND_DEMO              BEAUTY              0.086577    False\n",
+       "3            LIFESTYLE              EVENTS              0.105029    False\n",
+       "4            EDUCATION              EVENTS              0.087598    False\n",
+       "5       ART_AND_DESIGN      HOUSE_AND_HOME              0.189590    False\n",
+       "6               BEAUTY      ART_AND_DESIGN              0.102613    False\n",
+       "7               DATING              DATING              0.699165     True\n",
+       "8       HOUSE_AND_HOME      HOUSE_AND_HOME              0.131807     True\n",
+       "9              FINANCE  HEALTH_AND_FITNESS              0.183727    False\n",
+       "10  HEALTH_AND_FITNESS                GAME              0.348999    False\n",
+       "11           EDUCATION      ART_AND_DESIGN              0.112001    False\n",
+       "12  HEALTH_AND_FITNESS  HEALTH_AND_FITNESS              0.118400     True\n",
+       "13             FINANCE              BEAUTY              0.088732    False\n",
+       "14             FINANCE             FINANCE              0.124280     True"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1423,56 +1430,32 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "         True_Category  Predicted_Category  Predicted_Confidence  Correct\n",
-       "0              FINANCE             FINANCE              0.144609     True\n",
-       "1        COMMUNICATION            BUSINESS              0.094501    False\n",
-       "2   LIBRARIES_AND_DEMO              BEAUTY              0.086577    False\n",
-       "3            LIFESTYLE              EVENTS              0.105029    False\n",
-       "4            EDUCATION              EVENTS              0.087598    False\n",
-       "5       ART_AND_DESIGN      HOUSE_AND_HOME              0.189590    False\n",
-       "6               BEAUTY      ART_AND_DESIGN              0.102613    False\n",
-       "7               DATING              DATING              0.699165     True\n",
-       "8       HOUSE_AND_HOME      HOUSE_AND_HOME              0.131807     True\n",
-       "9              FINANCE  HEALTH_AND_FITNESS              0.183727    False\n",
-       "10  HEALTH_AND_FITNESS                GAME              0.348999    False\n",
-       "11           EDUCATION      ART_AND_DESIGN              0.112001    False\n",
-       "12  HEALTH_AND_FITNESS  HEALTH_AND_FITNESS              0.118400     True\n",
-       "13             FINANCE              BEAUTY              0.088732    False\n",
-       "14             FINANCE             FINANCE              0.124280     True"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "log_probs = log_model.predict_proba(scaled_X_test)\n",
-    "log_pred_df = prediction_sheet(y_test, y_test_pred_log, log_probs, 'Category', head_n=15)\n",
-    "log_pred_df"
-   ]
+   "execution_count": 72
   },
   {
    "cell_type": "markdown",
    "id": "2b7b85284cddecb2",
    "metadata": {},
    "source": [
-    "## KNN"
+    "## KNN\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
    "id": "a8d69c56d1a21825",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.447555200Z",
-     "start_time": "2026-04-26T14:22:29.769862300Z"
+     "end_time": "2026-04-26T14:56:45.930039900Z",
+     "start_time": "2026-04-26T14:56:45.351579800Z"
     }
    },
-   "outputs": [],
    "source": [
     "best_k = 1\n",
     "best_f1 = 0.0\n",
@@ -1502,30 +1485,19 @@
     "    if val_f1_k > best_f1:\n",
     "        best_f1 = val_f1_k\n",
     "        best_k = k"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 73
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
    "id": "8206b3ab050f67e9",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.632246600Z",
-     "start_time": "2026-04-26T14:22:30.551913300Z"
+     "end_time": "2026-04-26T14:56:46.108932900Z",
+     "start_time": "2026-04-26T14:56:46.021739200Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "plt.figure(figsize=(10, 6), dpi=120)\n",
     "plt.plot(k_candidates, test_error_rates, label='test error')\n",
@@ -1534,21 +1506,43 @@
     "plt.legend()\n",
     "plt.grid(alpha=0.3)\n",
     "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "a0f18919585864e0",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.686659800Z",
-     "start_time": "2026-04-26T14:22:30.633246300Z"
-    }
-   },
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "<Figure size 1200x720 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 74
+  },
+  {
+   "cell_type": "code",
+   "id": "a0f18919585864e0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-26T14:56:46.161829600Z",
+     "start_time": "2026-04-26T14:56:46.131951100Z"
+    }
+   },
+   "source": [
+    "knn_final = KNeighborsClassifier(n_neighbors=best_k, weights='distance')\n",
+    "knn_final.fit(scaled_X_train, y_train)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=19, weights='distance')"
+      ],
       "text/html": [
        "<style>#sk-container-id-4 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
@@ -2354,66 +2348,82 @@
        "}\n",
        "\n",
        "forceTheme('sk-container-id-4');</script></body>"
-      ],
-      "text/plain": [
-       "KNeighborsClassifier(n_neighbors=19, weights='distance')"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "knn_final = KNeighborsClassifier(n_neighbors=best_k, weights='distance')\n",
-    "knn_final.fit(scaled_X_train, y_train)"
-   ]
+   "execution_count": 75
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
    "id": "fff19d47a4e5d268",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.712628200Z",
-     "start_time": "2026-04-26T14:22:30.688659100Z"
+     "end_time": "2026-04-26T14:56:46.199632900Z",
+     "start_time": "2026-04-26T14:56:46.165028300Z"
     }
    },
-   "outputs": [],
    "source": [
     "y_val_pred_knn = knn_final.predict(scaled_X_val)\n",
     "knn_val_acc, knn_val_f1 = eval_metrics(y_val, y_val_pred_knn, f\"KNN k={best_k} (Validation)\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 76
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
    "id": "7bca71dd45a3207e",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.745997600Z",
-     "start_time": "2026-04-26T14:22:30.713628100Z"
+     "end_time": "2026-04-26T14:56:46.229948800Z",
+     "start_time": "2026-04-26T14:56:46.200633Z"
     }
    },
-   "outputs": [],
    "source": [
     "y_test_pred_knn = knn_final.predict(scaled_X_test)\n",
     "knn_test_acc, knn_test_f1 = eval_metrics(y_test, y_test_pred_knn, f\"KNN k={best_k} (Test)\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 77
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
    "id": "f6cef056afb2b5f4",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:30.797330100Z",
-     "start_time": "2026-04-26T14:22:30.746999400Z"
+     "end_time": "2026-04-26T14:56:46.275317300Z",
+     "start_time": "2026-04-26T14:56:46.231945600Z"
     }
    },
+   "source": [
+    "knn_probs = knn_final.predict_proba(scaled_X_test)\n",
+    "knn_pred_df = prediction_sheet(y_test, y_test_pred_knn, knn_probs, 'Category', head_n=15)\n",
+    "knn_pred_df"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "         True_Category  Predicted_Category  Predicted_Confidence  Correct\n",
+       "0              FINANCE             FINANCE              0.322858     True\n",
+       "1        COMMUNICATION             FINANCE              0.149371    False\n",
+       "2   LIBRARIES_AND_DEMO  HEALTH_AND_FITNESS              0.675556    False\n",
+       "3            LIFESTYLE             FINANCE              0.220342    False\n",
+       "4            EDUCATION                GAME              0.982053    False\n",
+       "5       ART_AND_DESIGN           LIFESTYLE              0.696031    False\n",
+       "6               BEAUTY              BEAUTY              0.331699     True\n",
+       "7               DATING              DATING              0.921884     True\n",
+       "8       HOUSE_AND_HOME           LIFESTYLE              0.187210    False\n",
+       "9              FINANCE  HEALTH_AND_FITNESS              0.577385    False\n",
+       "10  HEALTH_AND_FITNESS                GAME              0.308132    False\n",
+       "11           EDUCATION  HEALTH_AND_FITNESS              0.346198    False\n",
+       "12  HEALTH_AND_FITNESS  HEALTH_AND_FITNESS              0.185119     True\n",
+       "13             FINANCE      ART_AND_DESIGN              0.198243    False\n",
+       "14             FINANCE             FINANCE              0.365431     True"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -2548,59 +2558,24 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "         True_Category  Predicted_Category  Predicted_Confidence  Correct\n",
-       "0              FINANCE             FINANCE              0.322858     True\n",
-       "1        COMMUNICATION             FINANCE              0.149371    False\n",
-       "2   LIBRARIES_AND_DEMO  HEALTH_AND_FITNESS              0.675556    False\n",
-       "3            LIFESTYLE             FINANCE              0.220342    False\n",
-       "4            EDUCATION                GAME              0.982053    False\n",
-       "5       ART_AND_DESIGN           LIFESTYLE              0.696031    False\n",
-       "6               BEAUTY              BEAUTY              0.331699     True\n",
-       "7               DATING              DATING              0.921884     True\n",
-       "8       HOUSE_AND_HOME           LIFESTYLE              0.187210    False\n",
-       "9              FINANCE  HEALTH_AND_FITNESS              0.577385    False\n",
-       "10  HEALTH_AND_FITNESS                GAME              0.308132    False\n",
-       "11           EDUCATION  HEALTH_AND_FITNESS              0.346198    False\n",
-       "12  HEALTH_AND_FITNESS  HEALTH_AND_FITNESS              0.185119     True\n",
-       "13             FINANCE      ART_AND_DESIGN              0.198243    False\n",
-       "14             FINANCE             FINANCE              0.365431     True"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "knn_probs = knn_final.predict_proba(scaled_X_test)\n",
-    "knn_pred_df = prediction_sheet(y_test, y_test_pred_knn, knn_probs, 'Category', head_n=15)\n",
-    "knn_pred_df"
-   ]
+   "execution_count": 78
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
    "id": "afeb457d3f0b329d",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.008708Z",
-     "start_time": "2026-04-26T14:22:30.799331200Z"
+     "end_time": "2026-04-26T14:56:46.476719300Z",
+     "start_time": "2026-04-26T14:56:46.309592300Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "best_acc_idx = int(np.argmax(knn_val_acc_scores))\n",
     "best_f1_idx = int(np.argmax(knn_val_f1_scores))\n",
@@ -2630,21 +2605,64 @@
     "plt.grid(alpha=0.3)\n",
     "plt.legend()\n",
     "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "36c97146b1995aaf",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.060021900Z",
-     "start_time": "2026-04-26T14:22:31.010211900Z"
-    }
-   },
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 79
+  },
+  {
+   "cell_type": "code",
+   "id": "36c97146b1995aaf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-26T14:56:46.523885900Z",
+     "start_time": "2026-04-26T14:56:46.498250100Z"
+    }
+   },
+   "source": [
+    "results_df = pd.DataFrame([\n",
+    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Validation\", \"Accuracy\": log_val_acc, \"Weighted F1\": log_val_f1},\n",
+    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Test\",       \"Accuracy\": log_test_acc, \"Weighted F1\": log_test_f1},\n",
+    "    {\"Model\": f\"KNN (k={best_k})\",   \"Split\": \"Validation\", \"Accuracy\": knn_val_acc, \"Weighted F1\": knn_val_f1},\n",
+    "    {\"Model\": f\"KNN (k={best_k})\",   \"Split\": \"Test\",       \"Accuracy\": knn_test_acc, \"Weighted F1\": knn_test_f1},\n",
+    "])"
+   ],
+   "outputs": [],
+   "execution_count": 80
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-26T14:56:46.536747300Z",
+     "start_time": "2026-04-26T14:56:46.523885900Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "results_df",
+   "id": "26b1869358109b24",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                 Model       Split  Accuracy  Weighted F1\n",
+       "0  Logistic Regression  Validation  0.301802     0.302937\n",
+       "1  Logistic Regression        Test  0.270270     0.262607\n",
+       "2           KNN (k=19)  Validation  0.360360     0.354603\n",
+       "3           KNN (k=19)        Test  0.310811     0.290187"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -2702,53 +2720,24 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                 Model       Split  Accuracy  Weighted F1\n",
-       "0  Logistic Regression  Validation  0.301802     0.302937\n",
-       "1  Logistic Regression        Test  0.270270     0.262607\n",
-       "2           KNN (k=19)  Validation  0.360360     0.354603\n",
-       "3           KNN (k=19)        Test  0.310811     0.290187"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "results_df = pd.DataFrame([\n",
-    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Validation\", \"Accuracy\": log_val_acc, \"Weighted F1\": log_val_f1},\n",
-    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Test\",       \"Accuracy\": log_test_acc, \"Weighted F1\": log_test_f1},\n",
-    "    {\"Model\": f\"KNN (k={best_k})\",   \"Split\": \"Validation\", \"Accuracy\": knn_val_acc, \"Weighted F1\": knn_val_f1},\n",
-    "    {\"Model\": f\"KNN (k={best_k})\",   \"Split\": \"Test\",       \"Accuracy\": knn_test_acc, \"Weighted F1\": knn_test_f1},\n",
-    "])\n",
-    "\n",
-    "results_df"
-   ]
+   "execution_count": 81
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
    "id": "5e4472673a0665ef",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.336424300Z",
-     "start_time": "2026-04-26T14:22:31.062023500Z"
+     "end_time": "2026-04-26T14:56:46.830513200Z",
+     "start_time": "2026-04-26T14:56:46.537744Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "top_n = 10\n",
     "top_classes = y_test.value_counts().head(top_n).index.tolist()\n",
@@ -2782,32 +2771,34 @@
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "927a62088f56bd44",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.466999600Z",
-     "start_time": "2026-04-26T14:22:31.337425900Z"
-    }
-   },
+   ],
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1300x500 with 1 Axes>"
-      ]
+       "<Figure size 1500x600 with 2 Axes>"
+      ],
+      "image/png": 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"
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
+   "execution_count": 82
+  },
+  {
+   "cell_type": "code",
+   "id": "927a62088f56bd44",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-26T14:56:46.938393800Z",
+     "start_time": "2026-04-26T14:56:46.833501600Z"
+    }
+   },
    "source": [
-    "# Simple class-count comparison: actual vs each model's predicted categories.\n",
     "actual_counts_a = y_test.value_counts().reindex(top_classes, fill_value=0)\n",
     "log_pred_counts_a = pd.Series(y_test_pred_log).value_counts().reindex(top_classes, fill_value=0)\n",
     "knn_pred_counts_a = pd.Series(y_test_pred_knn).value_counts().reindex(top_classes, fill_value=0)\n",
@@ -2827,7 +2818,23 @@
     "ax.legend()\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1300x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 83
   },
   {
    "cell_type": "markdown",
@@ -2841,20 +2848,18 @@
     "to predict “Content Rating” (use the training/validation/test partition without\n",
     "cross-validation). **[7 marks]**\n",
     "\n",
-    "Below, I train **Logistic Regression** and **KNN** using exactly the inputs stated in the question. I then compare them using **Accuracy**, **Weighted F1**, and **Confusion Matrices**, and I also show the **raw predicted outputs** from each model on the test set so it’s clear what each model is actually predicting."
+    "For this part, I'll be training Logistic Regression and KNN using the inputs mentioned above. I'll compare how they perform using Accuracy, Weighted F1, and Confusion Matrices. I also added a table showing the raw predicted outputs from each model on the test set so it's clear what the models are actually predicting.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
    "id": "65e55b70ad44b2dc",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.482686500Z",
-     "start_time": "2026-04-26T14:22:31.467997400Z"
+     "end_time": "2026-04-26T14:56:46.983924300Z",
+     "start_time": "2026-04-26T14:56:46.959454Z"
     }
    },
-   "outputs": [],
    "source": [
     "cols_part_b = [\n",
     "    'Rating',\n",
@@ -2865,41 +2870,73 @@
     "    'Content Rating'\n",
     "]\n",
     "\n",
-    "df_part_b = df[cols_part_b].dropna().copy()\n"
-   ]
+    "df_part_b = df[cols_part_b].dropna().copy()"
+   ],
+   "outputs": [],
+   "execution_count": 84
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
    "id": "3a26f4d9fe9824a6",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.488424600Z",
-     "start_time": "2026-04-26T14:22:31.482686500Z"
+     "end_time": "2026-04-26T14:56:47.010597900Z",
+     "start_time": "2026-04-26T14:56:46.983924300Z"
     }
    },
-   "outputs": [],
    "source": [
     "app_inputs_b = df_part_b.drop('Content Rating', axis=1)\n",
-    "\n",
     "true_content_rating = df_part_b['Content Rating']\n",
-    "\n",
-    "app_inputs_b_encoded = pd.get_dummies(app_inputs_b, columns=['Category'])\n"
-   ]
+    "app_inputs_b_encoded = pd.get_dummies(app_inputs_b, columns=['Category'])"
+   ],
+   "outputs": [],
+   "execution_count": 85
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
    "id": "d3a9df5839db106f",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.502157100Z",
-     "start_time": "2026-04-26T14:22:31.488424600Z"
+     "end_time": "2026-04-26T14:56:47.049264100Z",
+     "start_time": "2026-04-26T14:56:47.013108700Z"
     }
    },
+   "source": [
+    "class_counts_b = true_content_rating.value_counts()\n",
+    "valid_mask_b = true_content_rating.map(class_counts_b) >= 2\n",
+    "\n",
+    "X_b = app_inputs_b_encoded.loc[valid_mask_b]\n",
+    "y_b = true_content_rating.loc[valid_mask_b]\n",
+    "\n",
+    "dropped_classes_b = class_counts_b[class_counts_b < 2]\n",
+    "\n",
+    "X_temp_b, X_test_b, y_temp_b, y_test_b = train_test_split(\n",
+    "    X_b, y_b, test_size=0.2, random_state=seed, stratify=y_b\n",
+    ")\n",
+    "\n",
+    "stratify_second_b = y_temp_b if y_temp_b.value_counts().min() >= 2 else None\n",
+    "part_b_fallback = stratify_second_b is None\n",
+    "\n",
+    "X_train_b, X_val_b, y_train_b, y_val_b = train_test_split(\n",
+    "    X_temp_b, y_temp_b, test_size=0.25, random_state=seed, stratify=stratify_second_b\n",
+    ")\n",
+    "\n",
+    "part_b_split_info = pd.DataFrame([\n",
+    "    {'Split': 'Train', 'Rows': len(X_train_b)},\n",
+    "    {'Split': 'Validation', 'Rows': len(X_val_b)},\n",
+    "    {'Split': 'Test', 'Rows': len(X_test_b)},\n",
+    "])\n",
+    "part_b_split_info"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "        Split  Rows\n",
+       "0       Train   663\n",
+       "1  Validation   221\n",
+       "2        Test   222"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -2942,62 +2979,42 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "        Split  Rows\n",
-       "0       Train   663\n",
-       "1  Validation   221\n",
-       "2        Test   222"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "class_counts_b = true_content_rating.value_counts()\n",
-    "valid_mask_b = true_content_rating.map(class_counts_b) >= 2\n",
-    "\n",
-    "X_b = app_inputs_b_encoded.loc[valid_mask_b]\n",
-    "y_b = true_content_rating.loc[valid_mask_b]\n",
-    "\n",
-    "dropped_classes_b = class_counts_b[class_counts_b < 2]\n",
-    "\n",
-    "# 2) First split (stratified)\n",
-    "X_temp_b, X_test_b, y_temp_b, y_test_b = train_test_split(\n",
-    "    X_b, y_b, test_size=0.2, random_state=seed, stratify=y_b\n",
-    ")\n",
-    "\n",
-    "# 3) Second split: stratify only if still valid\n",
-    "stratify_second_b = y_temp_b if y_temp_b.value_counts().min() >= 2 else None\n",
-    "part_b_fallback = stratify_second_b is None\n",
-    "\n",
-    "X_train_b, X_val_b, y_train_b, y_val_b = train_test_split(\n",
-    "    X_temp_b, y_temp_b, test_size=0.25, random_state=seed, stratify=stratify_second_b\n",
-    ")\n",
-    "\n",
-    "part_b_split_info = pd.DataFrame([\n",
-    "    {'Split': 'Train', 'Rows': len(X_train_b)},\n",
-    "    {'Split': 'Validation', 'Rows': len(X_val_b)},\n",
-    "    {'Split': 'Test', 'Rows': len(X_test_b)},\n",
-    "])\n",
-    "part_b_split_info"
-   ]
+   "execution_count": 86
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
    "id": "28b588c11c53a3c8",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.511669600Z",
-     "start_time": "2026-04-26T14:22:31.503157700Z"
+     "end_time": "2026-04-26T14:56:47.137869700Z",
+     "start_time": "2026-04-26T14:56:47.073286900Z"
     }
    },
+   "source": [
+    "scaler_b = StandardScaler()\n",
+    "X_train_b_scaled = scaler_b.fit_transform(X_train_b)\n",
+    "X_val_b_scaled = scaler_b.transform(X_val_b)\n",
+    "X_test_b_scaled = scaler_b.transform(X_test_b)\n",
+    "\n",
+    "display(pd.DataFrame([{\n",
+    "    'Dropped rare classes': int(dropped_classes_b.shape[0]),\n",
+    "    'Second split fallback used': part_b_fallback\n",
+    "}]))\n"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   Dropped rare classes  Second split fallback used\n",
+       "0                     1                       False"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3030,56 +3047,37 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Dropped rare classes  Second split fallback used\n",
-       "0                     1                       False"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "scaler_b = StandardScaler()\n",
-    "X_train_b_scaled = scaler_b.fit_transform(X_train_b)\n",
-    "X_val_b_scaled = scaler_b.transform(X_val_b)\n",
-    "X_test_b_scaled = scaler_b.transform(X_test_b)\n",
-    "\n",
-    "display(pd.DataFrame([{\n",
-    "    'Dropped rare classes': int(dropped_classes_b.shape[0]),\n",
-    "    'Second split fallback used': part_b_fallback\n",
-    "}]))"
-   ]
+   "execution_count": 87
   },
   {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "647213d276fb84a0",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-04-26T14:22:31.516600100Z",
      "start_time": "2026-04-26T14:22:31.512670300Z"
     }
    },
-   "outputs": [],
-   "source": [
-    "# --------------------\n",
-    "# Logistic Regression\n",
-    "# --------------------"
-   ]
+   "cell_type": "markdown",
+   "source": "## Logistic Regression",
+   "id": "2d369767c902755b"
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
    "id": "c4ccabce439988cc",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.542771500Z",
-     "start_time": "2026-04-26T14:22:31.516600100Z"
+     "end_time": "2026-04-26T14:56:47.181896500Z",
+     "start_time": "2026-04-26T14:56:47.159890900Z"
     }
    },
-   "outputs": [],
    "source": [
     "logreg_b = LogisticRegression(\n",
     "    max_iter=5000,\n",
@@ -3094,55 +3092,48 @@
     "logreg_b_test_pred = logreg_b.predict(X_test_b_scaled)\n",
     "\n",
     "logreg_b_val_acc, logreg_b_val_f1 = eval_metrics(y_val_b, logreg_b_val_pred, \"LogReg (Part B, Validation)\")\n",
-    "logreg_b_test_acc, logreg_b_test_f1 = eval_metrics(y_test_b, logreg_b_test_pred, \"LogReg (Part B, Test)\")\n",
-    "\n"
-   ]
+    "logreg_b_test_acc, logreg_b_test_f1 = eval_metrics(y_test_b, logreg_b_test_pred, \"LogReg (Part B, Test)\")"
+   ],
+   "outputs": [],
+   "execution_count": 88
   },
   {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "e990ef7a606e43e8",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-04-26T14:22:31.548622500Z",
      "start_time": "2026-04-26T14:22:31.544770800Z"
     }
    },
-   "outputs": [],
-   "source": [
-    "# --------------------\n",
-    "# KNN (tune k using Validation set only)\n",
-    "# --------------------"
-   ]
+   "cell_type": "markdown",
+   "source": "## KNN",
+   "id": "21142cf006b1ff7b"
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
    "id": "ce85e65e395a4512",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.555501500Z",
-     "start_time": "2026-04-26T14:22:31.548622500Z"
+     "end_time": "2026-04-26T14:56:47.200857200Z",
+     "start_time": "2026-04-26T14:56:47.182897100Z"
     }
    },
-   "outputs": [],
    "source": [
     "k_values_b = list(range(1, 51, 2))\n",
     "knn_val_acc_scores_b = []\n",
     "knn_val_f1_scores_b = []"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 89
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
    "id": "154c1a685a6e35b0",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.724659900Z",
-     "start_time": "2026-04-26T14:22:31.556505700Z"
+     "end_time": "2026-04-26T14:56:47.371213500Z",
+     "start_time": "2026-04-26T14:56:47.204857900Z"
     }
    },
-   "outputs": [],
    "source": [
     "for k in k_values_b:\n",
     "    knn_b = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
@@ -3160,39 +3151,40 @@
     "knn_b_test_pred = knn_final_b.predict(X_test_b_scaled)\n",
     "\n",
     "knn_b_val_acc, knn_b_val_f1 = eval_metrics(y_val_b, knn_b_val_pred, f\"KNN k={best_k_b} (Part B, Validation)\")\n",
-    "knn_b_test_acc, knn_b_test_f1 = eval_metrics(y_test_b, knn_b_test_pred, f\"KNN k={best_k_b} (Part B, Test)\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "7b0356ac3546ff01",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.730058400Z",
-     "start_time": "2026-04-26T14:22:31.725712500Z"
-    }
-   },
+    "knn_b_test_acc, knn_b_test_f1 = eval_metrics(y_test_b, knn_b_test_pred, f\"KNN k={best_k_b} (Part B, Test)\")"
+   ],
    "outputs": [],
-   "source": [
-    "# --------------------\n",
-    "# Visual comparison (real outputs from the models)\n",
-    "# --------------------"
-   ]
+   "execution_count": 90
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
    "id": "9e50cbc03f526dc2",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.754532200Z",
-     "start_time": "2026-04-26T14:22:31.730058400Z"
+     "end_time": "2026-04-26T14:56:47.424439200Z",
+     "start_time": "2026-04-26T14:56:47.372214200Z"
     }
    },
+   "source": [
+    "comparison_b = pd.DataFrame([\n",
+    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Validation\", \"Accuracy\": logreg_b_val_acc, \"Weighted F1\": logreg_b_val_f1},\n",
+    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Test\",       \"Accuracy\": logreg_b_test_acc, \"Weighted F1\": logreg_b_test_f1},\n",
+    "    {\"Model\": f\"KNN (k={best_k_b})\",   \"Split\": \"Validation\", \"Accuracy\": knn_b_val_acc, \"Weighted F1\": knn_b_val_f1},\n",
+    "    {\"Model\": f\"KNN (k={best_k_b})\",   \"Split\": \"Test\",       \"Accuracy\": knn_b_test_acc, \"Weighted F1\": knn_b_test_f1},\n",
+    "])\n",
+    "\n",
+    "display(comparison_b)"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "                 Model       Split  Accuracy  Weighted F1\n",
+       "0  Logistic Regression  Validation  0.583710     0.659486\n",
+       "1  Logistic Regression        Test  0.621622     0.690548\n",
+       "2           KNN (k=15)  Validation  0.873303     0.865222\n",
+       "3           KNN (k=15)        Test  0.864865     0.856732"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3250,52 +3242,26 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                 Model       Split  Accuracy  Weighted F1\n",
-       "0  Logistic Regression  Validation  0.583710     0.659486\n",
-       "1  Logistic Regression        Test  0.621622     0.690548\n",
-       "2           KNN (k=15)  Validation  0.873303     0.865222\n",
-       "3           KNN (k=15)        Test  0.864865     0.856732"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "comparison_b = pd.DataFrame([\n",
-    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Validation\", \"Accuracy\": logreg_b_val_acc, \"Weighted F1\": logreg_b_val_f1},\n",
-    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Test\",       \"Accuracy\": logreg_b_test_acc, \"Weighted F1\": logreg_b_test_f1},\n",
-    "    {\"Model\": f\"KNN (k={best_k_b})\",   \"Split\": \"Validation\", \"Accuracy\": knn_b_val_acc, \"Weighted F1\": knn_b_val_f1},\n",
-    "    {\"Model\": f\"KNN (k={best_k_b})\",   \"Split\": \"Test\",       \"Accuracy\": knn_b_test_acc, \"Weighted F1\": knn_b_test_f1},\n",
-    "])\n",
-    "\n",
-    "display(comparison_b)"
-   ]
+   "execution_count": 91
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
    "id": "e7f5bec0bd0e8318",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.892520800Z",
-     "start_time": "2026-04-26T14:22:31.755530300Z"
+     "end_time": "2026-04-26T14:56:47.605250500Z",
+     "start_time": "2026-04-26T14:56:47.459459900Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
     "\n",
@@ -3310,30 +3276,33 @@
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "7e764a950b7ddd0c",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:31.995247700Z",
-     "start_time": "2026-04-26T14:22:31.893521Z"
-    }
-   },
+   ],
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1400x500 with 2 Axes>"
-      ]
+       "<Figure size 1200x400 with 2 Axes>"
+      ],
+      "image/png": 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      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
+   "execution_count": 92
+  },
+  {
+   "cell_type": "code",
+   "id": "7e764a950b7ddd0c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-26T14:56:47.705350300Z",
+     "start_time": "2026-04-26T14:56:47.606249800Z"
+    }
+   },
    "source": [
     "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
     "\n",
@@ -3359,38 +3328,75 @@
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 93
   },
   {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "93a4849276258786",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-04-26T14:22:32.000732400Z",
      "start_time": "2026-04-26T14:22:31.996248700Z"
     }
    },
-   "outputs": [],
-   "source": [
-    "# --------------------\n",
-    "# Display the actual predicted outputs (so the marker can see what each model predicted)\n",
-    "# --------------------"
-   ]
+   "cell_type": "markdown",
+   "source": "## Displaying the predicted data",
+   "id": "525f5a31731fb4fa"
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
    "id": "d0789b435a8f8172",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.047616200Z",
-     "start_time": "2026-04-26T14:22:32.001766Z"
+     "end_time": "2026-04-26T14:56:47.814892400Z",
+     "start_time": "2026-04-26T14:56:47.740975700Z"
     }
    },
+   "source": [
+    "logreg_b_probs = logreg_b.predict_proba(X_test_b_scaled)\n",
+    "logreg_b_pred_df = prediction_sheet(y_test_b, logreg_b_test_pred, logreg_b_probs, 'Content_Rating', head_n=20)\n",
+    "logreg_b_pred_df"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   True_Content_Rating Predicted_Content_Rating  Predicted_Confidence  Correct\n",
+       "0             Everyone                 Everyone              0.993702     True\n",
+       "1             Everyone                 Everyone              0.482609     True\n",
+       "2             Everyone                 Everyone              0.994017     True\n",
+       "3             Everyone             Everyone 10+              0.522200    False\n",
+       "4             Everyone                     Teen              0.767573    False\n",
+       "5             Everyone                 Everyone              0.612729     True\n",
+       "6             Everyone                 Everyone              0.994448     True\n",
+       "7             Everyone                 Everyone              0.993697     True\n",
+       "8             Everyone             Everyone 10+              0.530097    False\n",
+       "9             Everyone                 Everyone              0.749141     True\n",
+       "10          Mature 17+               Mature 17+              0.940680     True\n",
+       "11            Everyone                 Everyone              0.991359     True\n",
+       "12                Teen                 Everyone              0.639161    False\n",
+       "13            Everyone                 Everyone              0.993842     True\n",
+       "14            Everyone                 Everyone              0.993532     True\n",
+       "15            Everyone             Everyone 10+              0.480918    False\n",
+       "16            Everyone                 Everyone              0.760410     True\n",
+       "17            Everyone             Everyone 10+              0.504585    False\n",
+       "18            Everyone                 Everyone              0.460006     True\n",
+       "19            Everyone                 Everyone              0.993453     True"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3560,55 +3566,55 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   True_Content_Rating Predicted_Content_Rating  Predicted_Confidence  Correct\n",
-       "0             Everyone                 Everyone              0.993702     True\n",
-       "1             Everyone                 Everyone              0.482609     True\n",
-       "2             Everyone                 Everyone              0.994017     True\n",
-       "3             Everyone             Everyone 10+              0.522200    False\n",
-       "4             Everyone                     Teen              0.767573    False\n",
-       "5             Everyone                 Everyone              0.612729     True\n",
-       "6             Everyone                 Everyone              0.994448     True\n",
-       "7             Everyone                 Everyone              0.993697     True\n",
-       "8             Everyone             Everyone 10+              0.530097    False\n",
-       "9             Everyone                 Everyone              0.749141     True\n",
-       "10          Mature 17+               Mature 17+              0.940680     True\n",
-       "11            Everyone                 Everyone              0.991359     True\n",
-       "12                Teen                 Everyone              0.639161    False\n",
-       "13            Everyone                 Everyone              0.993842     True\n",
-       "14            Everyone                 Everyone              0.993532     True\n",
-       "15            Everyone             Everyone 10+              0.480918    False\n",
-       "16            Everyone                 Everyone              0.760410     True\n",
-       "17            Everyone             Everyone 10+              0.504585    False\n",
-       "18            Everyone                 Everyone              0.460006     True\n",
-       "19            Everyone                 Everyone              0.993453     True"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "logreg_b_probs = logreg_b.predict_proba(X_test_b_scaled)\n",
-    "logreg_b_pred_df = prediction_sheet(y_test_b, logreg_b_test_pred, logreg_b_probs, 'Content_Rating', head_n=20)\n",
-    "logreg_b_pred_df"
-   ]
+   "execution_count": 94
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
    "id": "ce36a342b29e1424",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.067801900Z",
-     "start_time": "2026-04-26T14:22:32.050608200Z"
+     "end_time": "2026-04-26T14:56:47.868023600Z",
+     "start_time": "2026-04-26T14:56:47.815893400Z"
     }
    },
+   "source": [
+    "knn_b_probs = knn_final_b.predict_proba(X_test_b_scaled)\n",
+    "knn_b_pred_df = prediction_sheet(y_test_b, knn_b_test_pred, knn_b_probs, 'Content_Rating', head_n=20)\n",
+    "knn_b_pred_df"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   True_Content_Rating Predicted_Content_Rating  Predicted_Confidence  Correct\n",
+       "0             Everyone                 Everyone              1.000000     True\n",
+       "1             Everyone                 Everyone              1.000000     True\n",
+       "2             Everyone                 Everyone              1.000000     True\n",
+       "3             Everyone                 Everyone              0.564860     True\n",
+       "4             Everyone                     Teen              0.640995    False\n",
+       "5             Everyone                 Everyone              0.998311     True\n",
+       "6             Everyone                 Everyone              1.000000     True\n",
+       "7             Everyone                 Everyone              1.000000     True\n",
+       "8             Everyone                 Everyone              0.875223     True\n",
+       "9             Everyone                 Everyone              1.000000     True\n",
+       "10          Mature 17+               Mature 17+              1.000000     True\n",
+       "11            Everyone                 Everyone              1.000000     True\n",
+       "12                Teen                     Teen              0.998994     True\n",
+       "13            Everyone                 Everyone              1.000000     True\n",
+       "14            Everyone                 Everyone              1.000000     True\n",
+       "15            Everyone                 Everyone              0.911639     True\n",
+       "16            Everyone                 Everyone              1.000000     True\n",
+       "17            Everyone                 Everyone              0.919221     True\n",
+       "18            Everyone                 Everyone              0.886516     True\n",
+       "19            Everyone                 Everyone              1.000000     True"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3778,64 +3784,24 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   True_Content_Rating Predicted_Content_Rating  Predicted_Confidence  Correct\n",
-       "0             Everyone                 Everyone              1.000000     True\n",
-       "1             Everyone                 Everyone              1.000000     True\n",
-       "2             Everyone                 Everyone              1.000000     True\n",
-       "3             Everyone                 Everyone              0.564860     True\n",
-       "4             Everyone                     Teen              0.640995    False\n",
-       "5             Everyone                 Everyone              0.998311     True\n",
-       "6             Everyone                 Everyone              1.000000     True\n",
-       "7             Everyone                 Everyone              1.000000     True\n",
-       "8             Everyone                 Everyone              0.875223     True\n",
-       "9             Everyone                 Everyone              1.000000     True\n",
-       "10          Mature 17+               Mature 17+              1.000000     True\n",
-       "11            Everyone                 Everyone              1.000000     True\n",
-       "12                Teen                     Teen              0.998994     True\n",
-       "13            Everyone                 Everyone              1.000000     True\n",
-       "14            Everyone                 Everyone              1.000000     True\n",
-       "15            Everyone                 Everyone              0.911639     True\n",
-       "16            Everyone                 Everyone              1.000000     True\n",
-       "17            Everyone                 Everyone              0.919221     True\n",
-       "18            Everyone                 Everyone              0.886516     True\n",
-       "19            Everyone                 Everyone              1.000000     True"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "knn_b_probs = knn_final_b.predict_proba(X_test_b_scaled)\n",
-    "knn_b_pred_df = prediction_sheet(y_test_b, knn_b_test_pred, knn_b_probs, 'Content_Rating', head_n=20)\n",
-    "knn_b_pred_df"
-   ]
+   "execution_count": 95
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
    "id": "b7e45ff0a81c1be2",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.172646800Z",
-     "start_time": "2026-04-26T14:22:32.071801700Z"
+     "end_time": "2026-04-26T14:56:47.970823700Z",
+     "start_time": "2026-04-26T14:56:47.893047200Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "actual_counts_b = pd.Series(y_test_b).value_counts().sort_index()\n",
     "logreg_pred_counts_b = pd.Series(logreg_b_test_pred).value_counts().reindex(actual_counts_b.index, fill_value=0)\n",
@@ -3856,38 +3822,67 @@
     "ax.legend()\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 96
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
    "id": "7f69236f537f633c",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.179466Z",
-     "start_time": "2026-04-26T14:22:32.173646200Z"
+     "end_time": "2026-04-26T14:56:48.016130100Z",
+     "start_time": "2026-04-26T14:56:47.973410900Z"
     }
    },
-   "outputs": [],
    "source": [
     "best_model_b = (\n",
     "    f\"KNN (k={best_k_b})\" if knn_b_test_f1 > logreg_b_test_f1 else \"Logistic Regression\"\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 97
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
    "id": "6c63275f27f4efe3",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.191485200Z",
-     "start_time": "2026-04-26T14:22:32.179466Z"
+     "end_time": "2026-04-26T14:56:48.052039300Z",
+     "start_time": "2026-04-26T14:56:48.022133500Z"
     }
    },
+   "source": [
+    "part_b_final_summary = pd.DataFrame([\n",
+    "    {'Model': 'Logistic Regression', 'Accuracy': logreg_b_test_acc, 'Weighted F1': logreg_b_test_f1},\n",
+    "    {'Model': f'KNN (k={best_k_b})', 'Accuracy': knn_b_test_acc, 'Weighted F1': knn_b_test_f1},\n",
+    "]).sort_values('Weighted F1', ascending=False).reset_index(drop=True)\n",
+    "\n",
+    "display(part_b_final_summary)\n",
+    "display(pd.DataFrame([{'Part B Best Model': best_model_b}]))"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "                 Model  Accuracy  Weighted F1\n",
+       "0           KNN (k=15)  0.864865     0.856732\n",
+       "1  Logistic Regression  0.621622     0.690548"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3928,18 +3923,20 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                 Model  Accuracy  Weighted F1\n",
-       "0           KNN (k=15)  0.864865     0.856732\n",
-       "1  Logistic Regression  0.621622     0.690548"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
+      "text/plain": [
+       "  Part B Best Model\n",
+       "0        KNN (k=15)"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3970,25 +3967,28 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "  Part B Best Model\n",
-       "0        KNN (k=15)"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
+   "execution_count": 98
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
    "source": [
-    "part_b_final_summary = pd.DataFrame([\n",
-    "    {'Model': 'Logistic Regression', 'Accuracy': logreg_b_test_acc, 'Weighted F1': logreg_b_test_f1},\n",
-    "    {'Model': f'KNN (k={best_k_b})', 'Accuracy': knn_b_test_acc, 'Weighted F1': knn_b_test_f1},\n",
-    "]).sort_values('Weighted F1', ascending=False).reset_index(drop=True)\n",
+    "## Answer for Part B:\n",
+    "The best model to fit the prediction of the \"Category\" is the KNN model with it's K being set to 15, it seems to provide the highest accuracy and weighted F1 score on the test set, and also has a better confusion matrix distribution compared to Logistic Regression.\n",
+    "The data provided by the KNN model also shows a better distribution of predicted classes that is closer to the actual distribution of the test set.\n",
     "\n",
-    "display(part_b_final_summary)\n",
-    "display(pd.DataFrame([{'Part B Best Model': best_model_b}]))"
-   ]
+    "Therefore, KNN is the best classification model for predicting \"Content Rating\" in Part B."
+   ],
+   "id": "9a0e228af8fcee76"
   },
   {
    "cell_type": "markdown",
@@ -4002,20 +4002,18 @@
     "best classification model to predict “Installs_Num” (using Logistic regression and\n",
     "KNN) (use the training/test partition without cross-validation) **[8 marks]**\n",
     "\n",
-    "Below, I treat **Installs_Num** as the **categorical label** to predict, train **Logistic Regression** and **KNN** using only the inputs listed in the question, and compare them on the test set using **Accuracy**, **Weighted F1**, **Confusion Matrices**, plus a table showing the **actual per-row predictions** from both models."
+    "Here I'm treating Installs_Num as the categorical label we want to predict. I'll train Logistic Regression and KNN using only the requested inputs and compare them on the test set using Accuracy, Weighted F1, and Confusion Matrices. I'll also include a table showing the actual per-row predictions from both models.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
    "id": "f3535461938f94f5",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.198890900Z",
-     "start_time": "2026-04-26T14:22:32.193484800Z"
+     "end_time": "2026-04-26T14:56:48.104315400Z",
+     "start_time": "2026-04-26T14:56:48.076549300Z"
     }
    },
-   "outputs": [],
    "source": [
     "cols_part_c = [\n",
     "    'Rating',\n",
@@ -4027,40 +4025,70 @@
     "]\n",
     "\n",
     "df_part_c = df[cols_part_c].dropna().copy()"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 99
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
    "id": "abea4225feb4b17d",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.217483900Z",
-     "start_time": "2026-04-26T14:22:32.199891500Z"
+     "end_time": "2026-04-26T14:56:48.131175400Z",
+     "start_time": "2026-04-26T14:56:48.105313900Z"
     }
    },
-   "outputs": [],
    "source": [
     "app_inputs_c = df_part_c.drop('Numeric Installs', axis=1)\n",
-    "\n",
     "true_install_bucket = df_part_c['Numeric Installs'].astype(str)\n",
-    "\n",
     "app_inputs_c_encoded = pd.get_dummies(app_inputs_c, columns=['Category', 'Content Rating'])"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 100
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
    "id": "648d0b5bc8432817",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.232510100Z",
-     "start_time": "2026-04-26T14:22:32.218482700Z"
+     "end_time": "2026-04-26T14:56:48.212881400Z",
+     "start_time": "2026-04-26T14:56:48.141172400Z"
     }
    },
+   "source": [
+    "class_counts_c = true_install_bucket.value_counts()\n",
+    "valid_mask_c = true_install_bucket.map(class_counts_c) >= 2\n",
+    "\n",
+    "X_c = app_inputs_c_encoded.loc[valid_mask_c]\n",
+    "y_c = true_install_bucket.loc[valid_mask_c]\n",
+    "\n",
+    "dropped_classes_c = class_counts_c[class_counts_c < 2]\n",
+    "\n",
+    "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(\n",
+    "    X_c,\n",
+    "    y_c,\n",
+    "    test_size=0.2,\n",
+    "    random_state=seed,\n",
+    "    stratify=y_c\n",
+    ")\n",
+    "\n",
+    "display(pd.DataFrame([\n",
+    "    {'Split': 'Train', 'Rows': len(X_train_c)},\n",
+    "    {'Split': 'Test', 'Rows': len(X_test_c)},\n",
+    "]))\n",
+    "display(pd.DataFrame([{\n",
+    "    'Part C classes after filtering': int(y_c.nunique()),\n",
+    "    'Dropped rare classes': int(dropped_classes_c.shape[0])\n",
+    "}]))"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   Split  Rows\n",
+       "0  Train   884\n",
+       "1   Test   222"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -4098,18 +4126,20 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Split  Rows\n",
-       "0  Train   884\n",
-       "1   Test   222"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
+      "text/plain": [
+       "   Part C classes after filtering  Dropped rare classes\n",
+       "0                              15                     1"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -4142,57 +4172,53 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Part C classes after filtering  Dropped rare classes\n",
-       "0                              15                     1"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "# Safe split for Part C (handles classes with <2 samples)\n",
-    "class_counts_c = true_install_bucket.value_counts()\n",
-    "valid_mask_c = true_install_bucket.map(class_counts_c) >= 2\n",
-    "\n",
-    "X_c = app_inputs_c_encoded.loc[valid_mask_c]\n",
-    "y_c = true_install_bucket.loc[valid_mask_c]\n",
-    "\n",
-    "dropped_classes_c = class_counts_c[class_counts_c < 2]\n",
-    "\n",
-    "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(\n",
-    "    X_c,\n",
-    "    y_c,\n",
-    "    test_size=0.2,\n",
-    "    random_state=seed,\n",
-    "    stratify=y_c\n",
-    ")\n",
-    "\n",
-    "display(pd.DataFrame([\n",
-    "    {'Split': 'Train', 'Rows': len(X_train_c)},\n",
-    "    {'Split': 'Test', 'Rows': len(X_test_c)},\n",
-    "]))\n",
-    "display(pd.DataFrame([{\n",
-    "    'Part C classes after filtering': int(y_c.nunique()),\n",
-    "    'Dropped rare classes': int(dropped_classes_c.shape[0])\n",
-    "}]))"
-   ]
+   "execution_count": 101
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
    "id": "7177e6874e9c6064",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.257027600Z",
-     "start_time": "2026-04-26T14:22:32.232510100Z"
+     "end_time": "2026-04-26T14:56:48.267401100Z",
+     "start_time": "2026-04-26T14:56:48.212881400Z"
     }
    },
+   "source": [
+    "scaler_c = StandardScaler()\n",
+    "X_train_c_scaled = scaler_c.fit_transform(X_train_c)\n",
+    "X_test_c_scaled = scaler_c.transform(X_test_c)\n",
+    "display(y_c.value_counts().head(15).rename('Class_Count').reset_index().rename(columns={'index': 'Numeric_Installs'}))"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   Numeric Installs  Class_Count\n",
+       "0           1000000          302\n",
+       "1            100000          191\n",
+       "2          10000000          134\n",
+       "3            500000          125\n",
+       "4           5000000           99\n",
+       "5             10000           66\n",
+       "6             50000           56\n",
+       "7         100000000           51\n",
+       "8          50000000           21\n",
+       "9              1000           16\n",
+       "10             5000           12\n",
+       "11              100           11\n",
+       "12        500000000           10\n",
+       "13              500            9\n",
+       "14       1000000000            3"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -4295,66 +4321,37 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Numeric Installs  Class_Count\n",
-       "0           1000000          302\n",
-       "1            100000          191\n",
-       "2          10000000          134\n",
-       "3            500000          125\n",
-       "4           5000000           99\n",
-       "5             10000           66\n",
-       "6             50000           56\n",
-       "7         100000000           51\n",
-       "8          50000000           21\n",
-       "9              1000           16\n",
-       "10             5000           12\n",
-       "11              100           11\n",
-       "12        500000000           10\n",
-       "13              500            9\n",
-       "14       1000000000            3"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "scaler_c = StandardScaler()\n",
-    "X_train_c_scaled = scaler_c.fit_transform(X_train_c)\n",
-    "X_test_c_scaled = scaler_c.transform(X_test_c)\n",
-    "\n",
-    "display(y_c.value_counts().head(15).rename('Class_Count').reset_index().rename(columns={'index': 'Numeric_Installs'}))"
-   ]
+   "execution_count": 102
   },
   {
-   "cell_type": "code",
-   "execution_count": 115,
-   "id": "2af93ad40c17fac5",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-04-26T14:22:32.276694500Z",
      "start_time": "2026-04-26T14:22:32.258028300Z"
     }
    },
-   "outputs": [],
-   "source": [
-    "# --------------------\n",
-    "# Logistic Regression\n",
-    "# --------------------"
-   ]
+   "cell_type": "markdown",
+   "source": "## Logistic Regression",
+   "id": "cbae09c315aea2a2"
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
    "id": "7945f22b5340fd36",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.350406200Z",
-     "start_time": "2026-04-26T14:22:32.276694500Z"
+     "end_time": "2026-04-26T14:56:48.358676Z",
+     "start_time": "2026-04-26T14:56:48.294916600Z"
     }
    },
-   "outputs": [],
    "source": [
     "logreg_c = LogisticRegression(\n",
     "    max_iter=5000,\n",
@@ -4367,38 +4364,49 @@
     "\n",
     "logreg_c_test_pred = logreg_c.predict(X_test_c_scaled)\n",
     "logreg_c_test_acc, logreg_c_test_f1 = eval_metrics(y_test_c, logreg_c_test_pred, \"LogReg (Part C, Test)\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 103
   },
   {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "fc30ac2b6d7ed614",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-04-26T14:22:32.354959600Z",
      "start_time": "2026-04-26T14:22:32.350406200Z"
     }
    },
-   "outputs": [],
-   "source": [
-    "# --------------------\n",
-    "# KNN (tune k using a small internal split from training only)\n",
-    "# --------------------"
-   ]
+   "cell_type": "markdown",
+   "source": "## KNN",
+   "id": "4b14b40b684d7919"
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
    "id": "309c91f395a12138",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.370241100Z",
-     "start_time": "2026-04-26T14:22:32.354959600Z"
+     "end_time": "2026-04-26T14:56:48.433582500Z",
+     "start_time": "2026-04-26T14:56:48.364805600Z"
     }
    },
+   "source": [
+    "stratify_inner_c = y_train_c if y_train_c.value_counts().min() >= 2 else None\n",
+    "X_train_c_inner, X_val_c_inner, y_train_c_inner, y_val_c_inner = train_test_split(\n",
+    "    X_train_c, y_train_c, test_size=0.25, random_state=seed, stratify=stratify_inner_c\n",
+    ")\n",
+    "display(pd.DataFrame([{'Part C inner split fallback used': stratify_inner_c is None}]))\n",
+    "\n",
+    "scaler_c_inner = StandardScaler()\n",
+    "X_train_c_inner_scaled = scaler_c_inner.fit_transform(X_train_c_inner)\n",
+    "X_val_c_inner_scaled = scaler_c_inner.transform(X_val_c_inner)\n",
+    "X_test_c_scaled_inner = scaler_c_inner.transform(X_test_c)"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   Part C inner split fallback used\n",
+       "0                             False"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -4429,117 +4437,118 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   Part C inner split fallback used\n",
-       "0                             False"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "stratify_inner_c = y_train_c if y_train_c.value_counts().min() >= 2 else None\n",
-    "X_train_c_inner, X_val_c_inner, y_train_c_inner, y_val_c_inner = train_test_split(\n",
-    "    X_train_c, y_train_c, test_size=0.25, random_state=seed, stratify=stratify_inner_c\n",
-    ")\n",
-    "display(pd.DataFrame([{'Part C inner split fallback used': stratify_inner_c is None}]))\n",
-    "\n",
-    "scaler_c_inner = StandardScaler()\n",
-    "X_train_c_inner_scaled = scaler_c_inner.fit_transform(X_train_c_inner)\n",
-    "X_val_c_inner_scaled = scaler_c_inner.transform(X_val_c_inner)\n",
-    "X_test_c_scaled_inner = scaler_c_inner.transform(X_test_c)"
-   ]
+   "execution_count": 104
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
    "id": "8d076c79e4de34b1",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.374834900Z",
-     "start_time": "2026-04-26T14:22:32.370241100Z"
+     "end_time": "2026-04-26T14:56:48.477483100Z",
+     "start_time": "2026-04-26T14:56:48.454608500Z"
     }
    },
-   "outputs": [],
    "source": [
     "k_values_c = list(range(1, 51, 2))\n",
     "knn_val_acc_scores_c = []\n",
     "knn_val_f1_scores_c = []"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 105
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
    "id": "ea2c3ff63f5038c9",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.536734Z",
-     "start_time": "2026-04-26T14:22:32.375835600Z"
+     "end_time": "2026-04-26T14:56:48.632493300Z",
+     "start_time": "2026-04-26T14:56:48.478483700Z"
     }
    },
-   "outputs": [],
    "source": [
     "for k in k_values_c:\n",
     "    knn_c = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
     "    knn_c.fit(X_train_c_inner_scaled, y_train_c_inner)\n",
     "    y_val_pred_k = knn_c.predict(X_val_c_inner_scaled)\n",
     "    knn_val_acc_scores_c.append(accuracy_score(y_val_c_inner, y_val_pred_k))\n",
-    "    knn_val_f1_scores_c.append(f1_score(y_val_c_inner, y_val_pred_k, average='weighted'))\n"
-   ]
+    "    knn_val_f1_scores_c.append(f1_score(y_val_c_inner, y_val_pred_k, average='weighted'))"
+   ],
+   "outputs": [],
+   "execution_count": 106
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
    "id": "fa8b61af830f8448",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.556754800Z",
-     "start_time": "2026-04-26T14:22:32.536734Z"
+     "end_time": "2026-04-26T14:56:48.664301700Z",
+     "start_time": "2026-04-26T14:56:48.634493Z"
     }
    },
-   "outputs": [],
    "source": [
     "best_k_c = int(k_values_c[int(np.argmax(knn_val_f1_scores_c))])\n",
-    "\n",
     "knn_final_c = KNeighborsClassifier(n_neighbors=best_k_c, weights='distance')\n",
     "knn_final_c.fit(X_train_c_inner_scaled, y_train_c_inner)\n",
-    "\n",
     "knn_c_test_pred = knn_final_c.predict(X_test_c_scaled_inner)\n",
     "knn_c_test_acc, knn_c_test_f1 = eval_metrics(y_test_c, knn_c_test_pred, f\"KNN k={best_k_c} (Part C, Test)\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 107
   },
   {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "d791a90dc2193198",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-04-26T14:22:32.561270100Z",
      "start_time": "2026-04-26T14:22:32.556754800Z"
     }
    },
-   "outputs": [],
-   "source": [
-    "# --------------------\n",
-    "# Visual comparison (real outputs from the models)\n",
-    "# --------------------"
-   ]
+   "cell_type": "markdown",
+   "source": "## Visual comparison",
+   "id": "d9f03dba2a8e6d4e"
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
    "id": "78b84ffa610839ec",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.630796200Z",
-     "start_time": "2026-04-26T14:22:32.561270100Z"
+     "end_time": "2026-04-26T14:56:48.788059200Z",
+     "start_time": "2026-04-26T14:56:48.666808400Z"
     }
    },
+   "source": [
+    "comparison_c = pd.DataFrame([\n",
+    "    {\"Model\": \"Logistic Regression\", \"Accuracy\": logreg_c_test_acc, \"Weighted F1\": logreg_c_test_f1},\n",
+    "    {\"Model\": f\"KNN (k={best_k_c})\", \"Accuracy\": knn_c_test_acc, \"Weighted F1\": knn_c_test_f1},\n",
+    "])\n",
+    "\n",
+    "display(comparison_c)\n",
+    "fig, ax = plt.subplots(figsize=(7, 4))\n",
+    "ax.bar(comparison_c[\"Model\"], comparison_c[\"Weighted F1\"], label=\"Weighted F1\")\n",
+    "ax.plot(comparison_c[\"Model\"], comparison_c[\"Accuracy\"], marker='o', linewidth=2, label=\"Accuracy\")\n",
+    "ax.set_title(\"Part C: Model comparison on Test set\")\n",
+    "ax.set_ylim(0, 1)\n",
+    "ax.grid(True, axis='y', alpha=0.3)\n",
+    "ax.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "                 Model  Accuracy  Weighted F1\n",
+       "0  Logistic Regression  0.171171     0.182992\n",
+       "1           KNN (k=41)  0.324324     0.285951"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -4580,87 +4589,39 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                 Model  Accuracy  Weighted F1\n",
-       "0  Logistic Regression  0.171171     0.182992\n",
-       "1           KNN (k=41)  0.324324     0.285951"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
-      ]
+      ],
+      "image/png": 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"
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "comparison_c = pd.DataFrame([\n",
-    "    {\"Model\": \"Logistic Regression\", \"Accuracy\": logreg_c_test_acc, \"Weighted F1\": logreg_c_test_f1},\n",
-    "    {\"Model\": f\"KNN (k={best_k_c})\", \"Accuracy\": knn_c_test_acc, \"Weighted F1\": knn_c_test_f1},\n",
-    "])\n",
-    "\n",
-    "display(comparison_c)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(7, 4))\n",
-    "ax.bar(comparison_c[\"Model\"], comparison_c[\"Weighted F1\"], label=\"Weighted F1\")\n",
-    "ax.plot(comparison_c[\"Model\"], comparison_c[\"Accuracy\"], marker='o', linewidth=2, label=\"Accuracy\")\n",
-    "ax.set_title(\"Part C: Model comparison on Test set\")\n",
-    "ax.set_ylim(0, 1)\n",
-    "ax.grid(True, axis='y', alpha=0.3)\n",
-    "ax.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
+   "execution_count": 108
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
-   "id": "51a7ab76e87a33c6",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.635838700Z",
-     "start_time": "2026-04-26T14:22:32.631802200Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "# Confusion matrices (Test set)\n",
-    "# If there are many install classes, the full confusion matrix becomes unreadable.\n",
-    "# So I show the confusion matrix for the most frequent classes in the test set.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
    "id": "b3f55990f53222cd",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.897864100Z",
-     "start_time": "2026-04-26T14:22:32.635838700Z"
+     "end_time": "2026-04-26T14:56:49.112004400Z",
+     "start_time": "2026-04-26T14:56:48.796573100Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "top_n_c = 12\n",
     "\n",
@@ -4687,38 +4648,75 @@
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 109
   },
   {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "ae59b1c4d61d77fc",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-04-26T14:22:32.916355800Z",
      "start_time": "2026-04-26T14:22:32.898864500Z"
     }
    },
-   "outputs": [],
-   "source": [
-    "# --------------------\n",
-    "# Display the actual predicted outputs (so the marker can see what each model predicted)\n",
-    "# --------------------\n"
-   ]
+   "cell_type": "markdown",
+   "source": "### Predicted Data Outputs:",
+   "id": "b09c91f61b44cad6"
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
    "id": "1f20964c6c1d3861",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.926688100Z",
-     "start_time": "2026-04-26T14:22:32.916355800Z"
+     "end_time": "2026-04-26T14:56:49.143985700Z",
+     "start_time": "2026-04-26T14:56:49.115509800Z"
     }
    },
+   "source": [
+    "logreg_c_probs = logreg_c.predict_proba(X_test_c_scaled)\n",
+    "logreg_c_pred_df = prediction_sheet(y_test_c, logreg_c_test_pred, logreg_c_probs, 'Installs_Num', head_n=20)\n",
+    "logreg_c_pred_df"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   True_Installs_Num Predicted_Installs_Num  Predicted_Confidence  Correct\n",
+       "0            1000000                   5000              0.197939    False\n",
+       "1            1000000                  50000              0.469158    False\n",
+       "2           10000000               50000000              0.425556    False\n",
+       "3             500000                1000000              0.145711    False\n",
+       "4          100000000              500000000              0.470446    False\n",
+       "5          100000000               50000000              0.400632    False\n",
+       "6           10000000               50000000              0.492270    False\n",
+       "7           10000000                5000000              0.250524    False\n",
+       "8              10000                    500              0.647713    False\n",
+       "9            1000000                1000000              0.171517     True\n",
+       "10            100000                 500000              0.334840    False\n",
+       "11             10000                  50000              0.212020    False\n",
+       "12           5000000                    100              0.291118    False\n",
+       "13           1000000                 500000              0.241188    False\n",
+       "14              5000                    100              0.207994    False\n",
+       "15            500000                  50000              0.244060    False\n",
+       "16           5000000               10000000              0.237635    False\n",
+       "17          10000000               50000000              0.373763    False\n",
+       "18           1000000                1000000              0.196770     True\n",
+       "19            100000                5000000              0.161215    False"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -4888,55 +4886,55 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   True_Installs_Num Predicted_Installs_Num  Predicted_Confidence  Correct\n",
-       "0            1000000                   5000              0.197939    False\n",
-       "1            1000000                  50000              0.469158    False\n",
-       "2           10000000               50000000              0.425556    False\n",
-       "3             500000                1000000              0.145711    False\n",
-       "4          100000000              500000000              0.470446    False\n",
-       "5          100000000               50000000              0.400632    False\n",
-       "6           10000000               50000000              0.492270    False\n",
-       "7           10000000                5000000              0.250524    False\n",
-       "8              10000                    500              0.647713    False\n",
-       "9            1000000                1000000              0.171517     True\n",
-       "10            100000                 500000              0.334840    False\n",
-       "11             10000                  50000              0.212020    False\n",
-       "12           5000000                    100              0.291118    False\n",
-       "13           1000000                 500000              0.241188    False\n",
-       "14              5000                    100              0.207994    False\n",
-       "15            500000                  50000              0.244060    False\n",
-       "16           5000000               10000000              0.237635    False\n",
-       "17          10000000               50000000              0.373763    False\n",
-       "18           1000000                1000000              0.196770     True\n",
-       "19            100000                5000000              0.161215    False"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "logreg_c_probs = logreg_c.predict_proba(X_test_c_scaled)\n",
-    "logreg_c_pred_df = prediction_sheet(y_test_c, logreg_c_test_pred, logreg_c_probs, 'Installs_Num', head_n=20)\n",
-    "logreg_c_pred_df"
-   ]
+   "execution_count": 110
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
    "id": "948657d95acea7a",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:32.941786900Z",
-     "start_time": "2026-04-26T14:22:32.926688100Z"
+     "end_time": "2026-04-26T14:56:49.199982600Z",
+     "start_time": "2026-04-26T14:56:49.144985200Z"
     }
    },
+   "source": [
+    "knn_c_probs = knn_final_c.predict_proba(X_test_c_scaled_inner)\n",
+    "knn_c_pred_df = prediction_sheet(y_test_c, knn_c_test_pred, knn_c_probs, 'Installs_Num', head_n=20)\n",
+    "knn_c_pred_df"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "   True_Installs_Num Predicted_Installs_Num  Predicted_Confidence  Correct\n",
+       "0            1000000                1000000              0.293952     True\n",
+       "1            1000000                1000000              0.446849     True\n",
+       "2           10000000                1000000              0.522130    False\n",
+       "3             500000                1000000              0.287865    False\n",
+       "4          100000000                1000000              0.376263    False\n",
+       "5          100000000              100000000              0.995326     True\n",
+       "6           10000000               10000000              0.459153     True\n",
+       "7           10000000                1000000              0.304257    False\n",
+       "8              10000                 100000              0.291979    False\n",
+       "9            1000000                 100000              0.427634    False\n",
+       "10            100000                 500000              0.259322    False\n",
+       "11             10000                  50000              0.322032    False\n",
+       "12           5000000                1000000              0.234653    False\n",
+       "13           1000000                 500000              0.431720    False\n",
+       "14              5000                 100000              0.377805    False\n",
+       "15            500000                  50000              0.431116    False\n",
+       "16           5000000                1000000              0.443130    False\n",
+       "17          10000000               10000000              0.353770     True\n",
+       "18           1000000                 100000              0.376758    False\n",
+       "19            100000                1000000              0.595938    False"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5106,80 +5104,35 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "   True_Installs_Num Predicted_Installs_Num  Predicted_Confidence  Correct\n",
-       "0            1000000                1000000              0.293952     True\n",
-       "1            1000000                1000000              0.446849     True\n",
-       "2           10000000                1000000              0.522130    False\n",
-       "3             500000                1000000              0.287865    False\n",
-       "4          100000000                1000000              0.376263    False\n",
-       "5          100000000              100000000              0.995326     True\n",
-       "6           10000000               10000000              0.459153     True\n",
-       "7           10000000                1000000              0.304257    False\n",
-       "8              10000                 100000              0.291979    False\n",
-       "9            1000000                 100000              0.427634    False\n",
-       "10            100000                 500000              0.259322    False\n",
-       "11             10000                  50000              0.322032    False\n",
-       "12           5000000                1000000              0.234653    False\n",
-       "13           1000000                 500000              0.431720    False\n",
-       "14              5000                 100000              0.377805    False\n",
-       "15            500000                  50000              0.431116    False\n",
-       "16           5000000                1000000              0.443130    False\n",
-       "17          10000000               10000000              0.353770     True\n",
-       "18           1000000                 100000              0.376758    False\n",
-       "19            100000                1000000              0.595938    False"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "knn_c_probs = knn_final_c.predict_proba(X_test_c_scaled_inner)\n",
-    "knn_c_pred_df = prediction_sheet(y_test_c, knn_c_test_pred, knn_c_probs, 'Installs_Num', head_n=20)\n",
-    "knn_c_pred_df"
-   ]
+   "execution_count": 111
   },
   {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "97b114df88250967",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-04-26T14:22:32.947794900Z",
      "start_time": "2026-04-26T14:22:32.941786900Z"
     }
    },
-   "outputs": [],
-   "source": [
-    "# Visual: compare distribution of predicted labels vs actual labels on the test set\n",
-    "# Keep it readable by focusing on the most frequent install classes\n"
-   ]
+   "cell_type": "markdown",
+   "source": "comparing distribution of predicted labels vs actual labels on the test set",
+   "id": "881500a7bfe439cf"
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
    "id": "4b9d2236b6721b38",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:33.069212300Z",
-     "start_time": "2026-04-26T14:22:32.948794Z"
+     "end_time": "2026-04-26T14:56:49.301674300Z",
+     "start_time": "2026-04-26T14:56:49.200983700Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "dist_top_n_c = 12\n",
     "most_common_classes_c = pd.Series(y_test_c).value_counts().head(dist_top_n_c).index\n",
@@ -5203,55 +5156,67 @@
     "ax.legend()\n",
     "plt.tight_layout()\n",
     "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "c0cd0cf13f562969",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:33.086835200Z",
-     "start_time": "2026-04-26T14:22:33.071212500Z"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
-   },
-   "outputs": [],
-   "source": [
-    "# --------------------\n",
-    "# Decision + short discussion (based on the real metrics above)\n",
-    "# --------------------"
-   ]
+   ],
+   "execution_count": 112
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
    "id": "d475aa4180f2014e",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:33.092961500Z",
-     "start_time": "2026-04-26T14:22:33.087836500Z"
+     "end_time": "2026-04-26T14:56:49.337671200Z",
+     "start_time": "2026-04-26T14:56:49.302674600Z"
     }
    },
-   "outputs": [],
    "source": [
     "best_model_c = (\n",
     "    f\"KNN (k={best_k_c})\" if knn_c_test_f1 > logreg_c_test_f1 else \"Logistic Regression\"\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 113
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
    "id": "d0a4f6fb6c77d787",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-26T14:22:33.105337900Z",
-     "start_time": "2026-04-26T14:22:33.093959500Z"
+     "end_time": "2026-04-26T14:56:49.352706600Z",
+     "start_time": "2026-04-26T14:56:49.342668900Z"
     }
    },
+   "source": [
+    "part_c_final_summary = pd.DataFrame([\n",
+    "    {'Model': 'Logistic Regression', 'Accuracy': logreg_c_test_acc, 'Weighted F1': logreg_c_test_f1},\n",
+    "    {'Model': f'KNN (k={best_k_c})', 'Accuracy': knn_c_test_acc, 'Weighted F1': knn_c_test_f1},\n",
+    "]).sort_values('Weighted F1', ascending=False).reset_index(drop=True)\n",
+    "\n",
+    "display(part_c_final_summary)\n",
+    "display(pd.DataFrame([{'Part C Best Model': best_model_c}]))"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "                 Model  Accuracy  Weighted F1\n",
+       "0           KNN (k=41)  0.324324     0.285951\n",
+       "1  Logistic Regression  0.171171     0.182992"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5292,18 +5257,20 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "                 Model  Accuracy  Weighted F1\n",
-       "0           KNN (k=41)  0.324324     0.285951\n",
-       "1  Logistic Regression  0.171171     0.182992"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
+      "text/plain": [
+       "  Part C Best Model\n",
+       "0        KNN (k=41)"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5334,25 +5301,27 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
-      ],
-      "text/plain": [
-       "  Part C Best Model\n",
-       "0        KNN (k=41)"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
+   "execution_count": 114
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
    "source": [
-    "part_c_final_summary = pd.DataFrame([\n",
-    "    {'Model': 'Logistic Regression', 'Accuracy': logreg_c_test_acc, 'Weighted F1': logreg_c_test_f1},\n",
-    "    {'Model': f'KNN (k={best_k_c})', 'Accuracy': knn_c_test_acc, 'Weighted F1': knn_c_test_f1},\n",
-    "]).sort_values('Weighted F1', ascending=False).reset_index(drop=True)\n",
+    "## Answer for Part C:\n",
     "\n",
-    "display(part_c_final_summary)\n",
-    "display(pd.DataFrame([{'Part C Best Model': best_model_c}]))\n"
-   ]
+    "\n",
+    "The KNN Model with the K value being set to 41, provides the closest predicted data distribution to the actual distribution of the test set, and also has a higher weighted F1 score compared to Logistic Regression. Therefore, KNN is the best classification model for predicting \"Installs_Num\" in Part C."
+   ],
+   "id": "76125c5a3ab3075a"
   }
  ],
  "metadata": {