From 9d8ae2eea42161a665e86055a0d75420484be1e5 Mon Sep 17 00:00:00 2001
From: mudabbir-ahmad <mudabbira.uk@gmail.com>
Date: Sat, 25 Apr 2026 20:25:10 +0100
Subject: [PATCH] Part G done

---
 Q1.ipynb | 818 ++++++++++++++++++++++++++++++++++++++++++++-----------
 1 file changed, 656 insertions(+), 162 deletions(-)

diff --git a/Q1.ipynb b/Q1.ipynb
index cfc91a6..4cd26cc 100644
--- a/Q1.ipynb
+++ b/Q1.ipynb
@@ -24,8 +24,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.185419800Z",
-     "start_time": "2026-04-25T18:32:46.161551700Z"
+     "end_time": "2026-04-25T19:24:54.267637800Z",
+     "start_time": "2026-04-25T19:24:54.259719300Z"
     }
    },
    "cell_type": "code",
@@ -37,20 +37,20 @@
    ],
    "id": "edaea0c939a83b79",
    "outputs": [],
-   "execution_count": 868
+   "execution_count": 1287
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.231687200Z",
-     "start_time": "2026-04-25T18:32:46.204273900Z"
+     "end_time": "2026-04-25T19:24:54.304283700Z",
+     "start_time": "2026-04-25T19:24:54.282710700Z"
     }
    },
    "cell_type": "code",
    "source": "df = pd.read_csv('data/googleplaystore_new.csv')",
    "id": "e657e9baacc13e6b",
    "outputs": [],
-   "execution_count": 869
+   "execution_count": 1288
   },
   {
    "metadata": {},
@@ -61,8 +61,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.287290300Z",
-     "start_time": "2026-04-25T18:32:46.263732800Z"
+     "end_time": "2026-04-25T19:24:54.323566400Z",
+     "start_time": "2026-04-25T19:24:54.306281Z"
     }
    },
    "cell_type": "code",
@@ -72,13 +72,13 @@
    ],
    "id": "756c92821453bbb3",
    "outputs": [],
-   "execution_count": 870
+   "execution_count": 1289
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.328054400Z",
-     "start_time": "2026-04-25T18:32:46.288288200Z"
+     "end_time": "2026-04-25T19:24:54.360201700Z",
+     "start_time": "2026-04-25T19:24:54.324564700Z"
     }
    },
    "cell_type": "code",
@@ -290,12 +290,12 @@
        "</div>"
       ]
      },
-     "execution_count": 871,
+     "execution_count": 1290,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 871
+   "execution_count": 1290
   },
   {
    "metadata": {
@@ -311,8 +311,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.394614Z",
-     "start_time": "2026-04-25T18:32:46.330567300Z"
+     "end_time": "2026-04-25T19:24:54.367360500Z",
+     "start_time": "2026-04-25T19:24:54.360705700Z"
     }
    },
    "cell_type": "code",
@@ -328,26 +328,26 @@
    ],
    "id": "c15cb7f9831e0f81",
    "outputs": [],
-   "execution_count": 872
+   "execution_count": 1291
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.448348600Z",
-     "start_time": "2026-04-25T18:32:46.396618500Z"
+     "end_time": "2026-04-25T19:24:54.387843100Z",
+     "start_time": "2026-04-25T19:24:54.368358900Z"
     }
    },
    "cell_type": "code",
    "source": "df['Size in bytes'] = df['Size'].apply(parse_size)",
    "id": "c76da70de24ddc72",
    "outputs": [],
-   "execution_count": 873
+   "execution_count": 1292
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.538411700Z",
-     "start_time": "2026-04-25T18:32:46.451372100Z"
+     "end_time": "2026-04-25T19:24:54.424398600Z",
+     "start_time": "2026-04-25T19:24:54.388843Z"
     }
    },
    "cell_type": "code",
@@ -448,18 +448,18 @@
        "</div>"
       ]
      },
-     "execution_count": 874,
+     "execution_count": 1293,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 874
+   "execution_count": 1293
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.627364Z",
-     "start_time": "2026-04-25T18:32:46.563472Z"
+     "end_time": "2026-04-25T19:24:54.448197600Z",
+     "start_time": "2026-04-25T19:24:54.426507500Z"
     }
    },
    "cell_type": "code",
@@ -474,13 +474,13 @@
      ]
     }
    ],
-   "execution_count": 875
+   "execution_count": 1294
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.670395800Z",
-     "start_time": "2026-04-25T18:32:46.628364600Z"
+     "end_time": "2026-04-25T19:24:54.484354800Z",
+     "start_time": "2026-04-25T19:24:54.463753600Z"
     }
    },
    "cell_type": "code",
@@ -495,13 +495,13 @@
      ]
     }
    ],
-   "execution_count": 876
+   "execution_count": 1295
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.696603800Z",
-     "start_time": "2026-04-25T18:32:46.671400500Z"
+     "end_time": "2026-04-25T19:24:54.504827700Z",
+     "start_time": "2026-04-25T19:24:54.485357700Z"
     }
    },
    "cell_type": "code",
@@ -516,13 +516,13 @@
      ]
     }
    ],
-   "execution_count": 877
+   "execution_count": 1296
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.720540800Z",
-     "start_time": "2026-04-25T18:32:46.700613100Z"
+     "end_time": "2026-04-25T19:24:54.525543700Z",
+     "start_time": "2026-04-25T19:24:54.505826700Z"
     }
    },
    "cell_type": "code",
@@ -757,12 +757,12 @@
        "</div>"
       ]
      },
-     "execution_count": 878,
+     "execution_count": 1297,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 878
+   "execution_count": 1297
   },
   {
    "metadata": {
@@ -778,21 +778,21 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.749478600Z",
-     "start_time": "2026-04-25T18:32:46.738353Z"
+     "end_time": "2026-04-25T19:24:54.541583100Z",
+     "start_time": "2026-04-25T19:24:54.526548900Z"
     }
    },
    "cell_type": "code",
    "source": "df['Numeric Installs'] = df['Installs'].str.replace('+', '').str.replace(',', '').astype(int)",
    "id": "c8d4f46526918c20",
    "outputs": [],
-   "execution_count": 879
+   "execution_count": 1298
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.762648Z",
-     "start_time": "2026-04-25T18:32:46.751624500Z"
+     "end_time": "2026-04-25T19:24:54.577350700Z",
+     "start_time": "2026-04-25T19:24:54.542582200Z"
     }
    },
    "cell_type": "code",
@@ -1038,12 +1038,12 @@
        "</div>"
       ]
      },
-     "execution_count": 880,
+     "execution_count": 1299,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 880
+   "execution_count": 1299
   },
   {
    "metadata": {},
@@ -1054,15 +1054,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.785103900Z",
-     "start_time": "2026-04-25T18:32:46.763648900Z"
+     "end_time": "2026-04-25T19:24:54.593385100Z",
+     "start_time": "2026-04-25T19:24:54.578350400Z"
     }
    },
    "cell_type": "code",
    "source": "df.to_csv('data/googleplaystore_new_new.csv', index=False)",
    "id": "5a99edb9f8b29b12",
    "outputs": [],
-   "execution_count": 881
+   "execution_count": 1300
   },
   {
    "metadata": {},
@@ -1073,8 +1073,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.792121900Z",
-     "start_time": "2026-04-25T18:32:46.786105500Z"
+     "end_time": "2026-04-25T19:24:54.597888800Z",
+     "start_time": "2026-04-25T19:24:54.594384900Z"
     }
    },
    "cell_type": "code",
@@ -1086,26 +1086,26 @@
    ],
    "id": "8cc741d5b19eaa62",
    "outputs": [],
-   "execution_count": 882
+   "execution_count": 1301
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.803603800Z",
-     "start_time": "2026-04-25T18:32:46.793716400Z"
+     "end_time": "2026-04-25T19:24:54.608068800Z",
+     "start_time": "2026-04-25T19:24:54.597888800Z"
     }
    },
    "cell_type": "code",
    "source": "df_new = pd.read_csv('data/googleplaystore_new_new.csv')",
    "id": "bc158bb312aa3cd7",
    "outputs": [],
-   "execution_count": 883
+   "execution_count": 1302
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.810420900Z",
-     "start_time": "2026-04-25T18:32:46.803603800Z"
+     "end_time": "2026-04-25T19:24:54.614378300Z",
+     "start_time": "2026-04-25T19:24:54.608068800Z"
     }
    },
    "cell_type": "code",
@@ -1115,13 +1115,13 @@
    ],
    "id": "585677f9cc3efc16",
    "outputs": [],
-   "execution_count": 884
+   "execution_count": 1303
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.822566300Z",
-     "start_time": "2026-04-25T18:32:46.810925Z"
+     "end_time": "2026-04-25T19:24:54.625388Z",
+     "start_time": "2026-04-25T19:24:54.614378300Z"
     }
    },
    "cell_type": "code",
@@ -1388,31 +1388,31 @@
        "</div>"
       ]
      },
-     "execution_count": 885,
+     "execution_count": 1304,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 885
+   "execution_count": 1304
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.852368200Z",
-     "start_time": "2026-04-25T18:32:46.823731300Z"
+     "end_time": "2026-04-25T19:24:54.640595500Z",
+     "start_time": "2026-04-25T19:24:54.625388Z"
     }
    },
    "cell_type": "code",
    "source": "df_encoded = pd.get_dummies(df_min, columns=['Category', 'Content Rating'])",
    "id": "8c4742ab98b8b6ca",
    "outputs": [],
-   "execution_count": 886
+   "execution_count": 1305
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.860385300Z",
-     "start_time": "2026-04-25T18:32:46.853369300Z"
+     "end_time": "2026-04-25T19:24:54.645210300Z",
+     "start_time": "2026-04-25T19:24:54.640595500Z"
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    "cell_type": "code",
@@ -1422,26 +1422,26 @@
    ],
    "id": "7faed3f843076351",
    "outputs": [],
-   "execution_count": 887
+   "execution_count": 1306
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.878750100Z",
-     "start_time": "2026-04-25T18:32:46.860889300Z"
+     "end_time": "2026-04-25T19:24:54.663344300Z",
+     "start_time": "2026-04-25T19:24:54.647212400Z"
     }
    },
    "cell_type": "code",
    "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)",
    "id": "8646df724923b0f5",
    "outputs": [],
-   "execution_count": 888
+   "execution_count": 1307
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.884189Z",
-     "start_time": "2026-04-25T18:32:46.879749500Z"
+     "end_time": "2026-04-25T19:24:54.668052Z",
+     "start_time": "2026-04-25T19:24:54.663344300Z"
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    "cell_type": "code",
@@ -1460,20 +1460,20 @@
    ],
    "id": "44c698b18cdce613",
    "outputs": [],
-   "execution_count": 889
+   "execution_count": 1308
   },
   {
    "metadata": {
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-     "end_time": "2026-04-25T18:32:46.892720900Z",
-     "start_time": "2026-04-25T18:32:46.885190900Z"
+     "end_time": "2026-04-25T19:24:54.674935900Z",
+     "start_time": "2026-04-25T19:24:54.668052Z"
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    },
    "cell_type": "code",
    "source": "results = []",
    "id": "d8740e0256fe177b",
    "outputs": [],
-   "execution_count": 890
+   "execution_count": 1309
   },
   {
    "metadata": {},
@@ -1484,8 +1484,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.906083Z",
-     "start_time": "2026-04-25T18:32:46.892720900Z"
+     "end_time": "2026-04-25T19:24:54.704415700Z",
+     "start_time": "2026-04-25T19:24:54.675937Z"
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@@ -1496,13 +1496,13 @@
    ],
    "id": "db88942671bdf26a",
    "outputs": [],
-   "execution_count": 891
+   "execution_count": 1310
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:46.917527900Z",
-     "start_time": "2026-04-25T18:32:46.906083Z"
+     "end_time": "2026-04-25T19:24:54.721751600Z",
+     "start_time": "2026-04-25T19:24:54.705412700Z"
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    "cell_type": "code",
@@ -1931,18 +1931,18 @@
        "</div>"
       ]
      },
-     "execution_count": 892,
+     "execution_count": 1311,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 892
+   "execution_count": 1311
   },
   {
    "metadata": {
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-     "end_time": "2026-04-25T18:32:46.991337600Z",
-     "start_time": "2026-04-25T18:32:46.918530100Z"
+     "end_time": "2026-04-25T19:24:54.795055100Z",
+     "start_time": "2026-04-25T19:24:54.722755900Z"
     }
    },
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@@ -1974,7 +1974,7 @@
      }
     }
    ],
-   "execution_count": 893
+   "execution_count": 1312
   },
   {
    "metadata": {},
@@ -1985,8 +1985,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.011369400Z",
-     "start_time": "2026-04-25T18:32:46.992338400Z"
+     "end_time": "2026-04-25T19:24:54.823641Z",
+     "start_time": "2026-04-25T19:24:54.808608400Z"
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@@ -1997,13 +1997,13 @@
    ],
    "id": "b867289f4b8dd0ae",
    "outputs": [],
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+   "execution_count": 1313
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.042987600Z",
-     "start_time": "2026-04-25T18:32:47.012369900Z"
+     "end_time": "2026-04-25T19:24:54.854794Z",
+     "start_time": "2026-04-25T19:24:54.824641Z"
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@@ -2014,13 +2014,13 @@
    ],
    "id": "61b135564e36f71d",
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-   "execution_count": 895
+   "execution_count": 1314
   },
   {
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-     "end_time": "2026-04-25T18:32:47.112590600Z",
-     "start_time": "2026-04-25T18:32:47.042987600Z"
+     "end_time": "2026-04-25T19:24:54.919410Z",
+     "start_time": "2026-04-25T19:24:54.854794Z"
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@@ -2050,7 +2050,7 @@
      }
     }
    ],
-   "execution_count": 896
+   "execution_count": 1315
   },
   {
    "metadata": {
@@ -2066,8 +2066,8 @@
   {
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-     "start_time": "2026-04-25T18:32:47.128611600Z"
+     "end_time": "2026-04-25T19:24:54.935563200Z",
+     "start_time": "2026-04-25T19:24:54.920415Z"
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@@ -2078,13 +2078,13 @@
    ],
    "id": "f7b09be17154ad47",
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-   "execution_count": 897
+   "execution_count": 1316
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   {
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-     "start_time": "2026-04-25T18:32:47.139738100Z"
+     "end_time": "2026-04-25T19:24:55.000114100Z",
+     "start_time": "2026-04-25T19:24:54.935563200Z"
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@@ -2114,7 +2114,7 @@
      }
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    ],
-   "execution_count": 898
+   "execution_count": 1317
   },
   {
    "metadata": {
@@ -2130,8 +2130,8 @@
   {
    "metadata": {
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-     "end_time": "2026-04-25T18:32:47.231714300Z",
-     "start_time": "2026-04-25T18:32:47.211723200Z"
+     "end_time": "2026-04-25T19:24:55.014415700Z",
+     "start_time": "2026-04-25T19:24:55.000625Z"
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@@ -2142,13 +2142,13 @@
    ],
    "id": "11024fb6674354a3",
    "outputs": [],
-   "execution_count": 899
+   "execution_count": 1318
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.300748Z",
-     "start_time": "2026-04-25T18:32:47.232718700Z"
+     "end_time": "2026-04-25T19:24:55.080634600Z",
+     "start_time": "2026-04-25T19:24:55.014415700Z"
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    },
    "cell_type": "code",
@@ -2178,13 +2178,13 @@
      }
     }
    ],
-   "execution_count": 900
+   "execution_count": 1319
   },
   {
    "metadata": {
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-     "end_time": "2026-04-25T18:32:47.322487800Z",
-     "start_time": "2026-04-25T18:32:47.301252200Z"
+     "end_time": "2026-04-25T19:24:55.098288800Z",
+     "start_time": "2026-04-25T19:24:55.080634600Z"
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    },
    "cell_type": "code",
@@ -2262,18 +2262,18 @@
        "</div>"
       ]
      },
-     "execution_count": 901,
+     "execution_count": 1320,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 901
+   "execution_count": 1320
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    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.436110800Z",
-     "start_time": "2026-04-25T18:32:47.323487900Z"
+     "end_time": "2026-04-25T19:24:55.210961200Z",
+     "start_time": "2026-04-25T19:24:55.098288800Z"
     }
    },
    "cell_type": "code",
@@ -2308,7 +2308,7 @@
      }
     }
    ],
-   "execution_count": 902
+   "execution_count": 1321
   },
   {
    "metadata": {},
@@ -2333,8 +2333,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.471463900Z",
-     "start_time": "2026-04-25T18:32:47.438114200Z"
+     "end_time": "2026-04-25T19:24:55.229576600Z",
+     "start_time": "2026-04-25T19:24:55.225726800Z"
     }
    },
    "cell_type": "code",
@@ -2344,13 +2344,13 @@
    ],
    "id": "ab09192a122e6e8f",
    "outputs": [],
-   "execution_count": 903
+   "execution_count": 1322
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.480482700Z",
-     "start_time": "2026-04-25T18:32:47.472462700Z"
+     "end_time": "2026-04-25T19:24:55.242674500Z",
+     "start_time": "2026-04-25T19:24:55.229576600Z"
     }
    },
    "cell_type": "code",
@@ -2363,13 +2363,13 @@
    ],
    "id": "3f65e25b63cc8e93",
    "outputs": [],
-   "execution_count": 904
+   "execution_count": 1323
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.507956400Z",
-     "start_time": "2026-04-25T18:32:47.481574700Z"
+     "end_time": "2026-04-25T19:24:55.248417Z",
+     "start_time": "2026-04-25T19:24:55.242674500Z"
     }
    },
    "cell_type": "code",
@@ -2380,13 +2380,13 @@
    ],
    "id": "75e6c5289568ef2f",
    "outputs": [],
-   "execution_count": 905
+   "execution_count": 1324
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.539443500Z",
-     "start_time": "2026-04-25T18:32:47.509460400Z"
+     "end_time": "2026-04-25T19:24:55.257368100Z",
+     "start_time": "2026-04-25T19:24:55.249415300Z"
     }
    },
    "cell_type": "code",
@@ -2400,13 +2400,13 @@
    ],
    "id": "983db24d45054c97",
    "outputs": [],
-   "execution_count": 906
+   "execution_count": 1325
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.563723100Z",
-     "start_time": "2026-04-25T18:32:47.541490300Z"
+     "end_time": "2026-04-25T19:24:55.267514600Z",
+     "start_time": "2026-04-25T19:24:55.257368100Z"
     }
    },
    "cell_type": "code",
@@ -2460,18 +2460,18 @@
        "</div>"
       ]
      },
-     "execution_count": 907,
+     "execution_count": 1326,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 907
+   "execution_count": 1326
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.652441900Z",
-     "start_time": "2026-04-25T18:32:47.565723700Z"
+     "end_time": "2026-04-25T19:24:55.289362400Z",
+     "start_time": "2026-04-25T19:24:55.267514600Z"
     }
    },
    "cell_type": "code",
@@ -2481,8 +2481,21 @@
     "    value_vars=['MAE', 'RMSE', 'R2'],\n",
     "    var_name='Metric',\n",
     "    value_name='Score'\n",
-    ")\n",
-    "\n",
+    ")"
+   ],
+   "id": "4f73fdfeefd697e2",
+   "outputs": [],
+   "execution_count": 1327
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.369601700Z",
+     "start_time": "2026-04-25T19:24:55.290372600Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
     "plt.figure(figsize=(9, 5))\n",
     "sns.barplot(data=feature_melted, x='Feature', y='Score', hue='Metric')\n",
     "plt.title(\"Part F: Single-Feature Comparison (Linear Regression)\")\n",
@@ -2506,37 +2519,26 @@
      }
     }
    ],
-   "execution_count": 908
+   "execution_count": 1328
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.672589700Z",
-     "start_time": "2026-04-25T18:32:47.653756800Z"
+     "end_time": "2026-04-25T19:24:55.384665Z",
+     "start_time": "2026-04-25T19:24:55.369601700Z"
     }
    },
    "cell_type": "code",
-   "source": [
-    "best_feature = feature_results_df.iloc[0]['Feature']\n",
-    "print(f\"Most predictive single feature (lowest RMSE): {best_feature}\")"
-   ],
-   "id": "4b8fc4506bce35f8",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Most predictive single feature (lowest RMSE): Numeric Installs\n"
-     ]
-    }
-   ],
-   "execution_count": 909
+   "source": "best_feature = feature_results_df.iloc[0]['Feature']",
+   "id": "593913702f875fa0",
+   "outputs": [],
+   "execution_count": 1329
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.764101900Z",
-     "start_time": "2026-04-25T18:32:47.673589300Z"
+     "end_time": "2026-04-25T19:24:55.390688200Z",
+     "start_time": "2026-04-25T19:24:55.384665Z"
     }
    },
    "cell_type": "code",
@@ -2544,12 +2546,38 @@
     "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=101\n",
-    ")\n",
-    "\n",
+    ")"
+   ],
+   "id": "2be649a00dbb9fc7",
+   "outputs": [],
+   "execution_count": 1330
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.410333600Z",
+     "start_time": "2026-04-25T19:24:55.390688200Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
     "best_model = LinearRegression()\n",
     "best_model.fit(X_train_b, y_train_b)\n",
-    "y_pred_b = best_model.predict(X_test_b)\n",
-    "\n",
+    "y_pred_b = best_model.predict(X_test_b)"
+   ],
+   "id": "d36175ff718e84a1",
+   "outputs": [],
+   "execution_count": 1331
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.476257800Z",
+     "start_time": "2026-04-25T19:24:55.411338500Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
     "plt.figure(figsize=(6, 6))\n",
     "plt.scatter(y_test_b, y_pred_b, alpha=0.5)\n",
     "plt.plot([y_test_b.min(), y_test_b.max()], [y_test_b.min(), y_test_b.max()], 'r--')\n",
@@ -2559,7 +2587,7 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ],
-   "id": "593913702f875fa0",
+   "id": "86d5d9a4bc156e7f",
    "outputs": [
     {
      "data": {
@@ -2575,7 +2603,7 @@
      }
     }
    ],
-   "execution_count": 910
+   "execution_count": 1332
   },
   {
    "metadata": {},
@@ -2610,15 +2638,481 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T18:32:47.787646400Z",
-     "start_time": "2026-04-25T18:32:47.766103400Z"
+     "end_time": "2026-04-25T19:24:55.504530400Z",
+     "start_time": "2026-04-25T19:24:55.477257700Z"
     }
    },
    "cell_type": "code",
-   "source": "",
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.preprocessing import StandardScaler"
+   ],
    "id": "137267551f08dae5",
    "outputs": [],
-   "execution_count": 910
+   "execution_count": 1333
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.525724500Z",
+     "start_time": "2026-04-25T19:24:55.504530400Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "columns_to_keep_g = ['Category', 'Reviews', 'Content Rating', 'Rating', 'Numeric Installs', 'Size in bytes']\n",
+    "df_g = df_new[columns_to_keep_g]"
+   ],
+   "id": "725e85ef4abe3948",
+   "outputs": [],
+   "execution_count": 1334
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.542623400Z",
+     "start_time": "2026-04-25T19:24:55.526724Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_g_encoded = pd.get_dummies(df_g, columns=['Category', 'Content Rating'])",
+   "id": "7a6b3f84b1265599",
+   "outputs": [],
+   "execution_count": 1335
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.547176500Z",
+     "start_time": "2026-04-25T19:24:55.542623400Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "X_g = df_g_encoded.drop('Size in bytes', axis=1) if 'Size in bytes' in df_g_encoded.columns else df_g_encoded\n",
+    "y_g = df_encoded['Size in bytes']  # Use Size in bytes from df_encoded"
+   ],
+   "id": "679b52eb182f0e1c",
+   "outputs": [],
+   "execution_count": 1336
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.556497500Z",
+     "start_time": "2026-04-25T19:24:55.548176400Z"
+    }
+   },
+   "cell_type": "code",
+   "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=101)",
+   "id": "35f87078cd8053fa",
+   "outputs": [],
+   "execution_count": 1337
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.562749500Z",
+     "start_time": "2026-04-25T19:24:55.556497500Z"
+    }
+   },
+   "cell_type": "code",
+   "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",
+    "        'Model': name,\n",
+    "        'MAE (test)': mean_absolute_error(y_test, y_pred),\n",
+    "        'RMSE (test)': np.sqrt(mean_squared_error(y_test, y_pred)),\n",
+    "        'R2 (test)': r2_score(y_test, y_pred),\n",
+    "        'CV R2 (mean)': cv_scores.mean(),\n",
+    "        'CV R2 (std)': cv_scores.std()\n",
+    "    }\n",
+    "    return results, y_pred, cv_scores"
+   ],
+   "id": "9cbccc4044fd76e",
+   "outputs": [],
+   "execution_count": 1338
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.571894200Z",
+     "start_time": "2026-04-25T19:24:55.563749500Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "results_g = []",
+   "id": "7bd72610ea9b4047",
+   "outputs": [],
+   "execution_count": 1339
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Trying Linear Regression for Part G",
+   "id": "ec07cf5f77d93f3b"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.597732800Z",
+     "start_time": "2026-04-25T19:24:55.573890200Z"
+    }
+   },
+   "cell_type": "code",
+   "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)"
+   ],
+   "id": "5077d997183c52b7",
+   "outputs": [],
+   "execution_count": 1340
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.602480600Z",
+     "start_time": "2026-04-25T19:24:55.598734300Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "residual_lin_g = y_test_g - y_pred_lin_g",
+   "id": "dcf6fd613a1d2d79",
+   "outputs": [],
+   "execution_count": 1341
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.674989Z",
+     "start_time": "2026-04-25T19:24:55.602480600Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.scatter(y_pred_lin_g, residual_lin_g, alpha=0.5)\n",
+    "plt.axhline(y=0, color='r', linestyle='--')\n",
+    "plt.title(\"Part G - Linear Regression: Residual Plot\")\n",
+    "plt.xlabel(\"Predicted Size in Bytes\")\n",
+    "plt.ylabel(\"Residual (y - y_hat)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "39fd78fe960acb67",
+   "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": 1342
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Trying polynomial regression for Part G",
+   "id": "5566a6d1839e472f"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.686530600Z",
+     "start_time": "2026-04-25T19:24:55.674989Z"
+    }
+   },
+   "cell_type": "code",
+   "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)"
+   ],
+   "id": "b5e9cdb3ef42c22f",
+   "outputs": [],
+   "execution_count": 1343
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.832957600Z",
+     "start_time": "2026-04-25T19:24:55.687531Z"
+    }
+   },
+   "cell_type": "code",
+   "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)"
+   ],
+   "id": "8d5313ca848e4519",
+   "outputs": [],
+   "execution_count": 1344
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.900704700Z",
+     "start_time": "2026-04-25T19:24:55.833958Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(y_test_g, y_pred_poly_g, alpha=0.5)\n",
+    "plt.plot([y_test_g.min(), y_test_g.max()], [y_test_g.min(), y_test_g.max()], 'r--')\n",
+    "plt.title(\"Part G - Polynomial Regression: Actual vs Predicted\")\n",
+    "plt.xlabel(\"True Size in Bytes\")\n",
+    "plt.ylabel(\"Predicted Size in Bytes\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "8c81b3b6dab60fee",
+   "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": 1345
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "# Trying Ridge Regression for Part G",
+   "id": "636e6b9a053c8f8d"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.928066700Z",
+     "start_time": "2026-04-25T19:24:55.911232600Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ridge_model_g = Ridge(alpha=1.0, solver='lsqr')\n",
+    "ridge_res_g, y_pred_ridge_g, cv_ridge_g = evaluate_model_cv(ridge_model_g, X_train_g, X_test_g, y_train_g, y_test_g, \"Ridge Regression\")\n",
+    "results_g.append(ridge_res_g)"
+   ],
+   "id": "a31ad4ed704f4541",
+   "outputs": [],
+   "execution_count": 1346
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:55.990468200Z",
+     "start_time": "2026-04-25T19:24:55.928066700Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(y_test_g, y_pred_ridge_g, alpha=0.5)\n",
+    "plt.plot([y_test_g.min(), y_test_g.max()], [y_test_g.min(), y_test_g.max()], 'r--')\n",
+    "plt.title(\"Part G - Ridge Regression: Actual vs Predicted\")\n",
+    "plt.xlabel(\"True Size in Bytes\")\n",
+    "plt.ylabel(\"Predicted Size in Bytes\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "121ba346ffaea0e5",
+   "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": 1347
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "# Regression Model Results",
+   "id": "f238f4198de0c527"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:56.013586200Z",
+     "start_time": "2026-04-25T19:24:56.000480800Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "results_df_g = pd.DataFrame(results_g)\n",
+    "results_df_g"
+   ],
+   "id": "c7a5e958c9ab89e6",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                   Model    MAE (test)   RMSE (test)  R2 (test)  CV R2 (mean)  \\\n",
+       "0      Linear Regression  1.458116e+07  1.906474e+07   0.292880      0.245829   \n",
+       "1  Polynomial Regression  1.708302e+07  2.663426e+07  -0.380108     -0.452607   \n",
+       "2       Ridge Regression  1.629031e+07  2.162718e+07   0.090021      0.090846   \n",
+       "\n",
+       "   CV R2 (std)  \n",
+       "0     0.067041  \n",
+       "1     0.900419  \n",
+       "2     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>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>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>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</td>\n",
+       "      <td>1.629031e+07</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": 1348,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 1348
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:56.026691400Z",
+     "start_time": "2026-04-25T19:24:56.013586200Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "results_melted_g = results_df_g.melt(\n",
+    "    id_vars='Model',\n",
+    "    value_vars=['MAE (test)', 'RMSE (test)', 'R2 (test)', 'CV R2 (mean)'],\n",
+    "    var_name='Metric',\n",
+    "    value_name='Score'\n",
+    ")"
+   ],
+   "id": "a8b2b05314122ac3",
+   "outputs": [],
+   "execution_count": 1349
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T19:24:56.121605200Z",
+     "start_time": "2026-04-25T19:24:56.027692700Z"
+    }
+   },
+   "cell_type": "code",
+   "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()"
+   ],
+   "id": "28a6da6deea25737",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 1350
   }
  ],
  "metadata": {