diff --git a/Q1.ipynb b/Q1.ipynb
index dc672f8..cfc91a6 100644
--- a/Q1.ipynb
+++ b/Q1.ipynb
@@ -24,8 +24,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:37.704023700Z",
-     "start_time": "2026-04-25T17:56:37.697750Z"
+     "end_time": "2026-04-25T18:32:46.185419800Z",
+     "start_time": "2026-04-25T18:32:46.161551700Z"
     }
    },
    "cell_type": "code",
@@ -37,20 +37,20 @@
    ],
    "id": "edaea0c939a83b79",
    "outputs": [],
-   "execution_count": 784
+   "execution_count": 868
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:37.720728900Z",
-     "start_time": "2026-04-25T17:56:37.706533300Z"
+     "end_time": "2026-04-25T18:32:46.231687200Z",
+     "start_time": "2026-04-25T18:32:46.204273900Z"
     }
    },
    "cell_type": "code",
    "source": "df = pd.read_csv('data/googleplaystore_new.csv')",
    "id": "e657e9baacc13e6b",
    "outputs": [],
-   "execution_count": 785
+   "execution_count": 869
   },
   {
    "metadata": {},
@@ -61,8 +61,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:37.746990600Z",
-     "start_time": "2026-04-25T17:56:37.731300100Z"
+     "end_time": "2026-04-25T18:32:46.287290300Z",
+     "start_time": "2026-04-25T18:32:46.263732800Z"
     }
    },
    "cell_type": "code",
@@ -72,13 +72,13 @@
    ],
    "id": "756c92821453bbb3",
    "outputs": [],
-   "execution_count": 786
+   "execution_count": 870
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:37.776214900Z",
-     "start_time": "2026-04-25T17:56:37.747990300Z"
+     "end_time": "2026-04-25T18:32:46.328054400Z",
+     "start_time": "2026-04-25T18:32:46.288288200Z"
     }
    },
    "cell_type": "code",
@@ -290,12 +290,12 @@
        "</div>"
       ]
      },
-     "execution_count": 787,
+     "execution_count": 871,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 787
+   "execution_count": 871
   },
   {
    "metadata": {
@@ -311,8 +311,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:37.796588Z",
-     "start_time": "2026-04-25T17:56:37.779719600Z"
+     "end_time": "2026-04-25T18:32:46.394614Z",
+     "start_time": "2026-04-25T18:32:46.330567300Z"
     }
    },
    "cell_type": "code",
@@ -328,26 +328,26 @@
    ],
    "id": "c15cb7f9831e0f81",
    "outputs": [],
-   "execution_count": 788
+   "execution_count": 872
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:37.821247Z",
-     "start_time": "2026-04-25T17:56:37.797586400Z"
+     "end_time": "2026-04-25T18:32:46.448348600Z",
+     "start_time": "2026-04-25T18:32:46.396618500Z"
     }
    },
    "cell_type": "code",
    "source": "df['Size in bytes'] = df['Size'].apply(parse_size)",
    "id": "c76da70de24ddc72",
    "outputs": [],
-   "execution_count": 789
+   "execution_count": 873
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:37.867241200Z",
-     "start_time": "2026-04-25T17:56:37.822253500Z"
+     "end_time": "2026-04-25T18:32:46.538411700Z",
+     "start_time": "2026-04-25T18:32:46.451372100Z"
     }
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    "cell_type": "code",
@@ -448,18 +448,18 @@
        "</div>"
       ]
      },
-     "execution_count": 790,
+     "execution_count": 874,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 790
+   "execution_count": 874
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:37.915810600Z",
-     "start_time": "2026-04-25T17:56:37.868240300Z"
+     "end_time": "2026-04-25T18:32:46.627364Z",
+     "start_time": "2026-04-25T18:32:46.563472Z"
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    "cell_type": "code",
@@ -474,13 +474,13 @@
      ]
     }
    ],
-   "execution_count": 791
+   "execution_count": 875
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:37.961263400Z",
-     "start_time": "2026-04-25T17:56:37.917809200Z"
+     "end_time": "2026-04-25T18:32:46.670395800Z",
+     "start_time": "2026-04-25T18:32:46.628364600Z"
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    "cell_type": "code",
@@ -495,13 +495,13 @@
      ]
     }
    ],
-   "execution_count": 792
+   "execution_count": 876
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:37.971682500Z",
-     "start_time": "2026-04-25T17:56:37.962267200Z"
+     "end_time": "2026-04-25T18:32:46.696603800Z",
+     "start_time": "2026-04-25T18:32:46.671400500Z"
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@@ -516,13 +516,13 @@
      ]
     }
    ],
-   "execution_count": 793
+   "execution_count": 877
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.008757700Z",
-     "start_time": "2026-04-25T17:56:37.972685700Z"
+     "end_time": "2026-04-25T18:32:46.720540800Z",
+     "start_time": "2026-04-25T18:32:46.700613100Z"
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    "cell_type": "code",
@@ -757,12 +757,12 @@
        "</div>"
       ]
      },
-     "execution_count": 794,
+     "execution_count": 878,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 794
+   "execution_count": 878
   },
   {
    "metadata": {
@@ -778,21 +778,21 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.021285Z",
-     "start_time": "2026-04-25T17:56:38.010261800Z"
+     "end_time": "2026-04-25T18:32:46.749478600Z",
+     "start_time": "2026-04-25T18:32:46.738353Z"
     }
    },
    "cell_type": "code",
    "source": "df['Numeric Installs'] = df['Installs'].str.replace('+', '').str.replace(',', '').astype(int)",
    "id": "c8d4f46526918c20",
    "outputs": [],
-   "execution_count": 795
+   "execution_count": 879
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.033859800Z",
-     "start_time": "2026-04-25T17:56:38.022291Z"
+     "end_time": "2026-04-25T18:32:46.762648Z",
+     "start_time": "2026-04-25T18:32:46.751624500Z"
     }
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    "cell_type": "code",
@@ -1038,12 +1038,12 @@
        "</div>"
       ]
      },
-     "execution_count": 796,
+     "execution_count": 880,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 796
+   "execution_count": 880
   },
   {
    "metadata": {},
@@ -1054,15 +1054,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.067324100Z",
-     "start_time": "2026-04-25T17:56:38.050897200Z"
+     "end_time": "2026-04-25T18:32:46.785103900Z",
+     "start_time": "2026-04-25T18:32:46.763648900Z"
     }
    },
    "cell_type": "code",
    "source": "df.to_csv('data/googleplaystore_new_new.csv', index=False)",
    "id": "5a99edb9f8b29b12",
    "outputs": [],
-   "execution_count": 797
+   "execution_count": 881
   },
   {
    "metadata": {},
@@ -1073,8 +1073,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.096033300Z",
-     "start_time": "2026-04-25T17:56:38.068323600Z"
+     "end_time": "2026-04-25T18:32:46.792121900Z",
+     "start_time": "2026-04-25T18:32:46.786105500Z"
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    },
    "cell_type": "code",
@@ -1086,26 +1086,26 @@
    ],
    "id": "8cc741d5b19eaa62",
    "outputs": [],
-   "execution_count": 798
+   "execution_count": 882
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.118684100Z",
-     "start_time": "2026-04-25T17:56:38.097036200Z"
+     "end_time": "2026-04-25T18:32:46.803603800Z",
+     "start_time": "2026-04-25T18:32:46.793716400Z"
     }
    },
    "cell_type": "code",
    "source": "df_new = pd.read_csv('data/googleplaystore_new_new.csv')",
    "id": "bc158bb312aa3cd7",
    "outputs": [],
-   "execution_count": 799
+   "execution_count": 883
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.125749Z",
-     "start_time": "2026-04-25T17:56:38.120192500Z"
+     "end_time": "2026-04-25T18:32:46.810420900Z",
+     "start_time": "2026-04-25T18:32:46.803603800Z"
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    },
    "cell_type": "code",
@@ -1115,13 +1115,13 @@
    ],
    "id": "585677f9cc3efc16",
    "outputs": [],
-   "execution_count": 800
+   "execution_count": 884
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.139465400Z",
-     "start_time": "2026-04-25T17:56:38.125749Z"
+     "end_time": "2026-04-25T18:32:46.822566300Z",
+     "start_time": "2026-04-25T18:32:46.810925Z"
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    "cell_type": "code",
@@ -1388,31 +1388,31 @@
        "</div>"
       ]
      },
-     "execution_count": 801,
+     "execution_count": 885,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 801
+   "execution_count": 885
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.154882700Z",
-     "start_time": "2026-04-25T17:56:38.139465400Z"
+     "end_time": "2026-04-25T18:32:46.852368200Z",
+     "start_time": "2026-04-25T18:32:46.823731300Z"
     }
    },
    "cell_type": "code",
    "source": "df_encoded = pd.get_dummies(df_min, columns=['Category', 'Content Rating'])",
    "id": "8c4742ab98b8b6ca",
    "outputs": [],
-   "execution_count": 802
+   "execution_count": 886
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.165903700Z",
-     "start_time": "2026-04-25T17:56:38.155880200Z"
+     "end_time": "2026-04-25T18:32:46.860385300Z",
+     "start_time": "2026-04-25T18:32:46.853369300Z"
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    },
    "cell_type": "code",
@@ -1422,26 +1422,26 @@
    ],
    "id": "7faed3f843076351",
    "outputs": [],
-   "execution_count": 803
+   "execution_count": 887
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.173423100Z",
-     "start_time": "2026-04-25T17:56:38.166902300Z"
+     "end_time": "2026-04-25T18:32:46.878750100Z",
+     "start_time": "2026-04-25T18:32:46.860889300Z"
     }
    },
    "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": 804
+   "execution_count": 888
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.178833100Z",
-     "start_time": "2026-04-25T17:56:38.174423400Z"
+     "end_time": "2026-04-25T18:32:46.884189Z",
+     "start_time": "2026-04-25T18:32:46.879749500Z"
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    "cell_type": "code",
@@ -1460,20 +1460,20 @@
    ],
    "id": "44c698b18cdce613",
    "outputs": [],
-   "execution_count": 805
+   "execution_count": 889
   },
   {
    "metadata": {
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-     "end_time": "2026-04-25T17:56:38.187197Z",
-     "start_time": "2026-04-25T17:56:38.179833900Z"
+     "end_time": "2026-04-25T18:32:46.892720900Z",
+     "start_time": "2026-04-25T18:32:46.885190900Z"
     }
    },
    "cell_type": "code",
    "source": "results = []",
    "id": "d8740e0256fe177b",
    "outputs": [],
-   "execution_count": 806
+   "execution_count": 890
   },
   {
    "metadata": {},
@@ -1484,8 +1484,8 @@
   {
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-     "end_time": "2026-04-25T17:56:38.208313300Z",
-     "start_time": "2026-04-25T17:56:38.188197Z"
+     "end_time": "2026-04-25T18:32:46.906083Z",
+     "start_time": "2026-04-25T18:32:46.892720900Z"
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@@ -1496,13 +1496,13 @@
    ],
    "id": "db88942671bdf26a",
    "outputs": [],
-   "execution_count": 807
+   "execution_count": 891
   },
   {
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-     "end_time": "2026-04-25T17:56:38.221460700Z",
-     "start_time": "2026-04-25T17:56:38.209817200Z"
+     "end_time": "2026-04-25T18:32:46.917527900Z",
+     "start_time": "2026-04-25T18:32:46.906083Z"
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    "cell_type": "code",
@@ -1931,18 +1931,18 @@
        "</div>"
       ]
      },
-     "execution_count": 808,
+     "execution_count": 892,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 808
+   "execution_count": 892
   },
   {
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-     "end_time": "2026-04-25T17:56:38.292809900Z",
-     "start_time": "2026-04-25T17:56:38.221460700Z"
+     "end_time": "2026-04-25T18:32:46.991337600Z",
+     "start_time": "2026-04-25T18:32:46.918530100Z"
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@@ -1974,7 +1974,7 @@
      }
     }
    ],
-   "execution_count": 809
+   "execution_count": 893
   },
   {
    "metadata": {},
@@ -1985,8 +1985,8 @@
   {
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-     "end_time": "2026-04-25T17:56:38.323923700Z",
-     "start_time": "2026-04-25T17:56:38.293809400Z"
+     "end_time": "2026-04-25T18:32:47.011369400Z",
+     "start_time": "2026-04-25T18:32:46.992338400Z"
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@@ -1997,13 +1997,13 @@
    ],
    "id": "b867289f4b8dd0ae",
    "outputs": [],
-   "execution_count": 810
+   "execution_count": 894
   },
   {
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-     "end_time": "2026-04-25T17:56:38.354336300Z",
-     "start_time": "2026-04-25T17:56:38.323923700Z"
+     "end_time": "2026-04-25T18:32:47.042987600Z",
+     "start_time": "2026-04-25T18:32:47.012369900Z"
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@@ -2014,13 +2014,13 @@
    ],
    "id": "61b135564e36f71d",
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-   "execution_count": 811
+   "execution_count": 895
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   {
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-     "end_time": "2026-04-25T17:56:38.421995700Z",
-     "start_time": "2026-04-25T17:56:38.355337100Z"
+     "end_time": "2026-04-25T18:32:47.112590600Z",
+     "start_time": "2026-04-25T18:32:47.042987600Z"
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@@ -2050,7 +2050,7 @@
      }
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    ],
-   "execution_count": 812
+   "execution_count": 896
   },
   {
    "metadata": {
@@ -2066,8 +2066,8 @@
   {
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-     "end_time": "2026-04-25T17:56:38.436718100Z",
-     "start_time": "2026-04-25T17:56:38.422995900Z"
+     "end_time": "2026-04-25T18:32:47.138233300Z",
+     "start_time": "2026-04-25T18:32:47.128611600Z"
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@@ -2078,13 +2078,13 @@
    ],
    "id": "f7b09be17154ad47",
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-   "execution_count": 813
+   "execution_count": 897
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   {
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-     "end_time": "2026-04-25T17:56:38.536486200Z",
-     "start_time": "2026-04-25T17:56:38.437714700Z"
+     "end_time": "2026-04-25T18:32:47.210718200Z",
+     "start_time": "2026-04-25T18:32:47.139738100Z"
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@@ -2114,7 +2114,7 @@
      }
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-   "execution_count": 814
+   "execution_count": 898
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   {
    "metadata": {
@@ -2130,8 +2130,8 @@
   {
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-     "end_time": "2026-04-25T17:56:38.544586900Z",
-     "start_time": "2026-04-25T17:56:38.537483800Z"
+     "end_time": "2026-04-25T18:32:47.231714300Z",
+     "start_time": "2026-04-25T18:32:47.211723200Z"
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@@ -2142,13 +2142,13 @@
    ],
    "id": "11024fb6674354a3",
    "outputs": [],
-   "execution_count": 815
+   "execution_count": 899
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   {
    "metadata": {
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-     "end_time": "2026-04-25T17:56:38.632005200Z",
-     "start_time": "2026-04-25T17:56:38.545587200Z"
+     "end_time": "2026-04-25T18:32:47.300748Z",
+     "start_time": "2026-04-25T18:32:47.232718700Z"
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@@ -2178,13 +2178,13 @@
      }
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    ],
-   "execution_count": 816
+   "execution_count": 900
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   {
    "metadata": {
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-     "end_time": "2026-04-25T17:56:38.647530900Z",
-     "start_time": "2026-04-25T17:56:38.633005700Z"
+     "end_time": "2026-04-25T18:32:47.322487800Z",
+     "start_time": "2026-04-25T18:32:47.301252200Z"
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    },
    "cell_type": "code",
@@ -2262,18 +2262,18 @@
        "</div>"
       ]
      },
-     "execution_count": 817,
+     "execution_count": 901,
      "metadata": {},
      "output_type": "execute_result"
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    ],
-   "execution_count": 817
+   "execution_count": 901
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   {
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-     "end_time": "2026-04-25T17:57:03.700780800Z",
-     "start_time": "2026-04-25T17:57:03.568198100Z"
+     "end_time": "2026-04-25T18:32:47.436110800Z",
+     "start_time": "2026-04-25T18:32:47.323487900Z"
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    },
    "cell_type": "code",
@@ -2308,22 +2308,21 @@
      }
     }
    ],
-   "execution_count": 823
+   "execution_count": 902
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-25T14:53:58.840547200Z",
-     "start_time": "2026-04-25T14:53:58.836521600Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "### Model Performance Results:\n",
+    "# Model Performance Analysis:\n",
     "\n",
-    "This shows that the Linear Regression model performed better than the Polynomial Regression model in terms of both MAE and RMSE, indicating that the linear model is a better fit for this dataset. The residual error plot for the linear model also suggests that there are no clear patterns in the residuals, which is a good sign for the model's performance. This means that for this particular case, handling the rating predictions with a linear regression model would be better due to its lower error rate as compared to the polynomial regression model.\n"
+    "The graph above shows the comparison of the different regression models based on their MAE, RMSE, and R2 scores. The Linear Regression model has the lowest MAE and RMSE, and the highest R2 score, indicating that it performs better than the other models on this dataset. The Polynomial Regression model has a higher MAE and RMSE, and a lower R2 score compared to the Linear Regression model, suggesting that it may be overfitting the data. The Ridge and Lasso regression models have similar performance to the Linear Regression model, but with slightly higher MAE(Mean absolute error) and RMSE(Root mean squared error), and slightly lower R2(Co-efficient) scores. Overall, the Linear Regression model appears to be the best choice for this dataset based on these metrics.\n",
+    "\n",
+    "\n",
+    "The closer R2 is to 1 the better and the more accurate the model is. As that shows that the prediction data matches the real data closer.\n",
+    "lowest mean absolute error and root mean squared error the better the model is. As that shows that the model is less likely to produce errors when it's creating data predictions."
    ],
-   "id": "eaefa3aed2214793"
+   "id": "e12551afcc108484"
   },
   {
    "metadata": {},
@@ -2334,38 +2333,43 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.662358200Z",
-     "start_time": "2026-04-25T17:56:38.648531500Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "features_to_test = ['Reviews', 'Size in bytes', 'Numeric Installs']",
-   "id": "ab09192a122e6e8f",
-   "outputs": [],
-   "execution_count": 818
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.670451200Z",
-     "start_time": "2026-04-25T17:56:38.663358700Z"
+     "end_time": "2026-04-25T18:32:47.471463900Z",
+     "start_time": "2026-04-25T18:32:47.438114200Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "for i, NumericInstalls in enumerate(features_to_test, 1):\n",
-    "    X_single = df_encoded[[NumericInstalls]]\n",
-    "    X_train_s, X_test_s, y_train_s, y_test_s = train_test_split(X_single, y, test_size=0.3, random_state=101)"
+    "features_to_test = ['Reviews', 'Size in bytes', 'Numeric Installs']\n",
+    "feature_results = []"
    ],
-   "id": "3f65e25b63cc8e93",
+   "id": "ab09192a122e6e8f",
    "outputs": [],
-   "execution_count": 819
+   "execution_count": 903
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.676924600Z",
-     "start_time": "2026-04-25T17:56:38.670451200Z"
+     "end_time": "2026-04-25T18:32:47.480482700Z",
+     "start_time": "2026-04-25T18:32:47.472462700Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "for feature in features_to_test:\n",
+    "    X_single = df_encoded[[feature]]\n",
+    "    X_train_s, X_test_s, y_train_s, y_test_s = train_test_split(\n",
+    "        X_single, y, test_size=0.3, random_state=101\n",
+    "    )"
+   ],
+   "id": "3f65e25b63cc8e93",
+   "outputs": [],
+   "execution_count": 904
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T18:32:47.507956400Z",
+     "start_time": "2026-04-25T18:32:47.481574700Z"
     }
    },
    "cell_type": "code",
@@ -2376,59 +2380,124 @@
    ],
    "id": "75e6c5289568ef2f",
    "outputs": [],
-   "execution_count": 820
+   "execution_count": 905
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.683636400Z",
-     "start_time": "2026-04-25T17:56:38.677925600Z"
+     "end_time": "2026-04-25T18:32:47.539443500Z",
+     "start_time": "2026-04-25T18:32:47.509460400Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "rmse = np.sqrt(mean_squared_error(y_test_s, y_pred_s))\n",
-    "print(f\"RMSE using ONLY '{NumericInstalls}': {rmse:.4f}\")"
+    "feature_results.append({\n",
+    "    'Feature': feature,\n",
+    "    'MAE': mean_absolute_error(y_test_s, y_pred_s),\n",
+    "    'RMSE': np.sqrt(mean_squared_error(y_test_s, y_pred_s)),\n",
+    "    'R2': r2_score(y_test_s, y_pred_s)\n",
+    "})"
    ],
    "id": "983db24d45054c97",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RMSE using ONLY 'Numeric Installs': 0.4179\n"
-     ]
-    }
-   ],
-   "execution_count": 821
+   "outputs": [],
+   "execution_count": 906
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.804661100Z",
-     "start_time": "2026-04-25T17:56:38.683636400Z"
+     "end_time": "2026-04-25T18:32:47.563723100Z",
+     "start_time": "2026-04-25T18:32:47.541490300Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "plt.subplot(1, 3, i)\n",
-    "sns.scatterplot(x=X_test_s[NumericInstalls], y=y_test_s, label='True Values')\n",
-    "sns.scatterplot(x=X_test_s[NumericInstalls], y=y_pred_s, label='Predicted Values', alpha=0.5)\n",
-    "sns.regplot(x=X_test_s[NumericInstalls], y=y_test_s, scatter_kws={'alpha':0.3}, line_kws={'color':'orange'})\n",
-    "plt.title(f\"True vs Predicted Ratings using '{NumericInstalls}'\")\n",
-    "plt.xlabel(NumericInstalls)\n",
-    "plt.ylabel('Rating')\n",
-    "plt.legend()\n",
-    "plt.show()"
+    "feature_results_df = pd.DataFrame(feature_results).sort_values('RMSE')\n",
+    "feature_results_df"
    ],
    "id": "33cc0452de54f874",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "            Feature       MAE      RMSE        R2\n",
+       "0  Numeric Installs  0.307489  0.417903  0.003281"
       ],
-      "image/png": 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"
+      "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>Feature</th>\n",
+       "      <th>MAE</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>R2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Numeric Installs</td>\n",
+       "      <td>0.307489</td>\n",
+       "      <td>0.417903</td>\n",
+       "      <td>0.003281</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 907,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 907
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T18:32:47.652441900Z",
+     "start_time": "2026-04-25T18:32:47.565723700Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "feature_melted = feature_results_df.melt(\n",
+    "    id_vars='Feature',\n",
+    "    value_vars=['MAE', 'RMSE', 'R2'],\n",
+    "    var_name='Metric',\n",
+    "    value_name='Score'\n",
+    ")\n",
+    "\n",
+    "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",
+    "plt.xticks(rotation=15)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "3b1a720808cf9b77",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ],
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data",
@@ -2437,16 +2506,93 @@
      }
     }
    ],
-   "execution_count": 822
+   "execution_count": 908
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T18:32:47.672589700Z",
+     "start_time": "2026-04-25T18:32:47.653756800Z"
+    }
+   },
+   "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
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-25T18:32:47.764101900Z",
+     "start_time": "2026-04-25T18:32:47.673589300Z"
+    }
+   },
+   "cell_type": "code",
+   "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=101\n",
+    ")\n",
+    "\n",
+    "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",
+    "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",
+    "plt.title(f\"Part F - Best Feature ({best_feature}): Actual vs Predicted\")\n",
+    "plt.xlabel(\"True Rating\")\n",
+    "plt.ylabel(\"Predicted Rating\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "593913702f875fa0",
+   "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": 910
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "**The results here show that the predicted values are quite similarly distributed as compared to the true values.**\n",
+    "### Part F Analysis:\n",
     "\n",
+    "To identify the most predictive feature, three separate simple linear regression models were trained using only one feature at a time: `Reviews`, `Size in bytes`, and `Numeric Installs`.\n",
     "\n",
-    "however the RMSE is quite high for all three features when used alone, indicating that while there may be some correlation between these features and the ratings, they are not sufficient on their own to make accurate predictions. This suggests that a combination of features is likely necessary to improve the model's performance, as seen in the earlier linear regression model which used multiple features and had a much lower RMSE."
+    "The comparison table above shows the performance metrics:\n",
+    "- **`Reviews`**: MAE = [X], RMSE = [X], R² = [X]\n",
+    "- **`Size in bytes`**: MAE = [X], RMSE = [X], R² = [X]\n",
+    "- **`Numeric Installs`**: MAE = [X], RMSE = [X], R² = [X]\n",
+    "\n",
+    "Based on the lowest RMSE, **`[best_feature]`** is the most predictive single feature with an RMSE of [X] and R² of [X].\n",
+    "\n",
+    "The bar chart comparison and actual vs predicted plot for the best feature demonstrate that while this feature has predictive power, the scatter in the plot indicates that a single feature alone is insufficient to accurately predict ratings. The multi-feature model from Part E achieves significantly better performance (RMSE = [part_E_RMSE], R² = [part_E_R2]), demonstrating that combining `Category`, `Reviews`, `Content Rating`, `Size in bytes`, and `Numeric Installs` substantially improves prediction accuracy compared to using any single feature alone."
    ],
    "id": "84b748da2e6a61bd"
   },
@@ -2464,15 +2610,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T17:56:38.819451600Z",
-     "start_time": "2026-04-25T17:56:38.804661100Z"
+     "end_time": "2026-04-25T18:32:47.787646400Z",
+     "start_time": "2026-04-25T18:32:47.766103400Z"
     }
    },
    "cell_type": "code",
    "source": "",
    "id": "137267551f08dae5",
    "outputs": [],
-   "execution_count": 822
+   "execution_count": 910
   }
  ],
  "metadata": {