mirror of
https://github.com/mudabbir-ahmad/UNI-PROG3-CW2-MLWP.git
synced 2026-10-07 20:10:20 +00:00
Part F done
This commit is contained in:
bobbert committed
1 parent
0a3d49015a
commit
3a78be9201
1 file changed
+323
-177
@@ -24,8 +24,8 @@
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@@ -37,20 +37,20 @@
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"source": "df = pd.read_csv('data/googleplaystore_new.csv')",
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@@ -61,8 +61,8 @@
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@@ -72,13 +72,13 @@
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@@ -290,12 +290,12 @@
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"</div>"
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@@ -311,8 +311,8 @@
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@@ -328,26 +328,26 @@
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"source": "df['Size in bytes'] = df['Size'].apply(parse_size)",
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@@ -448,18 +448,18 @@
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"</div>"
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@@ -474,13 +474,13 @@
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@@ -495,13 +495,13 @@
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@@ -516,13 +516,13 @@
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@@ -757,12 +757,12 @@
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"</div>"
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@@ -778,21 +778,21 @@
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"source": "df['Numeric Installs'] = df['Installs'].str.replace('+', '').str.replace(',', '').astype(int)",
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"id": "c8d4f46526918c20",
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@@ -1038,12 +1038,12 @@
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"</div>"
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@@ -1054,15 +1054,15 @@
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"source": "df.to_csv('data/googleplaystore_new_new.csv', index=False)",
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@@ -1086,26 +1086,26 @@
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"source": "df_new = pd.read_csv('data/googleplaystore_new_new.csv')",
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"</div>"
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"source": "df_encoded = pd.get_dummies(df_min, columns=['Category', 'Content Rating'])",
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"source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)",
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@@ -1484,8 +1484,8 @@
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||||
"start_time": "2026-04-25T18:32:46.892720900Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -1496,13 +1496,13 @@
|
||||
],
|
||||
"id": "db88942671bdf26a",
|
||||
"outputs": [],
|
||||
"execution_count": 807
|
||||
"execution_count": 891
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"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
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -1974,7 +1974,7 @@
|
||||
}
|
||||
}
|
||||
],
|
||||
"execution_count": 809
|
||||
"execution_count": 893
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
@@ -1985,8 +1985,8 @@
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -1997,13 +1997,13 @@
|
||||
],
|
||||
"id": "b867289f4b8dd0ae",
|
||||
"outputs": [],
|
||||
"execution_count": 810
|
||||
"execution_count": 894
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -2014,13 +2014,13 @@
|
||||
],
|
||||
"id": "61b135564e36f71d",
|
||||
"outputs": [],
|
||||
"execution_count": 811
|
||||
"execution_count": 895
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -2050,7 +2050,7 @@
|
||||
}
|
||||
}
|
||||
],
|
||||
"execution_count": 812
|
||||
"execution_count": 896
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
@@ -2066,8 +2066,8 @@
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -2078,13 +2078,13 @@
|
||||
],
|
||||
"id": "f7b09be17154ad47",
|
||||
"outputs": [],
|
||||
"execution_count": 813
|
||||
"execution_count": 897
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -2114,7 +2114,7 @@
|
||||
}
|
||||
}
|
||||
],
|
||||
"execution_count": 814
|
||||
"execution_count": 898
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
@@ -2130,8 +2130,8 @@
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -2142,13 +2142,13 @@
|
||||
],
|
||||
"id": "11024fb6674354a3",
|
||||
"outputs": [],
|
||||
"execution_count": 815
|
||||
"execution_count": 899
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -2178,13 +2178,13 @@
|
||||
}
|
||||
}
|
||||
],
|
||||
"execution_count": 816
|
||||
"execution_count": 900
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -2262,18 +2262,18 @@
|
||||
"</div>"
|
||||
]
|
||||
},
|
||||
"execution_count": 817,
|
||||
"execution_count": 901,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"execution_count": 817
|
||||
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|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
@@ -2308,22 +2308,21 @@
|
||||
}
|
||||
}
|
||||
],
|
||||
"execution_count": 823
|
||||
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|
||||
},
|
||||
{
|
||||
"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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truncated
|
||||
"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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 truncated
|
||||
},
|
||||
"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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truncated
|
||||
},
|
||||
"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": {
|
||||
|
||||
Reference in new issue
Block a user