Refined Answers!

Q1.ipynb

@@ -4863,28 +4863,36 @@
    "source": [
     "### Part G Answer (with Cross-Validation)\n",
     "\n",
-    "For Part G, the goal is to predict **`Size in bytes`** using the other inputs:\n",
+    "For Part G, the goal was to predict **`Size in bytes`** using the other inputs:\n",
     "- `Category` \n",
     "- `Reviews` \n",
     "- `Content Rating` \n",
     "- `Rating` \n",
     "- `Numeric Installs` \n",
     "\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",
+    "I compared the regression models using a standard train/test split, but this time I also added 5-fold cross-validation on the training set.\n",
     "\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",
+    "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 the 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 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",
+    "- 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",
     "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 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"
    ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "bb62142045c39ce8"
   }
  ],
  "metadata": {

Q2.ipynb

@@ -5322,6 +5322,14 @@
     "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": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "157be5d5faea96fe"
   }
  ],
  "metadata": {