From 2115e1b662be1432113528d1e9ce9ffc7c716a1c Mon Sep 17 00:00:00 2001
From: mudabbir-ahmad <mudabbira.uk@gmail.com>
Date: Sat, 25 Apr 2026 20:43:31 +0100
Subject: [PATCH] Outlining Questions and Parts for Q2

---
 Q2.ipynb | 57 +++++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 56 insertions(+), 1 deletion(-)

diff --git a/Q2.ipynb b/Q2.ipynb
index cd75aca..ad45166 100644
--- a/Q2.ipynb
+++ b/Q2.ipynb
@@ -12,13 +12,68 @@
    ],
    "id": "bb3519b1aa083259"
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Part A\n",
+    "\n",
+    "Q: A. Using (Rating + Reviews + Content Rating + Size in Bytes +\n",
+    "Installs_Num), using Logistic regression and KNN, find and discuss the best\n",
+    "classification model to predict “Category” (use the training/validation/test\n",
+    "partition without cross-validation). **[8 marks]**\n"
+   ],
+   "id": "1baa7daa49445720"
+  },
   {
    "metadata": {},
    "cell_type": "code",
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "3f6e8a6b947be33b"
+   "id": "f6fd7137bf91f31e"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Part B\n",
+    "\n",
+    "Q: Using (Rating + Reviews + Category + Size in Bytes + Installs_Num),\n",
+    "using Logistic regression and KNN, find and discuss the best classification model\n",
+    "to predict “Content Rating” (use the training/validation/test partition without\n",
+    "cross-validation). **[7 marks]**"
+   ],
+   "id": "f15aa6cca58826fc"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "65e55b70ad44b2dc"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Part C\n",
+    "\n",
+    "Q: By considering Installs_Num as categorical feature and using (Rating +\n",
+    "Reviews + Category + Size in Bytes + Content Rating), find and discuss the\n",
+    "best classification model to predict “Installs_Num” (using Logistic regression and\n",
+    "KNN) (use the training/test partition without cross-validation) **[8 marks]**"
+   ],
+   "id": "86c040f5e043a39c"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "f3535461938f94f5"
   }
  ],
  "metadata": {