Part G done

Q1.ipynb

@@ -55,7 +55,14 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "## Part A\n",
+   "source": [
+    "## Part A\n",
+    "\n",
+    "\n",
+    "Q: Check for any missing values. If any, delete all rows that contain null values Then,\n",
+    "check for duplicates (similar contents for every feature). If any, delete the\n",
+    "redundant rows. **[1 mark]**"
+   ],
    "id": "751a6161e8e12bdd"
   },
   {
@@ -305,7 +312,12 @@
     }
    },
    "cell_type": "markdown",
-   "source": "## Part B",
+   "source": [
+    "## Part B\n",
+    "\n",
+    "Q: Create a new column called 'Size in bytes' (numeric) and convert the entries\n",
+    "from 'Size' column (M means megabyte and k means kilobytes). **[1 mark]**\n"
+   ],
    "id": "5e0e0e76635904b6"
   },
   {
@@ -772,21 +784,27 @@
     }
    },
    "cell_type": "markdown",
-   "source": "# Part C",
+   "source": [
+    "## Part C\n",
+    "\n",
+    "Q: Create a new column called ‘Numeric_installs' (numeric) and convert the entries\n",
+    "from ‘Installs’ column (remove ‘+’ and ‘,’; for example, “5,000,000+” becomes\n",
+    "“5000000” as an Integer). **[1 mark]**"
+   ],
    "id": "9883e69704d0a69e"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-25T19:24:54.541583100Z",
+     "end_time": "2026-04-25T19:39:31.412418700Z",
-     "start_time": "2026-04-25T19:24:54.526548900Z"
+     "start_time": "2026-04-25T19:39:31.404386600Z"
     }
    },
    "cell_type": "code",
    "source": "df['Numeric Installs'] = df['Installs'].str.replace('+', '').str.replace(',', '').astype(int)",
    "id": "c8d4f46526918c20",
    "outputs": [],
-   "execution_count": 1298
+   "execution_count": 1351
   },
   {
    "metadata": {
@@ -1048,7 +1066,11 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "## Part D",
+   "source": [
+    "## Part D\n",
+    "\n",
+    "Q: Save the updated dataset as “googleplaystore_new_new.csv” **[1 mark]**\n"
+   ],
    "id": "1818d3183dae5398"
   },
   {
@@ -1067,7 +1089,13 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "# Part E",
+   "source": [
+    "# Part E\n",
+    "\n",
+    "Q: Using (Category + Reviews + Content Rating + Size in Bytes +\n",
+    "Installs_Num), find and discuss the best regression model to predict “rating”\n",
+    "(use the standard training/test partition without cross-validation). **[7 marks]**\n"
+   ],
    "id": "5372c799c0b2662"
   },
   {
@@ -2327,7 +2355,12 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "## Part F",
+   "source": [
+    "## Part F\n",
+    "\n",
+    "Q: Train and test all necessary model(s) to show and discuss the most predictive\n",
+    "feature in the previous question **[8 marks]**\n"
+   ],
    "id": "3c89e2f8ae31de3b"
   },
   {
@@ -2632,7 +2665,13 @@
     }
    },
    "cell_type": "markdown",
-   "source": "## Part G",
+   "source": [
+    "## Part G\n",
+    "\n",
+    "Q: Using (Category + Reviews + Content Rating + Rating + Installs_Num),\n",
+    "find and discuss the best regression model to predict “Size in Bytes” (use the\n",
+    "standard training/test partition with cross-validation). **[8 marks]**\n"
+   ],
    "id": "de14342fb4966baf"
   },
   {