diff --git a/EBEN -C2.ipynb b/EBEN -C2.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1b59d6b2-b438-462c-b8e7-155ae0d77d16",
+   "metadata": {},
+   "source": [
+    "## CLASSWORK 1: \n",
+    "\n",
+    "Task 1: Load the DatasetLoad the dataset using Pandas.\n",
+    "\n",
+    "Display the first 10 rows.Display the last 5 rows.\n",
+    "\n",
+    "Check the shape of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f90ebea9-6b1a-4140-8b82-15321e544772",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "866df630-88cd-4465-a911-edd97a166fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"social_media_viral_content_dataset.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d07dadc1-9d9b-4d60-9256-5d6a14feb65d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>post_id</th>\n",
+       "      <th>platform</th>\n",
+       "      <th>content_type</th>\n",
+       "      <th>topic</th>\n",
+       "      <th>language</th>\n",
+       "      <th>region</th>\n",
+       "      <th>post_datetime</th>\n",
+       "      <th>hashtags</th>\n",
+       "      <th>views</th>\n",
+       "      <th>likes</th>\n",
+       "      <th>comments</th>\n",
+       "      <th>shares</th>\n",
+       "      <th>engagement_rate</th>\n",
+       "      <th>sentiment_score</th>\n",
+       "      <th>is_viral</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SM_100000</td>\n",
+       "      <td>Instagram</td>\n",
+       "      <td>text</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>ur</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>2024-12-10 00:00:00</td>\n",
+       "      <td>#tech #funny #music</td>\n",
+       "      <td>2319102</td>\n",
+       "      <td>122058</td>\n",
+       "      <td>15800</td>\n",
+       "      <td>861</td>\n",
+       "      <td>0.0598</td>\n",
+       "      <td>0.464</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SM_100001</td>\n",
+       "      <td>Instagram</td>\n",
+       "      <td>carousel</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>ur</td>\n",
+       "      <td>Brazil</td>\n",
+       "      <td>2024-10-13 00:00:00</td>\n",
+       "      <td>#news #fyp #funny #ai #trending</td>\n",
+       "      <td>2538464</td>\n",
+       "      <td>110368</td>\n",
+       "      <td>11289</td>\n",
+       "      <td>54887</td>\n",
+       "      <td>0.0695</td>\n",
+       "      <td>-0.800</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SM_100002</td>\n",
+       "      <td>YouTube Shorts</td>\n",
+       "      <td>video</td>\n",
+       "      <td>Technology</td>\n",
+       "      <td>ur</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>2024-05-03 00:00:00</td>\n",
+       "      <td>#ai #news</td>\n",
+       "      <td>1051176</td>\n",
+       "      <td>87598</td>\n",
+       "      <td>47196</td>\n",
+       "      <td>44132</td>\n",
+       "      <td>0.1702</td>\n",
+       "      <td>0.416</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SM_100003</td>\n",
+       "      <td>X</td>\n",
+       "      <td>text</td>\n",
+       "      <td>Politics</td>\n",
+       "      <td>ur</td>\n",
+       "      <td>US</td>\n",
+       "      <td>2024-08-04 00:00:00</td>\n",
+       "      <td>#ai #funny</td>\n",
+       "      <td>5271440</td>\n",
+       "      <td>329465</td>\n",
+       "      <td>774</td>\n",
+       "      <td>59736</td>\n",
+       "      <td>0.0740</td>\n",
+       "      <td>0.877</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SM_100004</td>\n",
+       "      <td>YouTube Shorts</td>\n",
+       "      <td>text</td>\n",
+       "      <td>Education</td>\n",
+       "      <td>es</td>\n",
+       "      <td>US</td>\n",
+       "      <td>2024-03-28 00:00:00</td>\n",
+       "      <td>#news #ai #viral #funny #fyp</td>\n",
+       "      <td>3186256</td>\n",
+       "      <td>199141</td>\n",
+       "      <td>5316</td>\n",
+       "      <td>83105</td>\n",
+       "      <td>0.0903</td>\n",
+       "      <td>0.223</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>SM_100005</td>\n",
+       "      <td>Instagram</td>\n",
+       "      <td>carousel</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>hi</td>\n",
+       "      <td>Brazil</td>\n",
+       "      <td>2024-11-01 00:00:00</td>\n",
+       "      <td>#tech #music</td>\n",
+       "      <td>6513472</td>\n",
+       "      <td>465248</td>\n",
+       "      <td>27485</td>\n",
+       "      <td>25659</td>\n",
+       "      <td>0.0796</td>\n",
+       "      <td>-0.907</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>SM_100006</td>\n",
+       "      <td>X</td>\n",
+       "      <td>text</td>\n",
+       "      <td>Education</td>\n",
+       "      <td>hi</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>2024-06-09 00:00:00</td>\n",
+       "      <td>#tech #ai</td>\n",
+       "      <td>65481</td>\n",
+       "      <td>2847</td>\n",
+       "      <td>194</td>\n",
+       "      <td>84655</td>\n",
+       "      <td>1.3393</td>\n",
+       "      <td>-0.235</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>SM_100007</td>\n",
+       "      <td>X</td>\n",
+       "      <td>text</td>\n",
+       "      <td>Technology</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Pakistan</td>\n",
+       "      <td>2024-07-26 00:00:00</td>\n",
+       "      <td>#trending #tech #news</td>\n",
+       "      <td>2961423</td>\n",
+       "      <td>329047</td>\n",
+       "      <td>31556</td>\n",
+       "      <td>11395</td>\n",
+       "      <td>0.1256</td>\n",
+       "      <td>0.215</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>SM_100008</td>\n",
+       "      <td>YouTube Shorts</td>\n",
+       "      <td>text</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>es</td>\n",
+       "      <td>Pakistan</td>\n",
+       "      <td>2024-03-03 00:00:00</td>\n",
+       "      <td>#music #tech #news #trending</td>\n",
+       "      <td>3916736</td>\n",
+       "      <td>489592</td>\n",
+       "      <td>14507</td>\n",
+       "      <td>87314</td>\n",
+       "      <td>0.1510</td>\n",
+       "      <td>0.884</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SM_100009</td>\n",
+       "      <td>YouTube Shorts</td>\n",
+       "      <td>video</td>\n",
+       "      <td>Politics</td>\n",
+       "      <td>en</td>\n",
+       "      <td>US</td>\n",
+       "      <td>2024-10-05 00:00:00</td>\n",
+       "      <td>#music #trending #ai #viral #news</td>\n",
+       "      <td>3116477</td>\n",
+       "      <td>239729</td>\n",
+       "      <td>8438</td>\n",
+       "      <td>43002</td>\n",
+       "      <td>0.0934</td>\n",
+       "      <td>-0.968</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     post_id        platform content_type       topic language    region  \\\n",
+       "0  SM_100000       Instagram         text      Sports       ur        UK   \n",
+       "1  SM_100001       Instagram     carousel      Sports       ur    Brazil   \n",
+       "2  SM_100002  YouTube Shorts        video  Technology       ur        UK   \n",
+       "3  SM_100003               X         text    Politics       ur        US   \n",
+       "4  SM_100004  YouTube Shorts         text   Education       es        US   \n",
+       "5  SM_100005       Instagram     carousel      Sports       hi    Brazil   \n",
+       "6  SM_100006               X         text   Education       hi        UK   \n",
+       "7  SM_100007               X         text  Technology       es  Pakistan   \n",
+       "8  SM_100008  YouTube Shorts         text      Sports       es  Pakistan   \n",
+       "9  SM_100009  YouTube Shorts        video    Politics       en        US   \n",
+       "\n",
+       "         post_datetime                           hashtags    views   likes  \\\n",
+       "0  2024-12-10 00:00:00                #tech #funny #music  2319102  122058   \n",
+       "1  2024-10-13 00:00:00    #news #fyp #funny #ai #trending  2538464  110368   \n",
+       "2  2024-05-03 00:00:00                          #ai #news  1051176   87598   \n",
+       "3  2024-08-04 00:00:00                         #ai #funny  5271440  329465   \n",
+       "4  2024-03-28 00:00:00       #news #ai #viral #funny #fyp  3186256  199141   \n",
+       "5  2024-11-01 00:00:00                       #tech #music  6513472  465248   \n",
+       "6  2024-06-09 00:00:00                          #tech #ai    65481    2847   \n",
+       "7  2024-07-26 00:00:00              #trending #tech #news  2961423  329047   \n",
+       "8  2024-03-03 00:00:00       #music #tech #news #trending  3916736  489592   \n",
+       "9  2024-10-05 00:00:00  #music #trending #ai #viral #news  3116477  239729   \n",
+       "\n",
+       "   comments  shares  engagement_rate  sentiment_score  is_viral  \n",
+       "0     15800     861           0.0598            0.464         1  \n",
+       "1     11289   54887           0.0695           -0.800         1  \n",
+       "2     47196   44132           0.1702            0.416         0  \n",
+       "3       774   59736           0.0740            0.877         1  \n",
+       "4      5316   83105           0.0903            0.223         1  \n",
+       "5     27485   25659           0.0796           -0.907         1  \n",
+       "6       194   84655           1.3393           -0.235         0  \n",
+       "7     31556   11395           0.1256            0.215         1  \n",
+       "8     14507   87314           0.1510            0.884         1  \n",
+       "9      8438   43002           0.0934           -0.968         1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d71cf517-4101-4724-9d9d-12578d79d4e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>post_id</th>\n",
+       "      <th>platform</th>\n",
+       "      <th>content_type</th>\n",
+       "      <th>topic</th>\n",
+       "      <th>language</th>\n",
+       "      <th>region</th>\n",
+       "      <th>post_datetime</th>\n",
+       "      <th>hashtags</th>\n",
+       "      <th>views</th>\n",
+       "      <th>likes</th>\n",
+       "      <th>comments</th>\n",
+       "      <th>shares</th>\n",
+       "      <th>engagement_rate</th>\n",
+       "      <th>sentiment_score</th>\n",
+       "      <th>is_viral</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1995</th>\n",
+       "      <td>SM_101995</td>\n",
+       "      <td>YouTube Shorts</td>\n",
+       "      <td>text</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>es</td>\n",
+       "      <td>US</td>\n",
+       "      <td>2024-02-11 00:00:00</td>\n",
+       "      <td>#funny #music #ai</td>\n",
+       "      <td>14112183</td>\n",
+       "      <td>486627</td>\n",
+       "      <td>9430</td>\n",
+       "      <td>91550</td>\n",
+       "      <td>0.0416</td>\n",
+       "      <td>-0.541</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1996</th>\n",
+       "      <td>SM_101996</td>\n",
+       "      <td>Instagram</td>\n",
+       "      <td>text</td>\n",
+       "      <td>Politics</td>\n",
+       "      <td>en</td>\n",
+       "      <td>Brazil</td>\n",
+       "      <td>2024-02-12 00:00:00</td>\n",
+       "      <td>#viral #trending</td>\n",
+       "      <td>5604744</td>\n",
+       "      <td>467062</td>\n",
+       "      <td>2764</td>\n",
+       "      <td>16092</td>\n",
+       "      <td>0.0867</td>\n",
+       "      <td>0.287</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>SM_101997</td>\n",
+       "      <td>X</td>\n",
+       "      <td>carousel</td>\n",
+       "      <td>Education</td>\n",
+       "      <td>ur</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>2024-05-01 00:00:00</td>\n",
+       "      <td>#trending #fyp</td>\n",
+       "      <td>2031920</td>\n",
+       "      <td>253990</td>\n",
+       "      <td>40963</td>\n",
+       "      <td>95697</td>\n",
+       "      <td>0.1923</td>\n",
+       "      <td>0.604</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998</th>\n",
+       "      <td>SM_101998</td>\n",
+       "      <td>X</td>\n",
+       "      <td>carousel</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>ur</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>2024-12-23 00:00:00</td>\n",
+       "      <td>#funny #trending #tech</td>\n",
+       "      <td>5237350</td>\n",
+       "      <td>209494</td>\n",
+       "      <td>15042</td>\n",
+       "      <td>67422</td>\n",
+       "      <td>0.0557</td>\n",
+       "      <td>-0.861</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999</th>\n",
+       "      <td>SM_101999</td>\n",
+       "      <td>X</td>\n",
+       "      <td>carousel</td>\n",
+       "      <td>Politics</td>\n",
+       "      <td>ur</td>\n",
+       "      <td>Pakistan</td>\n",
+       "      <td>2024-11-04 00:00:00</td>\n",
+       "      <td>#viral #funny</td>\n",
+       "      <td>3369429</td>\n",
+       "      <td>481347</td>\n",
+       "      <td>13455</td>\n",
+       "      <td>9492</td>\n",
+       "      <td>0.1497</td>\n",
+       "      <td>0.297</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        post_id        platform content_type      topic language    region  \\\n",
+       "1995  SM_101995  YouTube Shorts         text     Sports       es        US   \n",
+       "1996  SM_101996       Instagram         text   Politics       en    Brazil   \n",
+       "1997  SM_101997               X     carousel  Education       ur        UK   \n",
+       "1998  SM_101998               X     carousel     Sports       ur        UK   \n",
+       "1999  SM_101999               X     carousel   Politics       ur  Pakistan   \n",
+       "\n",
+       "            post_datetime                hashtags     views   likes  comments  \\\n",
+       "1995  2024-02-11 00:00:00       #funny #music #ai  14112183  486627      9430   \n",
+       "1996  2024-02-12 00:00:00        #viral #trending   5604744  467062      2764   \n",
+       "1997  2024-05-01 00:00:00          #trending #fyp   2031920  253990     40963   \n",
+       "1998  2024-12-23 00:00:00  #funny #trending #tech   5237350  209494     15042   \n",
+       "1999  2024-11-04 00:00:00           #viral #funny   3369429  481347     13455   \n",
+       "\n",
+       "      shares  engagement_rate  sentiment_score  is_viral  \n",
+       "1995   91550           0.0416           -0.541         1  \n",
+       "1996   16092           0.0867            0.287         1  \n",
+       "1997   95697           0.1923            0.604         1  \n",
+       "1998   67422           0.0557           -0.861         1  \n",
+       "1999    9492           0.1497            0.297         1  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "488572f2-7670-4ec3-912d-22f3089361fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2000, 15)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caf19d9d-9506-4482-a579-d414c5788290",
+   "metadata": {},
+   "source": [
+    "## Task 2: \n",
+    "Data Inspection Check the column names.\n",
+    "\n",
+    "Check data types of all columns.\n",
+    "\n",
+    "Identify columns that contain missing values.\n",
+    "\n",
+    "Count the number of missing values per column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f01d432e-66fd-48ec-be32-295bbf926744",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['post_id', 'platform', 'content_type', 'topic', 'language', 'region',\n",
+       "       'post_datetime', 'hashtags', 'views', 'likes', 'comments', 'shares',\n",
+       "       'engagement_rate', 'sentiment_score', 'is_viral'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "88e99e33-d214-42f8-9803-fcc3d74f514f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 2000 entries, 0 to 1999\n",
+      "Data columns (total 15 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   post_id          2000 non-null   object \n",
+      " 1   platform         2000 non-null   object \n",
+      " 2   content_type     2000 non-null   object \n",
+      " 3   topic            2000 non-null   object \n",
+      " 4   language         2000 non-null   object \n",
+      " 5   region           2000 non-null   object \n",
+      " 6   post_datetime    2000 non-null   object \n",
+      " 7   hashtags         2000 non-null   object \n",
+      " 8   views            2000 non-null   int64  \n",
+      " 9   likes            2000 non-null   int64  \n",
+      " 10  comments         2000 non-null   int64  \n",
+      " 11  shares           2000 non-null   int64  \n",
+      " 12  engagement_rate  2000 non-null   float64\n",
+      " 13  sentiment_score  2000 non-null   float64\n",
+      " 14  is_viral         2000 non-null   int64  \n",
+      "dtypes: float64(2), int64(5), object(8)\n",
+      "memory usage: 234.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f4914b78-45b9-4ec9-aeaa-64d235f27a63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "post_id            0\n",
+       "platform           0\n",
+       "content_type       0\n",
+       "topic              0\n",
+       "language           0\n",
+       "region             0\n",
+       "post_datetime      0\n",
+       "hashtags           0\n",
+       "views              0\n",
+       "likes              0\n",
+       "comments           0\n",
+       "shares             0\n",
+       "engagement_rate    0\n",
+       "sentiment_score    0\n",
+       "is_viral           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96765e3d-3a5c-45c5-a7d4-5982908c7c0e",
+   "metadata": {},
+   "source": [
+    "no missing value "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0bbef1d-d98e-4911-9e7c-f3d347569f28",
+   "metadata": {},
+   "source": [
+    "## TASK 3 \n",
+    "Numerical Analysis (NumPy + Pandas)\n",
+    "\n",
+    "1.Select one numerical column.\n",
+    "\n",
+    "2. Convert the column to a NumPy array.\n",
+    "3. Using NumPy methods, \n",
+    "calculate:MeanMedianMinimumMaximumStandard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e2b16367-54dc-419d-8f28-58e1c661034b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "like_col = df.loc[:, 'likes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b08344c5-e301-436f-9271-2ed0ac056370",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "likes_array = np.array(like_col)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2c715d2f-cbac-4f6b-9acf-8341452b3f66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 245329.244\n",
+      "Median: 239831.0\n",
+      "Minimum: 292\n",
+      "Maximum: 499983\n",
+      "Standard Deviation: 144996.16\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Calculating the statistics using NumPy methods\n",
+    "mean_val = np.mean(likes_array)\n",
+    "median_val = np.median(likes_array)\n",
+    "min_val = np.min(likes_array)\n",
+    "max_val = np.max(likes_array)\n",
+    "std_dev = np.std(likes_array)\n",
+    "\n",
+    "# Printing the results\n",
+    "print(f\"Mean: {mean_val}\")\n",
+    "print(f\"Median: {median_val}\")\n",
+    "print(f\"Minimum: {min_val}\")\n",
+    "print(f\"Maximum: {max_val}\")\n",
+    "print(f\"Standard Deviation: {std_dev:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e6b35b7-9fec-476e-9e67-7794b26c372e",
+   "metadata": {},
+   "source": [
+    "## Task 4:\n",
+    "Basic Visualization (Matplotlib)\n",
+    "\n",
+    "Plot a line chart showing how one numerical variable changes over time or index.\n",
+    "\n",
+    "Plot a bar chart of the selected numerical column.\n",
+    "\n",
+    "Task 5: InsightWrite 3 observations from the analysis and visualizations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e6225bd0-5888-494d-b5c3-e6f18d1a612f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0a248b2e-8372-402d-ad99-dab0c5a530bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df.index[:10], df[\"likes\"].head(10))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "be4d732a-1db5-4be2-b8a5-7f9e6588a796",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(df.index[:10], df[\"likes\"].head(10))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fdd3877-59a7-493d-92fc-88ad442a5eec",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/EBEN C-3.ipynb b/EBEN C-3.ipynb
new file mode 100644
index 0000000..01099bc
--- /dev/null
+++ b/EBEN C-3.ipynb	
@@ -0,0 +1,335 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2fff0675-bde2-48f1-8fbc-4de415ba72d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Platform</th>\n",
+       "      <th>Content_Type</th>\n",
+       "      <th>Likes</th>\n",
+       "      <th>Shares</th>\n",
+       "      <th>Comments</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TikTok</td>\n",
+       "      <td>Video</td>\n",
+       "      <td>3500</td>\n",
+       "      <td>200</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Instagram</td>\n",
+       "      <td>Video</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TikTok</td>\n",
+       "      <td>Video</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>300</td>\n",
+       "      <td>150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>TikTok</td>\n",
+       "      <td>Video</td>\n",
+       "      <td>4200</td>\n",
+       "      <td>250</td>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Platform Content_Type  Likes  Shares  Comments\n",
+       "1     TikTok        Video   3500     200       100\n",
+       "3  Instagram        Video   2300     120        80\n",
+       "4     TikTok        Video   5000     300       150\n",
+       "7     TikTok        Video   4200     250       120"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# STEP 1: Open the file\n",
+    "df = pd.read_csv('social_media_viral_content.csv')\n",
+    "\n",
+    "# STEP 2: Find the \"Average\" (the middle ground)\n",
+    "# If the average is 1,891, anything lower is 'normal', anything higher is 'viral'\n",
+    "average_likes = df['Likes'].mean()\n",
+    "\n",
+    "# STEP 3: Create the filter\n",
+    "# We tell Python: \"Keep only the rows where Likes are greater than our average\"\n",
+    "filtered_data = df[df['Likes'] > average_likes]\n",
+    "\n",
+    "# STEP 4: Save and Look at the result\n",
+    "# Save the 'winners' to a new file\n",
+    "filtered_data.to_csv('top_posts.csv', index=False)\n",
+    "\n",
+    "# Show the first few rows of our new \"Winners Only\" list\n",
+    "filtered_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8e1b2835-7395-4af3-b5ff-6ac8cedfcb31",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Grouped Analysis ---\n",
+      "                  mean  count\n",
+      "Platform                     \n",
+      "Instagram  1525.000000      4\n",
+      "TikTok     4233.333333      3\n",
+      "Twitter      39.000000      3\n",
+      "\n",
+      "--- Mean Likes as a NumPy Array ---\n",
+      "[1525.         4233.33333333   39.        ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Group the data by 'Platform'\n",
+    "grouped = df.groupby('Platform')\n",
+    "\n",
+    "# 2. Calculate the Mean (Average) Likes and the Count of posts per platform\n",
+    "# This creates a summary table\n",
+    "analysis = grouped['Likes'].agg(['mean', 'count'])\n",
+    "\n",
+    "# 3. Convert the 'mean' results into a NumPy array\n",
+    "# This is often done when you need to pass data into a mathematical model\n",
+    "mean_likes_array = analysis['mean'].to_numpy()\n",
+    "\n",
+    "# --- Display the results ---\n",
+    "print(\"--- Grouped Analysis ---\")\n",
+    "print(analysis)\n",
+    "print(\"\\n--- Mean Likes as a NumPy Array ---\")\n",
+    "print(mean_likes_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1c1bf02c-0904-4a2e-b657-e390e89effec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Set a clean visual style\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# --- 1. Histogram: Comparing Groups ---\n",
+    "# We use 'hue' to color the bars by Platform to compare their distributions\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(data=df, x='Likes', hue='Platform', multiple='stack', bins=10)\n",
+    "plt.title('Distribution of Likes across Different Platforms')\n",
+    "plt.xlabel('Number of Likes')\n",
+    "plt.ylabel('Frequency of Posts')\n",
+    "plt.savefig('likes_histogram.png') # Optional: saves the image\n",
+    "\n",
+    "# --- 2. Scatter Plot: Relationship between Likes and Shares ---\n",
+    "# This shows if more likes lead to more shares (Correlation)\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.scatterplot(data=df, x='Likes', y='Shares', hue='Platform', s=100)\n",
+    "plt.title('Correlation: Likes vs. Shares')\n",
+    "plt.xlabel('Number of Likes')\n",
+    "plt.ylabel('Number of Shares')\n",
+    "plt.legend(title='Platform')\n",
+    "plt.savefig('likes_vs_shares_scatter.png')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8c3d943a-19fe-44e1-b6e4-067aa62ea57d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Load the data\n",
+    "df = pd.read_csv('social_media_viral_content.csv')\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# --- 1. Trend Chart: Engagement Scaling ---\n",
+    "df_sorted = df.sort_values(by='Likes').reset_index(drop=True)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(df_sorted.index, df_sorted['Likes'], marker='o', label='Likes', color='blue')\n",
+    "plt.plot(df_sorted.index, df_sorted['Shares'] * 10, marker='s', label='Shares (x10)', linestyle='--')\n",
+    "plt.title('Trend of Engagement Growth')\n",
+    "plt.xlabel('Posts (Sorted by Least to Most Likes)')\n",
+    "plt.ylabel('Engagement Count')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# --- 2. Distribution Chart: Likes by Content Type (FIXED VERSION) ---\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "# Fixed: Added hue='Content_Type' and legend=False to stop the warning\n",
+    "sns.boxplot(data=df, x='Content_Type', y='Likes', hue='Content_Type', palette='Set2', legend=False)\n",
+    "plt.title('Distribution of Likes across Content Types')\n",
+    "plt.xlabel('Content Type')\n",
+    "plt.ylabel('Likes')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51b89502-c35d-4d3c-bf3c-bf6460823ced",
+   "metadata": {},
+   "source": [
+    "Report\n",
+    "\n",
+    "### **1. The Big Winner: TikTok & Video**\n",
+    "\n",
+    "If you want your content to be seen, **TikTok is the place to be, and Video is the format to use.** * TikTok posts get nearly **3 times more likes** than Instagram posts.\n",
+    "\n",
+    "* Videos are the \"superstars\" of this list. Every single post that did better than average was a video.\n",
+    "\n",
+    "### **2. The \"Slow and Steady\": Instagram & Images**\n",
+    "\n",
+    "Instagram is reliable but doesn't hit the massive peaks that TikTok does.\n",
+    "\n",
+    "* Images perform well on Instagram, but they don't \"explode\" the way videos do.\n",
+    "* Think of images as a safe bet for consistent views, while video is like trying to win the lottery.\n",
+    "\n",
+    "### **3. The Underperformer: Twitter & Text**\n",
+    "\n",
+    "In this specific group of data, **Twitter and text-only posts are struggling.** * They receive very little attention (likes or shares) compared to anything with a visual.\n",
+    "\n",
+    "* Spending a lot of time writing text posts for Twitter might not be the best use of resources right now.\n",
+    "\n",
+    "### **4. The \"Snowball Effect\"**\n",
+    "\n",
+    "We noticed that **Likes, Shares, and Comments all move together.** * When people start liking a post, they almost always start sharing it and commenting on it too.\n",
+    "\n",
+    "* This means you don't need to worry about \"getting more shares\" specifically; if you can make people click that Like button, the shares and comments will naturally follow.\n",
+    "\n",
+    "### **5. The Bottom Line (What should you do?)**\n",
+    "\n",
+    "* **Put your money and time into Video.** It’s the only format currently capable of \"going viral.\"\n",
+    "* **Focus on TikTok.** It has a much higher \"ceiling\" for success than the other platforms.\n",
+    "* **Don't expect much from Text.** If you have an important message, put it in a video or a picture rather than just typing it out.\n",
+    "\n",
+    "**In short: If you want to go viral, make a TikTok video!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4a1e79b-bf4e-4af8-9d90-06b23a7a5334",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}