diff --git a/Customer Segmentation Analysis/Customer Segmentation.ipynb b/Customer Segmentation Analysis/Customer Segmentation.ipynb
new file mode 100644
index 000000000..0c1742b6e
--- /dev/null
+++ b/Customer Segmentation Analysis/Customer Segmentation.ipynb	
@@ -0,0 +1,1040 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "b20360c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "a510b2b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= pd.read_csv(\"Shopping Mall Customer Segmentation Data .csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "f7fe197d",
+   "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>Customer ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Annual Income</th>\n",
+       "      <th>Spending Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>d410ea53-6661-42a9-ad3a-f554b05fd2a7</td>\n",
+       "      <td>30</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>151479</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1770b26f-493f-46b6-837f-4237fb5a314e</td>\n",
+       "      <td>58</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>185088</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>e81aa8eb-1767-4b77-87ce-1620dc732c5e</td>\n",
+       "      <td>62</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>70912</td>\n",
+       "      <td>76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9795712a-ad19-47bf-8886-4f997d6046e3</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>55460</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>64139426-2226-4cd6-bf09-91bce4b4db5e</td>\n",
+       "      <td>24</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>153752</td>\n",
+       "      <td>76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15074</th>\n",
+       "      <td>a0504768-a85f-4930-ac24-55bc8e4fec9e</td>\n",
+       "      <td>29</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>97723</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15075</th>\n",
+       "      <td>a08c4e0e-d1fe-48e7-9366-aab11ae409cd</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>73361</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15076</th>\n",
+       "      <td>0e87c25a-268c-401a-8ba1-7111dcde6f1a</td>\n",
+       "      <td>18</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>112337</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15077</th>\n",
+       "      <td>5f388cbe-3373-4e16-b743-38f508f2249f</td>\n",
+       "      <td>26</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>94312</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15078</th>\n",
+       "      <td>b8b8f561-ebca-4401-8afe-544c906554ba</td>\n",
+       "      <td>19</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>78045</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15079 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                Customer ID  Age  Gender  Annual Income  \\\n",
+       "0      d410ea53-6661-42a9-ad3a-f554b05fd2a7   30    Male         151479   \n",
+       "1      1770b26f-493f-46b6-837f-4237fb5a314e   58  Female         185088   \n",
+       "2      e81aa8eb-1767-4b77-87ce-1620dc732c5e   62  Female          70912   \n",
+       "3      9795712a-ad19-47bf-8886-4f997d6046e3   23    Male          55460   \n",
+       "4      64139426-2226-4cd6-bf09-91bce4b4db5e   24    Male         153752   \n",
+       "...                                     ...  ...     ...            ...   \n",
+       "15074  a0504768-a85f-4930-ac24-55bc8e4fec9e   29  Female          97723   \n",
+       "15075  a08c4e0e-d1fe-48e7-9366-aab11ae409cd   22    Male          73361   \n",
+       "15076  0e87c25a-268c-401a-8ba1-7111dcde6f1a   18  Female         112337   \n",
+       "15077  5f388cbe-3373-4e16-b743-38f508f2249f   26  Female          94312   \n",
+       "15078  b8b8f561-ebca-4401-8afe-544c906554ba   19    Male          78045   \n",
+       "\n",
+       "       Spending Score  \n",
+       "0                  89  \n",
+       "1                  95  \n",
+       "2                  76  \n",
+       "3                  57  \n",
+       "4                  76  \n",
+       "...               ...  \n",
+       "15074              30  \n",
+       "15075              74  \n",
+       "15076              48  \n",
+       "15077               5  \n",
+       "15078               2  \n",
+       "\n",
+       "[15079 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d06307f0",
+   "metadata": {},
+   "source": [
+    "#### Dropping customer ID column "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "35c95f61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= df.drop(columns=['Customer ID'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "82c41783",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15079, 4)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "777b0037",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age               0\n",
+       "Gender            0\n",
+       "Annual Income     0\n",
+       "Spending Score    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7433bc4a",
+   "metadata": {},
+   "source": [
+    "#### No null values are present"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "9a12a93b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 15079 entries, 0 to 15078\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column          Non-Null Count  Dtype \n",
+      "---  ------          --------------  ----- \n",
+      " 0   Age             15079 non-null  int64 \n",
+      " 1   Gender          15079 non-null  object\n",
+      " 2   Annual Income   15079 non-null  int64 \n",
+      " 3   Spending Score  15079 non-null  int64 \n",
+      "dtypes: int64(3), object(1)\n",
+      "memory usage: 471.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6339911b",
+   "metadata": {},
+   "source": [
+    "#### Distribution of Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "99e3f4b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Male      7595\n",
+       "Female    7484\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "150f6d10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Gender', ylabel='count'>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(4, 4))\n",
+    "sns.countplot(x= 'Gender', data=df, palette='Set2',edgecolor='black')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "069b2c30",
+   "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>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Annual Income</th>\n",
+       "      <th>Spending Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>0</td>\n",
+       "      <td>151479</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>58</td>\n",
+       "      <td>1</td>\n",
+       "      <td>185088</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>62</td>\n",
+       "      <td>1</td>\n",
+       "      <td>70912</td>\n",
+       "      <td>76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>55460</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>153752</td>\n",
+       "      <td>76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15074</th>\n",
+       "      <td>29</td>\n",
+       "      <td>1</td>\n",
+       "      <td>97723</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15075</th>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>73361</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15076</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>112337</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15077</th>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>94312</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15078</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>78045</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15079 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Age  Gender  Annual Income  Spending Score\n",
+       "0       30       0         151479              89\n",
+       "1       58       1         185088              95\n",
+       "2       62       1          70912              76\n",
+       "3       23       0          55460              57\n",
+       "4       24       0         153752              76\n",
+       "...    ...     ...            ...             ...\n",
+       "15074   29       1          97723              30\n",
+       "15075   22       0          73361              74\n",
+       "15076   18       1         112337              48\n",
+       "15077   26       1          94312               5\n",
+       "15078   19       0          78045               2\n",
+       "\n",
+       "[15079 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Gender']= df['Gender'].map({'Male':0, 'Female':1})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "4cda1ae0",
+   "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>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Annual Income</th>\n",
+       "      <th>Spending Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>15079.000000</td>\n",
+       "      <td>15079.000000</td>\n",
+       "      <td>15079.000000</td>\n",
+       "      <td>15079.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>54.191591</td>\n",
+       "      <td>0.496319</td>\n",
+       "      <td>109742.880562</td>\n",
+       "      <td>50.591617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>21.119207</td>\n",
+       "      <td>0.500003</td>\n",
+       "      <td>52249.425866</td>\n",
+       "      <td>28.726977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20022.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>64141.000000</td>\n",
+       "      <td>26.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>109190.000000</td>\n",
+       "      <td>51.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>155008.000000</td>\n",
+       "      <td>75.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>199974.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Age        Gender  Annual Income  Spending Score\n",
+       "count  15079.000000  15079.000000   15079.000000    15079.000000\n",
+       "mean      54.191591      0.496319  109742.880562       50.591617\n",
+       "std       21.119207      0.500003   52249.425866       28.726977\n",
+       "min       18.000000      0.000000   20022.000000        1.000000\n",
+       "25%       36.000000      0.000000   64141.000000       26.000000\n",
+       "50%       54.000000      0.000000  109190.000000       51.000000\n",
+       "75%       72.000000      1.000000  155008.000000       75.000000\n",
+       "max       90.000000      1.000000  199974.000000      100.000000"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "6429b2bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(df.corr(), annot= True, cmap= 'coolwarm', linewidth= 0.2, fmt= \"0.2f\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1512c3cc",
+   "metadata": {},
+   "source": [
+    "#### Age distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "3b7008a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df['Age'], kde=True, bins=24)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "726ed01c",
+   "metadata": {},
+   "source": [
+    "#### Distribution of income"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "e8bd038f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df['Annual Income'], kde=True, bins=30)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb483b8a",
+   "metadata": {},
+   "source": [
+    "#### Distribution of Spending Score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "4013f01a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df['Spending Score'], kde=True, bins=25)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f6bfece",
+   "metadata": {},
+   "source": [
+    "#### Outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "6c620e78",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "col=['Age', 'Annual Income', 'Spending Score']\n",
+    "\n",
+    "length= len(col)\n",
+    "row= (length+2)//3\n",
+    "\n",
+    "for k,var in enumerate(col):\n",
+    "    plt.figure(figsize=(8,4))\n",
+    "    plt.subplot(row,3,k+1)\n",
+    "    sns.boxplot(df[var])\n",
+    "    plt.title(f'boxplot of {var}')\n",
+    "    \n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d19e904e",
+   "metadata": {},
+   "source": [
+    "#### No outliers are present"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fc8fd2d",
+   "metadata": {},
+   "source": [
+    "#### Importing libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "fdd30a8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99528d8e",
+   "metadata": {},
+   "source": [
+    "#### Scaling of the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "86e0d3ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler= StandardScaler()\n",
+    "scaled_df= scaler.fit_transform(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4c1d755",
+   "metadata": {},
+   "source": [
+    "#### Customer Segmentation using K-Means Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "bb4241e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "inertia = []\n",
+    "K = range(1, 11)\n",
+    "for k in K:\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans.fit(scaled_df)\n",
+    "    inertia.append(kmeans.inertia_)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(K, inertia, 'bo-')\n",
+    "plt.title('Elbow Method For Optimal k')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "e63c741e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.14551615, -0.99266566,  0.79881267,  1.33705873],\n",
+       "       [ 0.18033514,  1.00738853,  1.44207552,  1.54592857],\n",
+       "       [ 0.36974247,  1.00738853, -0.74320756,  0.88450743],\n",
+       "       ...,\n",
+       "       [-1.71373813,  1.00738853,  0.04965041, -0.09021845],\n",
+       "       [-1.33492347,  1.00738853, -0.2953409 , -1.58711891],\n",
+       "       [-1.6663863 , -0.99266566, -0.60668478, -1.69155383]])"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "7c6d85da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimal_k = 4\n",
+    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
+    "df['Clusters'] = kmeans.fit_predict(scaled_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "7742ccba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df[['Age', 'Gender','Annual Income', 'Spending Score']]\n",
+    "y = df['Clusters']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "0b3d231d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_scaled= scaler.fit_transform(X_train)\n",
+    "X_test_scaled= scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "e6495daf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, multi_class=&#x27;ovr&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, multi_class=&#x27;ovr&#x27;)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression(max_iter=1000, multi_class='ovr')"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LogisticRegression(multi_class='ovr', max_iter=1000)\n",
+    "model.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "3063b8aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9993368700265252"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test_scaled)\n",
+    "accuracy_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88e59ee2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b87fb760",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Customer Segmentation Analysis/Shopping Mall Customer Segmentation Data .csv b/Customer Segmentation Analysis/Shopping Mall Customer Segmentation Data .csv
new file mode 100644
index 000000000..b394ac0c4
--- /dev/null
+++ b/Customer Segmentation Analysis/Shopping Mall Customer Segmentation Data .csv	
@@ -0,0 +1,15080 @@
+Customer ID,Age,Gender,Annual Income,Spending Score
+d410ea53-6661-42a9-ad3a-f554b05fd2a7,30,Male,151479,89
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+d93d2a18-c9e8-42ed-a97c-84912b1b0efd,44,Female,186527,6
+4884ace5-bbb8-4502-96e0-be564083da60,29,Male,48177,53
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