├── Mall_Customers.csv ├── README.md ├── main.py ├── output.png └── terminal-output.png /Mall_Customers.csv: -------------------------------------------------------------------------------- 1 | CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100) 2 | 1,Male,19,15,39 3 | 2,Male,21,15,81 4 | 3,Female,20,16,6 5 | 4,Female,23,16,77 6 | 5,Female,31,17,40 7 | 6,Female,22,17,76 8 | 7,Female,35,18,6 9 | 8,Female,23,18,94 10 | 9,Male,64,19,3 11 | 10,Female,30,19,72 12 | 11,Male,67,19,14 13 | 12,Female,35,19,99 14 | 13,Female,58,20,15 15 | 14,Female,24,20,77 16 | 15,Male,37,20,13 17 | 16,Male,22,20,79 18 | 17,Female,35,21,35 19 | 18,Male,20,21,66 20 | 19,Male,52,23,29 21 | 20,Female,35,23,98 22 | 21,Male,35,24,35 23 | 22,Male,25,24,73 24 | 23,Female,46,25,5 25 | 24,Male,31,25,73 26 | 25,Female,54,28,14 27 | 26,Male,29,28,82 28 | 27,Female,45,28,32 29 | 28,Male,35,28,61 30 | 29,Female,40,29,31 31 | 30,Female,23,29,87 32 | 31,Male,60,30,4 33 | 32,Female,21,30,73 34 | 33,Male,53,33,4 35 | 34,Male,18,33,92 36 | 35,Female,49,33,14 37 | 36,Female,21,33,81 38 | 37,Female,42,34,17 39 | 38,Female,30,34,73 40 | 39,Female,36,37,26 41 | 40,Female,20,37,75 42 | 41,Female,65,38,35 43 | 42,Male,24,38,92 44 | 43,Male,48,39,36 45 | 44,Female,31,39,61 46 | 45,Female,49,39,28 47 | 46,Female,24,39,65 48 | 47,Female,50,40,55 49 | 48,Female,27,40,47 50 | 49,Female,29,40,42 51 | 50,Female,31,40,42 52 | 51,Female,49,42,52 53 | 52,Male,33,42,60 54 | 53,Female,31,43,54 55 | 54,Male,59,43,60 56 | 55,Female,50,43,45 57 | 56,Male,47,43,41 58 | 57,Female,51,44,50 59 | 58,Male,69,44,46 60 | 59,Female,27,46,51 61 | 60,Male,53,46,46 62 | 61,Male,70,46,56 63 | 62,Male,19,46,55 64 | 63,Female,67,47,52 65 | 64,Female,54,47,59 66 | 65,Male,63,48,51 67 | 66,Male,18,48,59 68 | 67,Female,43,48,50 69 | 68,Female,68,48,48 70 | 69,Male,19,48,59 71 | 70,Female,32,48,47 72 | 71,Male,70,49,55 73 | 72,Female,47,49,42 74 | 73,Female,60,50,49 75 | 74,Female,60,50,56 76 | 75,Male,59,54,47 77 | 76,Male,26,54,54 78 | 77,Female,45,54,53 79 | 78,Male,40,54,48 80 | 79,Female,23,54,52 81 | 80,Female,49,54,42 82 | 81,Male,57,54,51 83 | 82,Male,38,54,55 84 | 83,Male,67,54,41 85 | 84,Female,46,54,44 86 | 85,Female,21,54,57 87 | 86,Male,48,54,46 88 | 87,Female,55,57,58 89 | 88,Female,22,57,55 90 | 89,Female,34,58,60 91 | 90,Female,50,58,46 92 | 91,Female,68,59,55 93 | 92,Male,18,59,41 94 | 93,Male,48,60,49 95 | 94,Female,40,60,40 96 | 95,Female,32,60,42 97 | 96,Male,24,60,52 98 | 97,Female,47,60,47 99 | 98,Female,27,60,50 100 | 99,Male,48,61,42 101 | 100,Male,20,61,49 102 | 101,Female,23,62,41 103 | 102,Female,49,62,48 104 | 103,Male,67,62,59 105 | 104,Male,26,62,55 106 | 105,Male,49,62,56 107 | 106,Female,21,62,42 108 | 107,Female,66,63,50 109 | 108,Male,54,63,46 110 | 109,Male,68,63,43 111 | 110,Male,66,63,48 112 | 111,Male,65,63,52 113 | 112,Female,19,63,54 114 | 113,Female,38,64,42 115 | 114,Male,19,64,46 116 | 115,Female,18,65,48 117 | 116,Female,19,65,50 118 | 117,Female,63,65,43 119 | 118,Female,49,65,59 120 | 119,Female,51,67,43 121 | 120,Female,50,67,57 122 | 121,Male,27,67,56 123 | 122,Female,38,67,40 124 | 123,Female,40,69,58 125 | 124,Male,39,69,91 126 | 125,Female,23,70,29 127 | 126,Female,31,70,77 128 | 127,Male,43,71,35 129 | 128,Male,40,71,95 130 | 129,Male,59,71,11 131 | 130,Male,38,71,75 132 | 131,Male,47,71,9 133 | 132,Male,39,71,75 134 | 133,Female,25,72,34 135 | 134,Female,31,72,71 136 | 135,Male,20,73,5 137 | 136,Female,29,73,88 138 | 137,Female,44,73,7 139 | 138,Male,32,73,73 140 | 139,Male,19,74,10 141 | 140,Female,35,74,72 142 | 141,Female,57,75,5 143 | 142,Male,32,75,93 144 | 143,Female,28,76,40 145 | 144,Female,32,76,87 146 | 145,Male,25,77,12 147 | 146,Male,28,77,97 148 | 147,Male,48,77,36 149 | 148,Female,32,77,74 150 | 149,Female,34,78,22 151 | 150,Male,34,78,90 152 | 151,Male,43,78,17 153 | 152,Male,39,78,88 154 | 153,Female,44,78,20 155 | 154,Female,38,78,76 156 | 155,Female,47,78,16 157 | 156,Female,27,78,89 158 | 157,Male,37,78,1 159 | 158,Female,30,78,78 160 | 159,Male,34,78,1 161 | 160,Female,30,78,73 162 | 161,Female,56,79,35 163 | 162,Female,29,79,83 164 | 163,Male,19,81,5 165 | 164,Female,31,81,93 166 | 165,Male,50,85,26 167 | 166,Female,36,85,75 168 | 167,Male,42,86,20 169 | 168,Female,33,86,95 170 | 169,Female,36,87,27 171 | 170,Male,32,87,63 172 | 171,Male,40,87,13 173 | 172,Male,28,87,75 174 | 173,Male,36,87,10 175 | 174,Male,36,87,92 176 | 175,Female,52,88,13 177 | 176,Female,30,88,86 178 | 177,Male,58,88,15 179 | 178,Male,27,88,69 180 | 179,Male,59,93,14 181 | 180,Male,35,93,90 182 | 181,Female,37,97,32 183 | 182,Female,32,97,86 184 | 183,Male,46,98,15 185 | 184,Female,29,98,88 186 | 185,Female,41,99,39 187 | 186,Male,30,99,97 188 | 187,Female,54,101,24 189 | 188,Male,28,101,68 190 | 189,Female,41,103,17 191 | 190,Female,36,103,85 192 | 191,Female,34,103,23 193 | 192,Female,32,103,69 194 | 193,Male,33,113,8 195 | 194,Female,38,113,91 196 | 195,Female,47,120,16 197 | 196,Female,35,120,79 198 | 197,Female,45,126,28 199 | 198,Male,32,126,74 200 | 199,Male,32,137,18 201 | 200,Male,30,137,83 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PRODIGY_TrackCode_Task2 -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # Importing necessary libraries 2 | import pandas as pd 3 | from sklearn.cluster import KMeans 4 | from sklearn.preprocessing import StandardScaler 5 | import matplotlib.pyplot as plt 6 | 7 | # Load the dataset 8 | mall_customers_df = pd.read_csv("Mall_Customers.csv") 9 | 10 | # Preprocessing 11 | # Drop irrelevant columns 12 | mall_customers_df.drop(columns=['CustomerID', 'Gender'], inplace=True) 13 | 14 | # Feature scaling 15 | scaler = StandardScaler() 16 | mall_customers_scaled = scaler.fit_transform(mall_customers_df) 17 | 18 | # Determine the optimal number of clusters (K) 19 | inertia = [] 20 | for k in range(1, 11): 21 | kmeans = KMeans(n_clusters=k, random_state=42) 22 | kmeans.fit(mall_customers_scaled) 23 | inertia.append(kmeans.inertia_) 24 | 25 | # Plot the elbow method graph 26 | plt.plot(range(1, 11), inertia) 27 | plt.title('Elbow Method') 28 | plt.xlabel('Number of Clusters') 29 | plt.ylabel('Inertia') 30 | plt.show() 31 | 32 | # Based on the elbow method, choose the optimal number of clusters (K) 33 | optimal_k = 5 34 | 35 | # Apply KMeans clustering 36 | kmeans = KMeans(n_clusters=optimal_k, random_state=42) 37 | kmeans.fit(mall_customers_scaled) 38 | 39 | # Add cluster labels to the original dataset 40 | mall_customers_df['Cluster'] = kmeans.labels_ 41 | 42 | # Interpretation 43 | # Analyze the characteristics of customers in each cluster 44 | cluster_means = mall_customers_df.groupby('Cluster').mean() 45 | print(cluster_means) 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