uploading initial files

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.cluster import KMeans
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.metrics import silhouette_score
+
+# Load dataset
+df = pd.read_csv("datasets/Mall_Customers.csv")
+df.drop(columns=['CustomerID'], inplace=True)
+
+# Encode Gender
+le = LabelEncoder()
+df['Genre'] = le.fit_transform(df['Genre'])
+
+# Standardize numerical columns
+scaler = StandardScaler()
+df_scaled = scaler.fit_transform(df)
+
+# Optimal K estimation
+k_optimal = 5
+kmeans = KMeans(n_clusters=k_optimal, init='k-means++', random_state=42)
+kmeans.fit(df_scaled)
+df['Cluster'] = kmeans.labels_
+
+sil_score = silhouette_score(df_scaled, kmeans.labels_)
+
+# Streamlit App
+st.title("Customer Segmentation using K-Means")
+st.caption("Dataset: Mall Customers")
+
+tab1, tab2, tab3 = st.tabs(["Model Performance", "Dataset", "Customer Predictor"])
+
+with tab1:
+    st.header("Model Performance")
+    st.write(f"**Silhouette Score:** {sil_score:.4f}")
+    
+    wcss = []
+    k_values = range(1, 11)
+    for k in k_values:
+        kmeans_temp = KMeans(n_clusters=k, init='k-means++', random_state=42)
+        kmeans_temp.fit(df_scaled)
+        wcss.append(kmeans_temp.inertia_)
+    
+    fig, ax = plt.subplots()
+    ax.plot(k_values, wcss, marker='o', linestyle='--')
+    ax.set_xlabel('Number of Clusters (K)')
+    ax.set_ylabel('WCSS')
+    ax.set_title('Elbow Method for Optimal K')
+    st.pyplot(fig)
+    
+with tab2:
+    st.header("Dataset")
+    st.dataframe(df.head())
+    
+    fig, ax = plt.subplots(figsize=(8, 6))
+    sns.heatmap(df.corr(), annot=True, fmt=".2f", cmap="coolwarm", linewidths=0.5, ax=ax)
+    ax.set_title("Correlation Matrix")
+    st.pyplot(fig)
+    
+with tab3:
+    st.header("Customer Segment Prediction")
+    
+    annual_income = st.slider("Annual Income (k$)", min_value=int(df['Annual Income (k$)'].min()), max_value=int(df['Annual Income (k$)'].max()), value=50)
+    spending_score = st.slider("Spending Score (1-100)", min_value=int(df['Spending Score (1-100)'].min()), max_value=int(df['Spending Score (1-100)'].max()), value=50)
+    gender = st.radio("Gender", ["Male", "Female"], index=0)
+    gender_encoded = 1 if gender == "Female" else 0
+    
+    input_data = np.array([[gender_encoded, annual_income, spending_score]])
+    input_scaled = scaler.transform(np.hstack((input_data, np.zeros((input_data.shape[0], 1)))))  # Ensure same feature shape
+    cluster_prediction = kmeans.predict(input_scaled)[0]
+    
+    st.subheader("Predicted Customer Segment")
+    st.markdown(f"<h1 style='color:blue;'>{cluster_prediction}</h1>", unsafe_allow_html=True)
+    
+    fig, ax = plt.subplots()
+    sns.scatterplot(x=df['Annual Income (k$)'], y=df['Spending Score (1-100)'], hue=df['Cluster'], palette='viridis', alpha=0.6)
+    ax.scatter(annual_income, spending_score, color='red', s=100, edgecolors='black', label='Input Customer')
+    ax.set_xlabel('Annual Income (k$)')
+    ax.set_ylabel('Spending Score')
+    ax.legend()
+    st.pyplot(fig)
+    st.divider()

datasets/Mall_Customers.csv

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+CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100)
+0001,Male,19,15,39
+0002,Male,21,15,81
+0003,Female,20,16,6
+0004,Female,23,16,77
+0005,Female,31,17,40
+0006,Female,22,17,76
+0007,Female,35,18,6
+0008,Female,23,18,94
+0009,Male,64,19,3
+0010,Female,30,19,72
+0011,Male,67,19,14
+0012,Female,35,19,99
+0013,Female,58,20,15
+0014,Female,24,20,77
+0015,Male,37,20,13
+0016,Male,22,20,79
+0017,Female,35,21,35
+0018,Male,20,21,66
+0019,Male,52,23,29
+0020,Female,35,23,98
+0021,Male,35,24,35
+0022,Male,25,24,73
+0023,Female,46,25,5
+0024,Male,31,25,73
+0025,Female,54,28,14
+0026,Male,29,28,82
+0027,Female,45,28,32
+0028,Male,35,28,61
+0029,Female,40,29,31
+0030,Female,23,29,87
+0031,Male,60,30,4
+0032,Female,21,30,73
+0033,Male,53,33,4
+0034,Male,18,33,92
+0035,Female,49,33,14
+0036,Female,21,33,81
+0037,Female,42,34,17
+0038,Female,30,34,73
+0039,Female,36,37,26
+0040,Female,20,37,75
+0041,Female,65,38,35
+0042,Male,24,38,92
+0043,Male,48,39,36
+0044,Female,31,39,61
+0045,Female,49,39,28
+0046,Female,24,39,65
+0047,Female,50,40,55
+0048,Female,27,40,47
+0049,Female,29,40,42
+0050,Female,31,40,42
+0051,Female,49,42,52
+0052,Male,33,42,60
+0053,Female,31,43,54
+0054,Male,59,43,60
+0055,Female,50,43,45
+0056,Male,47,43,41
+0057,Female,51,44,50
+0058,Male,69,44,46
+0059,Female,27,46,51
+0060,Male,53,46,46
+0061,Male,70,46,56
+0062,Male,19,46,55
+0063,Female,67,47,52
+0064,Female,54,47,59
+0065,Male,63,48,51
+0066,Male,18,48,59
+0067,Female,43,48,50
+0068,Female,68,48,48
+0069,Male,19,48,59
+0070,Female,32,48,47
+0071,Male,70,49,55
+0072,Female,47,49,42
+0073,Female,60,50,49
+0074,Female,60,50,56
+0075,Male,59,54,47
+0076,Male,26,54,54
+0077,Female,45,54,53
+0078,Male,40,54,48
+0079,Female,23,54,52
+0080,Female,49,54,42
+0081,Male,57,54,51
+0082,Male,38,54,55
+0083,Male,67,54,41
+0084,Female,46,54,44
+0085,Female,21,54,57
+0086,Male,48,54,46
+0087,Female,55,57,58
+0088,Female,22,57,55
+0089,Female,34,58,60
+0090,Female,50,58,46
+0091,Female,68,59,55
+0092,Male,18,59,41
+0093,Male,48,60,49
+0094,Female,40,60,40
+0095,Female,32,60,42
+0096,Male,24,60,52
+0097,Female,47,60,47
+0098,Female,27,60,50
+0099,Male,48,61,42
+0100,Male,20,61,49
+0101,Female,23,62,41
+0102,Female,49,62,48
+0103,Male,67,62,59
+0104,Male,26,62,55
+0105,Male,49,62,56
+0106,Female,21,62,42
+0107,Female,66,63,50
+0108,Male,54,63,46
+0109,Male,68,63,43
+0110,Male,66,63,48
+0111,Male,65,63,52
+0112,Female,19,63,54
+0113,Female,38,64,42
+0114,Male,19,64,46
+0115,Female,18,65,48
+0116,Female,19,65,50
+0117,Female,63,65,43
+0118,Female,49,65,59
+0119,Female,51,67,43
+0120,Female,50,67,57
+0121,Male,27,67,56
+0122,Female,38,67,40
+0123,Female,40,69,58
+0124,Male,39,69,91
+0125,Female,23,70,29
+0126,Female,31,70,77
+0127,Male,43,71,35
+0128,Male,40,71,95
+0129,Male,59,71,11
+0130,Male,38,71,75
+0131,Male,47,71,9
+0132,Male,39,71,75
+0133,Female,25,72,34
+0134,Female,31,72,71
+0135,Male,20,73,5
+0136,Female,29,73,88
+0137,Female,44,73,7
+0138,Male,32,73,73
+0139,Male,19,74,10
+0140,Female,35,74,72
+0141,Female,57,75,5
+0142,Male,32,75,93
+0143,Female,28,76,40
+0144,Female,32,76,87
+0145,Male,25,77,12
+0146,Male,28,77,97
+0147,Male,48,77,36
+0148,Female,32,77,74
+0149,Female,34,78,22
+0150,Male,34,78,90
+0151,Male,43,78,17
+0152,Male,39,78,88
+0153,Female,44,78,20
+0154,Female,38,78,76
+0155,Female,47,78,16
+0156,Female,27,78,89
+0157,Male,37,78,1
+0158,Female,30,78,78
+0159,Male,34,78,1
+0160,Female,30,78,73
+0161,Female,56,79,35
+0162,Female,29,79,83
+0163,Male,19,81,5
+0164,Female,31,81,93
+0165,Male,50,85,26
+0166,Female,36,85,75
+0167,Male,42,86,20
+0168,Female,33,86,95
+0169,Female,36,87,27
+0170,Male,32,87,63
+0171,Male,40,87,13
+0172,Male,28,87,75
+0173,Male,36,87,10
+0174,Male,36,87,92
+0175,Female,52,88,13
+0176,Female,30,88,86
+0177,Male,58,88,15
+0178,Male,27,88,69
+0179,Male,59,93,14
+0180,Male,35,93,90
+0181,Female,37,97,32
+0182,Female,32,97,86
+0183,Male,46,98,15
+0184,Female,29,98,88
+0185,Female,41,99,39
+0186,Male,30,99,97
+0187,Female,54,101,24
+0188,Male,28,101,68
+0189,Female,41,103,17
+0190,Female,36,103,85
+0191,Female,34,103,23
+0192,Female,32,103,69
+0193,Male,33,113,8
+0194,Female,38,113,91
+0195,Female,47,120,16
+0196,Female,35,120,79
+0197,Female,45,126,28
+0198,Male,32,126,74
+0199,Male,32,137,18
+0200,Male,30,137,83

requirements.txt

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+matplotlib==3.8.0
+numpy
+pandas==2.2.3
+scikit_learn==1.2.2
+seaborn==0.13.2
+streamlit==1.30.0