initial commit

Mall_Customers.csv

@@ -0,0 +1,201 @@
+CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100)
+1,Male,19,15,39
+2,Male,21,15,81
+3,Female,20,16,6
+4,Female,23,16,77
+5,Female,31,17,40
+6,Female,22,17,76
+7,Female,35,18,6
+8,Female,23,18,94
+9,Male,64,19,3
+10,Female,30,19,72
+11,Male,67,19,14
+12,Female,35,19,99
+13,Female,58,20,15
+14,Female,24,20,77
+15,Male,37,20,13
+16,Male,22,20,79
+17,Female,35,21,35
+18,Male,20,21,66
+19,Male,52,23,29
+20,Female,35,23,98
+21,Male,35,24,35
+22,Male,25,24,73
+23,Female,46,25,5
+24,Male,31,25,73
+25,Female,54,28,14
+26,Male,29,28,82
+27,Female,45,28,32
+28,Male,35,28,61
+29,Female,40,29,31
+30,Female,23,29,87
+31,Male,60,30,4
+32,Female,21,30,73
+33,Male,53,33,4
+34,Male,18,33,92
+35,Female,49,33,14
+36,Female,21,33,81
+37,Female,42,34,17
+38,Female,30,34,73
+39,Female,36,37,26
+40,Female,20,37,75
+41,Female,65,38,35
+42,Male,24,38,92
+43,Male,48,39,36
+44,Female,31,39,61
+45,Female,49,39,28
+46,Female,24,39,65
+47,Female,50,40,55
+48,Female,27,40,47
+49,Female,29,40,42
+50,Female,31,40,42
+51,Female,49,42,52
+52,Male,33,42,60
+53,Female,31,43,54
+54,Male,59,43,60
+55,Female,50,43,45
+56,Male,47,43,41
+57,Female,51,44,50
+58,Male,69,44,46
+59,Female,27,46,51
+60,Male,53,46,46
+61,Male,70,46,56
+62,Male,19,46,55
+63,Female,67,47,52
+64,Female,54,47,59
+65,Male,63,48,51
+66,Male,18,48,59
+67,Female,43,48,50
+68,Female,68,48,48
+69,Male,19,48,59
+70,Female,32,48,47
+71,Male,70,49,55
+72,Female,47,49,42
+73,Female,60,50,49
+74,Female,60,50,56
+75,Male,59,54,47
+76,Male,26,54,54
+77,Female,45,54,53
+78,Male,40,54,48
+79,Female,23,54,52
+80,Female,49,54,42
+81,Male,57,54,51
+82,Male,38,54,55
+83,Male,67,54,41
+84,Female,46,54,44
+85,Female,21,54,57
+86,Male,48,54,46
+87,Female,55,57,58
+88,Female,22,57,55
+89,Female,34,58,60
+90,Female,50,58,46
+91,Female,68,59,55
+92,Male,18,59,41
+93,Male,48,60,49
+94,Female,40,60,40
+95,Female,32,60,42
+96,Male,24,60,52
+97,Female,47,60,47
+98,Female,27,60,50
+99,Male,48,61,42
+100,Male,20,61,49
+101,Female,23,62,41
+102,Female,49,62,48
+103,Male,67,62,59
+104,Male,26,62,55
+105,Male,49,62,56
+106,Female,21,62,42
+107,Female,66,63,50
+108,Male,54,63,46
+109,Male,68,63,43
+110,Male,66,63,48
+111,Male,65,63,52
+112,Female,19,63,54
+113,Female,38,64,42
+114,Male,19,64,46
+115,Female,18,65,48
+116,Female,19,65,50
+117,Female,63,65,43
+118,Female,49,65,59
+119,Female,51,67,43
+120,Female,50,67,57
+121,Male,27,67,56
+122,Female,38,67,40
+123,Female,40,69,58
+124,Male,39,69,91
+125,Female,23,70,29
+126,Female,31,70,77
+127,Male,43,71,35
+128,Male,40,71,95
+129,Male,59,71,11
+130,Male,38,71,75
+131,Male,47,71,9
+132,Male,39,71,75
+133,Female,25,72,34
+134,Female,31,72,71
+135,Male,20,73,5
+136,Female,29,73,88
+137,Female,44,73,7
+138,Male,32,73,73
+139,Male,19,74,10
+140,Female,35,74,72
+141,Female,57,75,5
+142,Male,32,75,93
+143,Female,28,76,40
+144,Female,32,76,87
+145,Male,25,77,12
+146,Male,28,77,97
+147,Male,48,77,36
+148,Female,32,77,74
+149,Female,34,78,22
+150,Male,34,78,90
+151,Male,43,78,17
+152,Male,39,78,88
+153,Female,44,78,20
+154,Female,38,78,76
+155,Female,47,78,16
+156,Female,27,78,89
+157,Male,37,78,1
+158,Female,30,78,78
+159,Male,34,78,1
+160,Female,30,78,73
+161,Female,56,79,35
+162,Female,29,79,83
+163,Male,19,81,5
+164,Female,31,81,93
+165,Male,50,85,26
+166,Female,36,85,75
+167,Male,42,86,20
+168,Female,33,86,95
+169,Female,36,87,27
+170,Male,32,87,63
+171,Male,40,87,13
+172,Male,28,87,75
+173,Male,36,87,10
+174,Male,36,87,92
+175,Female,52,88,13
+176,Female,30,88,86
+177,Male,58,88,15
+178,Male,27,88,69
+179,Male,59,93,14
+180,Male,35,93,90
+181,Female,37,97,32
+182,Female,32,97,86
+183,Male,46,98,15
+184,Female,29,98,88
+185,Female,41,99,39
+186,Male,30,99,97
+187,Female,54,101,24
+188,Male,28,101,68
+189,Female,41,103,17
+190,Female,36,103,85
+191,Female,34,103,23
+192,Female,32,103,69
+193,Male,33,113,8
+194,Female,38,113,91
+195,Female,47,120,16
+196,Female,35,120,79
+197,Female,45,126,28
+198,Male,32,126,74
+199,Male,32,137,18
+200,Male,30,137,83

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.cluster import KMeans
+from sklearn.preprocessing import StandardScaler
+
+# Load dataset
+@st.cache_data
+def load_data():
+    file_path = "Mall_Customers.csv"
+    df = pd.read_csv(file_path)
+    return df
+
+df = load_data()
+
+# Sidebar for navigation
+st.sidebar.title("K-Means Clustering App")
+section = st.sidebar.radio("Go to", ["Introduction", "Data Exploration", "K-Means Model", "Test Model"])
+
+if section == "Introduction":
+    st.title("Introduction to K-Means Clustering")
+    
+    st.write("### About the Model")
+    st.write("K-Means Clustering is an unsupervised machine learning algorithm used for customer segmentation. It helps identify different groups of customers based on their spending behavior and income.")
+    
+    st.write("### About the Dataset")
+    st.write("The dataset consists of customer information, including:")
+    st.markdown("- **CustomerID**: Unique identifier for each customer.")
+    st.markdown("- **Gender**: Male or Female.")
+    st.markdown("- **Age**: Age of the customer.")
+    st.markdown("- **Annual Income (k$)**: Customer's yearly income.")
+    st.markdown("- **Spending Score (1-100)**: A score assigned based on spending behavior.")
+    
+    st.write("### How to Use the App")
+    st.markdown("1. **Go to 'Data Exploration'**: Understand the dataset using statistics and visualizations.")
+    st.markdown("2. **Go to 'K-Means Model'**: Train the model and visualize clusters.")
+    st.markdown("3. **Go to 'Test Model'**: Input values to predict customer cluster.")
+    
+    st.write("### Insights")
+    st.markdown("- Customers can be grouped into different segments based on their income and spending habits.")
+    st.markdown("- The Elbow Method helps determine the optimal number of clusters.")
+    st.markdown("- Businesses can use these insights to tailor marketing strategies and improve customer engagement.")
+
+elif section == "Data Exploration":
+    st.title("Data Exploration")
+    st.write("### First 5 rows of dataset")
+    st.dataframe(df.head())
+    
+    st.write("### Summary Statistics")
+    st.write(df.describe())
+    
+    st.write("### Pairplot")
+    sns.pairplot(df.drop(columns=["CustomerID", "Gender"]), diag_kind="kde")
+    st.pyplot()
+    
+    st.write("### Correlation Heatmap")
+    plt.figure(figsize=(8, 6))
+    sns.heatmap(df.drop(columns=["CustomerID", "Gender"]).corr(), annot=True, cmap="coolwarm")
+    st.pyplot()
+
+elif section == "K-Means Model":
+    st.title("K-Means Clustering")
+    
+    # Selecting features for clustering
+    features = df[["Annual Income (k$)", "Spending Score (1-100)"]]
+    scaler = StandardScaler()
+    scaled_features = scaler.fit_transform(features)
+    
+    # Finding the optimal number of clusters using Elbow Method
+    st.write("### Elbow Method")
+    inertia = []
+    for k in range(1, 11):
+        kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
+        kmeans.fit(scaled_features)
+        inertia.append(kmeans.inertia_)
+    
+    plt.figure(figsize=(8, 5))
+    plt.plot(range(1, 11), inertia, marker='o')
+    plt.xlabel('Number of Clusters')
+    plt.ylabel('Inertia')
+    plt.title('Elbow Method for Optimal k')
+    st.pyplot()
+    
+    # Train K-Means Model
+    k = st.slider("Select Number of Clusters", 2, 10, 5)
+    kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
+    df['Cluster'] = kmeans.fit_predict(scaled_features)
+    
+    st.write("### Clustered Data")
+    st.dataframe(df)
+    
+    # Visualization of clusters
+    plt.figure(figsize=(8, 6))
+    sns.scatterplot(x=df["Annual Income (k$)"], y=df["Spending Score (1-100)"], hue=df['Cluster'], palette='viridis')
+    plt.xlabel("Annual Income (k$)")
+    plt.ylabel("Spending Score (1-100)")
+    plt.title("Customer Segmentation using K-Means")
+    st.pyplot()
+
+    # Store the model and scaler globally
+    st.session_state['scaler'] = scaler
+    st.session_state['kmeans'] = kmeans
+
+elif section == "Test Model":
+    st.title("Test K-Means Model")
+    
+    income = st.number_input("Enter Annual Income (k$)", min_value=0, max_value=200, value=50)
+    score = st.number_input("Enter Spending Score (1-100)", min_value=1, max_value=100, value=50)
+    
+    if 'scaler' in st.session_state and 'kmeans' in st.session_state:
+        input_data = st.session_state['scaler'].transform([[income, score]])
+        prediction = st.session_state['kmeans'].predict(input_data)
+        st.write(f"### Predicted Cluster: {prediction[0]}")
+    else:
+        st.write("### Please run the K-Means Model section first.")

requirements.txt

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+streamlit
+pandas
+matplotlib
+seaborn
+scikit-learn