Upload 4 files

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+import matplotlib.pyplot as plt
+import plotly.express as px
+
+st.title("Customer Segmentation Using RFM")
+
+kmeans = joblib.load("customer_segmentation_model.pkl")
+rfm = pd.read_csv("Customer_Segmentation.csv")
+
+def predict_rfm(num1,num2,num3):
+    data = pd.DataFrame(data=[[num1,num2,num3]],columns=["Recency_Score","Frequency_Score","Monetary_Score"])
+    pred = kmeans.predict(data)
+    label = ['Loyal Customer','Champion','At Risk','New Customer']
+    return label[pred[0]]
+    
+
+
+col1,col2,col3 = st.columns(3)
+num1 = col1.number_input("Recency_Score (1-5):", min_value=1, max_value=5, step=1, value=1)
+num2 = col2.number_input("Frequency_Score (1-5):", min_value=1, max_value=5, step=1, value=1)
+num3 = col3.number_input("Monetary_Score (1-5):", min_value=1, max_value=5, step=1, value=1)
+
+value = ""
+if st.button(label="Predict"):
+    value = predict_rfm(num1,num2,num3)
+
+st.markdown(f"<span style='font-size:20px; font-weight:bold; font-style:italic'>{value}</span>",unsafe_allow_html=True)
+
+
+custom_colors = {
+    'Loyal Customers': '#99ff99',  
+    'Champions': '#66b3ff',        
+    'At Risk Customers': '#ff9999',  
+    'New Customers': '#ffcc99'     
+}
+
+figpx = px.scatter_3d(
+    rfm,
+    x='log_Recency',
+    y='log_Frequency',
+    z='log_Monetary',
+    color='Cluster Labels',  
+    color_discrete_map=custom_colors,  
+    labels={'log_Recency': 'Recency', 'log_Frequency': 'Frequency', 'log_Monetary': 'Monetary'},
+    title='Customer Segmentation Visualization'
+)
+st.plotly_chart(figpx)
+
+
+
+customers = rfm.shape[0]
+labels = ['Loyal Customers','Champions','At Risk Customers','New Customers']
+sizes = (rfm["Clusters"].value_counts()/customers)*100
+colors = ['#99ff99', '#66b3ff', '#ff9999', '#ffcc99']
+
+fig,ax = plt.subplots(figsize=(8,6))
+
+ax.pie(
+    sizes, labels=labels, colors=colors, autopct='%1.1f%%',
+    startangle=120, wedgeprops={'edgecolor': 'black'}
+)
+
+ax.set_title('Customer Segmentation', fontsize=14)
+ax.legend([0,1,2,3],title='Clusters',loc='best',)
+st.pyplot(fig)

customer_segmentation_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0dd6e91e9463737f0c56839ddfc1edd22dba75a921b5bf326a676b8587a2b2e7
+size 18547

requirements.txt

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+streamlit
+joblib
+scikit-learn==1.6.0
+matplotlib
+plotly