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app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import joblib
+import matplotlib.pyplot as plt
+import plotly.express as px
+
+st.title("Customer Segmentation")
+
+kmeans = joblib.load("kmeans.pkl")
+scaler = joblib.load("scaler.pkl")
+rfm = pd.read_csv("transformation.csv")
+
+cluster_label = {0: 'Loyal Customers', 1: 'At Risk', 2: 'Champions', 3: 'New Customers'}
+
+def customer_segmentation(num1,num2,num3):
+  print("Customer Segmentation")
+  data_recency = np.log1p(num1)
+  data_frequency = np.log1p(num2)
+  data_monetary = np.log1p(num3)
+  data = pd.DataFrame({'Recency': [data_recency], 'Frequency': [data_frequency], 'Monetary': [data_monetary]})
+  X_data = scaler.transform(data)
+  pred = kmeans.predict(X_data)
+  return cluster_label[pred[0]]
+
+col1,col2,col3 = st.columns(3)
+num1 = col1.number_input("Enter Recency",min_value=1,max_value=400,step=1)
+num2 = col2.number_input("Enter Frequency",min_value=1,max_value=6000,step=1)
+num3 = col3.number_input("Enter Monetary",min_value=1,step=10)
+
+value = ""
+if st.button(label="Predict"):
+    value = customer_segmentation(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': '#ff9999',
+    'New Customers': '#ffcc99'
+}
+
+figx = px.scatter_3d(
+    rfm,
+    x='Recency',
+    y='Frequency',
+    z='Monetary',
+    color='Cluster Labels',
+    color_discrete_map=custom_colors,
+    labels={'Recency': 'Recency', 'Frequency': 'Frequency', 'Monetary': 'Monetary'},
+    title='Customer Segmentation Visualization'
+)
+st.plotly_chart(figx)
+
+
+
+customers = rfm.shape[0]
+labels = ['Loyal Customers','At Risk','Champions','New Customers']
+sizes = (rfm["Cluster"].value_counts()/customers)*100
+colors = ['#99ff99', '#ff9999', '#66b3ff', '#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)
+

kmeans.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c89a3fc7ba1113d2bd13f4b863bff32e0ebddf5b847ce4524be22f9fdd7165c7
+size 17935

requirements.txt

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

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5fa6bf6808429bb53d112ac9385ef96b90bdda1ac8e899454d5ebe798ed0f04a
+size 1007