Added app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import datetime as dt
+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+
+# Set the page configuration
+st.set_page_config(page_title="Customer Segmentation", layout="wide")
+
+# Title and Description
+st.title("🛒 Advanced Customer Segmentation App")
+st.markdown("""
+    This application allows you to perform **Customer Segmentation** using RFM analysis and clustering. 
+    Upload your dataset, analyze the metrics, and visualize customer behaviors interactively.
+""")
+
+# Sidebar for uploading data
+st.sidebar.header("Upload Dataset")
+uploaded_file = st.sidebar.file_uploader("Choose a CSV file", type=["csv"])
+
+if uploaded_file:
+    # Load data
+    df = pd.read_csv(uploaded_file, encoding="ISO-8859-1", dtype={'CustomerID': str, 'InvoiceID': str})
+    st.sidebar.success("Dataset uploaded successfully!")
+else:
+    st.sidebar.warning("Please upload a CSV file to start!")
+    st.stop()
+
+# Data Cleaning and Preprocessing
+st.header("🧹 Data Cleaning and Preprocessing")
+
+# Create 'Amount' column
+df["Amount"] = df["Quantity"] * df["UnitPrice"]
+st.markdown("### Initial Data Preview")
+st.write(df.head())
+
+# Filter UK customers
+df = df[df["Country"] == "United Kingdom"]
+df = df[df["Quantity"] > 0]
+df.dropna(subset=['CustomerID'], inplace=True)
+df["InvoiceDate"] = pd.to_datetime(df["InvoiceDate"])
+df["date"] = df["InvoiceDate"].dt.date
+
+# Cleaned data preview
+st.markdown("### Cleaned Data Overview")
+st.write(df.describe())
+
+# Summary Statistics
+st.subheader("📊 Summary Statistics")
+metrics = {
+    "Number of Invoices": df['InvoiceNo'].nunique(),
+    "Number of Products Bought": df['StockCode'].nunique(),
+    "Number of Customers": df['CustomerID'].nunique(),
+    "Average Quantity per Customer": round(df.groupby("CustomerID").Quantity.sum().mean(), 0),
+    "Average Revenue per Customer (£)": round(df.groupby("CustomerID").Amount.sum().mean(), 2),
+}
+st.write(pd.DataFrame(metrics.items(), columns=["Metric", "Value"]))
+
+# Monthly Transactions Analysis
+st.subheader("📅 Monthly Transactions Analysis")
+df['month'] = df['InvoiceDate'].dt.month
+monthly_counts = df.groupby('month').size()
+
+# Plot using Plotly
+fig_monthly = px.bar(
+    monthly_counts,
+    x=monthly_counts.index,
+    y=monthly_counts.values,
+    labels={"x": "Month", "y": "Transactions"},
+    title="Transactions Per Month"
+)
+st.plotly_chart(fig_monthly)
+
+# RFM Analysis
+st.header("📈 RFM Analysis")
+
+# Recency Calculation
+now = pd.Timestamp("2011-12-09")
+recency_df = df.groupby("CustomerID")["date"].max().reset_index()
+recency_df["Recency"] = (now - pd.to_datetime(recency_df["date"])).dt.days
+
+# Frequency Calculation
+frequency_df = df.groupby("CustomerID")["InvoiceNo"].nunique().reset_index()
+frequency_df.rename(columns={"InvoiceNo": "Frequency"}, inplace=True)
+
+# Monetary Calculation
+monetary_df = df.groupby("CustomerID")["Amount"].sum().reset_index()
+monetary_df.rename(columns={"Amount": "Monetary"}, inplace=True)
+
+# Combine RFM
+rfm = recency_df.merge(frequency_df, on="CustomerID").merge(monetary_df, on="CustomerID")
+st.write("### RFM Data")
+st.write(rfm.head())
+
+# Visualize RFM Distributions
+fig_rfm = px.scatter_3d(
+    rfm,
+    x="Recency",
+    y="Frequency",
+    z="Monetary",
+    color="Monetary",
+    size="Monetary",
+    title="RFM Scatter Plot"
+)
+st.plotly_chart(fig_rfm)
+
+# K-Means Clustering
+st.header("📍 K-Means Clustering")
+st.sidebar.subheader("Clustering Parameters")
+num_clusters = st.sidebar.slider("Number of Clusters", 2, 10, value=4)
+
+kmeans = KMeans(n_clusters=num_clusters, random_state=42)
+rfm["Cluster"] = kmeans.fit_predict(rfm[["Recency", "Frequency", "Monetary"]])
+
+# Cluster Visualization
+fig_cluster = px.scatter_3d(
+    rfm,
+    x="Recency",
+    y="Frequency",
+    z="Monetary",
+    color="Cluster",
+    title=f"Customer Segmentation with {num_clusters} Clusters",
+    symbol="Cluster",
+    size="Monetary",
+)
+st.plotly_chart(fig_cluster)
+
+# Export Data
+st.header("📤 Export Processed Data")
+if st.button("Export RFM Data"):
+    rfm.to_csv("rfm_data.csv", index=False)
+    st.success("RFM data exported as `rfm_data.csv`!")
+
+st.markdown("### Enjoy exploring your customer data! 🚀")