app.py

import streamlit as st
import pandas as pd
import joblib
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import LabelEncoder

st.title("🛒 Online Shopping Recommendation System")

# Load dataset
csv_path = "file.csv"
df = pd.read_csv(csv_path)

# Handle categorical columns by encoding them
label_encoders = {}
for col in df.select_dtypes(include=['object']).columns:
    le = LabelEncoder()
    df[col] = le.fit_transform(df[col])
    label_encoders[col] = le

# Select relevant features
features = ['Avg_Price', 'Delivery_Charges', 'Discount_pct', 'Online_Spend', 'Offline_Spend', 'Tenure_Months']
target = 'Coupon_Status'

df = df.dropna()  # Remove missing values
X = df[features]
y = df[target]

# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train Decision Tree model
model = DecisionTreeClassifier()
model.fit(X_train, y_train)

# Save the model
joblib.dump(model, "decision_tree_model.pkl")

# Streamlit app with three tabs
tab1, tab2, tab3 = st.tabs(["📊 Dataset & Summary", "📈 Data Visualization", "🔍 Prediction"])

# Tab 1: Dataset & Summary
with tab1:
    st.subheader("Dataset Overview")
    st.write(df)  # Show full dataset
    st.write("### Summary Statistics")
    st.write(df.describe())

# Tab 2: Data Visualization
with tab2:
    st.subheader("📊 Correlation Matrix")
    fig, ax = plt.subplots(figsize=(10, 6))
    sns.heatmap(df.corr(), annot=True, cmap="coolwarm", fmt=".2f", ax=ax)
    st.pyplot(fig)

    st.subheader("📊 Pairplot")
    pairplot_fig = sns.pairplot(df[features])
    st.pyplot(pairplot_fig)

    st.subheader("📊 Feature Importance (Decision Tree)")
    feature_importance_fig, ax = plt.subplots()
    feature_importances = pd.Series(model.feature_importances_, index=features)
    feature_importances.nlargest(6).plot(kind='barh', ax=ax)
    st.pyplot(feature_importance_fig)

# Tab 3: Prediction
with tab3:
    st.subheader("🔍 Make a Prediction")

    # User inputs
    avg_price = st.number_input("Average Price of Product", min_value=0.0, step=1.0)
    delivery_charges = st.number_input("Delivery Charges", min_value=0.0, step=0.5)
    discount_pct = st.number_input("Discount Percentage", min_value=0.0, max_value=100.0, step=1.0)
    online_spend = st.number_input("Online Spend", min_value=0.0, step=10.0)
    offline_spend = st.number_input("Offline Spend", min_value=0.0, step=10.0)
    tenure = st.number_input("Tenure in Months", min_value=0, step=1)

    if st.button("Predict Coupon Usage"):
        # Load trained model
        model = joblib.load("decision_tree_model.pkl")
        
        # Make prediction
        prediction = model.predict([[avg_price, delivery_charges, discount_pct, online_spend, offline_spend, tenure]])
        
        # Display result
        if prediction[0] == 1:
            st.success("The customer is likely to use the coupon! 🎉")
        else:
            st.warning("The customer may not use the coupon.")