File size: 3,137 Bytes
26e2233 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
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.")
|