🛍️ Shopping Intent Prediction

Predict whether a website visitor will make a purchase based on their browsing behavior.
This project walks through a full machine-learning pipeline inside the notebook:

shopping_intent.ipynb

It includes data cleaning, feature engineering, class-imbalance handling, model building, tuning, and evaluation.

🎯 Objectives

• Analyze user behavior patterns\
• Predict the target variable MadePurchase (Yes/No)\
• Resolve missing values and outliers\
• Handle class imbalance using SMOTE\
• Compare multiple ML models\
• Choose and tune the best performer

🧰 Tech Stack

• Python
• Pandas, NumPy
• Matplotlib / Seaborn
• Scikit‑learn
• Imbalanced‑learn (SMOTE)

📂 Project Structure

shopping_intent.ipynb   # Main notebook
README.md               # Project documentation

🔎 Workflow

1. Data Understanding & EDA\
2. Data Cleaning\
3. Feature Engineering\
4. Encoding & Scaling\
5. Class Balancing (SMOTE)\
6. Modeling\
7. Hyperparameter Tuning\
8. Final Model Selection

▶️ How to Run

Install dependencies:

pip install pandas numpy scikit-learn imbalanced-learn matplotlib seaborn

Open the notebook:

jupyter notebook shopping_intent.ipynb

📈 Results (Overview)

Gradient Boosting showed the strongest performance after handling imbalance and tuning.

🚧 Future Improvements

• Add explainability tools (SHAP)
• Deploy as API / dashboard
• Try more advanced ensemble models

Feel free to modify or extend!