🛒 E-Commerce Purchase Prediction

📌 Problem Statement

An e-commerce company wants to predict whether a visitor will make a purchase based on their browsing session behavior. The goal is to build a Machine Learning model that can classify sessions into purchase (Revenue = True) or no purchase (Revenue = False).

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📊 Dataset

• ~12,330 user sessions
• Mix of numerical and categorical features
• Target variable: Revenue

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⚙️ Tech Stack

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn

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🔍 Approach

1. Exploratory Data Analysis (EDA)

• Checked data distribution
• Identified class imbalance
• Visualized key features

2. Data Preprocessing

• Handled categorical variables
• Feature transformations
• Prepared dataset for model training

3. Model Building

• Decision Tree Classifier
• Applied pruning to avoid overfitting
• Handled class imbalance using class weights
• Hyperparameter tuning using GridSearchCV

4. Evaluation

• Metric used: F1 Score (due to imbalanced dataset)

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🌳 Model Details

• Algorithm: Decision Tree Classifier

• Techniques used:

  • Pruning (max_depth, min_samples_split)
  • Class balancing
  • Hyperparameter tuning (GridSearchCV)

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📈 Results

• Achieved F1 Score: ~0.55+ (target met)

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📂 Project Structure

ECOMMERCE-PURCHASE-PREDICTION/
│
├── data/
│   └── raw/
│        └── shop_smart_ecommerce.csv   # Original dataset
│
├── models/
│   └── best_model.pkl           # Saved trained model
│
├── notebooks/
│    └── Ecommerce_P...ipynb      # Main notebook (EDA + training)
│
├── src/
│   ├── evaluate.py              # Model evaluation (F1, metrics)
│   ├── preprocess.py            # Data preprocessing
│   ├── train.py                 # Model training
│   └── utils.py                 # Helper functions
│
├── .gitignore
├── main.py                      # Entry point script
├── README.md
├── requirements.txt

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🚀 How to Run

1. Clone the repository

git clone https://github.com/your-username/ecommerce-purchase-prediction.git
cd ecommerce-purchase-prediction

2. Install dependencies

pip install -r requirements.txt

3. Run the project

python main.py

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📌 Author

Abhay Singh