Recommend products based on user behavior

Problem Description

Predict Price using User Behavior, Demographics, Purchase History

Source: direct_input
Original URL: N/A

Problem Understanding

The system analyzed the problem statement and identified it as a regression task with 80% confidence.

Problem Analysis:

• Task Type: Regression
• Confidence Level: 80%
• Key Features Identified: User Behavior, Demographics, Purchase History

Dataset

• Name: Predicting E-commerce User Repurchase Behavior
• Source: kaggle
• URL: https://www.kaggle.com/datasets/programmer3/predicting-e-commerce-user-repurchase-behavior

ML Solution Approach

The machine learning model solves this problem by:

1. Problem Formulation: Treats this as a regression task where the model learns to predict continuous numerical values.

2. Model Selection: After evaluating multiple algorithms, Unknown was selected as the best-performing model.

3. How It Works:

   • The model learns the relationship between input features and target values
   • It finds patterns and correlations in the training data
   • Given new data, it predicts a continuous numerical value
   • The model uses regression techniques to estimate outcomes

4. Solution Capability: The model can predict numerical values for new instances based on the relationships it learned during training.

Model Performance

• R² Score: 0.000
• Mean Squared Error: 804810834565716.500

Model Interpretability (SHAP)

This project includes SHAP (SHapley Additive exPlanations) outputs for global and local interpretability.

Top Features by Mean |SHAP|

• area: 24122075.130961
• floor: 5822495.339975
• district: 5781837.486989
• number_of_rooms: 5454021.861126
• quality: 3841310.538695
• Id: 3642154.996793
• year_of_construction: 2507355.508930
• structure_type: 2195602.974769

Generated SHAP Artifacts

• Summary plot: shap_summary.png
• Global bar importance: shap_bar.png
• Feature importances (JSON): feature_importance.json

Model Details

• Best Model: Unknown
• Framework: SCIKIT-LEARN
• Training Date: 2026-04-07
• Model File: regression_model_20260407_151308.pkl

Installation

pip install -r requirements.txt

Usage

Training

1. Place your dataset as data.csv
2. Update the target column name in train.py
3. Run:

python train.py

Prediction

1. Place new data as new_data.csv
2. Run:

python predict.py

Predictions will be saved to predictions.csv.

Limitations

• This is an automated solution and may require manual tuning
• Model performance depends on data quality
• Additional feature engineering may improve results

License

This project is generated by an automated ML pipeline. Use at your own discretion.

Generated By

Autonomous ML Automation Pipeline Generated on: 2026-04-07 15:20:13