The story
Developed supervised learning pipelines in Python to solve both
01
regression
and
02
classification
problems using real datasets. This project demonstrates the ability to preprocess structured data, build scikit-learn pipelines, train multiple models, and evaluate predictive performance with appropriate metrics.
Stack: Python, scikit-learn, pandas, NumPy, Matplotlib, Seaborn.
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Key Contributions
04
Regression Task (Customer Attributes Dataset)
- Target: Customer age prediction from demographic and behavioral features.
- Implemented preprocessing with SimpleImputer, StandardScaler, and OneHotEncoder.
- Trained Linear Regression and Random Forest Regressor, compared performance with MSE and R².
05
Classification Task (Sports Betting Dataset)
- Target: Match outcome prediction (Actual Winner vs Predicted Winner).
- Features: Team names, betting odds, draw odds, etc.
- Built pipelines with categorical encodings and scaling.
- Trained Decision Tree and Logistic Regression classifiers, evaluated with accuracy, precision, and recall.
06
Skills Demonstrated
- Machine Learning (supervised regression & classification)
- Data preprocessing (handling missing values, categorical encoding, scaling)
- Pipeline construction with ColumnTransformer and Pipeline
- Model evaluation (MSE, R², accuracy, precision, recall)
- Python ML stack: scikit-learn, pandas, numpy, matplotlib, seaborn
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