classification problem

Project Overview:

Built a machine learning model to classify data samples into categories using ensemble methods, improving accuracy and robustness compared to single models.

Key Steps:

Data Preparation – Cleaned and preprocessed the dataset, handled class imbalance, and performed feature scaling/encoding.

Exploratory Analysis – Identified key patterns and feature importance through visualization and correlation analysis.

Modeling – Implemented ensemble techniques:

Bagging (Random Forest) for reducing variance.

Boosting (XGBoost, AdaBoost) for improving weak learners.

Stacking to combine multiple classifiers for optimal performance.

Evaluation – Compared models using accuracy, precision, recall, F1-score, and ROC-AUC.

Results – Stacking ensemble achieved the highest classification accuracy and strong generalization across test data.

Tech Stack:

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

Impact:

The ensemble approach produced a robust classification system, showcasing the value of combining models for real-world decision-making.