MLPipelineOptimizationStudy: End-to-End ML Pipeline Exploration

MLPipelineOptimizationStudy is a rigorous, reproducible investigation into the art of building high-performance machine learning pipelines. Each stage of the pipeline — from raw data to final predictions — is analysed independently and in combination to understand where gains are achieved.

Pipeline Stages Covered

Data Preprocessing

• Missing value imputation strategies (mean, median, KNN, iterative).
• Outlier detection and treatment.
• Class imbalance handling (SMOTE, class weights).
• Feature scaling (Standard, MinMax, Robust).

Feature Engineering

• PCA for dimensionality reduction with explained-variance analysis.
• t-SNE for high-dimensional data visualisation.
• Manual feature construction and selection (correlation, mutual information, RFE).

Model Selection

Systematic comparison of:

• Logistic Regression, Kernel SVM
• Random Forest, XGBoost, LightGBM, CatBoost
• Voting Ensembles (hard and soft)

Hyperparameter Tuning

• GridSearchCV and RandomizedSearchCV with cross-validation.
• Analysis of resource-accuracy trade-offs across search strategies.

Technology

Python, scikit-learn, XGBoost, LightGBM, CatBoost, Matplotlib, Seaborn. All experiments in reproducible Jupyter Notebooks.