AI / Data ScienceSHIPPED

Data Science with ML Classification Algorithms

Comparative study of classification algorithms (Decision Tree, KNN, Logistic Regression, SVM, XGBoost) for predictive data analysis.

Timeline

2023

Role

Data Scientist

Team

3 people

Source Code

Data Science with ML Classification Algorithms – screenshot 1

OVERVIEW

A comparative study of classification algorithms for predictive data analysis, covering Decision Tree, KNN, Logistic Regression, SVM, and XGBoost. The project evaluates and compares the performance of each algorithm on various datasets to determine the most effective approach for different classification scenarios. The work was implemented in Python and documented in source code, with visualizations such as the decision tree used to understand how each model makes its predictions.

THE CHALLENGE

Each algorithm has different strengths, so a fair comparison needed consistent data handling and evaluation.
The datasets differed in nature, so no single algorithm could be assumed to be the best choice.

THE SOLUTION

Prepared each dataset in a consistent way before training any model.
Implemented and trained five algorithms in Python: Decision Tree, KNN, Logistic Regression, SVM, and XGBoost.
Evaluated every model with the same evaluation approach so results can be compared directly.
Visualized models such as the decision tree to interpret results and shared the full code as source code.