Analyzed 5,000+ movies with Pandas and Colab to build a machine learning model predicting movie revenue.

This repository explores the analysis and prediction of financial time series data using various machine learning and deep learning techniques. The project focuses on understanding historical index data, extracting meaningful features, and applying regression models and deep learning architectures for forecasting

Testing out ClearML.

Data Science Project (Classification via KNN & SVM M8)

This project aims to provide a comprehensive analysis of three different machine learning problems: classification, clustering, and regression. By utilizing publicly available datasets, we explore essential steps in machine learning workflows, including data preprocessing, feature selection, model training, and evaluation. The purpose is to showcas

A fully modular end-to-end Machine Learning pipeline for Iris classification. Includes data preparation, scaling, model training, evaluation, performance comparison, and a CLI-based prediction system using Logistic Regression, Decision Tree, and SVC. Designed as a clean template for beginners to understand core ML workflow.

Chicago real estate prediction project using machine learning, including data analysis, feature engineering, and model evaluation.

Healthcare Classification Problem

Built an end-to-end Customer Churn Prediction System using ML, achieving 80%+ accuracy with XGBoost. Project includes complete data cleaning, feature engineering, model comparison, & performance evaluation.Key churn drivers such as tenure, monthly charges, internet service, & contract duration were identified through EDA & PowerBI visual insights.

Binary classification model predicting loan approval outcomes using logistic regression