This project focuses on predicting retail sales using historical sales data and time-series regression techniques. It leverages Python, Scikit-learn, and XGBoost to build predictive models capable of forecasting sales trends. The goal is to provide actionable insights to retailers for inventory and sales strategy planning.

This project implements a Convolutional Neural Network (CNN) using TensorFlow and Keras to classify handwritten digits from the MNIST dataset. The model learns to recognize digits (0–9) from grayscale images and achieves high accuracy on the test set.

This project forecasts daily sales for Rossmann stores using historical data, store metadata, and engineered features. We use the Random Forest Regression & XGBoost regression model to capture complex patterns and improve predictive accuracy.

This project performs classification of human activities (e.g., walking, sitting, standing) based on smartphone sensor data using machine learning models. The dataset consists of preprocessed accelerometer and gyroscope readings from 30 participants.

A machine learning project to predict the presence of heart disease using the Kaggle Heart Disease Dataset. The project uses data preprocessing, exploratory data analysis (EDA), feature engineering, and a Random Forest Classifier to achieve high prediction accuracy.

This project predicts whether a loan application will be approved or not using machine learning classification models. The dataset used is from Kaggle’s Loan Prediction problem. The goal is to build a robust model to assist banks or financial institutions in making automated loan approval decisions.