Human Activity Recognition using Machine Learning

Developed a machine learning system for recognizing and classifying human activities using smartphone sensor data.

The project involved preparing and analyzing the dataset, applying data preprocessing and feature engineering techniques, training machine learning classification models, and evaluating their performance.

The goal was to build a reliable classification workflow capable of distinguishing between different human activities based on sensor measurements.

Technologies used:

Python, Pandas, NumPy, Scikit-learn, Machine Learning, Data Preprocessing, and Feature Engineering.

The project demonstrates the complete machine learning workflow from data preparation and exploration to model training and evaluation.