This repository contains three data science projects that demonstrate the application of machine learning techniques to solve real-world problems. Each project includes a detailed description, objectives, data sources, steps taken, and outcomes achieved.

This project demonstrates a machine learning application for predicting the future sale price of bulldozers based on their characteristics and previous sales data. The project is structured to include data pre-processing, model building, training, evaluation, hyperparameter tuning, and deployment. The final model will be able to make predictions on test data and extract feature importance to understand the factors influencing the sale price of bulldozers.

This project focuses on building a classification model to predict the presence of heart disease in patients based on various health metrics. The project utilizes machine learning techniques, including logistic regression, decision trees, and ensemble methods, to classify patients into two categories: those with heart disease and those without. The model is trained on a dataset containing patient health records and evaluated for its accuracy and performance.

This project focuses on building a multi-class classification model to identify dog breeds from images. The project utilizes deep learning techniques, specifically convolutional neural networks, to classify images of dogs into their respective breeds. The model is trained on a large dataset of dog images and evaluated for its accuracy and performance.