This repository contains three machine learning projects focused on different aspects of predictive modeling. Each project is implemented using linear regression and logistic regression and includes Google Colab notebook code for easy understanding and reproduction.
House Price Prediction: In this project, a machine learning model is developed to predict house prices. By utilizing linear regression techniques, the model analyzes various features of houses and learns patterns to estimate their prices accurately. The Google Colab notebook code provides detailed explanations and step-by-step instructions on how to train and evaluate the model.
Wine Quality Prediction: The Wine Quality Prediction project aims to determine the quality of wines based on specific attributes using linear regression. By training a machine learning model on a dataset comprising various wine characteristics, the model can predict the quality rating of a given wine sample. The Google Colab notebook code offers insights into the data preprocessing, model training, and evaluation stages.
Iris Flowers Classification: This project focuses on the classification of different species of iris flowers using logistic regression. By considering the length of petals and sepals, the machine learning model can identify the species accurately. The Google Colab notebook code provides comprehensive explanations of the data preprocessing, model training, and classification process.
All three projects are designed to demonstrate the application of regression in specific domains, showcasing how it can be used effectively for prediction tasks. By exploring the provided Google Colab notebook code, users can gain a deeper understanding of the implementation details and potentially apply similar techniques to their own projects.