"A set of Jupyter Notebooks on feature selection methods in Python for machine learning. It covers techniques like constant feature removal, correlation analysis, information gain, chi-square testing, univariate selection, and feature importance, with datasets included for practical application.

🚀Implementation of Logistic Regression and Linear Regression in Python for Classification Problems🏗

Data scraped from various sites for housing data around the greater Toronto area (GTA). Scrapes happen daily and data is in both JSON and CSV formats. Free to use for analysis.

This repository contains a collection of machine learning models and notebooks, primarily focused on the housing dataset. It includes implementations of linear regression, logistic regression, feature scaling techniques, and gradient descent using scikit-learn. Additionally, it features learnings from the University of Washington's ML Specializatio

Housing Price Prediction using Advanced Regression (Hyperparameter tuning)