This project involves predicting height based on age using polynomial regression in Jupyter Notebook. Polynomial regression is a variation of linear regression that models the relationship between the independent variable (age) and the dependent variable (height) as an nth-degree polynomial.

Quick analysis on multivariate regression using PCA and R2 score to select variables.

This project addresses the challenge of credit card fraud detection using machine learning techniques to identify suspicious transactions

House Price prediction in US

A machine learning project for predicting credit card approvals using logistic regression.

Predicting the popularity of a song based off of its audio features using data collected from Spotify

In this notebook, it will be assumed the role of a Data Analyst working at a real estate investment trust organization . It will be given a dataset containing detailed information about house prices in the region based on a number of property features. The job will be to analyze and predict the market price of houses given that information

Predicts ice cream sales based on temperature using Polynomial Regression. The model captures the non-linear relationship between temperature and sales, achieving R² ≈ 0.94 (train) and 0.89 (test). Includes data preprocessing, model training , Evaluation, and interactive results visualization.

BNA(Basketball dNA) is an machine learning project analyzing NBA player statistics and physical data to predict optimal basketball positions (PG-C) as part of a highschool course.

A little experiment to assest linear and polynomial decision regions in logistic regression.

Heart Risk Level Predicting Regression Model & Web using Feature Engineering and Data Preprocessing 🐤

Logistic Regression and Optimization (Tutorial)

Demonstration of how to use correlation matrices to pick the best variables for polynomial regression.

Linear Regression experiments on the California Housing dataset across five phases, using NumPy and scikit-learn only (no pandas). Includes EDA, polynomial features, SGD with scaling, residuals, 5-fold CV, and an LNCS-style report with figures.

Analysis and price prediction of house sales in King County, Seattle between May 2014 and May 2015. Explores key features such as square footage, grade, and waterfront views using Linear Regression, Ridge Regression, and Polynomial Features within a scikit-learn pipeline.