Machine learning using python

implementation of ridge and lasso regression from scratch

Implementation of different regression technique

R Data Mining and ML algo implementations(Learning Basics and notes)

Constructing a linear regression model which may help us predict the happiness score in the 155 countries.

In this project I used different regression algorithms to do regression to predict ticket price. I used Kaggles free GPUs and Machinehack Datasets in this project.

Comparing Ridge and LASSO model to find the best accuracy for Home Price Prediction

Developed a sophisticated Regression Model utilizing advanced algorithms like Linear Regression, Random Forest, to forecast Sales for historical sales data for 1,115 Rossmann stores in 7 European countries. Implemented various processing techniques, including Feature Scaling, Outlier Treatment, and Multicollinearity analysis, to improve accuracy.

IMDB Reviews Text Categorization Using Machine Learning Classifiers (Logistic Regression, Ridge Regression, Lasso Regression, Elastic Net Regression, KNN)

Machine Learning Regression model For Bike Sharing Demand Prediction

Simple Machine Learning Projects done for a Data Science Course

This repository contains code for predicting the price of mobile phones using regression models. It includes the implementation of various regression techniques such as Random Forest Regression, Decision Tree Regressor, Linear Regression, and Lasso Regression.

The objective of this study is to predict the max and min temp based on several parameters and represent it in a visual form.

This GitHub repository hosts the code and resources for predicting the closing prices of Yes Bank stock. Stock price prediction is a crucial task in the financial world, as accurate forecasts can assist investors and traders in making informed decisions.

Unsupervised and supervised learning algorithms performed on facial landmark coordinates data.