AI and ML

An app which can predict the prices of house based on training data and certain input parameters, deployed on Heroku.

I have applied the fundamental idea of Linear Regression with Single Variable input. I implemented the Gradian Descent algorithm simply from scratch with no libraries such as Scikit-Learn. I just used NumPy.

Detecting Payment Card Fraud -supervised learning using SageMaker built-in LinearLearner estimator

Demonstration of asymmetric cost functions in Linear Regression

Here the basic of the the Linear regression problem .

Regression is one of the foundational techniques in Machine Learning. Being one of the most well-understood algorithms, beginners always struggle to understand some fundamental terminology related to regression. In this series of projects, I will try to give you basic ideas of underlying concepts with the help of practical examples. If you are s…

📚📝 Types of Ml algos

Analyzing prototype suspension coils data for production troubles.

Stock price prediction is a crucial aspect of financial markets, involving the use of mathematical models and machine learning techniques to forecast the future price movements of stocks or financial assets.

EDA analysis and a couple of models from classical machine learning on actual data as of 12.07.2023 about video games. Dataset from kaggle link in readme.

Simple salary prediction using machine learning models.

This repository contains a machine learning model designed to predict car prices based on various features. Using historical data on car attributes such as make, model, year, mileage, and other relevant factors, the model aims to provide accurate and reliable price estimates for used cars.

Statistical Methods using R Programming

Codes and Project for Machine Learning

R programming

Pysprak

Linear Regression Multiple Variables : Sample problem of predicting home price in monroe, new jersey