This contains all the project in which i have used Simple Linear Regression to Predict output

My Learning Journey

ML, NN, NLP, ARIMA, clustering, classification, mapping

The "Linear Regression in Machine Learning using Python and Sklearn" article's source code

Pattern Recognition assignment

California Housing Price Prediction - Linear Regression, Support Vector Regression, Decision Trees, and Random Forest Regression

Create a platform that will predict a house price based on a user-input zip code and house type

Computational Intelligence Course - Spring 2023

Introduction to Machine Learning using data from the california_housing dataset.

California Housing Price prediction with web-hosting using Heroku and scikit-learn for predicting.

This project is full scale end to end Machine learning project that used to predict the price of the california housing dataset

Predicting California Housing Prices using Decision Tree Regressor

California house price prediction is done in this notebook

Build as part of "Building Your First scikit-learn Solution" Pluralsight course.

This is an educational workthrough project from the book "Hands-On ML with Scikit-Learn, Keras and TensorFlow" by Aurélien Géron. It is based on the well-known "California Housing Prices" dataset - through feature engineering I successfully improved the performance of the model used in the book.

How to train a XGBoost regression model on Amazon SageMaker and host inference as an API on a Docker container running on AWS App Runner.

How to train a XGBoost regression model on Amazon SageMaker, host inference on a Docker container running on Amazon ECS on AWS Fargate and optionally expose as an API with Amazon API Gateway.

How to train a XGBoost regression model on Amazon SageMaker, host inference on a serverless function in AWS Lambda and optionally expose as an API with Amazon API Gateway

Get started with Tensorflow/Keras API.