This project aims to develop a linear regression model to predict housing prices in California using the California Housing dataset. The project explores the impact of feature selection and scaling on the model's performance, with a focus on improving the accuracy of price predictions.

Regression using CNN 1D for House price prediction on California Housing Dataset

EDA and prediction of housing prices in California

California Housing Prices Prediction App

This is the experimental assignment of my course "Machine Learning and Data Mining", which requires completing the training, testing and evaluation of the linear regression model for house price prediction based on the California Housing Prices dataset

Python file for deploying the California Housing Price Prediction Streamlit Web App using AWS services

California housing price prediction with NN, Random Forest and Linear Regression

Capstone Project - California Housing Price Prediction: Used linear, DT, ensemble regression techniques (Random Forests), feature scaling and feature engineering using Principal component Analysis (PCA); achieved minimal RMSE with ensemble technique. Supervised learning, Machine Learning, Python, Jupyter Notebook.

This project focuses on predicting California housing prices using an XGBoost Regressor model trained on the California Housing dataset. The trained model is deployed via a Streamlit web application, allowing users to interactively input features and obtain price predictions.

This project provides a web application for predicting California housing prices using a trained machine learning model. The application allows users to input various features related to housing and get an estimated price prediction.

ML model for predicting California housing prices using gradient descent techniques, regularization, and early stopping. Includes a Flask-based web app with interactive UI for real-time predictions.

The California Housing Price Prediction project aims to leverage advanced machine learning techniques to forecast housing prices based on various features such as location, square footage, number of bedrooms and bathrooms, and other relevant factors. By analyzing historical data and patterns, the model intends to provide valuable insights into the

This project demonstrates the use of Explainable AI (XAI) techniques, specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), to explain the predictions of a machine learning model trained on the California and Boston Housing Prices dataset.

Linear Regression Model for California_Housing_Price_Prediction

This repository implements House Price Prediction Model for California housing dataset using XGBoost regression algortihm

Three PyTorch ML Projects: 1. MNIST Digit Classification (CNN) 2. IMDB Sentiment Analysis (Text Embedding + Feedforward) 3. California Housing Price Prediction (Regression NN). All scripts support GPU, include training, saving models.

⚡ Advanced ML Project: Real Estate Price Prediction This project uses a machine learning pipeline built with ZenML, MLflow, and Streamlit to predict California housing prices. It demonstrates end-to-end ML workflow orchestration, experiment tracking, and modern MLOps practices with future CI/CD integration.

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Regression Problem for Predicting House Price

Predicting median house values in Californian districts, given a number of features from these districts.

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This is a code i learned form Hands on machine learning with Scikit learn but i have added with some modifications for better looking

Housing Price Prediction for the California

ML - Project 1 - California Housing Price Prediction

California Housing Price Prediction with Linear Regression

California Housing Price Prediction using ML & Streamlit