This repository contains a machine learning algorithm that trains a model to predict house prices based on specified features of the homes, using the California Housing Dataset.
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Updated
Jul 13, 2023 - Jupyter Notebook
This repository contains a machine learning algorithm that trains a model to predict house prices based on specified features of the homes, using the California Housing Dataset.
Get started with Tensorflow/Keras API.
Create a platform that will predict a house price based on a user-input zip code and house type
Introduction to Machine Learning using data from the california_housing dataset.
This contains all the project in which i have used Simple Linear Regression to Predict output
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The "Linear Regression in Machine Learning using Python and Sklearn" article's source code
This repository contains a machine learning algorithm that trains a Random Forest model to predict house prices based on specified features of the homes, using the California Housing Dataset. The dataset used to train and evaluate the Random Forest model to predict median housing prices.
Computational Intelligence Course - Spring 2023
Machine Learning Python
This project is full scale end to end Machine learning project that used to predict the price of the california housing dataset
California housing price prediction with NN, Random Forest and Linear Regression
Build as part of "Building Your First scikit-learn Solution" Pluralsight course.
Analysis of the California Housing dataset using Python.
California Housing Price prediction with web-hosting using Heroku and scikit-learn for predicting.
California Housing Price Prediction - Linear Regression, Support Vector Regression, Decision Trees, and Random Forest Regression
Pattern Recognition assignment
Predicting California Housing Prices using Decision Tree Regressor
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