Get started with Tensorflow/Keras API.
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Updated
Sep 3, 2021 - Jupyter Notebook
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
🏡💲 Stochastic, full and mini-batch gradient descent for ridge regression using California Housing Dataset
The "Linear Regression in Machine Learning using Python and Sklearn" article's source code
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
Problem Statement The purpose of the project is to predict median house values in Californian districts, given many features from these districts. The project also aims at building a model of housing prices in California using the California census data. The data has metrics such as the population, median income, median housing price, and so on …
Computational Intelligence Course - Spring 2023
My Learning Journey
Machine Learning 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
Build as part of "Building Your First scikit-learn Solution" Pluralsight course.
Pattern Recognition assignment
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
Predicting California Housing Prices using Decision Tree Regressor
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