California Houses Prices prediction app using Scikit-learn served as a Flask web app.

Predict California housing prices using Linear Regression. Includes full EDA, data preprocessing, model training, evaluation (MSE, MAE, R²), and visualization. Make predictions for new houses based on features like location, rooms, income, and ocean proximity.

Predict houses prices with python by Machine learning techniques (Linear regression, Random forest )

Prediction Prices of Houses in different regions of California

this repository contains the prediction model of houses prices in California

This dataset provides a concise overview of California housing data. Through an analysis of this dataset, our primary objective is to estimate the expected prices of houses in California. The fundamental goal of working with this dataset is to make predictions regarding the median house prices in the state.

In this project I performed feature engineering and used Ridge and Lasso models for prediction of sales prices for houses in California also metric submissions are evaluated on Mean-Squared-Error, Root-Mean-Squared-Error, Mean Absolute Error, R2 (MSE, RMSE, MAE, R2)

This data is about California Housing Prices And you may see a lot of columns here, so these columns are about details about group of houses in a block Let's assume there are 100 blocks in a city So for each block the've calculated some statistical features out of a block median_income: Such as Median(statistics) of Income of people living a block median_house_value: Median(statistics) price of all the houses in a block. So, by using Univariate Linear Regression I have analysed the data and found the best fitting curve which would give the actual prediction price of the houses.