Sahaj-26 / House-Price-Prediction

The project aims to predict house prices in California based on various features using machine learning techniques. It uses the California housing dataset, comprising 20640 data entries and 8 attributes, with the target being the house price.

California House Price Prediction Model

The problem that we are going to solve here is that given a set of features that describe a house in California, our machine learning model must predict the house price. To train our machine learning model with California housing data, we will be using scikit-learn’s fetch_california_housing dataset.

In this dataset, each row describes a california town or suburb. There are 20640 rows and 8 attributes (features) with a target column (price). https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.names

Prerequisites

• You need to have Python 3 and the required dependencies installed on your system to run this script.
• Code is written in Jupyter Notebook