During my internship, I worked on a machine learning project to predict housing prices in California using various input features such as location, number of bedrooms, and number of bathrooms. To complete this project, I followed a structured process that involved framing the problem, obtaining and preprocessing the data, selecting and training a model, and launching and maintaining the model. I obtained the California Housing Price dataset, which contains information on more than 20,000 homes in the state, and used it to train a machine learning model. The model was able to learn from the data and make accurate predictions of housing prices based on the input features. The resulting model can be used to predict housing prices in California, and the code and resources for the project are available in this repository.
Table of Contents 1-Description 2-Installation 3-Usage 4-License
Description : During my internship, I was tasked with building a machine learning model to predict housing prices in California. I followed a structured process to complete this project, which involved framing the problem, obtaining and preprocessing the data, selecting and training a model, and launching and maintaining the model. This repository contains the code and resources for the resulting model, which can be used to predict housing prices in California based on various input features.
Installation : To use this project, you will need to install the following dependencies:
Python 3 NumPy SciPy scikit-learn You can install these dependencies using the following command:
pip install numpy scipy scikit-learn