House Price Prediction Model Readme Overview This repository contains code for a house price prediction model based on a regression model. The model is designed to predict the price of houses based on various features such as size, number of bedrooms, location, etc.
Model Architecture The house price prediction model is based on a regression model. Regression analysis is a statistical technique that models the relationship between a dependent variable (in this case, house price) and one or more independent variables (features). The model learns from the training data to make predictions on new data.
Dataset The dataset used for training the model consists of historical information about houses, including features such as:
Size (in square feet) Number of bedrooms Number of bathrooms Location (e.g., city, neighborhood) Age of the house Additional features (e.g., garage, swimming pool) The dataset also includes the corresponding prices of the houses.
Dependencies To run the code for this project, you'll need the following dependencies:
Python 3.x NumPy Pandas Scikit-learn Matplotlib (for data visualization)