House Price Prediction with Linear Regression

This project is a simple implementation of Linear Regression to predict house prices based on their size (in square meters).

Description

• The dataset (dataset/house_prices.csv) contains two columns:

  • size: size of the house in square meters
  • price: price of the house in dollars

• We train a linear regression model on this data to learn the relationship between house size and price.

• The user is prompted to input a house size, and the program predicts the expected price using the trained model.

Requirements

• Python 3.x
• pandas
• scikit-learn

You can install the required packages with:

pip install pandas scikit-learn

How to Run

Make sure the dataset CSV file is located at dataset/house_prices.csv.

When prompted, enter the size of the house in square meters, for example:

Enter with your house size m² that are you looking for: [INPUT_VALUE]

The program will output the predicted price formatted in USD currency, for example:

Regression results in price: $350,000.00

Code Explanation

We read the dataset using pandas.

Extract features (size) and labels (price).

Fit a LinearRegression model from scikit-learn.

Use locale to format the output price in US Dollar currency format.

The prediction is made based on user input.