PRODIGY_ML_01 - Linear Regression Model for House Price Prediction

This repository contains the code for implementing a Linear Regression model to predict house prices based on features such as square footage, number of bedrooms, and bathrooms, as part of my Prodigy InfoTech internship.

Task Overview

The objective of this task was to predict house prices using the Kaggle House Prices dataset. The model was implemented using Linear Regression, with a focus on:

• Data preprocessing
• Feature selection
• Model evaluation

Highlights

• Data Preprocessing: Cleaned the dataset, handled missing values, and scaled the features for consistency.
• Model: Built and trained a Linear Regression model to predict house prices.
• Performance Metrics: Evaluated the model using:
  • Mean Squared Error (MSE)
  • R-squared (R²)
• Visualization: Used scatter plots to compare actual vs predicted house prices.

Requirements

To run the project, make sure to have the following dependencies installed:

• Python 3.x
• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/Elysian0987/PRODIGY_ML_01.git
   cd PRODIGY_ML_01

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Download the Kaggle House Prices dataset (train.csv) from Kaggle.

2. Place the dataset (train.csv) in the root directory of the project.

3. Run the linear_regression_model.py script:

   python linear_regression_model.py

This will:

• Train the Linear Regression model on the data.
• Output the Mean Squared Error and R-squared values.
• Display visualizations comparing actual vs predicted prices.

Results

The model's performance will be displayed through evaluation metrics, and visual plots will showcase the relationship between predicted and actual house prices.