🏠 Task 01: House Price Prediction using Linear Regression

📈 Predicting House Prices with Linear Regression

A machine learning project that predicts residential house prices using a multiple linear regression model based on features such as square footage, number of bedrooms, and total bathrooms.

Before execution, install the required dependencies locally. This repository does not include the virtual environment folder, so create and activate your own environment before running the project.

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📌 Overview

This project implements a machine learning solution for estimating property values from structured housing data. It is designed to learn patterns from historical sales data and predict the sale price of a house based on essential property attributes.

The workflow includes:

• loading the dataset
• selecting relevant features
• engineering a combined bathroom metric
• training a linear regression model
• evaluating the model with metrics such as R² and RMSE
• generating submission-ready predictions
• visualizing actual versus predicted prices

This project was developed as Task 01 of the SkillCraft Technology Machine Learning Internship.

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🛠️ Technologies Used

• Python 3
• Pandas
• NumPy
• Scikit-Learn
• Matplotlib
• CSV dataset processing
• Jupyter / Python script execution

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📂 Project Structure

SCT_ML_1/
├── .gitignore                
├── .venv/                      # Local virtual environment(created from steps below)
├── README.md                   # Project documentation
├── requirements.txt            # Python dependencies
├── linear_regression.py        # Main ML script
├── actual_vs_predicted.png     # created as ouput
├── submission_task1.csv        
├── data/
   ├── train.csv              
   ├── test.csv               
   ├── sample_submission.csv  
   └── data_description.txt   

💻 How to Run

Prerequisites

• Python 3.10 or later
• pip package manager

Step 1: Create a virtual environment

python -m venv .venv

Step 2: Activate the environment

Windows (PowerShell):

.\.venv\Scripts\Activate.ps1

Windows (Command Prompt):

.venv\Scripts\activate.bat

Step 3: Install dependencies

pip install -r requirements.txt

Step 4: Run the project

python linear_regression.py

Step 5: View outputs

The script generates:

• submission_task1.csv
• actual_vs_predicted.png

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🎯 Learning Outcomes

• Understand the workflow of a regression-based machine learning project
• Learn how to clean and prepare structured housing data
• Apply feature engineering for more meaningful prediction inputs
• Train and evaluate a Linear Regression model
• Interpret evaluation metrics like R² Score and RMSE
• Save predictions in a Kaggle-style CSV format
• Visualize the relationship between actual and predicted prices

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📸 Project Preview

This project focuses on predicting household prices using features like:

• Living area in square feet
• Number of bedrooms
• Number of bathrooms
• Sale price as the target variable

A sample visualization, actual_vs_predicted.png, shows how closely the model's predictions match the actual prices.

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👨‍💻 Author

Author: Devarakonda Vignesh Varsha
Project Type: Machine Learning Internship Task

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📌 Internship Details

• Internship: SkillCraft Technology
• Domain: Machine Learning
• Task: Task 01 - House Price Prediction
• Objective: Build a Linear Regression model to predict house prices using relevant property features

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⭐ This project demonstrates a practical machine learning workflow for tabular data and highlights the value of regression models in real-world price prediction tasks.