House Price Prediction Using C++

This project implements a house price prediction system using a custom Linear Regression model in C++ and a data cleaning pipeline in Python.

Project Overview

• Data Cleaning:
  The clean.py script processes the raw dataset (data.csv) by selecting relevant features, removing missing values and duplicates, and saving the result as data_cleaned.csv.

• Model Training:
  The LinearRegression.cpp file contains the C++ implementation of the Linear Regression algorithm. It reads the cleaned dataset, applies a standard scalar (centering each feature and target variable by subtracting the mean and scaling by the standard deviation), trains the model, and evaluates predictions.
  Additionally, it provides a custom train_test_split function to split the dataset into training and testing sets.

Project Structure

• data.csv : Raw dataset with housing information.
• clean.py : Python script for data preprocessing.
• data_cleaned.csv : Clean dataset generated from clean.py.
• LinearRegression.cpp: C++ source code implementing the Linear Regression model.
• README.md : Project documentation.

Requirements

Python

• Python 3.x with the pandas library.
• Install dependencies with:

  pip install pandas

C++

• A C++ compiler supporting C++11 or later (e.g., g++).
• Standard C++ libraries.

Build and Run Instructions

1. Data Cleaning

Run the following command to clean the dataset:

python3 clean.py

This generates data_cleaned.csv which is required by the C++ model.

2. Compile and Run the C++ Model

Compile the C++ source code using:

 g++ HousePricePrediction.cpp DataPreprocessing.cpp LinearRegression.cpp -o ./out

Then execute the model with:

./out

Notes

• Ensure that data_cleaned.csv is available before running the C++ application.
• Adjust the parameters in the source files as needed.
• For any issues during compilation or execution, verify that all dependencies are properly installed.

License

This project is licensed under the Apache License. See the LICENSE file for details.