🏠 House Price Prediction Project (BP Course)

📌 Overview

This repository contains the final project for the Basics of Programming (BP) course. The project is implemented in C language using a modular and structured programming approach.

The main goal of this project is to analyze house-related data and predict house prices using statistical analysis and basic machine learning algorithms implemented from scratch, without using external libraries.

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🎯 Objectives

The objectives of this project are:

• Learning modular programming in C
• Working with CSV files and file I/O
• Dynamic memory management
• Data preprocessing and cleaning
• Exploratory Data Analysis (EDA)
• Implementing Linear Regression and Gradient Descent
• Understanding error metrics
• Teamwork using Git & GitHub
• Writing clean, maintainable, and documented code

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🧠 Features

• CSV data loading and parsing
• Handling missing values:
  • Mean Imputation
  • Median Imputation
  • Mode Imputation
• Statistical analysis (mean, min, max, etc.)
• Feature normalization (Min-Max Scaling)
• Linear Regression (analytical solution)
• Gradient Descent with adjustable learning rate
• Error evaluation:
  • MSE
  • MAE
  • MAPE
• Data visualization using Gnuplot
• Fully modular C project structure

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📂 Project Structure

bp-project/
│
├── data/
│ ├── raw/ # Original CSV files
│ └── processed/ # Preprocessed data
│
├── include/ # Header files (.h)
│ ├── config.h
│ ├── csv.h
│ ├── feature_engineering.h
│ ├── impute.h
│ ├── linear_regression.h
│ ├── metrics.h
│ ├── outlier.h
│ ├── plot.h
│ ├── scaling.h
│ ├── split.h
│ ├── stats.h
│ └── utils.h
│
├── src/ # Source files (.c)
│ ├── csv_numeric.c
│ ├── csv_ohe.c
│ ├── csv.c
│ ├── feature_engineering.c
│ ├── impute.c
│ ├── linear_regression.c
│ ├── main.c
│ ├── metrics.c
│ ├── outlier.c
│ ├── plot.c
│ ├── scaling.c
│ ├── split.c
│ ├── stats.c
│ └── utils.c
│
├── plots/ # Gnuplot scripts & outputs (.gp)
│
├── Makefile
└── README.md

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⚙️ Build & Run Guide

🔧 Requirements

• GCC Compiler
• Make
• Gnuplot (optional, for plotting)

⚠️ On Windows, it is recommended to use MinGW-w64 with make installed.

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🛠 Compile the Project

From the project root directory, run:

make

or

mingw32-make

This command: -Compiles all source files -Links object files -Produces the final executable

▶️ Run the Program

./project

(On Windows:)

project.exe

🧹 Clean Build Files

make clean

or

mingw32-make clean

This removes:

• Object files (.o)
• Executable files

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🔄 Teamwork & Git Workflow

This project is developed collaboratively using GitHub.

Workflow Rules:

• Each feature is implemented in a separate branch
• No direct commits to main
• Pull Requests are used for merging
• Small and meaningful commits are encouraged Example:

git checkout -b csv-module
git commit -m "Implement CSV file loader"
git push origin csv-module

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📊 Data Visualization

Gnuplot is used to visualize relationships between features such as:

• House size vs price
• Normalized vs raw data Plots are generated using data files exported from the program.

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📚 Course Information

• Course Name: Basics of Programming (BP)
• Programming Language: C
• Academic Level: Undergraduate
• Field: Computer Science / Software Engineering

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✨ Authors

• MohammadAmin Babaie
• Project Team Members

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🏁 Final Notes

This project focuses on learning core programming concepts, not using ready-made libraries. All algorithms and data processing techniques are implemented manually to ensure a deep understanding of the fundamentals.