Real Estate Price Prediction

This project predicts real estate prices using a linear regression model. It combines several datasets, cleans and preprocesses the data, and trains a machine learning model to provide accurate predictions. The project is modular and easy to extend, ensuring maintainability and scalability.

Project Structure

├── data/ # Folder for raw and processed datasets ├── graphs/ # Folder for generated plots ├── main.py # Main script to execute the pipeline ├── cleaning_datasets.py # Module for data cleaning and merging ├── cleaning_feature_engineering.py # Module for feature engineering ├── linear_regression_model.py # Module for training and evaluating the model ├── requirements.txt # List of dependencies └── README.md # Project documentation

bash Copier le code

How to Use

1. Setup

1. Clone the repository:

   git clone https://github.com/your-repo-name/real-estate-price-prediction.git
   cd real-estate-price-prediction

Install the required dependencies: bash Copier le code pip install -r requirements.txt Ensure the raw datasets are in the data/ directory. 2. Run the Pipeline Execute the pipeline by running:

bash Copier le code python main.py This script performs the following steps:

Merges and cleans the datasets. Preprocesses the data (handles outliers, missing values, and categorical variables). Trains a linear regression model. Saves cleaned data, metrics, and visualizations. 3. Outputs Processed Dataset: data/dataset-preprocessed.csv Model Evaluation: Metrics printed in the console. Visualization: A scatter plot comparing predicted and real values saved in the graphs/ folder. 4. Example Dataset Ensure your raw datasets have columns like:

Price, Property type, Living area, Building condition, Zip code, and more. Refer to the project scripts for detailed dataset expectations.

Modules Overview

1. cleaning_datasets.py Handles merging and cleaning of multiple datasets, renames columns, and ensures consistent formatting.

2. cleaning_feature_engineering.py Includes functions for handling outliers, replacing missing values, and transforming categorical variables into numerical data.

3. linear_regression_model.py Implements a linear regression model, evaluates it using metrics (MAE, RMSE, MAPE, R²), and generates visualizations.

Dependencies Python 3.8+ pandas numpy scikit-learn matplotlib For detailed dependencies, see requirements.txt.