House Prices - Advanced Regression Techniques

Overview

Welcome to the repository for the Kaggle "House Prices - Advanced Regression Techniques" competition. This project aims to predict the sale prices of homes based on a comprehensive set of features. The repository is organized to help you understand the data, preprocess it, build and evaluate models, and conduct research for improving predictions.

Kaggle Notebook Solution

Table of Contents

• Project Structure
• Data Preprocessing
• Dataset
• Helpers
• Main Script
• Models
• Research
• Acknowledgments

Project Structure

This project is organized into the following key components:

• config.py: Contains configuration files for setting up environment variables and parameters.
• data.py: Includes raw and processed datasets.
• data_preprocessing.py: Scripts for cleaning, transforming, and preparing data for modeling.
• dataset.py: Contains the raw dataset files used in the competition.
• helpers.py: Utility functions for various tasks such as data loading, feature engineering, and model evaluation.
• main.py: The main script for running the entire pipeline from data preprocessing to model training and evaluation.
• models.py: Scripts and notebooks related to model building, training, and evaluation.
• research.ipynb: Jupyter Notebooks for exploratory data analysis, feature engineering experiments, and model research.

Data Preprocessing

The datapreprocessing.py directory contains scripts that handle data cleaning, feature engineering, and transformation. Key preprocessing steps include:

1. Handling Missing Values: Imputation strategies for different types of missing data.
2. Feature Engineering: Creating new features based on domain knowledge and exploratory data analysis.
3. Data Normalization: Scaling numerical features and encoding categorical variables.

Dataset

The dataset.py directory contains the raw data files provided for the competition. The primary dataset files are:

• train.csv: Training data including both features and target values.
• test.csv: Test data with features only for which predictions are required.

Helpers (helpers.py)

The helpers.py file contains utility functions used across the project. These functions include:

• Data Loading: Functions to read and preprocess data files.
• Feature Engineering: Functions for creating and transforming features.
• Model Evaluation: Functions to evaluate model performance using metrics like RMSE.

Main Script (main.py)

The main.py script is the entry point for running the entire pipeline. It includes:

• Data Loading: Reading data from CSV files.
• Data Preprocessing: Applying transformations and feature engineering.
• Model Training: Training various regression models.
• Model Evaluation: Assessing model performance and generating predictions.

Models

The models.py directory contains scripts and notebooks for building and evaluating different regression models. This includes:

• Linear Regression: Baseline model for comparison.
• Random Forest: Ensemble model to capture complex interactions.
• Gradient Boosting: Advanced model for improved accuracy.
• XGBoost: Optimized gradient boosting for better performance.

Research

The research.ipynb directory includes Jupyter Notebooks for in-depth analysis and experimentation:

• Exploratory Data Analysis (EDA): Analyzing distributions, correlations, and patterns in the data.
• Feature Engineering Experiments: Testing different feature engineering techniques.
• Model Tuning: Experimenting with hyperparameter tuning and ensemble methods.

Acknowledgments

This project uses the Ames Housing dataset, compiled by Dean De Cock, and is part of the Kaggle "House Prices - Advanced Regression Techniques" competition. Special thanks to the Kaggle community and various tutorials that have provided valuable insights and guidance.

Getting Started

To get started with this project:

1. Clone the Repository: git clone https://github.com/Furk4nBulut/House-Prices-Advanced-Regression-Techniques
2. Run the Main Script: Execute main.py to start the data processing and model training pipeline.
3. Explore Research Notebooks: Use Jupyter Notebooks in the research.ipynb directory to review data exploration and model experiments.

Feel free to contribute to the project or open issues for any questions or feedback.