House Price Prediction

This repository contains Python code for predicting house prices using advanced regression techniques. The project focuses on preprocessing data, exploring various features, applying machine learning models, and optimizing predictions.

Project Structure

• Data/: Directory containing datasets used for training (train.csv), and testing (test.csv).
• requirements.txt: File listing all required Python libraries and dependencies.
• house_price_prediction.ipynb: Jupyter notebook containing the main code for data preprocessing, model building, and evaluation.

Description

This project aims as a solution for this problem to predict house prices using a dataset that includes various features such as lot size, neighborhood, number of rooms, and more. The steps involved in the project are as follows:

1. Data Preprocessing:

   • Importing and cleaning datasets.
   • Handling missing values and outliers.
   • Feature engineering to create new meaningful features.

2. Exploratory Data Analysis:

   • Visualizing relationships between features and target variable (SalePrice).
   • Analyzing distributions and correlations among variables.

3. Model Building:

   • Implementing machine learning models including:
     • Random Forest, Gradient Boosting, Ridge Regression, Lasso Regression, Support Vector Regression (SVR), XGBoost, LightGBM, CatBoost.
   • Hyperparameter tuning using Randomized Search Cross Validation.

4. Ensemble Learning:

   • Stacking multiple optimized models for improved prediction accuracy.

5. Evaluation and Submission:

   • Generating predictions using the ensemble model.
   • Formatting predictions according to submission requirements.

Requirements

All Python libraries and dependencies required for running the code are listed in requirements.txt. Install them using the following command:

pip install -r requirements.txt

Usage

1. Clone the repository:

   git clone https://github.com/Assem-ElQersh/Kaggle-Housing-Prices-Prediction
   cd Kaggle-Housing-Prices-Prediction  

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Jupyter notebook house_price_prediction.ipynb to execute the code step-by-step.

4. The final predicted house prices will be saved in my_prediction_ensemble.csv.

Contributors

• Assem ElQersh

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.