House Price Prediction AI/ML Project

This project involves building a machine learning model to predict house prices based on various features. The dataset used for this project is from the Kaggle competition "House Prices - Advanced Regression Techniques". The goal is to develop a model that accurately predicts house prices given a set of input features.

Kaggle Competition

• Dataset: House Prices - Advanced Regression Techniques
• Model Score: 87.16% (R-squared score)

File Structure

• house_price_prediction.ipynb: Jupyter Notebook containing the code for data preprocessing, exploratory data analysis (EDA), feature engineering, model training, and prediction.
• submission.csv: CSV file containing the predicted house prices for the test dataset.
• gbr.pkl: Pickle file containing the trained GradientBoostingRegressor model.

Libraries Used

• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• XGBoost

Data Loading and Analysis

• The training and test datasets are loaded from CSV files.
• Exploratory data analysis is performed to understand the structure and characteristics of the data.
• Data visualization techniques such as histograms, box plots, and heatmaps are used to analyze the distribution of features and identify missing values.

Data Preprocessing

• Missing values are handled using appropriate techniques such as imputation or dropping columns.
• Categorical variables are encoded using one-hot encoding.
• Numerical features are standardized to ensure uniformity and improve model performance.

Model Selection and Training

• Several regression models are considered, including Linear Regression, SVR, SGDRegressor, KNeighborsRegressor, DecisionTreeRegressor, RandomForestRegressor, GradientBoostingRegressor, XGBRegressor, and MLPRegressor.
• Cross-validation is used to evaluate each model's performance based on the R-squared score.
• The GradientBoostingRegressor model is selected based on its superior performance.

Model Evaluation and Prediction

• The selected model is trained on the training dataset.
• The trained model is used to make predictions on the test dataset.
• The predictions are saved to a CSV file (submission.csv) for submission.

Additional Notes

• The submission.csv file contains the predicted house prices for the test dataset.
• The trained model (gbr.pkl) is stored as a pickle file for future use or deployment.

For any further inquiries or improvements, feel free to reach out.

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