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. Connect me: Linkedin
