This project is part of the Housing Price Competition, where the goal is to predict housing prices based on a given dataset. The project uses various data analysis and machine learning techniques to create a model that can accurately predict the prices of houses.
- Data Cleaning: The initial step involves cleaning the data to ensure it is suitable for analysis. This includes handling missing values, outliers, and categorical data.
- Exploratory Data Analysis (EDA): This step involves analyzing the data to understand the distribution, correlation, and patterns within the data. Visualizations are used extensively to gain insights into the data.
- Feature Engineering: Based on the insights from EDA, new features are engineered to improve the model's predictive power. This includes creating, modifying, or removing features.
- Model Selection: Several machine learning models are evaluated to find the one that performs the best on the dataset. This could include linear regression, decision trees, random forests, and gradient boosting models, among others.
- Model Tuning: The selected model is then fine-tuned to optimize its performance. This involves adjusting hyperparameters and using techniques like cross-validation.
- Prediction and Evaluation: The final model is used to make predictions on the test dataset, and various metrics are used to evaluate its performance.
- Python
- Pandas, NumPy for data manipulation
- Matplotlib, Seaborn for data visualization
- Scikit-learn for machine learning
- Clone the repository to your local machine.
- Ensure you have Python and the necessary libraries installed.
- Run the Jupyter Notebook to go through the analysis and model building process.
The dataset used in this competition is provided by Kaggle. It includes various features of houses that are used to predict their selling prices.
The final Xgboost model achieved an RMSE (Root Mean Square Error) of 0.133 on the test dataset, indicating a high level of accuracy in predicting housing prices.
- Experiment with more advanced machine learning models.
- Incorporate additional data sources to improve the model's accuracy.
- Deploy the model as a web application for real-time predictions.