Iyamu Idahosa

The AI-Powered Research Assistant is a Python-based application that leverages AI agents to retrieve information on various topics, including weather, mathematics, news, definitions, and stock market data. The system follows a structured decision-making flow to determine the appropriate search route based on the user's query.

This Jupyter Notebook is designed for data preprocessing, model training, and evaluation using various machine learning models. It includes data visualization, model selection, and performance evaluation using cross-validation. The notebook leverages popular Python libraries such as pandas, numpy, scikit-learn, xgboost, lightgbm, catboost, ExtraTreesRegressor and shap for model explainability. Folder Structure all dataset, notebook and csv output are stored in the same directory or it follows the below structure for better organizing project/ │ ├── data/ # Folder to store datasets │ └── train.csv # Dataset file used in the notebook │ └── test.csv # Dataset file used in the notebook └──result/output -extratreesregressors.csv # Example of an output file │ ├── 1st place notebook # Main notebook file └── README.md # This README file dependencies: python=3.10.12 numpy pandas scikit-learn xgboost lightgbm catboost shap seaborn matplotlib tqdm tabulate