Kabr Prediction - v1.2.1
This is the initial release of the Kabr Prediction project. This release includes the complete workflow for predicting specific outcomes based on the provided dataset. The project utilizes multiple machine learning algorithms and data processing techniques to ensure accurate predictions.
Key Features:
- Data Preprocessing: Cleaning, handling missing values, and feature engineering.
- Machine Learning Models: Implemented using various algorithms, including linear models and ensemble methods.
- Model Evaluation: Comprehensive evaluation using cross-validation and other metrics.
- Hyperparameter Tuning: Utilizes GridSearchCV for optimal model parameters.
- Detailed Documentation: Step-by-step instructions provided in the Jupyter Notebook.
Installation:
- Clone the repository.
- Create and activate a virtual environment.
- Install dependencies using
pip install -r requirements.txt.
Usage:
Open model.ipynb in Jupyter Notebook or JupyterLab and follow the instructions.