This GitHub repository hosts an insurance prediction model developed to accurately forecast insurance outcomes. Leveraging three distinct machine learning algorithms—Decision Tree, Random Forest, and K-Nearest Neighbors (KNN)—this project aims to provide insightful predictions for insurance-related scenarios.
The primary objective of this model is to assess and compare the predictive capabilities of each algorithm, ultimately identifying the most effective approach for insurance prediction tasks. Through rigorous experimentation and evaluation, it was found that the KNN algorithm outperformed the others, achieving an impressive accuracy score of 87.7%.
The repository contains the following components:
The Python code used to implement and train the Decision Tree, Random Forest, and KNN models. This code includes data preprocessing, model training, evaluation, and comparison.
Relevant datasets utilized for training and testing the models. These datasets likely include features such as age, gender, medical history, and other pertinent variables for insurance prediction.
Detailed documentation providing insights into the project's methodology, model selection criteria, and experimental results. Additionally, instructions for replicating the experiments and utilizing the prediction model are included.
A summary of the experimental results, highlighting the accuracy scores achieved by each model and comparative analyses. Visualizations, such as accuracy charts or confusion matrices, may be provided to enhance understanding.
The test dataset used to validate the selected KNN model's predictive performance. This dataset is employed to assess how well the model generalizes to unseen data and real-world scenarios.
By sharing this repository, the aim is to contribute to the broader community of data scientists and machine learning enthusiasts, enabling collaboration, feedback, and further improvements in insurance prediction methodologies. Developers and researchers interested in enhancing insurance risk assessment and decision-making processes can leverage this model as a valuable resource. Contributions, suggestions, and feedback from the community are highly encouraged to foster continuous enhancement and refinement of the prediction model.