This project aims to develop a predictive model for an insurance company to determine whether a person will purchase travel insurance. The model is built using machine learning techniques, primarily focusing on the MLP (Multi-Layer Perceptron) classifier and Logistic Regression.

The challenge involves designing a model that accurately predicts the likelihood of a customer purchasing travel insurance based on various factors. This model will assist the insurance company in understanding customer behavior and tailoring their services accordingly.

The project involves several key steps:

1. Data Analysis and Preprocessing: Initial exploration and preparation of the data to make it suitable for modeling.
2. Model Development: Utilizing MLP and Logistic Regression algorithms to develop the predictive model.
3. Explanation and Evaluation: Each step in the data analysis is explained, including the rationale behind the chosen methods. The performance of the models is evaluated using a confusion matrix and other relevant metrics.

• HW2-4-v2.ipynb: Jupyter notebook containing the entire analysis and model development process.
• Q4.csv: The dataset used for the analysis.
• Report.pdf: A PDF file containing a detailed report of the analysis and findings.

• The comparison between MLPClassifier and LogisticRegression in terms of accuracy is depicted in the notebook.

  

• Insights on the best parameters and structure for MLP and other machine learning algorithms that yield the desired results are discussed.

• Clone the repository.
• Ensure you have Jupyter Notebook installed.
• Run HW2-4-v2.ipynb to view the analysis and results.