Health Insurance Cross Sell Prediction Classification Model
Welcome to the Health Insurance Cross Sell Prediction classification model repository. This project focuses on predicting whether customers would be interested in purchasing a health insurance policy based on various features and attributes. By leveraging machine learning algorithms, this project assists insurance providers in identifying potential customers for cross-selling health insurance products.
Overview
Cross-selling health insurance is a common strategy for insurance providers to increase revenue and customer retention. Predicting which customers are more likely to be interested in purchasing health insurance allows companies to target their marketing efforts more effectively. This project utilizes supervised learning techniques to build a classification model capable of identifying customers who are likely to purchase health insurance.
Dataset
The dataset used in this project contains information about customers, including demographic data, their interactions with insurance products, past purchases, and other relevant attributes. The dataset is preprocessed to handle missing values, encode categorical variables, and normalize numerical features to prepare it for model training.
Approach
Data Preprocessing: The dataset undergoes preprocessing steps such as handling missing values, encoding categorical variables, and scaling numerical features to ensure compatibility with the chosen classification algorithm.
Feature Engineering: Relevant features are selected or engineered to capture meaningful patterns and relationships within the data, enhancing the model's predictive performance.
Model Selection: Various classification algorithms such as logistic regression, decision trees, random forests, and gradient boosting are explored to identify the most suitable model for health insurance cross sell prediction.
Model Training: The selected classification model is trained on the preprocessed dataset to learn patterns and relationships between customer features and their likelihood of purchasing health insurance.
Model Evaluation: The trained model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score to assess its classification performance.
Hyperparameter Tuning: Hyperparameters of the selected model are fine-tuned using techniques like grid search or random search to optimize classification performance further.
Prediction and Deployment: Once the model is trained and evaluated satisfactorily, it can be deployed to predict whether new customers are likely to purchase health insurance based on their features.