bansalkiran49 / health-insurance-lead-prediction Goto Github PK

This project aims to develop a machine learning model to predict customer interest in a health insurance policy based on their profile and previous policies. The client for this project is a leading financial services company, LIA, specializing in general insurance. LIA offers a wide range of insurance products, including life, health, and accident coverage, and operates in multiple countries, including 10 in Asia.

LIA wishes to cross-sell health insurance to its existing customer base, which comprises over 40 million customers. To optimize their sales efforts, LIA wants to identify potential leads who are more likely to be interested in the proposed health insurance policy. Currently, customers visit the company's website, browse the recommended health insurance policy, and fill out an application form. If the customer's response towards the policy is positive, they are considered a lead. Sales advisors then approach these leads to convert them into policyholders.

To streamline this process and improve efficiency, LIA wants to leverage machine learning techniques to predict customer interest in the proposed health policy. By analyzing customer information, including their profile and previous policies, the goal is to build a predictive model that can identify potential leads automatically.

The dataset provided for this project includes the following features:

• Customer Id: Unique identifier for each customer
• City_Code: Code representing the customer's city
• Region_Code: Code representing the customer's region
• Accommodation_Type: Type of accommodation (e.g., owned, rented)
• Reco_Insurance_Type: Recommended insurance type (e.g., joint, single)
• Upper_Age: Upper age of the customer
• Lower_Age: Lower age of the customer
• Is_Spouse: Customer's relation (in case of a joint policy)
• Health Indicator: Current health indicator category
• Holding_Policy_Duration: Duration of the current holding policy
• Reco_Policy_Cat: Recommended policy category
• Holding_Policy_Type: Current holding policy type
• Reco_Policy_Premium: Recommended policy premium price
• Response: Customer's response towards the proposed health policy (target variable: 1 indicates interest, 0 indicates no interest)

To solve this problem, we will follow these steps:

1. Perform exploratory data analysis (EDA) to understand the distribution of features, identify any missing values, and analyze the relationship between variables.
2. Preprocess the data by handling missing values, encoding categorical features, and scaling numerical features as necessary.
3. Perform feature engineering by creating new columns from existing ones, such as Average age, premium per age to capture additional insights.
4. Split the dataset into training and testing sets for model development and evaluation.
5. Train various machine learning models using the training data, such as logistic regression, decision trees, random forests, or XGBoost algorithms.
6. Evaluate the performance of each model using appropriate evaluation metrics, such as accuracy and F1-score.

By building an accurate predictive model, LIA can focus its sales efforts on customers who are more likely to be interested in the proposed health insurance policy, leading to improved conversion rates and overall business growth.

Feel free to refer to the code examples and feature engineering suggestions provided earlier in this document to assist you in the development of the machine learning model.