Insurance Premium Prediction

Problem Statement :

The primary objective of this initiative is to offer individuals an insight into the potential financial requirements for their health coverage, tailored to their specific health circumstances. Armed with this understanding, individuals can then navigate through the offerings of different health insurance providers, taking into consideration the projected expenses from our assessment. This approach empowers individuals to focus their attention on the essential health-related aspects of an insurance plan, while sidestepping any convoluted or unnecessary complexities.

Dataset:

The dataset has been sourced from a Kaggle repository, and you have the option to download it from there: download the dataset

Approach

Executing machine learning tasks such as Data Exploration, Data Cleaning, Feature Engineering, Model Building, and Model Testing to construct a solution capable of predicting health insurance premiums for individuals.

Here's how each step was carried out:

Data Exploration: Investigated the dataset using libraries like pandas, numpy, matplotlib, plotly, and seaborn.

Exploratory Data Analysis: Generated various graphs to gain deeper insights into both dependent and independent variables.

Feature Engineering: Rescaled numerical features and encoded categorical ones.

Model Building: Started with dataset splitting, then proceeded to train different Machine Learning Algorithms, including:

• Linear Regression
• Support vector regression
• Random Forest Regressor
• Gradient Boosting Regressor

Model Selection: Evaluated all models based on their mean absolute error(mae) and R-squared metrics.

Pickle File: Chose the model with the best RMSE score and R-squared value, then generated a pickle file using the pickle library.

Webpage & Deployment: Designed a web application that collects user inputs and presents outputs. The project was subsequently deployed on the streamlit Platform.

Each of these steps was meticulously executed to create a comprehensive solution for predicting health insurance premiums, involving everything from initial data exploration to the final deployment of the predictive model on a web interface.

Web app interface

Libraries used:

1) Pandas
2) Numpy
3) Matplotlib, Seaborn, Plotly
4) Scikit-Learn
5) Streamlit
6) HTML
7) CSS

This repository contains a Streamlit web application for predicting insurance claim amounts based on user input. The predictive model is trained using machine learning techniques.

Usage

1. Clone the Repository:

   git clone https://github.com/MuhammadSheraza002/insurance-claim-prediction.git
   cd insurance-claim-prediction

2. Install Dependencies:

   pip install -r requirements.txt

3. Run the App:

   streamlit run app.py

4. Open in Browser: Visit http://localhost:8501 in your web browser.

App Interface

The web application allows users to input various parameters such as age, gender, BMI, blood pressure, diabetic status, number of children, smoker status, and region. After providing the input, the user can click the "Predict" button to receive an estimated insurance claim amount.

Files

• app.py: The main application file containing the Streamlit app.
• insurance.pkl: Pickle file containing the trained machine learning model.
• requirements.txt: File listing the Python dependencies for the project.

Model Training

The predictive model is trained on an insurance dataset, and the trained model is saved using pickle.