p-h-1-l-z-a / insuranceprediction_ml-project

Insurance Premium Prediction

Problem Statement :

We analyse the personal health data to predict insurance premium of individuals. Four regression models naming Linear Regression, Ridge Regression, Support Vector Regression and Gradient Boosting Regression have been used to compare and contrast the performance of these algorithms.

Dataset :

The dataset is taken from a Kaggle. You can download the dataset from here

Approach :

Applying machine learing tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building and model testing to build a solution that should able to predict the premium of the personal for health insurance.

• Data Exploration : Exploring the dataset using pandas, numpy, matplotlib, plotly and seaborn.

• Exploratory Data Analysis : Plotted different graphs to get more insights about dependent and independent variables/features.

• Feature Engineering : Numerical features scaled down and Categorical features encoded.

• Model Building : In this step, first dataset Splitting is done. After that model is trained on different Machine Learning Algorithms such as:

  1. Linear Regression
  2. Ridge Regressor
  3. Support Vector Regression.
  4. Gradient Boosting Regression

• Model Selection : Tested all the models to check the RMSE , MAE and R-squared.

• Pickle File : Selected model as per best RMSE score & R-squared and created pickle file using pickle library.

• Webpage &Deployment : Created a web application that takes all the necessary inputs from the user & shows the output.

Deployment Link :

Web Inerface :

Libraries used :

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

Technical Aspects :

1) Python 
2) Front-end : HTML, CSS
3) Back-end : Flask
4) Deployment :