Insurance_internshipProject

Problem Statement:

The goal of this project is to give people an estimate of how much they need based on their individual health situation. After that, customers can work with any health insurance carrier and its plans and perks while keeping the projected cost from our study in mind. This can assist a person in concentrating on the health side of an insurance policy rather han the ineffective part.

Approach:

The classical machine learning tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building and Model Testing. Try out different machine learning algorithms that’s best fit for the above case. Some Famous Algorithms: - Multiple Linear Regression, Decision tree Regression and Gradient Boosting, Adaboost Regressor, RandomForestRegression and Elasticnet.

Result:

We have build a solution that should able to predict the amount of health insurance.

Application Link

Follow the steps after making github repository and cloning in the working folder.

STEPS ARE MENTIONED BELOW FOR MAKING THE ENTIRE PIPELINE

Step 1:- Create env

conda create -n insurance python=3.7 -y

Step 2:- Activate env

conda activate insurance

Step 6:- Download dataset :- insurance

Step 7:- Creat template for project

code present in template.py

Step 8:- Initialize dvc

dvc init

Step 9:- Add data into dvc for tracking

dvc add data_given/dataname.csv

Step 10:- Add all the file to github

git add -A
git commit -m "first commit"
git push -u origin main

Step 11:- Create params.yaml and dvc.yaml params.yaml and dvc.yaml both very important file for the project.

Step 12:- Start working in src directory and for load data and train model

get_data.py
load_data.py
split_data.py
train_and_evaluate.py

Step 13:- After finish model building now time to create webapp:-

In webapp folder we have templates of the webpage and for styling we used bootstrap and css. CSS available in static folder.

Step 14:- app.py on root dir for creating flask api Now make routes like \ for rendering home page and /predict for rendering predictions.

step 15: For automation of the project create dir .github\workflow\ci-cd.yaml we used here github actions for automating our project.

Author: Abhishek Kumar
For any queries related to ml/dl contact me abhiprasad7042@gmail.com

Thank You