This app predicts Insurance premium price based on some data. 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.
https://project-insurance.herokuapp.com/
This project is created with below technologies/tools/resorces:
- Python: 3.7
- Machine Learning
- Jupyter Notebook
- HTML/CSS
- Docker
- Git
- CI/CD Pipeline
- Heroku
Create a conda environment
conda create -p venv python==3.7 -y
activate conda environment
conda activate venv/
To install requirement file
pip install -r requirements.txt
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Add files to git
git add .orgit add <file_name> -
To check the git status
git status -
To check all version maintained by git
git log -
To create version/commit all changes by git
git commit -m "message" -
To send version/changes to github
git push origin main -
To check all versions maintained by git
git log -
To check remote url
git remote -v -
Note: To ignore file or folder from git we can write the name of the file/folder in .gitignore folder -
BUILD DOCKER IMAGE
docker build -t <image_name>:<tagname> . -
Note: Image name for docker must be lowercas -
To list docker image
docker images -
Run docker image
docker run -p 5000:5000 -e PORT=5000 f8c749e73678 -
To check running container in docker
docker ps -
To stop docker conatiner
docker stop <container_id> -
To install ipykernel
pip install ipykernel -
-e . means install all packages in current directory
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setup.py is required whenever you want to install -e .
- Data Ingestion
- Data Validation
- Data Transformation
- Model Training
- Model Evaluation
- Model Deployement
- Data ingestion is the process in which unstructured data is extracted from one or multiple sources and then prepared for training machine learning models.
- Data validation is an integral part of ML pipeline. It is checking the quality of source data before training a new mode
- It focuses on checking that the statistics of the new data are as expected (e.g. feature distribution, number of categories, etc).
- Data transformation is the process of converting raw data into a format or structure that would be more suitable for model building.
- It is an imperative step in feature engineering that facilitates discovering insights.
- Model training in machine learning is the process in which a machine learning (ML) algorithm is fed with sufficient training data to learn from.
- Model evaluation is the process of using different evaluation metrics to understand a machine learning model’s performance, as well as its strengths and weaknesses.
- Model evaluation is important to assess the efficacy of a model during initial research phases, and it also plays a role in model monitoring.
- Deployment is the method by which we integrate a machine learning model into production environment to make practical business decisions based on data.
To setup CI/CD pipeline in heroku we need 3 information
HEROKU_EMAIL = [email protected]
HEROKU_API_KEY = <>
HEROKU_APP_NAME = project-insurance