/Insurance-Premium-Prediction

Insurance Premium Prediction

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0

Application URL : InsurancePremiumPredictor

Table of contents

About project

This app predicts Insurance premium price based on some data.

Technologies

This project is created with below technologies/tools/resorces:

  • Python: 3.7
  • Machine Learning
  • Jupyter Notebook
  • HTML/CSS
  • Docker
  • Git
  • CI/CD Pipeline
  • Heroku

Software and account Requirement

  1. Github Account
  2. Heroku Account
  3. VS Code IDE
  4. GIT CLI

Setup

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
  • Add files to git git add . or git 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

Project Pipeline

  1. Data Ingestion
  2. Data Validation
  3. Data Transformation
  4. Model Training
  5. Model Evaluation
  6. Model Deployement

1. Data Ingestion:

  • Data ingestion is the process in which unstructured data is extracted from one or multiple sources and then prepared for training machine learning models.

2. Data Validation:

  • 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).

3. Data Transformation

  • 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.

4. Model Training

  • Model training in machine learning is the process in which a machine learning (ML) algorithm is fed with sufficient training data to learn from.

5. Model Evaluation

  • 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.

6. Model Deployement

  • Deployment is the method by which we integrate a machine learning model into production environment to make practical business decisions based on data.