Application URL : InsurancePremiumPredictor

Table of contents

• About project
• Technologies
• Software and account requirement
• Setup
• Project Pipeline

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.