End-to-End-Insurance-Premium-Prediction

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UI of Application:-

Application URL Links : InsurancePremiumPredictor

Table of contents

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

About project

Insurance Premium Prediction is a Machine Learning Project that predicts Insurance premium prices based on some Input data.

Technologies

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

• Python: 3.7
• Machine Learning
• Jupyter Notebook
• Docker
• Git
• CI/CD Pipeline
• Streamlit

Software and account Requirement

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

Setup

To install the required 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 versions 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 Architecture

## Project Pipeline 1. [Data Ingestion](#1-data-ingestion) 2. [Data Validation](#2-data-validation) 3. [Data Transformation](#3-data-transformation) 4. [Model Training](#4-model-training) 5. [Model Evaluation](#5-model-evaluation) 6. [Model Deployement](#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 the 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 Deployment * Deployment is the method by which we integrate a machine-learning model into the production environment to make practical business decisions based on data.

### Thanks & Regards ### Ambarish Singh

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