End-to-End-Insurance-Premium-Prediction

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End-to-End-Insurance-Premium-Prediction

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Insurance Premium Prediction is an Machine Learning Project which predicts Insurance premium price based on some Input data.

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# End-to-End-Insurance-Premium-Prediction

[Linkedin](https://www.linkedin.com/in/ambarish-224/) 
[Medium](https://medium.com/@Ambarish_224) 
[Github](https://github.com/Ambarish-224)

## UI of Application:-

Application URL Links : [InsurancePremiumPredictor](https://ambarish-224-insurance-prediction-project.streamlit.app/)

## Table of contents
* [About project](#about-project)
* [Technologies](#technologies)
* [Software and account requirement](#software-and-account-requirement)
* [Setup](#setup)
* [Project Architecture](#project-architecture)
* [Project Pipeline](#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](https://github.com/)
2. [Streamlit Account](https://streamlit.io/cloud)
3. [VS Code IDE](https://code.visualstudio.com/download)
4. [GIT CLI](https://git-scm.com/downloads)

## 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.

<img src="https://lh5.googleusercontent.com/49NljwFVuPL1zR5z6rrBsLh8fEQBDTLCmG9Z9xScq1sLWdtR89KhtKS702hUDN566WIE42eems8Fb_y0jbb6N7Cv-noJ_W3pt7JDlblCE_0POna1AUAZ6aSNERqPC9nfMFrXL8g"/>
 
 
 
 ### Thanks & Regards
 ### Ambarish Singh

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