Application URL Links : https://insurance-premium-prediction-final2023.streamlit.app/
UI of Application :-
Table of contents About project Technologies Software and account requirement Setup Project Architecture Project Pipeline
About project Insurance Premium Prediction is an Machine Learning Project which predicts Insurance premium price based on some Input data.
Technologies This project is created with below technologies/tools/resorces:
Python: 3.7 Machine Learning Jupyter Notebook Docker Git CI/CD Pipeline Streamlit
Software and account Requirement Github Account Streamlit Account VS Code IDE GIT CLI
Setup 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 Architecture Project Pipeline Data Ingestion Data Validation Data Transformation Model Training Model Evaluation Model Deployement
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.
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).
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.
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.
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.
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