Insurance Premium Prediction Application

Overview

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This is a machine learning application designed for predicting insurance premiums. The project leverages a variety of tools and frameworks to streamline data management, experiment tracking, and model deployment.

🛠️ Tools Utilized

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• DVC (Data Version Control): Used for managing and versioning data pipeline.
• Git: Version control system for tracking code changes.
• MLflow: Used for tracking the model training and model evaluation.
• GitHub Actions Server: Used for continuous integration and deployment.
• Dagshub: Facilitates MLflow experiment tracking and DVC data pipeline.

🛢️ Machine Learning Pipeline

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Data Ingestion 📥

The application ingests insurance premium data from the data/insurance.csv data path and saves it into artifacts/DataIngestionArtifacts.

Data Transformation 🔧

Data undergoes transformation to prepare it for model training. Transformed data and preprocessing artifacts are saved into artifacts/DataTransformationArtifacts. Preprocessors are also stored in models/.

Model Training 🤖

Multiple machine learning models are trained: Linear Regression, Ridge Regression, Lasso Regression, Polynomial Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, Catboost. The top 4 performing models based on training metrics are selected. Both models and associated metrics are saved into artifacts/ModelTrainerArtifacts. MLflow is used to track model parameters and metrics throughout this process.

Model Evaluation 📊

The best-performing model on test data is selected and saved into artifacts/ModelEvaluationArtifacts and models/. Model evaluation metrics are tracked using MLflow.

Streamlit App Development 💻

A Streamlit application is developed to allow users to input data and receive predictions from the trained model.

Model Deployment 🚀

The model is deployend on the AWS EC2 using Docker and Github Action Server.

📋 Model tracking with MLFlow

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🖇️ Data pipeline tracking with DVC

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📁 Directory Structure

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📂.github/
└── 📂workflows/
      └── main.yaml
📂docs/
├── 📂docs/
│     ├── index.md
│     └── getting-started.md
├── mkdocs.yml
└── README.md
📂src/
├── init.py
├── 📂components/
│     ├── init.py
│     ├── data_ingestion.py
│     ├── data_transformation.py
│     ├── model_trainer.py
│     └── model_evaluation.py
├── 📂constants/
│     └── init.py
├── 📂entity/
│     ├── init.py
│     ├── config_entity.py
│     └── artifact_entity.py
├── 📂pipeline/
│     ├── init.py
│     ├── training_pipeline.py
│     └── prediction_pipeline.py
├── 📂utils/
│     ├── init.py
│     └── utils.py
├── 📂logger/
│     └── init.py
└── 📂exception/
      └── init.py
📂data/
  └── insurance.csv
📂experiment/
  └── experiments.ipynb
requirements.txt
requirements_app.txt
setup.py
app.py
main.py
README.md
implement.md
.gitignore
template.py
prediction.py
init_setup.ps1
dvc.yaml
Dockerfile
demo.py
config.json
.dockerignore
.dvcignore

📈 Models

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• Linear Regression
• Ridge Regression
• Lasso Regression
• Polynomial Regression
• Random Forest
• Gradient Boosting
• XGBoost
• LightGBM
• Catboost

🖥️ Installation

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🛠️ Requirements:

• Python 3.10
• mkdocs
• dvc
• numpy
• pandas
• colorama
• mlflow==2.2.2
• dagshub
• scikit-learn
• xgboost
• lightgbm
• catboost
• streamlit

⚙️ Setup

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To reproduce the model and run the application:

1. Clone the repository:

   git clone <repository_url>
cd <repository_name>

2. Set up the virtual environment and install the requirements:

   ./init_setup.ps1
   

3. Execute the whole pipeline:

   python main.py
   Now run the streamlit app.

🎯 Inference demo

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1. Run the Streamlit app:

   streamlit run app.py 2. Enter the input values and get prediction

Contributors 👨🏼‍💻

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• Ravi Kumar