Loan_prediction_tracking_using_mlflow/README.md

Project Title

The project aims to develop a machine learning application for predicting loan approval using data from historical loan applications. Leveraging machine learning techniques, the application aims to provide insights into whether a loan application is likely to be approved or rejected based on various factors such as income, credit history, loan amount, etc.

Overview

Explain the purpose and goals of your project.

MLflow Integration

This project utilizes MLflow for experiment tracking, packaging code into reproducible runs, and sharing and deploying models. MLflow helps streamline the machine learning lifecycle.

Dagshub Credentials

MLFLOW_TRACKING_URI=https://dagshub.com/naimurborno/Loan_prediction_tracking_using_mlflow.mlflow MLFLOW_TRACKING_USERNAME=naimurborno MLFLOW_TRACKING_PASSWORD=94ebc629914b4f17304744d67eec0b421a8f74d1 \

MLFLOW_TRACKING_URI=https://dagshub.com/naimurborno/Loan_prediction_tracking_using_mlflow.mlflow MLFLOW_TRACKING_USERNAME=naimurborno MLFLOW_TRACKING_PASSWORD=94ebc629914b4f17304744d67eec0b421a8f74d1 python script.py

MLflow on Aws

Login to AWS console.
Create IAM user with AdministratorAccess
Export the credentials in your AWS CLI by running "aws configure"
Create a s3 bucket
Create EC2 machine (Ubuntu) & add Security groups 5000 port

Run the following command on EC2 machine sudo apt update

sudo apt install python3-pip

sudo pip3 install pipenv

sudo pip3 install virtualenv

mkdir mlflow

cd mlflow

pipenv install mlflow

pipenv install awscli

pipenv install boto3

pipenv shell

Then set aws credentials

aws configure

#Finally mlflow server -h 0.0.0.0 --default-artifact-root s3://mlflow-test-23

#open Public IPv4 DNS to the port 5000

#set uri in your local terminal and in your code export MLFLOW_TRACKING_URI=http://ec2-54-147-36-34.compute-1.amazonaws.com:5000/