This project aims to predict the approval of US visa applications for workers using machine learning techniques. The model is trained on a dataset of historical visa applications and produces a prediction of approval based on various features such as employer details, job title, wage levels, and more.
The primary objective of this project is to classify visa applications as either "approved" or "denied" using machine learning algorithms.
By analyzing features, the model can predict whether a visa application is likely to be approved or not.
git clone https://github.com/VivekShinde7/US-Visa-Approval-Prediction.git
cd US-Visa-Approval-Prediction
```bash
conda create -n visa python=3.8 -yconda activate visapip install -r requirements.txtpython app.py- constants
- entity
- components
- pipeline
- Main file
export MONGODB_URL="mongodb+srv://<username>:<password>...."
export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>
export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>
#with specific access
1. EC2 access : It is virtual machine
2. ECR: Elastic Container registry to save your docker image in aws
#Description: About the deployment
1. Build docker image of the source code
2. Push your docker image to ECR
3. Launch Your EC2
4. Pull Your image from ECR in EC2
5. Lauch your docker image in EC2
#Policy:
1. AmazonEC2ContainerRegistryFullAccess
2. AmazonEC2FullAccess
- Save the URI: 315865595366.dkr.ecr.us-east-1.amazonaws.com/visarepo
#optinal
sudo apt-get update -y
sudo apt-get upgrade
#required
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker ubuntu
newgrp docker
setting>actions>runner>new self hosted runner> choose os> then run command one by one
- AWS_ACCESS_KEY_ID
- AWS_SECRET_ACCESS_KEY
- AWS_DEFAULT_REGION
- ECR_REPO