Problem Statement

Build an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features.

The main goal of this project is to perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset and build an appropriate Machine Learning Model that will help various Zomato Restaurants to predict their respective Ratings based on certain features.

How to run?

Before we run the project, make sure that you are having MongoDB in your local system, with Compass since we are using MongoDB for data storage. You also need AWS account to access the service like S3, ECR and EC2 instances.

Dataset information

The training set contains 57000 examples in total number of Attributes is 13.

Tech Stack Used

1. Python
2. FastAPI
3. Machine learning algorithms
4. Docker
5. MongoDB

Infrastructure Required.

1. AWS S3
2. AWS EC2
3. AWS ECR
4. Git Actions

How to run?

Before we run the project, make sure that you are having MongoDB in your local system, with Compass since we are using MongoDB for data storage. You also need AWS account to access the service like S3, ECR and EC2 instances.

Project Pipeline

Project Archietecture

Deployment Archietecture

Step 1: Clone the repository

git clone https://github.com/MaheshKumarMK/Restaurant-rating-prediction.git

Step 2- Create a conda environment after opening the repository

conda create -n venv python=3.7.6 -y

conda activate venv

Step 3 - Install the requirements

pip install -r requirements.txt

Step 4 - Export the environment variable

export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>

export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>

export AWS_DEFAULT_REGION=<AWS_DEFAULT_REGION>

export MONGODB_URL>

Step 5 - Run the application server

python main.py
  or
uvicorn main:app

Step 6. Train application

http://localhost:8080/train

Step 7. Prediction application

http://localhost:8080/predict

Run locally

1. Check if the Dockerfile is available in the project directory

2. Build the Docker image

docker build -t ratings-predictions . 

3. Run the Docker image

docker run -d -e AWS_ACCESS_KEY_ID="${{ secrets.AWS_ACCESS_KEY_ID }}" -e AWS_SECRET_ACCESS_KEY="${{ secrets.AWS_SECRET_ACCESS_KEY }}" -e AWS_DEFAULT_REGION="${{ secrets.AWS_DEFAULT_REGION }}" -e MONGODB_URL="${{ secrets.MONGODB_URL }}" -p 8080:8080 ratings-predictions