mlzoomcamp-midterm-project-2022: A Jupyter Notebook repository from msinha251

Midterm Project - Spaceship Titanic

In this project we are building and API for predicting whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly. To help you make these predictions, you're given a set of personal records recovered from the ship's damaged computer system.

Prediction API : https://mlzoomcamp-midterm-project.herokuapp.com/

Curl example:

curl -X 'POST' \
  'https://mlzoomcamp-midterm-project.herokuapp.com/predict' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "PassengerId": "0013_01",
  "HomePlanet": "Earth",
  "CryoSleep": true,
  "Cabin": "G/3/S",
  "Destination": "TRAPPIST-1e",
  "Age": 27,
  "VIP": false,
  "RoomService": 0,
  "FoodCourt": 0,
  "ShoppingMall": 0,
  "Spa": 0,
  "VRDeck": 0,
  "Names": "Nelly Carsoning"
}'

Data

Data is available in the data folder. The train.csv file contains the training set.

Dataset reference: https://www.kaggle.com/competitions/spaceship-titanic/data

Note: In order to download data from kaggle, you need to create an account on kaggle.com and download the kaggle.json file from your account. Then, you need to put this file in the ~/.kaggle folder. Then execute the ml/download_data.py script to download the data under the data folder.

Run with Docker

You can run the project with Docker. To do so, you need to have Docker installed on your machine. Then, you need to build the image with the following command:

docker build -t mlzoomcamp-midterm-2022 .

Then, you can run the project with the following command:

docker run -it -p 8000:8000 mlzoomcamp-midterm-2022:latest

COMMANDS

1. Create pipenv environment

pipenv install

2. Activate pipenv environment

pipenv shell
{get the virtual environment name}

3. Add pipenv environment to jupyter notebook

python -m ipykernel install --user --name={virtual environment name}

4. Run jupyter notebook

jupyter notebook

5. Train model Script

pipenv run python train_script.py

5. Trained Random Forest model and XGBoost model. In order to test the prediction, you need to run the main.py file with the following command:

pipenv run python -m uvicorn main:app

msinha251/mlzoomcamp-midterm-project-2022