amdp-chauhan / complete-deployable-ml-solution

API-First approach to make Machine Learning solution usable

API-First approach to make Machine Learning solution usable

Introduction

In this application I have solved 'Titanic Survival Prediction problem' and using simple VotingClassifier to make predictions. I have trained many other models as well and if you want to then you can view their performance and can choose any of the desired model (by making some changes in Src/utils/ClassificationModelBuilder.py file) to make predictions.

Application Setup

I have used Python's venv module for creating/managing virtual environment and flask framework for API creation.

If you're not much aware of venv environment setup, than you can go through this documentation. I learnt from same.

Once you have venv installed and got basic understanding, follow below steps to run this application:

1. git clone https://github.com/amdp-chauhan/titanic-survival-complete-ml-solution.git && cd titanic-survival-complete-ml-solution
2. python -m venv ./ - It will create a virtual environment in application directory.
3. Scripts\activate.bat - It will run this virtual environment.
4. pip install -r packages.txt - it will install all required dependencies.
5. python application.py - it will run the application.
6. deactivate - If you want to exit from virtual environment.

Upper commands will work fine in Windows 10, for Linux you can find alternatives in venv documentation.

Note that in application.py file, second import statement is commented out, it is because if it is enabled then it starts retraining classifier models, which is not required if you already have created a final model and data-set is same. Final models exists in Src/ml-model/voting_classifier_v1.pk we use same model to make predictions for requested JSON record.

Making Predictions

For predictions I have created an POST API:

http://{domain}/titanic-survival-classification-model/predict

It accepts list of JSON of test records and in return will give you a predicted Survival values in 0/1.

For example, for below input parameters:

[{
    "PassengerId": 892,
    "Pclass": 3,
    "Name": "Kelly, Mr. James",
    "Sex": "male",
    "Age": 34.5,
    "SibSp": 0,
    "Parch": 0,
    "Ticket": 330911,
    "Fare": 7.8292,
    "Cabin": "",
    "Embarked": "Q"
  },{
    "PassengerId": 893,
    "Pclass": 3,
    "Name": "Wilkes, Mrs. James (Ellen Needs)",
    "Sex": "female",
    "Age": 47,
    "SibSp": 1,
    "Parch": 0,
    "Ticket": 363272,
    "Fare": 7,
    "Cabin": "",
    "Embarked": "S"
  },{
    "PassengerId": 894,
    "Pclass": 2,
    "Name": "Myles, Mr. Thomas Francis",
    "Sex": "male",
    "Age": 62,
    "SibSp": 0,
    "Parch": 0,
    "Ticket": 240276,
    "Fare": 9.6875,
    "Cabin": "",
    "Embarked": "Q"
  },{
    "PassengerId": 895,
    "Pclass": 3,
    "Name": "Wirz, Mr. Albert",
    "Sex": "male",
    "Age": 27,
    "SibSp": 0,
    "Parch": 0,
    "Ticket": 315154,
    "Fare": 8.6625,
    "Cabin": "",
    "Embarked": "S"
  },{
    "PassengerId": 896,
    "Pclass": 3,
    "Name": "Hirvonen, Mrs. Alexander (Helga E Lindqvist)",
    "Sex": "female",
    "Age": 22,
    "SibSp": 1,
    "Parch": 1,
    "Ticket": 3101298,
    "Fare": 12.2875,
    "Cabin": "",
    "Embarked": "S"
  },{
    "PassengerId": 897,
    "Pclass": 3,
    "Name": "Svensson, Mr. Johan Cervin",
    "Sex": "male",
    "Age": 14,
    "SibSp": 0,
    "Parch": 0,
    "Ticket": 7538,
    "Fare": 9.225,
    "Cabin": "",
    "Embarked": "S"
  },{
    "PassengerId": 898,
    "Pclass": 3,
    "Name": "Connolly, Miss. Kate",
    "Sex": "female",
    "Age": 30,
    "SibSp": 0,
    "Parch": 0,
    "Ticket": 330972,
    "Fare": 7.6292,
    "Cabin": "",
    "Embarked": "Q"
  },{
    "PassengerId": 899,
    "Pclass": 2,
    "Name": "Caldwell, Mr. Albert Francis",
    "Sex": "male",
    "Age": 26,
    "SibSp": 1,
    "Parch": 1,
    "Ticket": 248738,
    "Fare": 29,
    "Cabin": "",
    "Embarked": "S"
  },{
    "PassengerId": 900,
    "Pclass": 3,
    "Name": "Abrahim, Mrs. Joseph (Sophie Halaut Easu)",
    "Sex": "female",
    "Age": 18,
    "SibSp": 0,
    "Parch": 0,
    "Ticket": 2657,
    "Fare": 7.2292,
    "Cabin": "",
    "Embarked": "C"
  },{
    "PassengerId": 901,
    "Pclass": 3,
    "Name": "Davies, Mr. John Samuel",
    "Sex": "male",
    "Age": 21,
    "SibSp": 2,
    "Parch": 0,
    "Ticket": "A/4 48871",
    "Fare": 24.15,
    "Cabin": "",
    "Embarked": "S"
  },{
    "PassengerId": 902,
    "Pclass": 3,
    "Name": "Ilieff, Mr. Ylio",
    "Sex": "male",
    "Age": "",
    "SibSp": 0,
    "Parch": 0,
    "Ticket": 349220,
    "Fare": 7.8958,
    "Cabin": "",
    "Embarked": "S"
  },{
    "PassengerId": 903,
    "Pclass": 1,
    "Name": "Jones, Mr. Charles Cresson",
    "Sex": "male",
    "Age": 46,
    "SibSp": 0,
    "Parch": 0,
    "Ticket": 694,
    "Fare": 26,
    "Cabin": "",
    "Embarked": "S"
}]

We will get below output:

{
	"predictions": "[{\"PassengerId\":892,\"Survived\":1},{\"PassengerId\":893,\"Survived\":1},{\"PassengerId\":894,\"Survived\":0},{\"PassengerId\":895,\"Survived\":1},{\"PassengerId\":896,\"Survived\":1},{\"PassengerId\":897,\"Survived\":1},{\"PassengerId\":898,\"Survived\":0},{\"PassengerId\":899,\"Survived\":0},{\"PassengerId\":900,\"Survived\":1},{\"PassengerId\":901,\"Survived\":0},{\"PassengerId\":902,\"Survived\":1},{\"PassengerId\":903,\"Survived\":0}]"
}