prediction-model 1.0.0

Loan Prediction Model

Packaging the ML Model of Classification

Problem Statement

• Company wants to automate the loan eligibility process based on customer detail provided while filling online application form.
• It is a classification problem where we have to predict whether a loan would be approved or not.

Data

The data corresponds to a set of financial requests associated with individuals.

Variables	Description
Loan_ID	Unique Loan ID
Gender	Male/ Female
Married	Applicant married (Y/N)
Dependents	Number of dependents
Education	Applicant Education (Graduate/ Under Graduate)
Self_Employed	Self employed (Y/N)
ApplicantIncome	Applicant income
CoapplicantIncome	Coapplicant income
LoanAmount	Loan amount in thousands
Loan_Amount_Term	Term of loan in months
Credit_History	credit history meets guidelines
Property_Area	Urban/ Semi Urban/ Rural
Loan_Status	Loan approved (Y/N)

Source: Kaggle

Running Locally

Add PYTHONPATH variable for ~/.bash_profile for MacOS

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<h2>Virtual Environment</h2>
<p>Install virtualenv</p>
<pre lang="python"><code>python3 -m pip install virtualenv

Check version

virtualenv --version

Create virtual environment

virtualenv ml_package

Activate virtual environment

For Linux/Mac

source ml_package/bin/activate

For Windows

ml_package\Scripts\activate

Deactivate virtual environment

deactivate

Directory structure

prediction_model


├── MANIFEST.in
├── prediction_model
│   ├── config
│   │   ├── config.py
│   │   └── __init__.py
│   ├── datasets
│   │   ├── __init__.py
│   │   ├── test.csv
│   │   └── train.csv
│   ├── __init__.py
│   ├── pipeline.py
│   ├── predict.py
│   ├── processing
│   │   ├── data_handling.py
│   │   ├── __init__.py
│   │   └── preprocessing.py
│   ├── trained_models
│   │   ├── classification.pkl
│   │   └── __init__.py
│   ├── training_pipeline.py
│   └── VERSION
├── README.md
├── requirements.txt
├── setup.py
└── tests
    ├── pytest.ini
    └── test_prediction.py

Build the Package

1. Goto Project directory and install dependencies pip install -r requirements.txt

2. Create Pickle file after training: python prediction_model/training_pipeline.py

3. Create source distribution and wheel python setup.py sdist bdist_wheel

Installation of Package

Go to project directory where setup.py file is located

1. To install it in editable or developer mode

pip install -e .

. refers to current directory

-e refers to --editable mode

2. Normal installation

pip install .

. refers to current directory

3. Also can be installed from git as well after pushing to github

pip install git+https://github.com/manifoldailearning/prediction_model.git

Testing the Package Working

1. Remove the PYTHONPATH from environment variables
2. Goto a separate location which is outside of package directory
3. Create a new virual environment using the commands mentioned above & activate it
4. Before installing, test whether you are able to import the package of prediction_model - (you should not be able to do it)
5. Now in the new environment install the package from github pip install git+https://github.com/manifoldailearning/prediction_model.git
6. Now try importing the prediction_model, you should be able to do it successfully
7. Extras : Run training pipeline using the package, and also conduct the test