prediction-model-manifoldailearning 1.0.0
Loan Prediction Model
License: MIT
Requires Python: >=3.7.0



# 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
```export PYTHONPATH="/Users/nachiketh/Desktop/author-repo/Complete-MLOps-BootCamp/Packaging-ML-Model/packaging-ml-model:$PYTHONPATH"
```

## Virtual Environment
Install virtualenv

```python
python3 -m pip install virtualenv
```

Check version
```python
virtualenv --version
```

Create virtual environment

```python
virtualenv ml_package
```

Activate virtual environment

For Linux/Mac
```python
source ml_package/bin/activate
```
For Windows
```python
ml_package\Scripts\activate
```

Deactivate virtual environment

```python
deactivate
```


## Directory structure

```bash
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
```python
pip install -e .
```
```.``` refers to current directory

```-e``` refers to --editable mode

2. Normal installation
```python
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