Metadata-Version: 2.1
Name: mhpp
Version: 0.3.0
Summary: A median house price prediction project to demonstrate packaging.
Description-Content-Type: text/markdown
Requires-Dist: scipy
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Provides-Extra: interactive
Requires-Dist: matplotlib ; extra == 'interactive'
Requires-Dist: seaborn ; extra == 'interactive'
Requires-Dist: jupyter ; extra == 'interactive'

# Introduction

A median house price prediction (mhpp) project is dealt to demonstrate packaging.
This package helps to fetch, preprocess and split the data set into train and test and also
train models using Linear Regression, Decision Tree and Random Forest algorithms and also
evaluate them using MSE (Mean Squarred Error), RMSE (Root mean square error) and MAS (Mean 
Absolute Error).


# Installation

Create a virtual environment using conda before installation.
```
  conda env create -f deploy/conda/env.yaml
```

#### Using pip
```
  pip install mhpp
```
#### From source

1. Clone the repo using git clone command.
   **Note**: Requires colab access as of now.
  ```
      git clone git@github.com:varun-mle/mle-training.git
  ```
2. Execute the below command.
  ```
      pip install . 
  ```

# Usage 
1. For fetching data **fetch-data** command is used.  Below command helps to know its usage
    ```
        fetch-data -h
    ```
 2. For preprocessing and splitting the data into train & test datasets **train-test-data** is used. Below command helps to know its usage.
    ```
        train-test-data -h 
    ```
 3. For training the model **train** command is used. Below command helps to know its usage
    ```
        train -h 
    ```
 4. For evaluating the model **evaluate** command is used. Below command helps to know its usage
    ```
        evaluate -h
    ```
  Refer **docs** folder for the detailed documentation. 



