dataVisualisation 0.0.1

Linear Regression

Intelligent Prediction

A simple linear regression model that lets you select a Google Sheet from the drive and calculates all the optimised parameters of the model using Gradient_Descent_Algorithm and lets you save all the parameters and evaluation metrics of the model to your Google Drive.

1. It not just calculates the optimised parameters of your model but lets you graphically visualise the relationship between various other parameters.

2. It lets you save files to your drive after your proper authentication.

You Come up  with a Dataset and walk out with the most optimised parameters of the model...Everything else(Data Preprocessing and Data Modelling) is taken care of.

It's kind of a drag and drop model.

How to install?

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pip install dataVisualisation

How to use:

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1. import the module

2. call the main function using the module

import dataVisualisation
dataVisualisation.main()

Prerequisties required for the module to run successfully:

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Before you call the module make sure you have installed all the following modules

pip install pydrive
pip install gspread
pip install pandas
pip install sklearn
pip install matplotlib

Set-Up:

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Before you go ahead with running the module...there are a few steps that needs to be followed:

1. Create a new project on google cloud console
2. Activate the Google Drive API
3. Create Credentials for the google drive API
4. Save the downloaded file as credentials.json
5. Share the desired google sheet with the client email in credentials.json file
6. Activate the google sheets API
7. Create oAuth2 Client credentials and save the file as client_secrets.json

Usage and functionality:

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1. This module can be used with any of the non categorical dataset but make sure to have the target variable at the last column of the spreadsheet.
2. Make sure client_secrets.json and credentials.json is inside the same directory as the Python program.
3. The directory(path) should not contain any subdirectories inside it.
4. Make sure to share the google sheet to client_email from the drive.

References:

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1. Creating a python library
2. Hosting the library on PyPI
3. working with google sheets
4. working with markdown Language

License:

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This Package is distributed under the MIT license