ML-powered Loan-Marketer Customer Filtering Engine

Overview

Loan-Web

ML powered Customer Filtering Engine for Loan Marketing

Streamlit Server Deployment

Developed with

Description :

In Loan-Marketing business employees are required to call the user's to buy loans of several fields and in several magnitudes. If employees are calling everybody in the network it is also very lengthy and uncertain that most of the customers will buy it. So, there is a very need for a filtering system that segregates the customers who are unlikely to buy loans and the opposite. Loan-Web is visualized and made up on that context.


Goals of the Project :

 1. Ease the job to find the correct vendor/customer.
 2. Reduce time and stress to find out the right customer.
 3. Gaining knowledge from the percentages of likelihood of buying loans and creating campaigns on similar agendas.
 4. Very accurately choosing the right customer.
 5. Reduce asset maintenance expenses as daily calls are getting reduced.

Dataset :

The data has been collected from kaggle Banking Dataset-Marketing Targets. More about the dataset are in this link.

Tech-Stack :

Pyhton environment (v3.8 or higher) equipped with -

       Pyhton Prompt
       Pandas
       Pillow
       Pybase64
       Scikit Learn
       Streamlit

Deployments :

This web app has been successfully deployed on HEROKU. Follow this link to try it online - https://loan-webapp.herokuapp.com/

Or you can fork the whole project in your local machine and try it locally :

Process:

  1. Create a Virtual Environment : Tutorial

  2. After activating the virtual environment download the dependency libraries through the "requirements.txt" file in Shell.

    $pip install -r requirements.txt
    
  3. After installing all the libraries paste the whole repository in that environment base folder and then write a code in Shell.

    $streamlit run loan_web_app.py
    

Interface :

PC interface Android Interface

HOW to Use :

You can download the use approach.

STAR this repository to keep the developer sane :)

And also follow me on github & kaggle for other Machine Learning / Deep Learning projects.

THANK YOU for visiting :)

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Comments
  • Bump pillow from 8.0.1 to 8.1.1

    Bump pillow from 8.0.1 to 8.1.1

    Bumps pillow from 8.0.1 to 8.1.1.

    Release notes

    Sourced from pillow's releases.

    8.1.1

    https://pillow.readthedocs.io/en/stable/releasenotes/8.1.1.html

    8.1.0

    https://pillow.readthedocs.io/en/stable/releasenotes/8.1.0.html

    Changes

    Dependencies

    Deprecations

    ... (truncated)

    Changelog

    Sourced from pillow's changelog.

    8.1.1 (2021-03-01)

    • Use more specific regex chars to prevent ReDoS. CVE-2021-25292 [hugovk]

    • Fix OOB Read in TiffDecode.c, and check the tile validity before reading. CVE-2021-25291 [wiredfool]

    • Fix negative size read in TiffDecode.c. CVE-2021-25290 [wiredfool]

    • Fix OOB read in SgiRleDecode.c. CVE-2021-25293 [wiredfool]

    • Incorrect error code checking in TiffDecode.c. CVE-2021-25289 [wiredfool]

    • PyModule_AddObject fix for Python 3.10 #5194 [radarhere]

    8.1.0 (2021-01-02)

    • Fix TIFF OOB Write error. CVE-2020-35654 #5175 [wiredfool]

    • Fix for Read Overflow in PCX Decoding. CVE-2020-35653 #5174 [wiredfool, radarhere]

    • Fix for SGI Decode buffer overrun. CVE-2020-35655 #5173 [wiredfool, radarhere]

    • Fix OOB Read when saving GIF of xsize=1 #5149 [wiredfool]

    • Makefile updates #5159 [wiredfool, radarhere]

    • Add support for PySide6 #5161 [hugovk]

    • Use disposal settings from previous frame in APNG #5126 [radarhere]

    • Added exception explaining that repr_png saves to PNG #5139 [radarhere]

    • Use previous disposal method in GIF load_end #5125 [radarhere]

    ... (truncated)

    Commits
    • 741d874 8.1.1 version bump
    • 179cd1c Added 8.1.1 release notes to index
    • 7d29665 Update CHANGES.rst [ci skip]
    • d25036f Credits
    • 973a4c3 Release notes for 8.1.1
    • 521dab9 Use more specific regex chars to prevent ReDoS
    • 8b8076b Fix for CVE-2021-25291
    • e25be1e Fix negative size read in TiffDecode.c
    • f891baa Fix OOB read in SgiRleDecode.c
    • cbfdde7 Incorrect error code checking in TiffDecode.c
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 0
  • Having Error when working with pickle loaded files

    Having Error when working with pickle loaded files

    When I tried to make the pickle file through this sequence done in this notebook. It shows an error when trying to load the model through this code load_clf = pickle.load(open('clf_LOAN.pkl', 'rb'))

    the error shows

         EOF Error. Ran out of Input.
    

    If anybody has any better approach or fix in this scenario, please help.

    bug enhancement question 
    opened by sagnik1511 0
Owner
Sagnik Roy
Kaggle Expert ● Deep learning & Machine Learning Enthusiast ●Competitive Programmer ●............... Discord ID - tensored__
Sagnik Roy
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