ML-powered Loan-Marketer Customer Filtering Engine

Sagnik Roy

Last update: Jul 2, 2022

Related tags

Machine Learning shell random-forest webapp banking python-3 binaryclassification streamlit-application

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:

Create a Virtual Environment : Tutorial
After activating the virtual environment download the dependency libraries through the "requirements.txt" file in Shell.
```
$pip install -r requirements.txt
```
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.

Comments

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

Fix TIFF OOB Write error #5175 [@radarhere]

Fix for Buffer Read Overrun in PCX Decoding #5174 [@radarhere]

Fix for SGI Decode buffer overrun #5173 [@radarhere]

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

Add support for PySide6 #5161 [@hugovk]

Moved QApplication into one test #5167 [@radarhere]

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

Revert "skip wheels on 3.10-dev due to wheel#354" #5163 [@radarhere]

Better _binary module use #5156 [@radarhere]

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

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

Do not catch a ValueError only to raise another #5090 [@radarhere]

Allow putpalette to accept 1024 integers to include alpha values #5089 [@radarhere]

Fix OOB Read when writing TIFF with custom Metadata #5148 [@wiredfool]

Removed unused variable #5140 [@radarhere]

Fix dereferencing of potential null pointers #5111 [@cgohlke]

Fixed warnings assigning to "unsigned char *" from "char *" #5127 [@radarhere]

Add append_images support for ICO #4568 [@ziplantil]

Fixed comparison warnings #5122 [@radarhere]

Block TIFFTAG_SUBIFD #5120 [@radarhere]

Fix dereferencing potential null pointer #5108 [@cgohlke]

Replaced PyErr_NoMemory with ImagingError_MemoryError #5113 [@radarhere]

Remove duplicate code #5109 [@cgohlke]

Moved warning to end of execution #4965 [@radarhere]

Removed unused fromstring and tostring C methods #5026 [@radarhere]

init() if one of the formats is unrecognised #5037 [@radarhere]

Dependencies

Updated libtiff to 4.2.0 #5153 [@radarhere]

Updated openjpeg to 2.4.0 #5151 [@radarhere]

Updated harfbuzz to 2.7.4 #5138 [@radarhere]

Updated harfbuzz to 2.7.3 #5128 [@radarhere]

Updated libraqm to 0.7.1 #5070 [@radarhere]

Updated libimagequant to 2.13.1 #5065 [@radarhere]

Update FriBiDi to 1.0.10 #5064 [@nulano]

Updated libraqm to 0.7.1 #5063 [@radarhere]

Updated libjpeg-turbo to 2.0.6 #5044 [@radarhere]

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

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dependencies
opened by dependabot[bot] 0
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

ML-powered Loan-Marketer Customer Filtering Engine

Related tags

Overview

Loan-Web

Streamlit Server Deployment

Description :

Goals of the Project :

Dataset :

Tech-Stack :

Deployments :

Process:

Interface :

HOW to Use :

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

8.1.1

8.1.0

Changes

Dependencies

Deprecations

8.1.1 (2021-03-01)

8.1.0 (2021-01-02)

Having Error when working with pickle loaded files

Owner

Sagnik Roy

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