https://github.com/zeyadusf/credit-score-classification
Credit Score Classification - ML
https://github.com/zeyadusf/credit-score-classification
classification machine-learning missing-values pycaret random-forest tableau
Last synced: about 1 month ago
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Credit Score Classification - ML
- Host: GitHub
- URL: https://github.com/zeyadusf/credit-score-classification
- Owner: zeyadusf
- Created: 2023-07-18T15:25:39.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-26T13:41:06.000Z (almost 3 years ago)
- Last Synced: 2025-02-25T03:41:58.226Z (over 1 year ago)
- Topics: classification, machine-learning, missing-values, pycaret, random-forest, tableau
- Language: Jupyter Notebook
- Homepage:
- Size: 43.7 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Credit Score Classification
## :dart: About : ##
The Credit Score Classification project aimed to develop a robust and accurate system for classifying credit scores. Credit score classification plays a vital role in assessing an individual's creditworthiness and determining their eligibility for loans, credit cards, and other financial services. It is a crucial component of risk assessment for financial institutions, helping them make informed decisions while minimizing the risk of defaults.
The missing data was dealt with in a way that is correct for the real values, and then the models were built.
Two models were built with different approaches:
* PyCaret:
- Extra Trees Classifier.
- Random Forest Classifier
- Extreme Gradient Boosting.
- K Neighbors Classifier.
- Decision Tree Classifier.
* RandomForest.
🔗 [More Details](Credit%20Score%20Classification.pdf) in Presentaion .
NoteBooks in Kaggel :
- 🔗 Part One : [CreditScore_Part1_HandleMissingWithCorrectValues](https://www.kaggle.com/zeyadusf/creditscore-part1-handlemissing)
- 🔗 Part Two : [CreditScoreClassification_Part2(EDA&Models)86.6%](https://www.kaggle.com/code/zeyadusf/creditscoreclassification-part2-eda-models-86-6)
🔗Dataset in kaggle : [Credit score classification](https://www.kaggle.com/datasets/parisrohan/credit-score-classification)
## :rocket: Technologies ##
## 🚩 Requirement ##
* Pycaret Library.
```
#install pycaret
! pip install pycaret
```
```
# check installed version
import pycaret
pycaret.__version__
```
- You can read this content to know more about [tutorials Pycaret](https://nbviewer.org/github/pycaret/pycaret/blob/master/tutorials/Tutorial%20-%20Binary%20Classification.ipynb) .
* Download this dataset to save yourself time running codes to eliminate Missing Values - [preprocessedMissing_dataset](Preprocessed_Missing_dataset.csv) [Optional]
### :email: Contact ##
