https://github.com/hariprasath-v/hackerearth--cipla-data-scientist-hiring-challenge
This machine learning challenge is about predicting the loan sanction amount from the customer's basic account transaction details and requested loan amount.
https://github.com/hariprasath-v/hackerearth--cipla-data-scientist-hiring-challenge
exploratory-data-analysis gridsearchcv machine-learning matplotlib seaborn xgboost-regression
Last synced: 8 months ago
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This machine learning challenge is about predicting the loan sanction amount from the customer's basic account transaction details and requested loan amount.
- Host: GitHub
- URL: https://github.com/hariprasath-v/hackerearth--cipla-data-scientist-hiring-challenge
- Owner: hariprasath-v
- Created: 2021-06-29T08:23:34.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-01-15T14:41:49.000Z (almost 4 years ago)
- Last Synced: 2025-01-13T01:44:56.716Z (9 months ago)
- Topics: exploratory-data-analysis, gridsearchcv, machine-learning, matplotlib, seaborn, xgboost-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 1.81 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# HackerEarth--cipla-data-scientist-hiring-challenge
## Data Scientist hiring challenge hosted in HackerEarth website
## This machine learning challenge is about predicting the loan sanction amount from the customer's basic account transaction details and requested loan amount.
# File information
* cipla-data-scientist-hiring-challenge--EDA.ipynb
#### Exploratory data analysis done by using seaborn,matplotlib packages
#### Missing value visualization using missingno package
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* Model---cipla-data-scientist-hiring-challenge.ipynb
#### Missing value imputation and feature engineering
#### Model creation
#### Hyperparameter tuning with gridsearch cross validation
#### cross validation results visualization