Credit Card Approval Based on Historical Data
An Applicant is "Good" or "Bad"
Since, Credit Card is very sought feature of A public / private sectors Bank. Since most of the banks makes profit by lending Credit Card to potential customers. They want to sell their product to the potetial customers but at the same time Bank also want to minimize the risk.
In this project, I am trying to find some insights from the given data of applicant and predict which condidate/applicant is Ideal for lenders.
The datasets can be downloaded from https://www.kaggle.com/rikdifos/credit-card-approval-prediction
Modules:
- Matplotlib
- Numpy
- Seaborn
- Jovian
- Pandas
I have taken the course ZeroToPandas, and learned the techniques of EDA.
I have learned key libraries such Numpy, Pandas, Seaborn and matplotlib which is very important and must have skills in Data Science field.
As a first step, let's upload our Jupyter notebook to Jovian.ml.
project_name = "an_applicant_is_good_or_bad_for_credit_card"!pip install jovian --upgrade -qimport jovianjovian.commit(project=project_name)[jovian] Attempting to save notebook..
[jovian] Updating notebook "anandrishu/an-applicant-is-good-or-bad-for-credit-card" on https://jovian.ml/
[jovian] Uploading notebook..
[jovian] Capturing environment..
[jovian] Error: Failed to read Anaconda environment using command: "conda env export -n base --no-builds"
[jovian] Committed successfully! https://jovian.ml/anandrishu/an-applicant-is-good-or-bad-for-credit-card