Competetion conducted by American Express on HackerEarth Platform. We are given relevant information about the customers of a company. And we're required to build a Machine Learning Model that can predict if there will be Credit Card Defaulters.
Column Name | Description |
---|---|
customer_id | unique identification of customer |
name | name of customer |
age | age of customer (Years) |
gender | gender of customer (M or F) |
owns_car | whether a customer owns a car (Y or N) |
owns_house | whether a customer owns a house (Y or N) |
no_of_children | number of children of a customer |
net_yearly_income | net yearly income of a customer (USD) |
no_of_days_employed | no. of days employed |
occupation_type | occupation type of customer |
total_family_members | no. of family members of customer |
migrant_worker | customer is migrant worker (Yes or No) |
yearly_debt_payments | yearly debt of customer (USD) |
credit_limit | credit limit of customer (USD) |
credit_limit_used(%) | credit limit used by customer |
credit_score | credit score of customer |
prev_defaults | no. of previous defaults |
default_in_last_6months | whether a customer has defaulted (Yes or No) |
credit_card_default | whether there will be credit card default (Yes or No) |
Score: 100 * (metrics.f1_score(actual, predicted, average='macro'))
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PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.
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In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and few more.
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The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise.