Loan Eligibility Prediction in Python using H2O.ai
Loan Eligibility Prediction in Python using H2O.ai
In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.
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Camille St. Omer
I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More
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SUBHABRATA BISWAS
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Arvind Sodhi
I have extensive experience in data management and data processing. Over the past few years I saw the data management technology transition into the Big Data ecosystem and I needed to follow suit. I... Read More
Nathan Elbert
This was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... Read More
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What will you learn
Project Description
Business Objective
When a customer applies for a loan at our company, we use statistical models to determine whether or not to grant the loan based on the likelihood of the loan being repaid. The factors involved in determining this likelihood are complex, and extensive statistical analysis and modelling are required to predict the outcome for each individual case.
Aim
You must implement a model that predicts if a loan should be granted to an individual based on the data provided
Tech Stack
- Language : Python
- Libraries : Scikit-learn, H2O, pandas, numpy, flask, Seaborn, Matplotlib
- Containerization : Docker
Dataset Description
The dataset used is an anonymized synthetic data that was generated specifically for use in this project. The data is designed to exhibit similar characteristics to genuine loan data.
In this dataset, you must explore and cleanse a dataset consisting of over 1,00,000 loan records to determine the best way to predict whether a loan applicant should be granted a loan or not.
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