Credit Risk Prediction Project From Scratch in Python
Credit Risk Prediction Project From Scratch in Python
This is based on prediction of defaulters in bank credit based on the data provided by the bank using past analysis. The result of this project ...
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What you'll learn
- Dataset Setup
- Data Cleaning
- Plotting
- Model Creation
- Testing the Data
This course consist of two parts: Problem statement explanation and Solution explanation with source code.
Part 1: This is the introduction part of the CREDIT RISK PREDICTION Project where we provide the details and procedures of the coming project that we will build in Part2 of this Project. This is based on prediction of defaulters in bank credit based on the data provided by the bank using past analysis. The result of this project will be that we will be able to forecast what are the chances of a person with certain credentials that will be a defaulter or a successful player.
Part 2: This is the second part of the CREDIT RISK PREDICTION Project where we create a complete project on Kaggle Community Platform regarding prediction of Credit Failure of customers based on their credentials. We use data cleaning, data plotting and utilised Random Forest Classifier, Support Vector Machine and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.
Whom is This Course for?
Aspiring machine learning students want to learn on machine learning projects but struggle hard to find interesting ideas and how to build the project. How should students build Machine learning projects, find data science or machine learning project ideas that motivate you, when deciding on a machine project to get started. You can decide the domain and dataset based on your interest. Size of the dataset and complexity of the dataset. If you are a fresher or a beginner, We recommend you get started with ML projects that focus on data cleaning and then move on to analytics, machine learning, and deep learning
