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Loan Amount Prediction for Applicants Using Regression Techniques
This project introduces learners to various Data Management, Data Visualizations and Machine Learning techniques to predict loan amount.
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Difficulty: IntermediateFoundational knowledge or experience in statistics or analytics is recommended.
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Duration: Approximately 4 hours
Case Overview
- Regression techniques is one of the commonly used methods when building a machine learning model for accurate future predictions.
- Home Loan Amount eligibility determines the amount of Home Loan that a customer is eligible to get based on the background details provided. Individuals all throughout the world rely on banks in some form to lend them money for a variety of reasons, including overcoming financial limits and achieving personal ambitions.
- This case helps companies to create an analytical model based on historical evidence regarding the amount of home loans based on their eligibility.
- Learn to apply various machine learning techniques and prepare an accurate loan amount prediction model.
What’s included
Flexible Scheduling
Start learning online immediately, at your own pace
Desktop Only
We recommend completing this project on a desktop
Skills You Will Learn
Associated Learning Tasks
- Buying own house is a dream for most people. There’s nothing quite as exciting as purchasing a home for the first time. One of the most important things to be considered for buying a home is to prepare for finances.
- XYZ is a Housing Finance company that deals with home loans. They have a presence across all the major global regions. Customer-first applies for a home loan and after that, the company validates the customer’s loan amount eligibility.
- The company wants to automate the loan amount eligibility process (real-time) based on customers detail provided while filling out the online application form.
- The data is summarized and data treatment operations are executed to clean the data and make it model ready.
- Feature engineering techniques such as Multicollinearity check were performed to select suitable features for model building and numerical values were scaled using the Normalization method.
- Later, the Linear Regression model was trained, and their assumptions were evaluated. Decision Tree and Random Forest were built and their predictions were analyzed.
- Loan amount prediction by machine learning saves time and reduces the human workforce.
- Helps Insurance companies to make a strategic decisions regarding funds allocation.
- Improved customer satisfaction as loan eligibility can determined almost instantly.
How it Works
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