This classification Project for the banking domain provides hands-on skills to handle tabular data for banking domain, and build classification models to predict the Loan status of the customer.
Loan granting and status prediction is a classification project for the banking domain. It is used to predict whether a loan will be granted or not. The project is based on data from the UCI Machine Learning Repository.
The project uses a Support Vector Machine (SVM) to learn from the data and predict the status of new loan applications. The SVM is trained on 80% of the data and tested on the remaining 20%. The accuracy of the project is 96.2%.
What will you Learn in the Project classification Project for banking domain?
As part of this project you will learn the following:
- Doing Exploring Data Analysis for a better understanding of the data
- Handling tabular data for predictive modelling
- Visualizing data for a better understanding
- Understanding corrupted data such as missing values, and outliers and treating it
- Building linear and ensemble-based models
- Google Colab for training the model
- Matplotlib/seaborn library for visualizing the loss & accuracy of the model
- scikit-learn for model building and evaluation
- Working knowledge of scikit-learn library
- Theoretical understanding of handling missing values and outliers
- Theoretical understanding of linear regularized models such as (lasso/ridge) and different ensembling learning approaches.
- Understanding of different classification models evaluation metrics
You will get hint/theory videos for each task to help you better understand the project.
As part of this project we will be performing following tasks:
Task-1: Load the data and perform EDA for data understanding
Task-2: Cleaning and preprocessing of the data
Task-3: Treat outliers and impute null values
Task-4: Prepare the data for model building
Task-5: Build different Machine Learning models and compare the performance