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Loan Granting and Status Prediction

In this project, you will study the bank’s data and create a machine learning model that can determine whether or not to grant the loan based on the livelihood of the loan being repaid. We will learn to handle the tabular data for predictive modelling and will build a classification model to predict the Loan status of a customer i.e. ‘Fully Paid’ and ‘Charged off’, based on the given features. We will build different classifiers from simple linear models to ensemble-based models.
Course
Curriculum

What will you Learn in the Project?

As part of this project you will learn following:

  1. Doing Exploring Data Analysis for a better understanding of the data
  2. Handling tabular data for predictive modelling 
  3. Visualizing data for a better understanding
  4. Understanding corrupted data such as missing values, and outliers and treating it 
  5. Building linear and ensemble-based models

Tools Used

  1. Google Colab for training the model
  2. Matplotlib/seaborn library for visualizing the loss & accuracy of the model 
  3. scikit-learn for model building and evaluation

Prerequisite

  1. Working knowledge of scikit-learn library
  2. Theoretical understanding of handling missing values and outliers  
  3. Theoretical understanding of linear regularized models such as (lasso/ridge) and different ensembling learning approaches.
  4. Understanding of different classification models evaluation metrics

You will get hint/theory videos for each task to help you better understand the project.

Tasks Performed

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

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Skills you will develop

Exploring Data Analysis

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

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