eSigning Classification

This repo consists of the eSigning Classification Case Study from Super Data Science's course, Machine Learning Practical: 6 Real-World Applications.

Problem Statement

The objective of this Case Study is to develop a model to predict for Quality Applicants. In this case study, Quality Applicants are those who reach a Key Part of the Loan Application Process.

Model Used

The case study utilises Logistic Regression Model from the Scikit Learn Library, which is evaluated a number of Metrics like Accuracy Score, Precision Score, F1 Score, Recall Score and many more. Also, we try different models such as SVM (both with Linear and RBF Kernel) and Random Forest Model, to compare the different Models. The Best Model among them is later improved, using 2 different rounds of Grid Search.

Libraries Used

• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scikit Learn

Visualisation of the data

The following plot (Matplotlib Subplots) shows Histograms of the Features from the entire Dataset

Correlation with Response Variable

The following plot (Pandas Bar Plot) shows a measure of the Correlation of the features, with the Response Variable (Churn Likelihood of the User)

Correlation Among the Features

The following plot (Seaborn Heatmap) shows the Correlation among the Features, with each other