This project aims understand a cohort of people who have difficulties paying back loans to make better business decisions as well as ensure that capable loan applicants are not rejected. It involves the use of Explanatory Data Analysis (EDA) to analyze patterns in the data and find a solution to challenges faced by a financial company.
The approach for this project is defined in the following steps:
- RESEARCH, it was done to understand the dataset, the values and the questions for proper Exploratory Data Analysis.
- Then, the dataset was loaded into the tech-stack that I wanted to use. It was prepared by cleaning it – removing empty and unnecessary values, filtering out rows and columns not important to my analysis.
- Then, analysis was done on this cleaned data.
Python Programming Language, Jupyter Notebook, Pandas Module, Matplotlib Module, Seaborn Module, MS Excel and MS Word were used to execute this project.
A large part of the analysis was done using Python, in Jupyter Notebook, Python modules - Pandas, Matplotlib and Seaborn were used for the analysis and visualisation. Excel was used to visualise and analyze data. MS Word was used to present all this analysis and insight.