CREDIT EDA

Exploratory Data Analysis (EDA)

IIIT Bengaluru (2023)

• The loan providing companies find it hard to give loans to the people due to their insufficient or non-existent credit history. Because of that, some consumers use it to their advantage by becoming a defaulter.
• In this project used EDA to analyse the patterns present in the data and this will ensure that the applicants capable of repaying the loan are not rejected.

⚡️ Business Objectives

This case study aims to identify patterns which indicate if a client has difficulty paying their instalments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc.

⏳ Dataset

This dataset has 3 files as explained below :

• 'application_data.csv' contains all the information of the client at the time of application. The data is about whether a client has payment difficulties.
• 'previous_application.csv' contains information about the client’s previous loan data. It contains the data on whether the previous application had been Approved, Cancelled, Refused or Unused offer.
• 'columns_description.csv' is data dictionary which describes the meaning of the variables.

• Download dataset from here : https://drive.google.com/drive/folders/19PROZVH489GlSHo16bJZ_kzhnaDXbT3X?usp=share_link

🖥️ Installation

🛠️ Requirements

• Python 3.5+

⚙️ Setup

1. Install Numpy :-

$ pip install numpy

2. Install Pandas :-

$ pip install pandas

3. Install Matplotlib :-

pip install matplotlib

4. Install Seaborn :-

$ pip install seaborn

📝 Description

Presenting the overall approach of the analysis in a presentation. Mention the problem statement and the analysis approach briefly.

Identifying the missing data and used appropriate method to deal with it. (Removing columns/or replace it with an appropriate value)

Identifying if there are outliers in the dataset. Also, mentioned why it is an outlier.

Identifying if there is data imbalance in the data. And Finding the ratio of data imbalance.

Explaining the results of univariate, segmented univariate, bivariate analysis, etc. in business terms.

Finding the top 10 correlation for the Client with payment difficulties and all other cases (Target variable)

Included visualisations and summarised the most important results in the presentation.

Used graphs to explain the numerical/categorical variables.

Explained Insights, why the variable is important for differentiating the clients with payment difficulties with all other cases.

Conclusion and Recommendation.

📜 Credits

Rishabh Tiwari