Bank Loan Case Study
Bank Loan Case Study
DURATION
1 Week
DURATION
1 Week
CLIENT
Loan Approval Analytics: Identifying Key Factors for Bank Loan Success
CLIENT
Loan Approval Analytics: Identifying Key Factors for Bank Loan Success
Data Cleaning & Preprocessing
Data Cleaning & Preprocessing
Risk Pattern Identification
Risk Pattern Identification
Outlier Detection
Outlier Detection
EDA
EDA
PROJECT OVERVIEW
PROJECT OVERVIEW
This project simulates real-world financial risk analysis by investigating customer data to prevent loan defaults. The insights aim to help lenders reduce financial losses while ensuring eligible applicants are not wrongly rejected.
This project simulates real-world financial risk analysis by investigating customer data to prevent loan defaults. The insights aim to help lenders reduce financial losses while ensuring eligible applicants are not wrongly rejected.
The Challenge
The Challenge
Managing missing values and outliers without distorting key financial indicators was critical for maintaining analysis integrity.
Managing missing values and outliers without distorting key financial indicators was critical for maintaining analysis integrity.
WHAT WE DID
WHAT WE DID
Performed Exploratory Data Analysis (EDA) using Excel to clean data, handle missing values, detect outliers, and analyze applicant behavior. Visualized customer and loan patterns to identify risk factors influencing defaults and improve approval decisions.
Performed Exploratory Data Analysis (EDA) using Excel to clean data, handle missing values, detect outliers, and analyze applicant behavior. Visualized customer and loan patterns to identify risk factors influencing defaults and improve approval decisions.
🔍 What I Learned / Gained:
Improved my data wrangling skills and gained practical knowledge of classification problems using logistic regression.
Developed a keen sense for identifying risk indicators in loan applications.
🛠️ Tools & Technologies Used:
Python | NumPy | Pandas | Scikit-learn | Logistic Regression | Jupyter Notebook
🔍 What I Learned / Gained:
Improved my data wrangling skills and gained practical knowledge of classification problems using logistic regression.
Developed a keen sense for identifying risk indicators in loan applications.
🛠️ Tools & Technologies Used:
Python | NumPy | Pandas | Scikit-learn | Logistic Regression | Jupyter Notebook
Tannu Antil
Tannu Antil
