BANK LOAN CASE STUDY 

By Sumit Kumar Prajapat

This Photo by Unknown author is licensed under CC BY-SA-NC.

PROJECT DESCRIPTION

Objective

•  Analyze a loan application dataset for patterns, outliers, imbalances, and correlations related to loan defaults. Build insights for accurate prediction models.

Approach:

• Missing Data: Use Python functions for identification (e.g., isnull()) and impute missing values (e.g., mean, median).
• Outlier Detection: Utilize Python's statistical functions and visualizations to find outliers in numerical variables.
• Data Imbalance: Calculate class frequencies in Python (pandas) and address imbalance through techniques like oversampling/undersampling.
• Descriptive & Bivariate Analysis: Perform univariate analysis (describe()) and explore relationships between variables (correlation).
• Correlation Analysis: Calculate variable-target correlations with Python visualize using heatmaps.

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APPROACH

• I began the project by analyzing a application dataset to understand its structure and identify any missing data. I used Python and its pandas library to handle missing values, employing functions like isnull() to detect missing entries.
• To identify potential outliers that could impact the analysis, I utilized Python's statistical functions and visualizations, such as box plots and scatter plots.
• Next, I addressed data imbalance issues in the dataset using Python's pandas library. I calculated  target variable with value_counts() and considered techniques to balance the data.
• For gaining insights into the dataset, I conducted univariate, segmented univariate, and bivariate analyses using Python's describe() function for descriptive statistics, for segmented analysis, and  bivariate analysis.
• To explore the relationships between variables and the target variable, I calculated correlation coefficients using Python's corr() function and visualized the correlations with heatmaps.
• Throughout the project, I leveraged various Python libraries, including pandas and matplotlib, to efficiently handle, analyze, and visualize the data

TECH STACK USED

• Tech-Stack Used:
• Python: Utilized for data analysis and manipulation.
• PyCharm : Facilitated efficient coding and debugging.
• Libraries:
• pandas: Employed for data handling and analysis.
• Seaborn : Used for creating visualizations, including box plots, scatter plots, and heatmaps.
• matplotlib : Used for additional data visualization capabilities and customization.

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INSIGHT

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• Addressed missing data using Python, ensuring dataset completeness.
• Identified outliers in numerical variables, understanding extreme data points' impact.
• Balanced data distribution, improving prediction model accuracy.
• Discovered patterns and trends through descriptive and bivariate analysis.
• Uncovered significant correlations, highlighting loan default indicators.
• Built accurate prediction models for risk assessment and informed decision-making.
• Enhanced risk management and loan approval procedures for financial institutions.

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RESULT 

• Analyzed the loan application dataset, gaining insights into loan default factors.
• Utilized Python and its libraries for proficient data analysis.
• Handled missing data, identified outliers, and addressed data imbalances.
• Built accurate loan default prediction models, improving risk assessment.
• Emphasized the importance of data-driven decision-making in financial institutions.
• Acquired transferable skills applicable to diverse datasets and real-world scenarios.
• A transformative and enlightening journey in data analysis and financial risk management.

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TASK 1 MISSING DATA

TASK 2 OUTLIERS

TASK 3 DATA IMBALANCED

TASK 4: UNIVARIATE ANALYSIS

TASK 4: SEGMENT UNIVARIATE ANALYSIS

TASK 4: BIVARIATE ANALYSIS

TASK 5: CORELATION FOR PAYMENT DIFFICULTIES

TASK 5: CORELATION FOR NO PAYMENT DIFFICULTIES

THANK YOU 

By Sumit K Prajapat

Drive Link: Data

Video Explanation: First Video

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Third Vedio

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