Exploratory Data Analysis and Modeling on Loan Dataset

In this project, I conducted an in-depth exploratory data analysis (EDA) and statistical testing on a loan dataset using Python in a Jupyter Notebook environment using libraries such as pandas for data manipulation, seaborn and matplotlib for visualization, scipy for statistical hypothesis testing, and scikit-learn for model development to uncover hidden insights, examine relationships between variables, and understand patterns that may influence loan decisions.

Understand the dataset’s structure, key variables, and overall trends.

Identify demographic, financial, and credit-related factors impacting loan decisions.

Detect and address missing values, outliers, and inconsistencies in the data.

Explore relationships between predictor variables and the loan outcome variable.

Build and evaluate a regression model to estimate target outcomes.

Imported and cleaned the dataset using pandas and numpy, standardizing formats and handling missing values.

Performed descriptive statistical analysis for both numerical and categorical features.

Visualized data distributions and relationships using seaborn and matplotlib.

Conducted correlation analysis and statistical hypothesis testing using scipy.

Encoded categorical features, scaled numerical variables, and prepared data for modeling.

Developed and evaluated a regression model using scikit-learn.

Found that credit history, applicant income, and loan amount were the strongest predictors of loan approval.

Identified demographic influences, such as marital status and education, on loan decisions.

Successfully built a regression model that demonstrated strong explanatory power for predicting loan approval probabilities.

Delivered a comprehensive analysis report with clear visuals, insights, and actionable recommendations.