This data analysis project focuses on automating the loan eligibility process based on customer details provided in online applications. The dataset, provided by Dream Housing Finance, covers data from four counties in Kenya. The analysis aims to gain insights into loan distribution, borrower demographics, loan book evolution over time, and factors influencing funding decisions.

The project consists of Python code for data analysis using libraries such as Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn. The code includes:

1. Loading and understanding the dataset
2. Exploratory Data Analysis (EDA) to visualize loan distribution, demographics, and numerical features
3. Data cleaning, handling missing values, and converting date formats
4. Advanced analysis, including clustering borrowers, predicting loan approval, and deriving new measures
5. Segmentation and potential behavioral insights based on income groups, age brackets, and overconfidence bias

To explore this project:

1. Clone the repository to your local machine.
2. Run the provided Python code in a Jupyter notebook or any Python environment.
3. Ensure you have Python installed on your machine. Specific analyses may require additional packages.
4. Ensure you have the required libraries installed using tools like pip. The comments within the code provide details on necessary installations and steps.

Feel free to modify the code, experiment with different parameters, and expand upon the analysis to suit your goals or specific requirements.

The project is organized into sections, each addressing a specific aspect of the Dream Housing Finance dataset. The structure includes:

• Data Loading and Understanding
• EDA and Data Visualization
• Data Cleaning and Preprocessing
• Advanced Analysis and Modeling
• Segmentation and Behavioral Insights

You can explore the sections sequentially or focus on areas of interest. Each section is self-contained, allowing for independent exploration.

This project wouldn't have been possible without the power of Python and libraries like NumPy, Pandas, and Scikit-learn. I'm especially grateful to the NumPy community for their efficient array manipulation tools and to the Scikit-learn developers for their accessible machine learning algorithms. Additionally, online tutorials and Stack Overflow responses provided invaluable guidance throughout the project.