---

Kiva Loan Data Analysis

Unlocking Insights in Microfinance with Data Science

Welcome to the Kiva Loan Data Analysis project, a comprehensive exploration of microfinance data from Kiva.org. This repository offers a complete pipeline for preparing and analyzing loan data, transforming raw datasets into actionable insights through state-of-the-art methods and compelling visualizations.

---

Project Overview

This project is divided into two main components:

1. Data Preparation:
   Innovative data cleaning, stratification, and feature engineering processes ensure the dataset is analysis-ready. Techniques include:

   • Filling missing values intelligently.
   • Stratifying data for balanced machine learning models.
   • Creating derived features to enrich the dataset.

2. Exploratory Data Analysis:
   Visual storytelling and statistical analysis are used to uncover trends and patterns in Kiva loans. Highlights include:

   • Interactive dashboards built with Plotly.
   • Static and dynamic visualizations using Seaborn and Matplotlib.
   • Detailed analysis of loan distributions, repayment patterns, and demographic insights.

---

Repository Structure

/Kiva-Loan-Analysis
├── Data-Preparation
│   ├── Kiva_Analysis_01_DM.ipynb    # Data preparation notebook
│   ├── README.md                    # Description of the preparation phase
│   └── requirements.txt             # Dependencies for preparation
├── Visualization
│   ├── Kiva_Analysis_02_EDA.ipynb   # Visualization and analysis notebook
│   ├── README.md                    # Description of the visualization phase
│   └── requirements.txt             # Dependencies for visualization
├── LICENSE                          # License details
└── README.md                        # Main project description

---

Key Features

🛠 Data Preparation

• Advanced cleaning methods to handle missing and inconsistent data.
• Stratification of data for improved analytical accuracy.
• Feature engineering for richer insights.

📊 Data Visualization

• Interactive dashboards for deep dives into data patterns.
• Geospatial and temporal visualizations for global insights.
• Statistical comparisons across demographics and loan categories.

---

Tools and Technologies

• Programming: Python
• Libraries: Pandas, Matplotlib, Seaborn, Plotly, Scikit-learn
• Environment: Jupyter Notebooks
• Data Source: Kaggle Dataset - Kiva Loans

---

Getting Started

1. Clone the repository:

   git clone git@github.com:whellcome/KivaLoanDataAnalysis.git
   cd KivaLoanDataAnalysis

2. Install dependencies:

   • For data preparation:

     pip install -r Data-Preparation/requirements.txt

   • For visualization:

     pip install -r Visualization/requirements.txt

3. Open the notebooks:

   jupyter notebook

4. Run:

   • Data Preparation: Data-Preparation/Kiva_Analysis_01_DM.ipynb
   • Visualization: Visualization/Kiva_Analysis_02_EDA.ipynb

---

Future Improvements

• Automate data cleaning and visualization pipelines.
• Expand analysis with external datasets.
• Integrate machine learning models for predictive insights.
• Create a web app for real-time data exploration.

---

Contributing

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.

---

Support

If you find this project insightful, consider supporting it by buying me a coffee.

---

Kiva Loan Data Analysis
Empowering microfinance through data.

---