Consumer-Loans-Analysis

This project explores and analyzes the Consumer Loans dataset from Kaggle.

Final data product is described here.

Data Source: Consumer loans dataset | Kaggle

Data Description

The dataset contains information about loan applicants, including their income , marital status, education, and loan outcomes (if the loan was finalized), etc...

Project Goals

• Understand consumer loan characteristics and trends.
• Identify factors influencing loan finalization.
• Develop models to predict loan approval.

Data Analysis and Exploration

• Conduct exploratory data analysis (EDA) to understand distributions of loan amounts and borrower characteristics.
• Visualizing the data using histograms, boxplots, scatter plots, and correlation plots.
• Performing data cleaning and pre-processing (handling missing values, outliers, etc.).

Feature Engineering

• Creating new features

Model Development (still in progress)

Project Structure

Consumer-Loans-Analysis/
  ├── data/               # Raw and processed data
  ├── notebooks/          # Jupyter Notebooks 
  ├── models/             # Trained machine learning models
  ├── pipelines/          # pipelines for processing data 
  ├── reqirements.txt     # needed tool versions
  └── README.md           # This file

Dependencies

• Pandas
• NumPy
• Matplotlib.pyplot
• Scikit-learn (for machine learning)
• Seaborn

Usage

1. Clone the project repository.
2. Install dependencies: pip install -r requirements.txt
3. Run analysis and modeling scripts (e.g., jupyter notebook eda_processing.py).