Loans Datasets Project
Exloratory Data Analysis - Loan Data for Dummy Bank
Exploratory Data Analysis (EDA) is the process of exploring, investigating and gathering meaningful insights and nuggets using different kind of statistical measures and visualizations. The objective of EDA is to develop an understanding of data by uncovering trends, relationships and patterns.
When it comes to the requirement of statistical knowledge, visulaization technique and data analysis tools like Numpy, Pandas, Matplotlib, etc. we categories it as an art. When there is reqirement of asking interesting questions to guide the investigation for generating meaningful insight we call it a science. So it is a mixture of both art and science.
How to Run the Code
The best way to learn the material is to execute the code and experiment with it yourself. This tutorial is an executable Jupyter notebook. You can run this tutorial and experiment with the code examples in a couple of ways: using free online resources (recommended) or on your computer.
Option 1: Running using free online resources (1-click, recommended)
The easiest way to start executing the code is to click the Run button at the top of this page and select Run on Binder. You can also select "Run on Colab" or "Run on Kaggle", but you'll need to create an account on Google Colab or Kaggle to use these platforms.
Option 2: Running on your computer locally
To run the code on your computer locally, you'll need to set up Python, download the notebook and install the required libraries. We recommend using the Conda distribution of Python. Click the Run button at the top of this page, select the Run Locally option, and follow the instructions.
- Download and read the dataset.
- Data Processing & Cleaning with Pandas
- Exploratory Analysis and Visualization
- Asking and Answering Questions
- References and Future Work
Download and read the dataset
Let's start by installing the required libraries and importing the useful modules.