Exploratory-data-analysis-EDA-with-pandas-in-banking

Introduction

The purpose of this project is to conduct the exploratory data analysis (EDA) in banking using Pandas framework.

During this project we will do the following

• Explore a banking dataset with Pandas framework.
• Build pivot tables.
• Visualize the dataset with various plot types.

Objectives

In this project, we will try to give answers to a set of questions that may be relevant when analyzing banking data:

• What is the share of clients attracted in our source data?
• What are the mean values of numerical features among the attracted clients?
• What is the average call duration for the attracted clients?
• What is the average age among the attracted and unmarried clients?
• What is the average age and call duration for different types of client employment?

Code and Resources used

Python Version:3.9.12

Packages:pandas,numpy,matplotlib

Data Source:https://archive.ics.uci.edu

Data Collection

The datasets used in this project were downloaded from https://archive.ics.uci.edu. I then read the csv files using the pd.read_csv() command.

Data Cleaning

The data did not require any type of cleaning. There were no missing values and the data was of the correct type.

Exploratory Data Analysis (EDA)

I looked at different distributions for both the numeric and categorical data. Below are highlights from the data visualization section