E-Commerce Exploratory Data Analysis

Exploratory data analysis is an approach of analyzing data sets to summarize their main characteristics, using different data visualization methods like Matplotlib, Seaborn, Pandas etc.

This project is aimed at conducting exploratory data analysis on e-commerce data using Python. Our goal is to understand the dataset, discover interesting patterns, and extract valuable insights from the data. You can access the dataset we will use for this application by clicking on it.

Project Description

This project will be carried out using the Python programming language. Our main objectives are:

• Gain a general overview of the e-commerce dataset and its contents.
• Apply data cleaning and preprocessing steps, handle missing data, and address data inconsistencies.
• Visualize the data to identify interesting patterns, trends, and outliers.
• Perform basic statistical analyses to answer relevant questions.
• Understand customer profiles through segmentation and grouping analyses.

Technologies Used

• Python: The core programming language for data analysis, manipulation, visualization, and statistical analysis.
• Pandas: A powerful library for data manipulation and analysis.
• NumPy: A fundamental package for numerical computations in Python.
• Matplotlib and Seaborn: Libraries for data visualization.
• PyCharm: An integrated development environment for Python.
• Jupyter Notebook: Used for interactive coding and documenting analysis results.

Analysis Steps

1- Data Loading and Overview:

• Load the e-commerce dataset using Pandas.

• Display basic information about the dataset like column names, data types, and first few rows.

2- Data Cleaning and Preprocessing:

• Handle missing data by imputation or removal.

• Address data inconsistencies and anomalies.

• Convert data types if needed.

3- Data Visualization:

• Create histograms, box plots, and scatter plots to visualize distributions and relationships.

• Use bar plots and pie charts to show categorical data proportions.

• Identify outliers and potential anomalies through visual exploration.