shibinashraf / superstore-da-ibmskillsbuild

Superstore Data Analytics Project

This repository contains the project files and documentation for the Superstore Data Analytics project, completed as part of the IBM SkillsBuild Data Analytics Internship.

Overview

The objective of this project is to perform data analysis on the Superstore dataset to gain insights into the sales and profitability of the store. The analysis involves exploring various attributes such as Ship Mode, Segment, Country, City, State, Postal Code, Region, Category, Sub-Category, Sales, Quantity, Discount, and Profit. By examining these variables, we aim to uncover patterns, trends, and correlations that can inform decision-making and improve business performance.

Dataset

The dataset used for this analysis is the Superstore dataset, which provides information about sales and profit for a variety of products sold by the Superstore. The dataset includes attributes such as Ship Mode, Segment, Region, Category, Sub-Category, and more. It offers a comprehensive view of the store's operations and customer behavior.

Project Files

• SuperstoreDataset.csv: CSV file containing the Superstore dataset.
• analysis.ipynb: Jupyter Notebook containing the code for data analysis and exploration.
• superstore_analysis.pptx: PowerPoint presentation summarizing the key findings and insights from the analysis.

Analysis Steps

1. Data Loading and Preprocessing: The dataset is loaded into a pandas DataFrame, and any necessary preprocessing steps are performed, such as dropping irrelevant columns or handling missing values.

2. Exploratory Data Analysis (EDA): Various EDA techniques are applied to gain insights into the dataset. This includes analyzing the distribution of sales, profit, quantity, and discount, as well as exploring relationships between different attributes. Visualizations such as histograms, bar plots, and correlation matrices are utilized.

3. Key Findings and Insights: The analysis reveals important patterns and trends within the data. Key findings related to sales performance, profitability, customer segments, regional variations, and category-wise performance are highlighted.

4. Conclusion and Recommendations: Based on the analysis, conclusions are drawn regarding the performance of the Superstore. Recommendations for business decisions, such as optimizing shipping methods, focusing on profitable categories, or targeting specific customer segments, are provided.

Getting Started

To reproduce the analysis and view the results, follow these steps:

1. Clone the repository: git clone https://github.com/shibinashraf/superstore-da-ibmskillsbuild.git

2. Install the necessary dependencies: pip install pandas pip install numpy pip install matplotlib pip install seaborn

3. Run the Jupyter Notebook