This repository contains the code and analysis for my Superstore data analysis project. The project focuses on exploring and visualizing the Superstore dataset to gain insights into the sales and customer behavior.
In this project, I performed a comprehensive analysis of the Superstore dataset, which includes data on sales, products, customers, and regions. The main objectives of the analysis were to identify patterns, trends, and correlations within the data and provide actionable insights for improving business performance.
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Data Cleaning: I conducted thorough data cleaning to handle missing values, outliers, and inconsistencies in the dataset. This step ensured the accuracy and reliability of the subsequent analysis.
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Exploratory Data Analysis (EDA): Using various statistical techniques and visualization libraries such as Pandas, Matplotlib, and Seaborn, I explored the Superstore dataset. I investigated sales trends, customer demographics, product categories, and regional performance to uncover meaningful patterns and relationships.
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Data Visualization: I employed data visualization techniques to present the findings in a visually appealing and understandable manner. Through interactive charts, graphs, and maps, I highlighted key insights and trends within the data.
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Statistical Analysis: I applied statistical methods, including hypothesis testing and correlation analysis, to derive insights and validate observations. This helped in identifying significant factors impacting sales and customer behavior.
Some of the key findings and outcomes of the analysis include:
- Identification of the top-selling product categories and subcategories.
- Analysis of regional sales performance and identification of high-performing regions.
- Understanding customer demographics and their buying preferences.
- Correlation analysis between different variables, such as discounts, profit, and quantity sold.
- Identification of potential areas for improvement and strategies to optimize sales and profitability.