This project focuses on analyzing customer shopping data to identify trends, patterns, and useful business insights. The workflow includes loading and exploring the dataset in Python, cleaning the data, performing SQL analysis, and creating a dashboard for visualization.
The dataset contains information about customer shopping behavior, including:
^ Customer details
^ Items purchased
^ Purchase amount
^ Review ratings
^ Shipping types
^ Other transaction-related data
^ Python – Data loading, data cleaning, and exploratory data analysis (EDA)
^ MySQL – Running SQL queries for deeper analysis
^ Power BI – Creating an interactive dashboard
^ Gamma – Creating the project presentation (PPT)
^ Jupyter Notebook – Running Python code and analysis
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Data Loading The dataset was loaded and explored using Python.
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Exploratory Data Analysis (EDA) Basic statistics and visual exploration were performed to understand the dataset.
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Data Cleaning Missing values and inconsistencies were handled to prepare the dataset for analysis.
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SQL Analysis SQL queries were executed in MySQL Server to analyze customer behavior and product trends.
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Dashboard Creation A Power BI dashboard was built to visualize key insights and trends.
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Reporting Findings were summarized in a report and presentation.
The Power BI dashboard visualizes key metrics such as:
^ Total sales and revenue trends
^ Top purchased products
^ Customer rating analysis
^ Shipping type distribution
^ Identified popular products based on customer purchases
^ Analyzed average product ratings
^ Compared different shipping methods
^ Highlighted important customer behavior trends
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Download or clone this repository
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Open the Python notebook to explore the dataset and perform EDA
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Run the SQL queries on MySQL Server for data analysis
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Open the Power BI file to view the dashboard
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Review the report and presentation for project insights
⭐ This project demonstrates skills in data cleaning, data analysis, SQL querying, and data visualization.