This project involves analyzing sales data from a fictional coffee shop chain, Maven Roasters. The dataset contains detailed information about transactions, such as product details, transaction amounts, store locations, and more. The goal of this analysis is to gain insights into sales trends, customer behavior, and product performance.
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Data Loading & Exploration:
- Libraries such as Pandas, NumPy, Seaborn, and Matplotlib were used.
- The dataset was loaded and its structure examined.
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Data Cleaning:
- Missing values were checked, and the data types were examined for consistency.
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Exploratory Data Analysis (EDA):
- Visualizations were created to explore sales patterns, such as sales distribution across different locations and products.
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Insights:
- Key findings related to customer behavior, best-selling products, and store performance were drawn from the data.
- Install necessary libraries:
pip install pandas numpy seaborn matplotlib
- Load the dataset and run the analysis by following the steps in the Jupyter notebook.
The analysis revealed key insights such as:
- Top-selling products.
- Sales trends across different stores and time periods.
- Revenue generation across various store locations.
This project provides useful insights into the coffee shop’s sales performance and customer behavior. These insights can help in decision-making for inventory management, store expansion, and product marketing strategies.