Here's how you can approach the task:
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Data Loading: You will need to access multiple CSV files representing monthly sales data. The first step is to load all of these files into a single dataset. In Python, this can be done by reading each CSV file and concatenating them into a single DataFrame.
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Data Cleaning: The data might have missing or incorrect values. You'll need to inspect the dataset for issues like:
Missing values: These need to be identified and either filled in or removed.
Data formatting: Columns like dates, prices, or product IDs might need to be standardized. For example, dates should be converted into a proper datetime format for
analysis over time.
Duplicate entries: Sometimes datasets contain duplicated rows that need to be removed to ensure accurate analysis.
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Feature Engineering: After cleaning the data, you can create new columns (features) to make your analysis easier. Examples include:
Month/Year: Extract the month and year from the date for time-based analysis.
Sales: If not already available, you can calculate the total sales for each row by multiplying the quantity sold by the price per item.
City: If needed, extract the city from the address or other location-based columns.
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Exploratory Data Analysis (EDA): Now that the data is clean and ready, you can start exploring trends and patterns:
Sales trends: Analyze how sales fluctuate over time (by month, quarter, or year). This can help identify seasonal trends.
Best-selling products: Determine which products are generating the most revenue.
Sales by region: Break down sales data by city or state to see which regions are performing best.
Correlations: Check if there are any relationships between different variables, such as sales volume and price.
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Visualization: Visualization is key to understanding data patterns. You can create:
Line charts for sales trends over time.
Bar charts for comparing sales by product, region, or category.
Heatmaps for visualizing correlations between different variables.