Course Content
SQL Data Analytics Project
Purpose: Analyze sales data to identify trends, top-selling products, and revenue metrics for business decision-making. Description: In this project, you will dive into a large sales dataset to extract valuable insights. You will explore sales trends over time, identify the best-selling products, calculate revenue metrics such as total sales and profit margins, and create visualizations to present your findings effectively. This project showcases your ability to manipulate and derive insights from large datasets, enabling you to make data-driven recommendations for optimizing sales strategies. Additional Tools: Excel or data visualization tools like Tableau or Power BI can help you create visually appealing charts and graphs to present your findings effectively. Excel: Used for data manipulation, calculations, and basic visualizations. Data visualization tools like Tableau, Power BI, or Google Data Studio: Used for creating interactive and visually appealing dashboards and charts.
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SQL Projects
About Lesson

Purpose: Segment customers based on attributes to understand target audience and develop targeted marketing strategies.

Description: Understanding your customers is crucial for businesses. In this project, you will work with a customer dataset and use SQL to segment customers based on attributes such as demographics, purchasing behavior, or engagement metrics. By creating customer segments, you will gain insights into different customer groups, enabling you to develop targeted marketing strategies and enhance customer satisfaction. This project highlights your ability to identify valuable customer segments and provide actionable insights for marketing and sales teams.

Additional Tools: Excel or data visualization tools can assist in creating visualizations that illustrate the distinct customer segments you identify.

  • Excel: Used for data manipulation, calculations, and basic visualizations.
  • Data visualization tools like Tableau, Power BI, or Google Data Studio: Used for creating interactive and visually appealing dashboards and charts.

 

1. Data Preparation

2. Exploring the content of variables

  • 2.1 Countries
  • 2.2 Customers and products
    • 2.2.1 Cancelling orders
    • 2.2.2 StockCode
    • 2.2.3 Basket price

3. Insight on product categories

  • 3.1 Product description
  • 3.2 Defining product categories
    • 3.2.1 Data encoding
    • 3.2.2 Clusters of products
    • 3.2.3 Characterizing the content of clusters

4. Customer categories

  • 4.1 Formating data
    • 4.1.1 Grouping products
    • 4.1.2 Time spliting of the dataset
    • 4.1.3 Grouping orders
  • 4.2 Creating customer categories
    • 4.2.1 Data enconding
    • 4.2.2 Creating categories

5. Classifying customers

  • 5.1 Support Vector Machine Classifier (SVC)
    • 5.1.1 Confusion matrix
    • 5.1.2 Leraning curves
  • 5.2 Logistic regression
  • 5.3 k-Nearest Neighbors
  • 5.4 Decision Tree
  • 5.5 Random Forest
  • 5.6 AdaBoost
  • 5.7 Gradient Boosting Classifier
  • 5.8 Let’s vote !

6. Testing the predictions

7. Conclusion

For more data visit – https://www.kaggle.com/code/fabiendaniel/customer-segmentation

 

Exercise Files
data.csv.zip
Size: 7.20 MB