Correlation Heatmaps for Feature Relationships
Understanding how features relate to each other is essential in retail analytics, where many variables—such as sales, inventory, discounts, and customer behavior—can be connected. A correlation matrix lets you quickly see the strength and direction of relationships between every pair of numerical features in your dataset.
In a retail context, a correlation matrix can help you:
- Identify if higher discounts are linked to increased sales;
- Discover whether certain product categories tend to move together;
- Spot connections between inventory levels and customer activity;
- Reveal hidden patterns that influence business outcomes.
By summarizing all pairwise correlations in a single table, you get a clear overview of how your features interact. This insight can guide deeper analysis and support smarter business decisions.
1234567891011121314import pandas as pd # Example retail dataset data = { "sales": [200, 260, 240, 310, 280], "discount": [12, 25, 18, 10, 22], "inventory": [85, 95, 80, 90, 70], "customer_count": [40, 32, 48, 36, 44] } df = pd.DataFrame(data) # Compute the correlation matrix for selected numerical features corr_matrix = df[["sales", "discount", "inventory", "customer_count"]].corr() print(corr_matrix)
12345678import seaborn as sns import matplotlib.pyplot as plt # Visualize the correlation matrix as a heatmap plt.figure(figsize=(6, 4)) sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", fmt=".2f") plt.title("Correlation Heatmap of Retail Features") plt.show()
When reading a correlation heatmap, focus on the following points:
- Values close to
1: strong positive relationship; as one feature increases, the other tends to increase as well. For example, ifsalesandcustomer_counthave a value near1, higher customer count is associated with higher sales; - Values close to
-1: strong negative relationship; as one feature increases, the other tends to decrease. For instance, a value near-1betweendiscountandinventorysuggests higher discounts are linked to lower inventory levels; - Values close to
0: weak or no linear relationship between the features.
Look for the darkest or brightest colors in the heatmap to quickly spot feature pairs with the strongest relationships. These pairs are often the most important for deeper analysis in your retail dataset.
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Understanding how features relate to each other is essential in retail analytics, where many variables—such as sales, inventory, discounts, and customer behavior—can be connected. A correlation matrix lets you quickly see the strength and direction of relationships between every pair of numerical features in your dataset.
In a retail context, a correlation matrix can help you:
- Identify if higher discounts are linked to increased sales;
- Discover whether certain product categories tend to move together;
- Spot connections between inventory levels and customer activity;
- Reveal hidden patterns that influence business outcomes.
By summarizing all pairwise correlations in a single table, you get a clear overview of how your features interact. This insight can guide deeper analysis and support smarter business decisions.
1234567891011121314import pandas as pd # Example retail dataset data = { "sales": [200, 260, 240, 310, 280], "discount": [12, 25, 18, 10, 22], "inventory": [85, 95, 80, 90, 70], "customer_count": [40, 32, 48, 36, 44] } df = pd.DataFrame(data) # Compute the correlation matrix for selected numerical features corr_matrix = df[["sales", "discount", "inventory", "customer_count"]].corr() print(corr_matrix)
12345678import seaborn as sns import matplotlib.pyplot as plt # Visualize the correlation matrix as a heatmap plt.figure(figsize=(6, 4)) sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", fmt=".2f") plt.title("Correlation Heatmap of Retail Features") plt.show()
When reading a correlation heatmap, focus on the following points:
- Values close to
1: strong positive relationship; as one feature increases, the other tends to increase as well. For example, ifsalesandcustomer_counthave a value near1, higher customer count is associated with higher sales; - Values close to
-1: strong negative relationship; as one feature increases, the other tends to decrease. For instance, a value near-1betweendiscountandinventorysuggests higher discounts are linked to lower inventory levels; - Values close to
0: weak or no linear relationship between the features.
Look for the darkest or brightest colors in the heatmap to quickly spot feature pairs with the strongest relationships. These pairs are often the most important for deeper analysis in your retail dataset.
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