Dataset: data provides information about a video streaming service company, where they want to predict if the customer will churn or not. The CSV consists of around 2000 rows and 16 columns.
Some visualizations to better understand the data. We'll look at:
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Distribution of age and weekly minutes watched.
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Churn rate based on gender.
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Relationship between the number of customer support calls and churn.
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Distribution of the number of days subscribed.
- Distribution of Age: The first plot shows the distribution of age among the customers. It provides insights into the age range and the most common ages of the customers.
- Distribution of Weekly Minutes Watched: This plot illustrates how many minutes customers watch on average per week. It's useful for understanding customer engagement.
- Churn Rate Based on Gender: This chart compares the churn rate between different genders. It helps in identifying if there's a significant difference in churn rates among genders.
- Customer Support Calls vs Churn: This plot shows the relationship between the number of customer support calls made and churn. It helps to understand if frequent calls to customer support are correlated with higher churn rates.
These visualizations can be used to derive insights and make informed decisions based on customer behavior and characteristics. For instance, understanding which age groups are more engaged or which behaviors are more associated with churn can help in targeting specific customer segments more effectively.