Learn how to segment and profile the source data to understand patterns hidden in data.
- [Instructor] Segmentation and profiling of data…is one of the primary goals of exploratory data analysis.…Data is summarized through multiple profiling variables…and analysis is done to understand causal relationships.…In this example, we are going to find conversion ratio…based on the discount percentage offered.…Conversion ratio is the total number of customers…who converted divided by the total number of email offers…that was sent.…
In order to do this analysis,…we first summarize data using the groupby…to find total offers and total conversions…by the discount percentage.…We then merge the data into a single DataFrame.…Then we compute conversion ratio…by dividing the number of conversions…by the number of offers.…We finally print the DataFrame.…The same data is then used to create a bar graph…to show conversion ratios by discount rate.…
From the bar graph, it is easy to interpret…that higher the discount rate,…the higher the conversion rate.…
- Setting up Cloud DataLlb for exploratory data analytics
- Segmentation and profiling
- Reading and writing data from BigQuery
- Managing cloud storage buckets
- Creating visualizations of BigQuery data with the GCP Charting API
- Managing Datalab instances
Skill Level Intermediate
Predictive Customer Analyticswith Kumaran Ponnambalam1h 37m Intermediate
1. Exploration Options in GCP
2. Cloud Datalab Basics
3. Datalab: BigQuery
4. Datalab: Cloud Storage
5. Datalab: Visualizations
6. EDA with GCP: Use Case
7. Managing Datalab
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