Complete a predictive analytics exercise in Python to create item groups based on common buyers and use them for recommendations.
- [Instructor] In this lecture,…we are going to see an implementation…of the use case for building recommendations.…So when a customer comes to your website…and has bought a product,…you want to recommend him products…that similar people bought.…So in this exercise, we're going to use data about…which customers bought which product…and, based on that, build an item to item affinity score,…and then use it for recommendation.…The exercise files are available under the folder 03_05.…
The data file is the ratings.csv,…and the notebook is the Recommendations.…This is the data file.…So data file is very simple, which user bought which item.…So we have user IDs and item IDs.…So users 1,001…seem to have bought 5,001, 5,002, and 5,005,…and so on, pretty simple, straightforward data file.…Moving on to the notebook,…the notebook is the Recommendations notebook…that we just talked about.…
What we're going to do here is, first,…we're going to load the ratings.csv file and take a look at it.…Now we have to build an affinity score between items…
Start off by learning about the various phases in a customer's life cycle. Explore the data generated inside and outside your business, and ways the data can be collected and aggregated within your organization. Then review three use cases for predictive analytics in each phase of the customer's life cycle, including acquisition, upsell, service, and retention. For each phase, you also build one predictive analytics solution in Python. In the final videos, author Kumaran Ponnambalam introduces best practices for creating a customer analytics process from the ground up.
- Understanding the customer life cycle
- Acquiring customer data
- Applying big data concepts to your customer relationships
- Finding high propensity prospects
- Upselling by identifying related products and interests
- Generating customer loyalty by discovering response patterns
- Predicting customer lifetime value (CLV)
- Identifying dissatisfied customers
- Uncovering attrition patterns
- Applying predictive analytics in multiple use cases
- Designing data processing pipelines
- Implementing continuous improvement
Skill Level Intermediate
Business Analytics: Prescriptive Analyticswith Alan Simon2h 40m Intermediate
Data Science Foundations: Python Scientific Stackwith Miki Tebeka3h 37m Intermediate
1. Customer Analytics Overview
2. Will You Become My Customer?
3. What Else Are You Interested In?
4. How Much Is Your Future Business Worth?
5. Are You Happy With Me?
6. Will You Leave Me?
7. Best Practices
Choose the right data1m 19s
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