Qlik AutoML and Data+: Predicting Netflix Movie Ratings?
- larissamiddendorf7
- Mar 11
- 1 min read
In the realm of data-driven decision-making, the integration of machine learning into business intelligence platforms has opened new avenues for predictive analytics. A compelling example of this is the use of Qlik AutoML and Data+ to forecast IMDb ratings for Netflix movies, enabling users to harness advanced analytics without extensive programming expertise.
Seamless Integration of Qlik AutoML and Data+
Qlik AutoML empowers users to develop and deploy machine learning models effortlessly. By integrating Data+, these models can be seamlessly incorporated into Qlik Sense applications, facilitating interactive and dynamic predictions within a familiar environment.
Predicting Netflix Movie Ratings
A recent tutorial demonstrates how to leverage Qlik AutoML and Data+ to predict IMDb ratings for Netflix movies. By considering various features such as genre, director, lead actors, and production year, users can create accurate forecasts of audience ratings.
Enhancing Transparency with SHAP Values
Understanding the factors influencing machine learning predictions is crucial. Qlik AutoML provides SHAP (Shapley Additive Explanations) values, elucidating the impact of each feature on the prediction. This transparency fosters trust in the model and aids in making informed decisions.
Real-Time Updates with Data+
Data+ enables real-time updates and refinements of predictions. Users can adjust input values within Qlik Sense and instantly observe the effects on the forecasts, promoting an interactive and exploratory data analysis experience.
Conclusion
The synergy between Qlik AutoML and Data+ offers a robust framework for predictive analytics within the Qlik platform. The capability to predict complex outcomes, such as Netflix movie ratings, understand the driving factors, and update predictions in real-time provides valuable insights, supporting data-driven decision-making processes.
For a detailed walkthrough and further information, we recommend watching the following tutorial: