The following use cases summarize how the Netflix Dataset Model Application assists different types of users in analyzing content trends, making predictions, and exploring global and genre-based recommendations.
The following use cases summarize how the Netflix Dataset Model Application and Chat Bot assists different types of users in analyzing content trends, making predictions, and exploring global and genre-based recommendations.
These use cases focus on key application functionalities like personalized recommendations, country-wise content analysis, and trend visualization.
These use cases focus on key application functionalities like personalized recommendations, country-wise content analysis, and trend visualization.
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@@ -95,36 +96,56 @@ The team wants to analyze how the duration of TV shows and movies in different g
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@@ -95,36 +96,56 @@ The team wants to analyze how the duration of TV shows and movies in different g
The team identifies meaningful trends in content duration, enhancing their project’s analytical depth.
The team identifies meaningful trends in content duration, enhancing their project’s analytical depth.
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# Use Cases for Chatbot
## **Global Viewer**
## Use Case 1: Personalized Movie Recommendations
### **Use Case 5: Exploring Popular Titles in Specific Countries**
### Title: Recommending Tailored Movies for Users
**Title**: Discovering Country-Wise Popular Content
### Actors:
**Actors**: Michael Edwards (primary user), Netflix Dataset Application
-**User:** Movie enthusiast seeking recommendations
-**Chatbot:** Recommending movies based on user preferences
**Scenario**:
### Scenario:
Michael, a global viewer, is curious about the most popular Netflix titles in India.
A user wants the chatbot to suggest movies based on their preferences to enjoy a weekend watch party.
**Steps**:
### Steps:
1. Michael opens the Netflix Dataset Application and selects the "Country-Wise Recommendations" feature.
1. The user initiates the chatbot and requests movie recommendations.
2. He inputs:
2. The chatbot asks the user for preferences:
-**Country**: India
-**Genre:** Action, Comedy
3. The application filters titles popular in India, highlighting the most frequently watched content.
-**Rating:** Family-friendly (G, PG, PG-13)
4. Michael reviews the list of top 20 titles sorted by rating priority and release year.
3. The chatbot filters its dataset to find movies matching the user's criteria.
5. He saves a few titles for later viewing.
4. The chatbot presents a list of recommendations sorted by popularity or relevance.
5. The user selects a title and asks for additional details, such as runtime or cast.
**Outcome**:
### Outcome:
Michael discovers culturally rich and highly rated titles in India, expanding his viewing experience.
The user receives tailored movie suggestions and selects a title for their watch party, enhancing their experience with personalized recommendations.
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## **Additional Use Cases**
## Use Case 2: Providing Model Evaluation Metrics
1.**Discovering Trends in Ratings**:
- Visualize how ratings (e.g., PG-13, R) have evolved over time for movies and TV shows.
### Title: Explaining Machine Learning Model Performance
2.**Custom Predictions**:
### Actors:
- Allow users to input custom preferences and predict release years or duration for new titles.
-**User:** Data science enthusiast
-**Chatbot:** Explaining model evaluation metrics and insights
### Scenario:
A user wants the chatbot to explain the evaluation metrics of a genre prediction model and provide insights.
### Steps:
1. The user interacts with the chatbot to learn about the model evaluation process.
2. The chatbot explains key metrics used for evaluating the model:
-**Accuracy:** Measures correct predictions.
-**F1-Score:** Balances precision and recall, useful for imbalanced datasets.
-**ROC-AUC:** Evaluates the model's ability to distinguish genres.
3. The user asks for the latest accuracy figure, and the chatbot responds with the current accuracy (e.g., 85%).
4. The chatbot offers additional details, such as performance across specific genres or datasets.
### Outcome:
The user gains a clear understanding of the model’s evaluation metrics and its overall performance, aiding in their data science learning journey.
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## **Summary**
## **Summary**
The Netflix Dataset Model Application serves diverse users, enabling them to explore personalized recommendations, global content trends, and data-driven insights. Each use case showcases the application’s versatility in enhancing the Netflix viewing experience and supporting data science learning.
The Netflix Dataset Model Application and Chat Bot serves diverse users, enabling them to explore personalized recommendations, global content trends, and data-driven insights. Each use case showcases the application’s versatility in enhancing the Netflix viewing experience and supporting data science learning.
