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.
These use cases focus on key application functionalities like personalized recommendations, country-wise content analysis, and trend visualization.
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## **Movie Enthusiast**
### **Use Case 1: Personalized Recommendations**
**Title**: Discovering Movies and TV Shows Based on Preferences
**Actors**: Sarah Collins (primary user), Netflix Dataset Application
**Scenario**:
Sarah is a movie enthusiast who wants to find personalized content recommendations based on her preferred genres and ratings.
**Steps**:
1. Sarah opens the Netflix Dataset Application.
2. She inputs her preferences:
-**Genres**: Sci-fi, Drama
-**Ratings**: PG-13, R
3. The application filters titles matching her preferences and sorts them by relevance (e.g., release year).
4. Sarah reviews the top 20 recommendations and saves her favorite titles.
5. She explores additional details about each title, such as the duration and cast.
**Outcome**:
Sarah discovers new titles tailored to her preferences, making her next movie night enjoyable and hassle-free.
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### **Use Case 2: Exploring Genre Trends**
**Title**: Analyzing Content Trends Over Time
**Actors**: Sarah Collins (primary user), Netflix Dataset Application
**Scenario**:
Sarah is curious about how the duration of sci-fi and drama movies has evolved over the years.
**Steps**:
1. Sarah selects the "Trend Analysis" feature in the application.
2. She inputs:
-**Content Type**: Movies
-**Genres**: Sci-fi, Drama
3. The application processes the data and displays a line graph showing the average duration of movies in these genres over time.
4. Sarah uses the graph to identify trends, such as the increasing length of sci-fi movies over the last decade.
**Outcome**:
Sarah gains insights into how her favorite genres have evolved, enhancing her appreciation of Netflix's diverse catalog.
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## **Data Science Student**
### **Use Case: Building a Predictive Model**
**Title**: Predicting Release Year Based on Features
Priya is a data science student using the application to predict the release year of Netflix titles based on features like genre and duration.
**Steps**:
1. Priya loads the Netflix Dataset Application and selects the "Model Training" feature.
2. She preprocesses the data:
-**Features**: Genre, Duration
-**Target Variable**: Release Year
3. The application trains a linear regression model and evaluates its performance.
4. Priya uses the trained model to predict release years for test data.
5. She visualizes the predictions with a scatter plot comparing actual vs. predicted release years.
**Outcome**:
Priya successfully builds and evaluates a predictive model, gaining hands-on experience with machine learning workflows.
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## **University Project Team**
### **Use Case: Visualizing Duration Trends**
**Title**: Analyzing Genre-Based Duration Trends
**Actors**: Alex, Maria, and John (team members), Netflix Dataset Application
**Scenario**:
The team wants to analyze how the duration of TV shows and movies in different genres has evolved over time for their data science project.
**Steps**:
1. The team selects the "Trend Analysis" feature in the application.
2. They input:
-**Content Type**: TV Shows
-**Genres**: Comedy, Drama
3. The application preprocesses the data and generates a line graph showing average duration trends by genre.
4. The team saves the graph and incorporates it into their project report.
**Outcome**:
The team identifies meaningful trends in content duration, enhancing their project’s analytical depth.
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## **Global Viewer**
### **Use Case: Exploring Popular Titles in Specific Countries**
**Title**: Discovering Country-Wise Popular Content
**Actors**: Michael Edwards (primary user), Netflix Dataset Application
**Scenario**:
Michael, a global viewer, is curious about the most popular Netflix titles in India.
**Steps**:
1. Michael opens the Netflix Dataset Application and selects the "Country-Wise Recommendations" feature.
2. He inputs:
-**Country**: India
3. The application filters titles popular in India, highlighting the most frequently watched content.
4. Michael reviews the list of top 20 titles sorted by rating priority and release year.
5. He saves a few titles for later viewing.
**Outcome**:
Michael discovers culturally rich and highly rated titles in India, expanding his viewing experience.
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## **Additional Use Cases**
1.**Discovering Trends in Ratings**:
- Visualize how ratings (e.g., PG-13, R) have evolved over time for movies and TV shows.
2.**Custom Predictions**:
- Allow users to input custom preferences and predict release years or duration for new titles.
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## **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.
