@@ -30,9 +31,6 @@ Chatbot: You’re welcome! Enjoy the movie, and let me know if you’d like any
#### Dialog 2: Family-Friendly Action Flicks
##### User Persona: Movie Enthusiast
##### System Persona: Netflix Dataset Chatbot
User: Hello!
Chatbot: Hi there! I can help you find movies, predict genres, or explore any film data you need. What can I assist you with today?
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@@ -57,52 +55,95 @@ Chatbot: You’re welcome! Feel free to come back if you’d like more recommend
### User Persona: Content Strategist
### System Persona: Netflix Dataset Chatbot
#### Dialog 2: Analyzing Genre Trends
#### Dialog 1: Delving Into Viewer Demographics
User: Hi.
**User**: Hi.
Chatbot: Hello and welcome! I can assist you with exploring movie genre predictions, data insights, or any analysis you need on the Netflix dataset. How can I help you today?
**Chatbot**: Hello and welcome! I am here to assist you with predicting the genre of any movie you have in mind. By providing some details like the director's name, movie duration, and launch date, I can make an educated guess about its genre. How can I help you get started today?
User: I’d like to know about viewer demographics for comedy movies. Any insights?
**User**: I’m looking for genre trends. Can you help?
**Chatbot**: Absolutely! The most popular genres on Netflix are:
1. Drama (1,200 movies)
2. Comedy (900 movies)
3. Action (700 movies)
Chatbot: Sure! According to our data:
- Comedy films have the highest viewership among the 18-34 age group.
- Peak streaming times are usually weekends between 6 PM and 9 PM.
**User**: What about the most popular directors?
**Chatbot**: Here are the top directors based on the number of movies:
- Steven Spielberg: 50 movies
- Martin Scorsese: 45 movies
- Quentin Tarantino: 40 movies
User: How about drama? Do they also appeal to the same demographic?
**User**: This is helpful. Thanks!
**Chatbot**: Glad I could help! Let me know if you'd like more data insights.
Chatbot: Dramas show a broader appeal, typically watched by a 25-45 age range, with viewership spikes on weekday evenings.
User: This is great info. Thank you!
Chatbot: Glad to help! Let me know if you need any more strategic insights.
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#### Dialog 2: Comparing Seasonal Trends
User: Hi again!
Chatbot: Hello! Ready for more data-driven insights on Netflix trends?
User: Yes. I want to compare viewership trends for different genres across seasons. Can you help?
Chatbot: Certainly! Here’s what we see in the data:
- Winter: Higher viewership in drama and romance genres (holidays boost these categories).
- Summer: Action and comedy tend to spike, likely due to more family gatherings and free time.
User: Interesting. Any specific months that stand out?
Chatbot: December and January show notably high drama viewership, while June and July see a surge in action/comedy streaming.
User: That’s very useful. Thanks!
Chatbot: You’re most welcome! I’m here if you need more insights for your content strategy.
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### User Persona: Data Science Student
### System Persona: Netflix Dataset Chatbot
#### Dialog 3: Exploring Data Preprocessing
#### Dialog 1: Model Evaluation Metrics
User: Hello.
Chatbot: Hi there! I can explain our data preprocessing steps, show you model metrics, or help with any other data-related insights you might need. What are you interested in today?
User: Can you tell me about how you evaluate your genre prediction model?
Chatbot: Absolutely! We use several metrics:
1. Accuracy: Gives the percentage of correctly predicted genres.
2. F1-Score: Balances precision and recall, especially useful if some genres have fewer movies.
3. ROC-AUC: Measures how well the model distinguishes between genres.
User: Can you share the latest accuracy figure?
Chatbot: Our current genre classification model is at about 85% accuracy across 5 major genres.
User: That’s impressive. Thank you!
**User**: Hi.
Chatbot: You’re welcome! Let me know if you need additional model details.
**Chatbot**: Hello and welcome! I am here to assist you with predicting the genre of any movie you have in mind. By providing some details like the director's name, movie duration, and launch date, I can make an educated guess about its genre. How can I help you get started today?
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#### Dialog 2: Feature Engineering Approaches
User: Hello again.
Chatbot: Welcome back! How can I help with your data science queries today?
**User**: Can you explain how the data is preprocessed?
**Chatbot**: Sure! Here are the preprocessing steps:
1. Missing values are filled or imputed.
2. Columns like duration and release year are scaled using StandardScaler.
3. Genres are encoded into numerical values for analysis.
User: I’m curious about how you engineer new features from the raw data. Any examples?
**User**: Can you show me the correlation heatmap?
**Chatbot**: Here is the correlation heatmap of the dataset: (*Heatmap displayed*)
Chatbot: Sure! Here are a few:
1. Director Frequency: We count how often a director appears in the dataset to capture their popularity.
2. Seasonal Release: We categorize launch dates into seasons (e.g., winter, summer) to see if release timing influences genre.
3. Title Keyword Extraction: We perform basic NLP to identify key words in titles that might hint at genres.
**User**: What are the most important features in your model?
**Chatbot**: The top features are:
- Duration (45% importance)
- Release Year (30% importance)
- Director Count (25% importance)
User: That’s great. Do you also use one-hot encoding for any categorical data?
**User**: Thanks for the details!
**Chatbot**: You're welcome! Feel free to explore more insights with me.
Chatbot: Yes, for columns like country and language, we apply one-hot encoding to convert them into numerical form for the model.
User: Perfect. Thanks so much!
Chatbot: You’re welcome! Feel free to ask if you need more details on feature engineering or model architecture.
