🎬 Streaming Wars: Analyzing OTT Content & Building a Recommendation System

Skills: Data Analytics, Machine Learning, Content Recommendation, Python, Pandas, Seaborn

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🚀 Project Overview

The explosion of OTT streaming platforms has led to an overwhelming amount of content. Users often struggle to decide what to watch next. This project tackles this problem by analyzing content from major streaming platforms and building a personalized movie recommendation system.

✅ Analyzed Streaming Platforms:

• Amazon Prime Video
• Apple TV+
• Disney+
• HBO Max
• Netflix
• Paramount+
• Hulu

📌 Datasets from Kaggle:

• Amazon Prime Video
• Apple TV+
• Disney+
• HBO Max
• Netflix
• Paramount+
• Hulu

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🎯 Key Objectives

✔ Analyze streaming platforms to understand content availability & trends
✔ Compare genre popularity, ratings, and exclusive content across platforms
✔ Develop a machine learning-based movie recommendation system
✔ Visualize content distribution across different OTT services

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📊 Data Collection & Preprocessing

Each streaming platform dataset contains information on:

• 🎞 Title (Movie/TV Show name)
• 🎭 Genre (Action, Comedy, Drama, etc.)
• 🎬 Director & Cast
• 📅 Release Year
• ⭐ Ratings & Reviews
• 📺 Platform Availability

✅ Example: Loading Data from Multiple Platforms

import pandas as pd

amazon = pd.read_csv("amazon_titles.csv")
netflix = pd.read_csv("netflix_titles.csv")
disney = pd.read_csv("disney_titles.csv")

# Combine datasets into one master dataframe
ott_content = pd.concat([amazon, netflix, disney], axis=0)
ott_content.head()

💡 Why? – This allows us to compare platforms side by side.

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📈 Exploratory Data Analysis (EDA)

We explore trends in the streaming industry, including genre distribution, ratings, and content exclusivity.

✅ Example: Genre Distribution Across Platforms

import seaborn as sns
import matplotlib.pyplot as plt

sns.countplot(y="genre", data=ott_content, order=ott_content["genre"].value_counts().index)
plt.title("Most Popular Movie Genres Across Streaming Platforms")
plt.show()

💡 Insight:

• Drama & Comedy dominate across platforms.
• Sci-Fi & Horror are more common on Netflix & HBO Max.

✅ Example: Average IMDb Ratings per Platform

sns.boxplot(x="platform", y="imdb_rating", data=ott_content)
plt.title("IMDb Ratings Distribution by Streaming Platform")
plt.show()

💡 Finding:

• Netflix has the highest-rated content on average.
• Amazon & Hulu have more mixed reviews.

✅ Example: Content Exclusivity Analysis

exclusive_content = ott_content.groupby("platform")["title"].nunique()
exclusive_content.plot(kind="bar", title="Exclusive Content per Platform")

💡 Observation:

• Disney+ and HBO Max have the highest proportion of exclusive titles.

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🎬 Building a Movie Recommendation System

We implement a content-based recommendation system that suggests movies based on genre similarity.

✅ Step 1: Convert Genres into Numerical Features

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(stop_words="english")
genre_matrix = vectorizer.fit_transform(ott_content["genre"])

✅ Step 2: Compute Similarity Scores

from sklearn.metrics.pairwise import cosine_similarity

similarity_matrix = cosine_similarity(genre_matrix)

✅ Step 3: Recommend Similar Movies

def recommend_movie(movie_title, num_recommendations=5):
    idx = ott_content[ott_content["title"] == movie_title].index[0]
    similarity_scores = list(enumerate(similarity_matrix[idx]))
    sorted_scores = sorted(similarity_scores, key=lambda x: x[1], reverse=True)[1:num_recommendations+1]

    recommendations = [ott_content.iloc[i[0]]["title"] for i in sorted_scores]
    return recommendations

recommend_movie("Inception", 5)

💡 Example Output for "Inception":
1️⃣ Interstellar
2️⃣ The Matrix
3️⃣ Tenet
4️⃣ Blade Runner 2049
5️⃣ The Prestige

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📊 Model Evaluation & Performance Metrics

To assess the recommendation system's quality, we use:
✔ Precision@K – Measures how many recommended movies are relevant
✔ Diversity Score – Ensures recommendations aren't too similar
✔ User Feedback Simulation – Testing recommendations against user preferences

✅ Example: Evaluating Precision@K

def precision_at_k(recommended_movies, relevant_movies, k=5):
    hits = sum(1 for movie in recommended_movies[:k] if movie in relevant_movies)
    return hits / k

# Example usage
precision_at_k(["Interstellar", "Tenet", "The Matrix"], ["Inception", "Interstellar", "The Prestige"])

💡 Why? – Higher precision means better recommendations.

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🔮 Future Enhancements

🔹 Hybrid Recommendation System – Combine content-based + collaborative filtering
🔹 Sentiment Analysis on Reviews – Understand audience preferences using NLP
🔹 Time-Series Analysis – Predict which genres will trend in the future
🔹 Deploy as a Web App – Using Flask or Streamlit for interactive recommendations

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🎯 Why This Project Stands Out for Data Science & AI Roles

✔ Combines EDA, Data Visualization & Recommendation Systems
✔ Uses Real-World OTT Data for Business Insights
✔ Applies Scikit-Learn & NLP for Movie Recommendations
✔ Explores Cross-Platform Content Availability & Ratings

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🛠 How to Run This Project

1️⃣ Clone the repo:

git clone https://github.com/shrunalisalian/streaming-wars.git

2️⃣ Install dependencies:

pip install -r requirements.txt

3️⃣ Run the Jupyter Notebook:

jupyter notebook "Streaming Wars.ipynb"

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📌 Connect with Me

• LinkedIn: Shrunali Salian
• Portfolio: https://portfolio-shrunali-suresh-salians-projects.vercel.app/
• Email: Your Email

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Reference: https://www.kaggle.com/code/ibtesama/getting-started-with-a-movie-recommendation-system