feder-cr/DataScienceProject

University project of the course "INTRODUZIONE ALLA DATA SCIENCE" of the computer science university of Genoa

Streaming Platform Data Analysis

Key Features

Objectives

• Perform comparative analysis between Netflix and Disney+ platforms
• Apply full data science pipeline from data collection to insights
• Hands-on practice of data science concepts and techniques

Datasets

• Disney and Netflix title datasets
  • Metadata like title, type, date added, genre etc.
  • Enrichment data like IMDB, TMDB scores
• 4 datasets integrated provide multifaceted view

Data Loading and Preparation

• Importing CSV datasets into dataframes
• Combining titles, enrichment and country datasets
• Handling missing values and duplicate rows
• Data transformations for analysis suitability
  • Extracting year/month from dates
  • Determining number of genres

Exploratory Data Analysis

• Statistical summaries of key variables
• Analysis across dimensions like certification, type etc.
• Comparative analysis across the platforms
• Hypothesis testing for distribution differences

Multidimensional Analysis

• Constructed OLAP cube with dimensions:
  • Month
  • Content type
  • Production country
• Slicing, filtering and aggregation capabilities

Predictive Modeling

• Developed classification model using Logistic Regression
• Predict content type - movie or TV show
• Features: popularity, ratings, metadata
• 85% accuracy on test data

Interactive Visualizations

• Graphics for distributions, comparisons and trends
• Dashboards for slicing OLAP cube on multiple axes

Key Technologies

• Python
• Jupyter Notebook
• Pandas
• Numpy
• Scikit-Learn
• Matplotlib
• Seaborn

What I Learned

Importing, cleaning and transforming medium-size datasets Identifying optimal data formats and structures Applying multivariate analysis techniques Training and evaluating classification models Using visualizations to extract insights