Problem Statement:

• The main goal was to create a performance dashboard to evaluate and analyze the success of Netflix movies and shows.
• Key performa
  • Ratings (user ratings and average ratings).
  • Genre Popularity (how different genres perform on Netflix).
  • Release Patterns (impact of release date on viewership).
  • Lead Actors and Directors (relationship between cast/crew and performance).
  • Country-specific Performance (regional preferences and success).

Project Objectives:

• To help Netflix’s content and marketing teams understand the factors that contribute to a movie/show’s success.
• To identify trends, such as the most popular genres or the impact of specific actors/directors on ratings and viewership.
• To allow stakeholders to explore data and make informed decisions for future content creation and marketing.

---

Tools & Technologies Used:

• Python: Used for data cleaning, handling missing values, and preprocessing the data.
  • Libraries like Pandas and NumPy were utilized for data manipulation and preparation.
• Power BI: Used to create an interactive, visually appealing dashboard with:
  • Various charts (bar, line, pie, heatmaps) for visualizing Netflix performance metrics.
  • Custom interactive filters for users to drill down into specific data points (e.g., genre, region, or director).
• DAX Functions in Power BI: Used to create calculated measures for:
  • Top Actors (e.g., identifying which actors are associated with the most successful movies/shows).
  • Top Directors (assessing the impact of directors on content performance).
  • Top Genres (analyzing genre popularity based on user ratings and views).

Data Cleaning & Preprocessing:

• Handling Null Values:
  • Applied appropriate techniques to handle missing or null values. For example, used forward filling, mean imputation, or removed rows based on the criticality of the missing data.
• Data Standardization:
  • Ensured consistency across categorical variables such as genre, country, and director names.
• Duplicate Removal:
  • Identified and removed duplicate entries to ensure the dataset’s integrity.
• Data Transformation:
  • Categorical variables were encoded, and numerical features were normalized or standardized as necessary to prepare the data for analysis.

DAX Functions for Calculated Measures:

• Top Actors Measure:
  • Created DAX measures to calculate the top-performing actors by aggregating ratings and viewership.
• Top Directors Measure:
  • Used DAX to measure director performance based on the success of the content they directed (viewership, ratings).
• Top Genres Measure:
  • Created measures to identify which genres are performing best on Netflix by aggregating ratings and views across genres.

Dashboard Features & Visualizations:

• Ratings Analysis:
  • A bar chart showing average ratings across different genres or regions.
• Genre Popularity:
  • Pie charts illustrating the distribution of genres based on viewer ratings and views.
• Top Actors & Directors:
  • Calculated measures using DAX to identify the top actors and directors based on average ratings and viewership.
  • Bar charts highlighting the actors and directors who contributed to successful movies and shows.
• Release Patterns:
  • Line charts showing the correlation between the release date and performance (viewership spikes, seasonal patterns).
• Country-specific Insights:
  • Heatmaps and bar charts visualizing Netflix’s performance in different countries, helping to understand regional preferences.

Key Insights & Findings:

• Genre Preferences:
  • Identified the most successful genres based on ratings and views (e.g., drama, action, comedy).
• Actor/Director Impact:
  • Found that certain actors or directors consistently contributed to higher ratings or more successful content.
• Release Timing:
  • Discovered patterns showing that content released during specific months or seasons saw more viewership, helping to identify optimal release windows.
• Regional Performance:
  • Revealed which countries have distinct content preferences, such as particular genres or actor choices.

Challenges Faced:

• Handling Large Datasets:
  • Working with a massive volume of data, requiring careful optimization for speed and performance.
• Data Quality:
  • Managing incomplete data from multiple sources and ensuring consistency in ratings, genre names, and country codes.
• Complex Calculations in DAX:
  • Developing the correct DAX measures for top actors, directors, and genres required a deep understanding of data relationships.

Impact of the Project:

• The dashboard provided actionable insights for Netflix’s content team, marketing team, and decision-makers by enabling them to assess content performance in real time.
• It helped Netflix optimize content strategy by identifying top-performing content types and guiding future content creation based on successful patterns.

Future Enhancements:

• Predictive Analysis:
  • Implementing machine learning models to predict the future performance of upcoming content based on past trends.
• Sentiment Analysis:
  • Analyzing user reviews with NLP to gain deeper insights into viewer sentiment.
• Viewer Retention Analysis:
  • Adding features to track how long viewers stay engaged with content and identifying patterns related to viewer retention.