Problem

⊳ With the rise of streaming services, viewers now have access to thousands of movies across platforms.

⊳ As a result, many viewers spend more time browsing than actually watching.

⊳ This problem can lead to frustration, lower satisfaction and less time spent on the platform.

⊳ Which can impact both the user experience and business performance.

Solution

⊳ A content-based movie recommender system built with clean and modular code with proper version control.

⊳ It analyzes metadata of 5000+ movies to recommend top 5 similar titles based on a user selected input.

⊳ The system uses techniques like CountVectorizer and CosineSimilarity to recommend similar movies.

⊳ The project not only focuses on functionality but on building a clean and scalable solution.

Impact

If this system gets scaled and integrated with a streaming service, this could :

⊳ Reduce the time users spend choosing what to watch.

⊳ Increase user engagement, watch time and customer satisfaction.

⊳ Help streaming platforms retain users by offering better personalized content.

Problem Statement

⊳ To analyze netflix content data, uncovering valuable insights into how the platform evolve its offerings over time.

Some Key Findings

⊳ Cleaned and analyzed dataset of 8000+ netflix movies and tv shows.

⊳ More than 60% of the content on netflix is rated for mature audience only.

⊳ More than 20% of the movies and tv shows are uploaded on 1st day of the month.

⊳ More than 30% of the content is exclusive for united states.

Problem Statement

⊳ To analyze supermarket sales data, identifying key factors for improving profitability and operational efficiency.

Some Key Findings

⊳ Analyzed purchasing pattern of 9000+ customers of supermarket.

⊳ More than 15% of the products sold were snacks.

⊳ More than 32% of the sales were occurred in west region of the supermarket.

⊳ Health and Soft drinks are the most profitable category in beverages.

⊳ November was the most profitable month contributing about 15% of the total annual profits.