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 count_vectorizer and cosine_similarity 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 evolves over time.

Some Key Findings

⊳ Cleaned and analyzed dataset of 8000+ Netflix Movies and TV Shows.

⊳ More than 60% of content on Netflix is rated for mature audiences.
→ Suggests that Netflix targets adult viewers to boost engagement and retention.

⊳ More than 25% of Movies and TV Shows are released on 1st day of the month.
→ Shows a consistent release schedule, likely to align with subscription cycles.

⊳ More than 40% of the content on Netflix is exclusive to United States.
→ Shows a strong focus on the U.S. market and content availability by location.

⊳ More than 20% of the content on Netflix falls under the "Drama" genre.
→ Confirms that "Drama" is a key part of Netflix's content library.

⊳ More than 23% of the content on Netflix was released in 2019 alone.
→ Indicates a major content push that year, possibly tied to growth or user acquisition goals.

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.
→ Shows that Snacks are a convenient choice and a big source of revenue.

⊳ More than 32% of the sales were occurred in West region of Supermarket.
→ Suggests that West region is a strong performing area as compared to others.

⊳ Health and Soft drinks are the most profitable category in Beverages.
→ Shows that both type of drinks option sells well.

⊳ November was the most profitable month contributing about 15% of the total annual profits.
→ Makes it an ideal time for running promotions and special offers.