Hi π, I'm Mrityunjay Pathak
I'm a Data Scientist with a knack for uncovering patterns and trends that drive smarter decisions.
π― Tools and Technologies
β’ Programming Language : I'm familiar with Python, a powerful language for data science and machine learning.
β’ Libraries : I'm also familiar with essential data science libraries like NumPy, Pandas, Matplotlib, Seaborn and Plotly.
β’ Machine Learning : I have experience with Sklearn, a famous machine learning library used widely across industries.
β’ Database : I can work with MySQL, a popular database management system to handle and retrieve data effectively.
β’ BI Tools : I'm familiar with Power BI and Excel to perform data analysis, create dynamic dashboards and extract meaningful insights.
β’ Version Control : I'm familiar with Git, which helps in keeping track of changes in code and collaborating effectively with a team.
π« Connect with Me
Kaggle | LinkedIn | GitHub | Medium | Portfolio
β 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.
β 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.
β 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.
Link : GitHub | Application
β Objective
- To analyze Netflix content data, uncovering valuable insights into how the platform evolves over time.
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- 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.
β Objective
- To analyze Supermarket Sales data, identifying key factors for improving profitability and operational efficiency.
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- 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.