Hi 👋, I'm Mrityunjay Pathak
I'm a Data Scientist with a knack for uncovering patterns and trends that drive smarter decisions.
🎯 Tools and Technologies
• Programming Language : I'm familiar with Python, a powerful language for data science and machine learning.
• Libraries : I'm also familiar with essential data science libraries like NumPy, Pandas, Matplotlib, Seaborn and Plotly.
• Machine Learning : I have experience with Sklearn, a famous machine learning library used widely across industries.
• Database : I can work with MySQL, a popular database management system to handle and retrieve data effectively.
• BI Tools : I'm familiar with Power BI and Excel to perform data analysis, create dynamic dashboards and extract meaningful insights.
• Version Control : I'm familiar with Git, which helps in keeping track of changes in code and collaborating effectively with a team.
📫 Connect with Me
Kaggle | LinkedIn | GitHub | Medium | Portfolio
➔ 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.
Link : GitHub | Application
➔ Objective
- 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.
➔ Objective
- 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.
