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.
• Data Science 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 Tool : I'm familiar with Power BI, which makes it easy to create dynamic dashboards and generate business reports.
• Data Analysis : I can use Excel for data cleaning, data analysis and applying advance functions like formulas and pivot tables.
• Web Application : I have experience with Streamlit, a library that helps create custom web applications for machine learning.
• 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
➔ Objective
- To develop a model that can accurately predict the price of used cars based on various features and attributes.
- The predicted price will assist both buyers and sellers in making informed decisions and ensure fair transactions.
➔ Some Key Findings
- Developed a highly accurate linear model to predict used car prices using various features and attributes.
- Achieved an average prediction accuracy of 82% demonstrating strong model performance.
- Validated model robustness through rigorous k-fold cross-validation, resulting in a mean cross-val score of 83%.
- Created an interactive application using streamlit, enabling users to input data and receive real-time predictions.
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.
