Hello! My name is Mrityunjay Pathak.

I'm a data scientist passionate about building real-world, end-to-end data solutions - from data analysis and dashboards to machine learning and deployment. I love creating projects that don't just stay in notebooks, but live on the internet - making them interactive, accessible and valuable for everyone.

Some projects I've worked on :

• AutoIQ : Car Price Prediction
  • Built a car price prediction system using FastAPI and Docker, trained on 2,800+ scraped car records from Cars24.
  • Deployed an interactive HTML/CSS/JS application on GitHub Pages that connects to the API, allowing users to get real-time price predictions.
• Pickify : Movie Recommender System
  • Built a content-based movie recommender system using metadata from 5,000+ movies.
  • Integrated the TMDB API to fetch and display movie posters dynamically, delivering a personalized user experience.
• Dashly : Live Sales Dashboard
  • Built a live Power BI dashboard connected to a Neon PostgreSQL database, containing 50,000+ sales records.
  • Developed an automated ETL pipeline using GitHub Actions to collect and ingest data daily, keeping the dashboard continuously updated with the latest insights.

Tools and Technologies I've worked with :

• Programming Language : Python
• Libraries : NumPy, Pandas, Matplotlib, Seaborn, Plotly
• Machine Learning : Scikit-learn
• Database : MySQL, PostgreSQL
• BI Tool : Power BI
• Web Framework : FastAPI
• Containerization : Docker
• Version Control : Git
• Automation : GitHub Actions

I'm currently looking for opportunities in Data Science/Data Analytics, where I can contribute to building data-driven solutions that create measurable business impact.

If you're looking for someone who's eager to learn, collaborate and deliver results, I'd love to connect and explore how I can add value to your team.

📫 Connect with Me

Kaggle  |  LinkedIn  |  GitHub  |  Medium  |  Portfolio

➔ Problem

• In the used car market, buyers and sellers often struggle to determine a fair price for their vehicle.
• This project aims to provide accurate and transparent pricing for used cars by analyzing real-world data.

➔ Solution

• Built and deployed an end-to-end machine learning pipeline to predict used car prices from real-world data.
• Collected and cleaned 2,800+ used car records from Cars24 using Selenium and BeautifulSoup.
• Optimized memory consumption of the dataset by downcasting data types and converting to Parquet format.
• Trained models with Scikit-learn Pipelines & ColumnTransformer to avoid leakage.
• Deployed the machine learning model as an API using FastAPI on Render.
• Built a HTML/CSS/JS frontend hosted on GitHub Pages to interact with the API and display predictions in real-time.
• Containerized the entire application using Docker and pushed to Docker Hub for reproducibility.

➔ Results

• Reduced dataset memory usage by 90% using optimized storage techniques.
• Achieved a 30% lower MAE and a 12% higher R2-score compared to the baseline model.
• Improved model stability by 70%, ensuring more stable and reliable predictions.

➔ Impact

• Helps car owners quickly find the right selling price for their vehicles based on real-world data.
• Makes it easier for buyers to know if a car is fairly priced before making a purchase.

➔ 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.
• Ultimately, this impacts both user experience and business performance.

➔ Solution

• Built a content-based movie recommender system trained on 5,000+ movie metadata records.
• Recommends the top 5 similar titles for any selected movie in ~2.5 seconds per recommendation.
• Integrated the TMDB API to dynamically fetch and display movie posters, enhancing user experience.
• Deployed as a Streamlit web app, used by 100+ users to discover personalized movie suggestions.

➔ 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 a dataset of 8,000+ Netflix Movies and TV Shows.
• More than 60% of the content on Netflix is rated for mature audiences.
  • Suggests that Netflix targets adult viewers to boost engagement and retention.
• More than 25% of the Movies and TV Shows were released on 1st day of the month.
  • Shows a consistent release schedule, likely aligned with subscription renewal cycles.
• More than 40% of the content on Netflix is exclusive to United States.
  • Shows a strong focus on 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 efforts.

➔ Problem Statement

• To analyze Supermarket Sales data, identifying key factors for improving profitability and operational efficiency.

➔ Some Key Findings

• Analyzed purchasing patterns of 9,000+ customers of a Supermarket.
• More than 15% of the products sold were Snacks.
  • Shows that Snacks are a convenient choice and a major source of revenue.
• More than 32% of total sales came from the West region of the Supermarket.
  • Suggests that West region is a strong performing area as compared to others.
• Health and Soft drinks were the most profitable sub-categories in Beverages.
  • Shows that both type of drink options perform well among customers.
• 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.