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Achievement: Pull SharkAchievement: Quickdraw

Achievements

Achievement: Pull SharkAchievement: Quickdraw

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Kapil1917T/README.md

Hi there! I'm Kapil 👋

About Me

🎓 MS in Applied Data Science student at Syracuse University
💼 Former Data Scientist with experience in building scalable analytics solutions
📊 Passionate about transforming complex data into actionable business insights

🛠️ Tech Stack

  • Languages: Python, R, SQL, HIVE
  • ML/Data: NumPy, Pandas, Scikit-Learn, TensorFlow, Keras
  • Big Data: PySpark, Databricks, Snowflake
  • Cloud: AWS (S3, EC2, Redshift), Azure
  • Visualization: Tableau, Power BI, Salesforce Datorama
  • Tools: Git, Docker, JIRA

🌟 Featured Projects

Housing Market Prediction

  • Built ML pipeline using PySpark to predict market conditions with 80%+ accuracy
  • Implemented Logistic Regression achieving 87% precision
  • Engineered features for analyzing seasonal trends across US markets

Reddit Sentiment Analysis for Stock Market

  • Processed 50k+ comments using NLP techniques
  • Achieved 78% prediction accuracy using BERT and Random Forest
  • Developed interactive Streamlit dashboards for stakeholders

OrangePath: Syracuse Navigation Platform

  • Developed location-based system using Azure and Power Apps
  • Reduced orientation staffing needs by 40%
  • Achieved 90% student satisfaction rate

🎯 Professional Impact

  • Enhanced guardrail prices by 40% through demand elasticity analysis
  • Boosted lead conversion rates by 20% through ETL operations
  • Optimized database processes reducing execution time by 70%

📫 Connect With Me

Always open to collaborating on innovative data science projects!

Pinned Loading

  1. Housing_Bubble_Detection_GCP Housing_Bubble_Detection_GCP Public

    Detect U.S. housing market bubbles using macroeconomic signals. Forecast HPI, score speculative risk, and visualize insights using a fully modular, cloud-native GCP pipeline.

    Python

  2. Real_Estate_Market_Segmentation_and_Forecasting Real_Estate_Market_Segmentation_and_Forecasting Public

    Predicting Housing Market Conditions using PySpark and Machine Learning

    Jupyter Notebook

  3. Social_Sentiment_Insights_for_Equity_Markets Social_Sentiment_Insights_for_Equity_Markets Public

    A full-stack data science application that performs real-time sentiment analysis on stock-related discussions from Reddit. The project uses Natural Language Processing (NLP) and Machine Learning to…

    Python 1

  4. NBA-Roster-Optimization NBA-Roster-Optimization Public

    Forked from aadit2697/NBA-Roster-Optimization

    This project aims to aid in the process of benching players using effective machine learning models to learn outcomes from various scenarios and predict the right players to bench according to vari…

    Jupyter Notebook

  5. real-estate-collapse-model real-estate-collapse-model Public

    Forked from Housing-Bubble-Project/real-estate-collapse-model

    Full-stack pipeline to detect U.S. housing market bubbles and forecast price trends. Merges 6+ macroeconomic datasets in Snowflake to compute risk scores and price predictions using walk-forward ML…

    Python 1