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ganeshmorye/README.md

👨‍💻 About Me

  • Experienced data scientist delivering multi-million-dollar projects for global companies.
  • Adept at handling complex, cross-functional datasets to identify and extract insights.
  • Proven track record of data-driven decision-making and predictive modeling.
  • Reliable team player with excellent communication and analytical skills.
  • Passionate about innovation and driving sustainability goals.

🛠️Languages and Tools



💼 My Portfolio Overview

With a strong emphasis on machine learning, my portfolio encompasses a range of end-to-end projects in NLP, regression, classification, and deep learning. Additionally, my projects are underpinned by comprehensive exploratory data analysis techniques that help me to identify key trends and patterns in data sets. By combining these skills, I am able to deliver high-quality data-driven solutions that drive business outcomes.

🖥 My Machine Learning Projects

Text Classification Model to Optimize Ad Campaign Targeting
Methodology: Binary classification
Algorithms: LogisticRegression, MNB, RF, SVC
Performance: Accuracy=85%, ROC AUC=0.92
Summary: Developed a classifier model to create an ad campaign strategy for appropriate subreddits.




Topic Modeling to Contextualize Search Algorithms Results
Methodology: Unstructured Data-Cluster Analysis
Algorithms: LDA, GSDMM
Performance: Identified 50 clusters
Summary: Identified several clusters to summarize the context of tweeting activity on Twitter




Image Classification Model to Detect Driver Distraction
Methodology: Image Classification
Algorithms: CNN
Performance: log-loss=0.73, accuracy=0.7
Summary: Developed an image classification model to detect driver behavior




Charting Kobe Byrant's Career
Methodology: EDA using Python, Pandas & MatplotLib
Algorithms: Data Analysis
Performance: Insights on game play
Summary: Developed several visualizations to depict the journey of Kobe Bryant's professional career.



Price Forecasting for Housing Sales
Methodology: Regression
Algorithms: Linear Regression, Regularization Using Ridge & Lasso
Performance: RMSE score = 18,000
Summary: Predictive Modeling for Sales Price: Unveiling Influential Predictors through Regression Analysis


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  1. twitter_topic_modeling twitter_topic_modeling Public

    Topic modeling on tweets from users in India over a 2-year period to discover trending topics and discussions

    Jupyter Notebook 2 1

  2. distracted_driver_detection distracted_driver_detection Public

    This repository contains a machine learning project aimed at identifying distracted driver behaviors from dashboard camera images.

    Jupyter Notebook

  3. Ames_Housing_Linear_Regression_Modeling Ames_Housing_Linear_Regression_Modeling Public

    Ames housing dataset used to build linear regression models for accurate sale price prediction.

    Jupyter Notebook

  4. kobe_career_shots_EDA_visualization kobe_career_shots_EDA_visualization Public

    Dive into an intriguing dataset documenting Kobe Bryant's remarkable NBA career spanning two decades. With 1558 game entries and 645 attributes, uncover the visualizations that showcase Kobe's NBA …

    Jupyter Notebook

  5. NLP_SubReddits_Classification NLP_SubReddits_Classification Public

    Leveraged advanced text classification techniques to create a powerful model that accurately determines the subreddit of a post, revolutionizing targeted advertising on Reddit. Algorithmically plac…

    Jupyter Notebook

  6. performance_analysis_SAT_scores_by_state_20years performance_analysis_SAT_scores_by_state_20years Public

    Investigating SAT scores over the last 20 years to identify any trends in the scores and the factors influencing the student's performance in this test.

    Jupyter Notebook