Parth Shah

New York City Metropolitan Area Contact Info
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About

I am a Data Scientist at Feedzai, where I work on transaction and loan application fraud…

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Publications

Projects

  • Chemex Diffused

    Fine-tuned Stable Diffusion on Chemex coffee brewer images to implant in fun output domain of the model.

    See project

  • Corny AI

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    Prompt engineered GPT3 to write corny jokes from any user question. The app is powered by Streamlit.

    See project

  • Spanish Reviews Sentiment Classifier

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    Electra transformer (discriminator) fine-tuned on Spanish reviews dataset (muchocine) to predict star rating.

    See project

  • GPT2-Horoscopes

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    - GPT2 model fine-tuned on Horoscope.com data. The model generates horoscopes given a category and sign!
    - Link to GitHub: https://github.com/shahp7575/gpt2-horoscopes

    See project

  • Modern Slavery Statements Analysis

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    Part-of-speech and subject-object syntactic representation analysis in an attempt to produce additional insights on the modern slavery statements dataset.

    See project

  • Data Visualization: LaLiga 2018/19

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    • A data visualization project summarizing the Spanish soccer league.
    • Implemented a web crawler to scrape the league stats from multiple pages and cleaned with SQL.
    • Created bar plots, scatter plots, radial charts to visualize match results and detailed statistics.

    Language: Python, SQL
    Tools: Tableau, Jupyter Notebook, DBeaver
    Libraries: Pandas, BeautifulSoup

    See project

  • Text mining: Is it a disaster?

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    • A text mining project analyzing a corpus of over 10,000 tweets.
    • Used Latent Dirichlet Allocation (LDA) to explore what words actually relate to disasters, along with different visualization methods such as histograms, wordclouds, unigram/bigram/trigram bar plots etc. Implemented t-SNE for multi-dimensional topics visualization.
    • Constructed text features from the corpus using Bag-of-Words, TF-IDF and Word2Vec.
    • Fitted logistic, SVM, Random Forest and XGBoost models. Best model…

    • A text mining project analyzing a corpus of over 10,000 tweets.
    • Used Latent Dirichlet Allocation (LDA) to explore what words actually relate to disasters, along with different visualization methods such as histograms, wordclouds, unigram/bigram/trigram bar plots etc. Implemented t-SNE for multi-dimensional topics visualization.
    • Constructed text features from the corpus using Bag-of-Words, TF-IDF and Word2Vec.
    • Fitted logistic, SVM, Random Forest and XGBoost models. Best model was XGBoost with Bag-of-Words achieving 82.9% AUC.

    Language: Python
    Libraries: Scikit-learn, NLTK, regex, plotly, xgboost, gensim

    See project

  • Wine Quality Prediction

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    • Analyzed a small dataset with 1500 instances.
    • Conducted different visualization graphs such as barplots, boxplots, histograms, heatmaps etc. to explore the relationship of features with the quality of wine.
    • Used scikit-learn package to fit a random forest model to build a wine quality classifier with 94% accuracy.

    Tools/Languages: Python, Minitab
    Libraries: scikit-learn, matplotlib, seaborn, numpy, pandas, scipy

    See project

  • ERP Software

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    Contributed as a Billing Software Developer using VB.Net and SQL for the company Satindar Pal Rubber Products.

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