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Achievement: Pull Shark

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Achievement: Pull Shark

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a-memme/README.md

Data Professional

  • 🌱 Always exploring the world of data science
  • 👀 Currently working on projects involved around targeting recommendation and revenue prediction in AdTech. Generally inolved in all areas where predictive modelling and advanced analytical techniques can be applied to online media, included but not limited to content recognition, user segmentation, and predicting asset performance - feel free to reach out!
  • 🍳 Ask me about food! I'm also a Chef, classically trained in French and Italian cuisine.

Connect:

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Languages and Tools:

Python R JavaScript HTML5 CSS3 Markdown

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  1. revenue_prediction_imputation revenue_prediction_imputation Public

    Random forest regression + ARIMA timeseries modeling to impute metric values and forecast revenue for reporting purposes.

    SQL

  2. Customer_Churn_and_CLTV Customer_Churn_and_CLTV Public

    BG/NBD and Gamma Gamma probabilistic models to evaluate and predict customer churn, retention, and lifetime value of an e-commerce business

    Jupyter Notebook 4 1

  3. Text_Analysis_Reddit Text_Analysis_Reddit Public

    Tokenization/Lemmatization vs keyBERT algorithms to analyze keywords and infer top performing topics on a subreddit of choice. Data is accessed via Python Reddit API Wrapper (PRAW).

    Jupyter Notebook

  4. Credit_Risk_Analysis Credit_Risk_Analysis Public

    Comparing sampling techniques and classification algorithms to predict credit risk

    Jupyter Notebook 1

  5. predicting_crime_pca predicting_crime_pca Public

    Comparing the use of original data vs PCA in multiple regression. Analysis also showcases how principal components can be transformed back to their original vector space after model fitting for des…

    R

  6. snap_dynamic_scheduling snap_dynamic_scheduling Public

    Auto-ARIMA timeseries forecasting in combination with PELT changepoint detection to predict social media viewership performance and identify major changes in performance trends. Models are deployed…

    Python 2