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NataliaVelasquez18/README.md

Hi there, I am Natalia 👋

👩‍🦱 Pronouns: She/her

  • ♾️ I love data science, business, and providing delightful client experiences
  • 🤓 I am obsessed with growing and learning new things every day
  • 🇨🇴 I value diversity in the workplace.
  • ♾️ I am mission driven and like to collaborate with people who share the same purpose to solve customer's most difficult challenges through data driven projects
  • 📚 Technical programming skills in Python; Pandas, Numpy, Seaborn, Matplotlib, scikit-learn, SQL Databases, PowerBI, Tableau, expossure to AWS Glue ETL, AWS Sagemaker, AWS S3, AWS Athena.
  • 📫 How to find me:

Current Projects (WIP)

Tech Stack: Python; Pandas, Numpy, Seaborn, Matplotlib, scikit-learn

Tech Stack: Jupyter, Python: Scikit-learn, Naive Random Oversampling, SMOTE Oversampling, Cluster Centroids, SMOTEEN, Random Forest Classifier, Easy Ensemble Classifier - AdaBoost

  • Movies ETL - A company in the live streaming business. Their data science team would like to develop an algorithm to predict which low budget movies being released will become popular. In order to do that it's necessary to go through the entire Data Pipeline (ETL)

Tech Stack: PostgreSQL, RStudio

  • Predicting Car Performance: Statistical Analysis using R - The purpose of this analysis is to help Mechacar's Manufacturing team to understand what car features impact car performance the most. The manufacturing team will incorporate the insights into the manufacturing process aiming to produce the best performing cars in the market, rebrand the company image, and regain market share.

Tech Stack: R, RStudio

Tech Stack: Google Colab, Python: Pyspark, PostgreSQL, AWS Database Service, Jupyter

Tech Stack: Python, Pandas, Numpy, Seaborn, Matplotlib, Tableau

Natalia's github stats

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  1. Car_Data_Statistical_Analysis Car_Data_Statistical_Analysis Public

    The purpose of this analysis is to help Mechacar's Manufacturing team to understand what car features impact car performance the most. The manufacturing team will incorporate the insights into the …

    R

  2. Amazon-Reviews-ETL Amazon-Reviews-ETL Public

    The purpose of this study is to help the executive team of DreamGames, an online videogame vendor, to decide if by joining the Amazon paid review program will generate a positive return on investme…

    Jupyter Notebook 1

  3. credit-risk credit-risk Public

    The purpose of this study is to recommend whether PureLending should use machine learning to predict credit risk. Several machine learning models are built employing different techniques, then they…

    Jupyter Notebook