Welcome to my GitHub page. In this space I share Data Science codes, written in Python and SQL. They contain predictive and exploratory analyzes in order to use data to generate insights.

I work to improve my knowledge in Data Analysis, Data Science, Programming, Machine Learning and Artificial Intelligence.

🎓 Bachelor in Information Systems
👩🏻‍💻 MBA in Strategic Management of IT
🎨 I can make realistic drawings
🏊🏼‍♀️ I love to swim

Languages	Enviroments	Data Manipulation	Data Visualization	Machine Learning	Data Base

• Content Creator

  • Creation of educational videos in partnership with Comunidade Data Science. I present content related to Data Science, Excel and Power BI.

  Video - PT - Importing Data from Web to Power BI

• Exploratory Data Analysis

  • Data Manipulation and Visualization with SQL, Python and R: Exploratory data analysis with python, R and SQL in Jupyter Notebook.

  Tools: Pandas, R magic, Tidyverse, SQL

• Data Extraction

  • Data Extraction using Google Analytics 4 API: Creation of connection to GA4's API to retrieve e-commerce data.

  Tools: Pandas, Google Analytics 4 API

• Machine Learning

  • InStyle Net Promoter Score Prediction: Score prediction for clients data, identifying if the client is more likely to be satisfied or not with the sales experience. A machine learning model was created with LGBM Regressor.
  • Rossman Stores Sales Prediction: Sales prediction for each store for the next six weeks in order to define a budget for stores renovation. A machine learning model was created with XGBoost Regressor in order to forecast store sales.
  • Prediction of Cancellations of Hotel Reservations: Analysis of customers behavior, who frequently cancel their reservations. The objective is to predict which customers would cancel their reservations in order to make better strategic decisions. Machine learning models were tested using classification algorithms like XGBoost, Random Forest and CatBoost.
  • Customer Clustering for Loyalty Program: Customer clustering developed with the objective of identifying the most valuable customers for an e-commerce loyalty program. The project uses data analysis and machine learning techniques to separate customers according to their purchasing profile.

  Tools: scikit-learn, statsmodels, xgboost, random forest, catboost, decision tree, PCA, t-SNE, UMAP, K-Means, pandas, pandas profiling, numpy, matplotlib, seaborn