Welcome to my Data Science & Data Analysis Portfolio! 🚀
Here, you'll find a collection of projects showcasing my skills in data cleaning, analysis, visualization, machine learning, and more.
Feel free to explore the repositories and reach out if you have any questions or feedback!
- 🎓 Graduate Student: Pursuing a Master’s in Computer Engineering at the University of Pernambuco.
- 🔬 Research Experience: Conducted a scientific initiation project on multivariate time series analysis with missing data.
- 💼 Professional Experience: Participated in a university extension project with Avanade.
- 🛠 Technical Skills: Intermediate in Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn. Basic knowledge of SQL.
- 🌍 Languages: Fluent in Portuguese, Intermediate in English.
- 🔍 Interests: Passionate about Machine Learning, Time Series Analysis, and Business Intelligence.
📩 Contact me: [www.linkedin.com/in/thaís-m-45a25a1a5]
| Project | Description | Technologies |
|---|---|---|
| 📊 Exploratory Data Analysis | In-depth analysis of a dataset with visualizations and insights | Python, Pandas, Seaborn |
| 📈 Time Series Forecasting | Predicting future trends using machine learning models | Python, LSTM, SARIMA |
| 📡 SQL Data Analysis | Querying and analyzing datasets using SQL | SQL, PostgreSQL |
| 🛠 Machine Learning Model | Building and evaluating predictive models | Scikit-learn, XGBoost |
- Description: This section contains Jupyter notebooks, each covering a different Python topic.
- Key Techniques: Condicional,Loops,dictionary,List,Object-Oriented Programming (OOP),
- Technologies: Python
- 📌 Repo: [https://github.com/metsumesquita/Python_resumo]
- Description: LDA (Latent Dirichlet Allocation) was applied to a CSV dataset containing research papers selected during my PIBIC project. The goal is to identify latent topics in the dataset.
- Key Features:
- Preprocessing: Data cleaning, tokenization, stopword removal, lemmatization, word cloud generation.
- LDA Modeling: Training an LDA model to discover hidden topics.
- Visualization: Interactive topic modeling visualizations.
- 📌 Repo: LDA Topic Modeling - PIBIC
- Topic Modeling with Gensim & Scikit-learn
- LDA Topic Modeling in Python
- How to Model Topics with LDA & Gensim
- Introduction to Topic Modeling using Latent Semantic Analysis
- Topic Modeling & Unigrams
- Interpreting Topic Models
- Beginner’s Guide to Topic Modeling
- Description: Performed an in-depth analysis of [Dataset Name].
- Key Techniques: Data cleaning, visualization, and feature engineering.
- Technologies: Python, Pandas, Matplotlib, Seaborn.
- 📌 Repo: [Link to project]
- Description: Forecasted future values using [LSTM/SARIMA/Prophet].
- Key Techniques: Data preprocessing, sliding windows, hyperparameter tuning.
- Technologies: Python, TensorFlow, Nixtla, Statsmodels.
- 📌 Repo: [Link to project]
- Description: Queried and analyzed a structured database for insights.
- Key Techniques: SQL joins, aggregations, and optimization.
- Technologies: SQL, PostgreSQL, SQLite.
- 📌 Repo: [Link to project]
- Description: Built and evaluated a predictive model for [classification/regression].
- Key Techniques: Feature selection, model training, evaluation metrics.
- Technologies: Python, Scikit-learn, XGBoost.
- 📌 Repo: [Link to project]
- Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow)
- SQL (PostgreSQL, MySQL, SQLite)
- R (Optional, if applicable)
- Data Cleaning & Preprocessing
- Exploratory Data Analysis (EDA)
- Machine Learning & Forecasting
- Big Data & Cloud Computing (if applicable)
- Power BI
- Matplotlib, Seaborn
- LinkedIn: [www.linkedin.com/in/thaís-m-45a25a1a5]
- GitHub: [https://github.com/metsumesquita]
- Email: [yuukosan98@gmail.com]
🚀 Thank you for visiting my portfolio! Feel free to check out my projects and reach out!