Introduction

In the predictive analysis discipline we aim to develop statistical models, based on data, for a particular outcome of interest. From this model, we make sure that behavioral learning and past experiences have a model with good generalization.

In this project, we developed a set of tasks to go further in Predictive Analytics by exploring some Deep Learning techniques.

This website intends to present the work analysis for the "Análise Preditiva Avançada" class assignment.

Trabalho em grupo de 3 a 4 alunos envolvendo técnicas de aprendizado supervisionado 
de máquina com Deep learning.

Possíveis trabalhos:

- Classificação ou Previsão, Dados Numéricos ou Categóricos, Estilo de problema 
similar às Regressões Logística ou Linear;
- Principalmente no caso de processamento de texto em linguagem natural;
- Classificação de imagens com Redes Convolutivas;
- Previsão de Texto com Redes Sequenciais;
- Geração de conteúdo (música por exemplo) com redes Auto-Generativas;
-  Mix de estilos artísticos com Redes Convolutivas / Auto-Generativas.

Material a ser entregue:

Se fizer em Python (recomendado): Jupyter Notebook com base e resultados (no caso de 
dados não submetidos à confidencialidade) ou apenas o Jupyter Notebook (com algumas 
referências de dados que possam "validar" o modelo de rede neural entregue).

Se fizer em R: Entregar a base de dados e o Rmarkdown. As bibliotecas de machine 
learning mais utilizadas (scikit-learn, tensorflow e keras) estão disponíveis também 
no R, porém com um print-end. É necessário instalar o Python para executalas.

🔗 See the final website report in Rodrigo Gonçalves' Kaggle profile at: https://www.kaggle.com/rodrigonca/advanced-predictive-analysis-cnn-implementation.

Alternatively, run a binder container:

Professor

• Gustavo Mirapalheta

Authors / Students

Profile	Name	E-mail
	Daniel Campos	(daniel.ferraz.campos@gmail.com)
	Leandro Daniel	(contato@leandrodaniel.com)
	Rodrigo Goncalves	(rodrigo.goncalves@me.com)
	Ygor Lima	(ygor_redesocial@hotmail.com)

Project Organization

---

├── LICENSE
├── Makefile              <- Makefile with commands like `make data` or `make train`
├── README.md             <- The top-level README for developers using this project.
├── data
│   ├── external          <- Data from third party sources.
│   ├── interim           <- Intermediate data that has been transformed.
│   ├── processed         <- The final, canonical data sets for modeling.
│   └── raw               <- The original, immutable data dump.
│
├── docs                  <- A default Sphinx project; see sphinx-doc.org for details
│
├── models                <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks             <- Jupyter notebooks. 
│   └── external_examples <- Other interesting notebooks
│   │
│   └── fgv_assignment    <- The final class assignment given by Professor Mirapalheta
│   │
│   └── fgv_classes       <- All notebooks given by Professor Mirapalheta and Professor Hithoshi
│                            in theirs respective classes
│                            
├── references            <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports               <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures           <- Generated graphics and figures to be used in reporting
│
├── requirements.txt      <- The requirements file for reproducing the analysis environment, e.g.
│                            generated with `pip freeze > requirements.txt`
│
├── setup.py              <- makes project pip installable (pip install -e .) so src can be imported
├── src                   <- Source code for use in this project.
│   ├── __init__.py       <- Makes src a Python module
│   │
│   ├── data              <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── exercises         <- Scripts for FGV's class assignments
│   │   └── __init__.py    
│   │   └── playground.py
│   │
│   ├── features          <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models            <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization     <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini               <- tox file with settings for running tox; see tox.testrun.org

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Project based on the cookiecutter data science project template. #cookiecutterdatascience