LSTM and GRU to predict Amazon’s stock prices

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Time series problem

Time series forecasting is an intriguing area of Machine Learning that requires attention and can be highly profitable if allied to other complex topics such as stock price prediction. Time series forecasting is the application of a model to predict future values based on previously observed values.

By definition, a time series is a series of data points indexed in time order. This type of problem is important because there is a variety of prediction problems that involve a time component, and finding the data/time relationship is key to the analysis (e.g. weather forecasting and earthquake prediction). …


Exploration of frameworks for deep learning classification

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When we start exploring the deep learning field, the first question that comes to mind is, “What framework should I use?”. There is a variety of frameworks out there, but the leaders of the segment are Tensorflow and PyTorch.

Tensorflow had its initial release in early 2015, supported by Google. It has gained popularity because of the ease of use and syntactic simplicity, facilitating fast development. On the other hand, we have PyTorch, released in late 2016, and Facebook supports it. It has attained wider usage among researchers because of its pythonic structure and flexibility.

Since Tensorflow was released earlier…


Improve marketing campaign profit using Data Science techniques

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Content

  • Context
  • Exploratory Data Analysis (EDA)
  • Outlier Detection
  • Feature Engineering
  • Feature Selection
  • Data Modeling
  • Conclusion

Context

Marketing is one of the most important things a business can do. Not only does marketing build brand awareness, but it can also increase sales, grow businesses, and engage customers. On the other hand, marketing is also a generous source of expenses, and the investment in this segment should be well thought. …


Genre classification of Netflix’s content based on its description

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Content

  • Context
  • Exploratory Data Analysis (EDA)
  • Preprocessing
  • Multi-label models
  • Conclusion

Context

Multi-label classification is the generalization of a single-label problem, and a single instance can belong to more than one single class. According to the documentation of the scikit-learn package, “This can be thought of as predicting properties of a sample that are not mutually exclusive.” There are no constraints about the number of classes that an instance can be assigned to in a multi-label problem. In a similar context, there exists the multi-class classification problem. …


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Iterative graph and plots showing the evolution of the disease

While most of the countries in Europe seem to have passed the worst of the pandemic, the same cannot be said about Brazil. Despite the series of statements that the Brazilian president Jair Bolsonaro released undermining the problem, the country has a long way to be out of the woods, since two days ago passed China in the number of deaths and the situation keeps escalating.

In this article, I try to portray the Covid-19 scenario that Brazil is currently facing by showing some plots with Python. Such a language has available many libraries for data analysis and data visualization…

Rodolfo Saldanha

Data Scientist/Data Engineer looking for challenges https://www.linkedin.com/in/rodolfo-saldanha-46a05185/

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