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Implementation of supervised, unsupervised and reinforcement learning algorithms; applying preprocessing and evaluation techniques.

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AlexisBaladon/Machine-Learning-FING

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Libraries

Using the most popular libraries in data science:

  • [scikit-learn] - Machine Learning Algorithms and helper methods.
  • [numpy] - Simplifies linear algebra operations using C code for increased efficiency.
  • [pandas] - Library for structured data manipulation.
  • [matplotlib] - Visualization functionalities for data analysis.
  • [nltk] - Natural Language Processing tool.
  • [gym] - Reinforcement Learning applications.

scikit-learn numpy pandas matplotlib nltk gym

Projects

Unsupervised Learning (K-means):

  • Implementation of the k-means algorithm and evaluation using the elbow method, the silhouette coefficient and PCA.

pca

Reinforcement Learning:

  • Train an agent from the mini-grid game through reinforcement learning

minigrid

Supervised Learning (Decision Trees):

  • Implementation of a decision tree for continuous attributes and application to a real problem with a heavily unbalanced dataset.

Decision Tree

Supervised Learning (Naive Bayes for text):

  • Implementation of naive bayes to identify spam emails using stemming, tokenization and stopword elimination techniques.

mail-length

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Implementation of supervised, unsupervised and reinforcement learning algorithms; applying preprocessing and evaluation techniques.

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