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Performance analysis of Decisions Trees, Boosting & Bagging, KNN, Neural Network and Linear Regression algorithms. Over two Data Sets (meant-to-be) very different in nature and volume.

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Requirements:

  • PYTHON 3
  • Basic librairies included in anaconda package : pandas, numpy, sklearn, matplotlib, mpl_toolkits

datasets are in ./data folder

Codes are in ./src folder :

Separate codes :

. /src/decision-trees.py : part with decision trees code . /src/neural-nets.py : part with neural nets code . /src/boosting.py : part with boosting code . /src/svm.py : part with svm code . /src/knn.py : part with knn code

Optional Notebook :

. /src/AS1-Supervised-Learning.ipynb : Complete jupyter-notebook . /src/AS1-Supervised-Learning.py : Complete notebook in python file

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Performance analysis of Decisions Trees, Boosting & Bagging, KNN, Neural Network and Linear Regression algorithms. Over two Data Sets (meant-to-be) very different in nature and volume.

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