- Execute all the steps using Python Scripts
- Build a report about all the decisions taken in all steps of the project. Explain the choices made.
- Make experiments with K-NN, LVQ, Decision Tree, SVM, Random Forest and an heterogen "committee".
- It will be necessary to make searches in the configuration committee.
- Utilize the cross validation with stratified k-fold with k=10
- Show the average accuracy and the standard deviation, confusion matrix, boxplot. Analyse the results with a classification report, present the tests that had most statistical significance.
- Extract a decision tree and identify which are the first attributes utilized on the decision tree building.
- Propose suggestions of the decisions based on the found attributes. (?)
- Deliver the final report that has all the decisions and discussion about each step of the CRISP-DM.
https://www.simplilearn.com/scikit-learn-vs-tensorflow-article
"Scikit-learn’s true strength resides in its model assessment and selection architecture, which allows us to cross-validate and perform multiple hyperparameter searches on our models. Scikit-learn also helps us choose the best model for our work."
"The machine learning algorithm is also implemented using Scikit-learn, a higher-level library."
Install Python:
Install the Pyhton package manager (PIP):
python3 -m venv .
Install the dependencies:
pip3 install -r requirements.txt
python3 main.py
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