This repository contains a Jupyter Notebook in which I am going to perform an analysis of the Breast cancer dataset, and then apply machine learning models, K Folds, Grid Search, among other methods, and thus comparing their results and which one fits better.
This repository, has as a personal goal to familiarize myself with machine learning techniques , and to help disseminate what I have learned regarding the same topic.
In addition, what one should take away after reading this paper is the following:
- Familiarize oneself with the models.
- Familiarize yourself with hyper-parameters and machine learning models
- Learning how to build machine learning models
- A well-formed foundation of the tools, such as Scikit-Learn, Pandas, MatplotLib, Seaborn, among others
The Notebook will have the following structure in the form of steps.
- Visualize data, view summations.
- Divide between training and test
- Training a simple model (KNN)
- View performance metrics
- Normalize features - do you get better at doing that?
- Optimize hyperparameters - how much does it improve?
- Apply more complex models how much better does it get with more complex models?
- Modify hyper-parameter optimization and use K-Folds
- Training of the final model
Python 3.7 required
Use the package manager conda to install the same virtual environment that I used, this command will create a new virtual environment with the same libraries that I used:
conda env create -f my_environment.yml- MatPlotLib data visualization.
- SeaBorn more data visualization.
- Numpy the fundamental package for scientific computing with Python.
- Pandas data analysis and manipulation tool.
Closures is provided under the MIT License.
MIT License
Copyright (c) 2021 Pértile Franco Giuliano
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