The python examples in this repository cover how to:
- load a labeled dataset
- examine the dataset
- manipulate the dataset
- graph the dataset
- use a supervised classification algorithm
- train the machine learning algorithm
- evaluate the performance of the trained model
- use the trained model to make predictions
Please visit the wiki for the project documentation. It's a "machine learning with python" tutorial:
- What to find in this repository
- About the Python libraries used in this repo
- Euclidean distance vs Manhattan distance
- machine learning introduction
- machine learning algorithms overview
- Introduction to arrays using numpy
- visualize a dataset using seaborn
- manipulate dataset with pandas
- classification using SVC (Support vector classifier)
- Graph a dataset using matplotlib
- Use k-Fold Cross Validation to evaluate the performance of a trained model
- Remove irrelevant features to reduce overfitting using RFE (Recursive Feature Elimination)
- transform non numerical labels to numerical labels with LabelEncoder
- split a column into multiple columns (One Hot Encode)
- data preparation example