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