Classification, regression, and clustering are some of the most commonly used machine learning techniques and there are various algorithms available for these tasks. Explore their application in Pandas ML.

Machine & Deep Learning Algorithms: Data Preperation in Pandas ML

load data from a CSV file into a Pandas dataframe and prepare the data for training a classification model

use the scikit-learn library to build and train a LinearSVC classification model and then evaluate its performance using the available model evaluation functions

install Pandas ML and then define and configure a ModelFrame

compare training and evaluation in Pandas ML with the equivalent tasks in scikit-learn

use Pandas for feature extraction and one-hot encoding, load its contents into a ModelFrame, and initialize and train a linear regression model

evaluate a regression model using metrics such as r-square and mean squared error and visualize its performance using Matplotlib

work with ModelFrames for feature extraction and label encoding

configure and build a clustering model using the K-Means algorithm and analyze data clusters to determine characteristics that are unique to them

distinguish between the use of scikit-learn and Pandas ML when training a model and identify some of the metrics used to evaluate a model