scikit-learn Cookbook , Second Edition - Second Edition

Getting ready

1. To start, load the Pima diabetes dataset from the UCI repository:

import pandas as pddata_web_address = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"column_names = ['pregnancy_x','plasma_con','blood_pressure','skin_mm','insulin','bmi','pedigree_func','age','target']feature_names = column_names[:-1]all_data = pd.read_csv(data_web_address , names=column_names)

2. Split the data into training and testing sets:

import numpy as npimport pandas as pdX = all_data[feature_names]y = all_data['target']from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7,stratify=y)

3. To review the previous ...