import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
import pandas as pd

# Load the diabetes dataset
diabetes_data = datasets.load_diabetes()

# Print all keys and number of …

import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
import statsmodels.api as sm
import scipy.stats as st
from statsmodels.tools import add_constant as add_constant

df = load_breast_cancer()
df_cancer = pd.DataFrame(np …

from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest

iris = load_iris()

X, y = iris.data, iris …

from pydotplus import graphviz

# Example graph creation
graph = graphviz.Dot(graph_type='digraph')
node_a = graphviz.Node('A')
node_b = graphviz.Node('B')
graph.add_node(node_a)
graph.add_node(node_b)
graph.add_edge(graphviz.Edge(node_a, node_b))

# Save and visualize
graph.write_png("example_graph.png")
from IPython.display import Image
Image(graph.create_png())

from sklearn import tree

X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)

print(clf.predict([[21., 21.]]))

[1]