import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import os import graphviz

iris_data=pd.read_csv('/work/Iris.csv')

iris_data.head()

species = iris_data[['Species']] species.head()

iris_data.columns

iris_data.Species.unique()

from sklearn.preprocessing import OrdinalEncoder #encoding the species column using the ordinal encoder to make it easier to handle numerically ordinal_encoder = OrdinalEncoder() iris_data['encoded_species'] = ordinal_encoder.fit_transform(species)

ordinal_encoder.categories_

iris_setosa = iris_data.loc[iris_data.Species == 'Iris-setosa'] iris_versicolor = iris_data.loc[iris_data.Species == 'Iris-versicolor'] iris_virginica = iris_data.loc[iris_data.Species == 'Iris-virginica']

iris_data.head()

iris_data.describe(include="all")

iris_data.groupby(['Species']).mean()

plt.figure(figsize=(16,8)) sns.boxplot(iris_setosa.SepalLengthCm)

plt.figure(figsize=(16,8)) sns.boxplot(iris_setosa.SepalWidthCm)

plt.figure(figsize=(16,8)) sns.boxplot(iris_setosa.PetalWidthCm)

plt.figure(figsize=(16,8)) sns.boxplot(iris_setosa.PetalLengthCm)

import plotly.express as px fig1 = px.scatter(iris_data, x='PetalLengthCm', y='SepalLengthCm', color='Species', marginal_x='box', marginal_y='box') fig2 = px.scatter(iris_data, x='PetalWidthCm', y='SepalWidthCm', color='Species', marginal_x='box', marginal_y='box') fig3 = px.scatter(iris_data, x='PetalWidthCm', y='PetalLengthCm', color='Species') fig1.show() fig2.show() fig3.show()

iris_corr = iris_data.iloc[:, 1:].corr() plt.figure(figsize=(16,8)) sns.heatmap(iris_corr)

fig = px.imshow(iris_corr, title="Iris") fig.show()

setosa_corr = iris_setosa.iloc[:, 1:].corr() versicolor_corr = iris_versicolor.iloc[:, 1:].corr() virginica_corr = iris_virginica.iloc[:, 1:].corr()

fig1 = px.imshow(setosa_corr, title='Iris Setosa') fig2 = px.imshow(versicolor_corr, title='Iris Versicolor') fig3 = px.imshow(virginica_corr, title='Iris Virginica') fig1.show() fig2.show() fig3.show()

X = iris_data.loc[:,('PetalLengthCm', 'PetalWidthCm')] y = iris_data.encoded_species feature_name = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] target_names = np.array(['setosa', 'versicolor', 'virginica'])

from sklearn.tree import DecisionTreeClassifier tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42) tree_clf.fit(X, y)

from graphviz import Source from sklearn.tree import export_graphviz IMAGES_PATH = '/work/' export_graphviz( tree_clf, out_file=os.path.join(IMAGES_PATH, "iris_tree.dot"), feature_names=feature_name[2:], class_names=target_names, rounded=True, filled=True ) Source.from_file(os.path.join(IMAGES_PATH, "iris_tree.dot"))

from matplotlib.colors import ListedColormap #Function from the Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True): x1s = np.linspace(axes[0], axes[1], 100) x2s = np.linspace(axes[2], axes[3], 100) x1, x2 = np.meshgrid(x1s, x2s) X_new = np.c_[x1.ravel(), x2.ravel()] y_pred = clf.predict(X_new).reshape(x1.shape) custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0']) plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap) if not iris: custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50']) plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8) if plot_training: plt.plot(X.iloc[:, 0][y==0], X.iloc[:, 1][y==0], "yo", label="Iris setosa") plt.plot(X.iloc[:, 0][y==1], X.iloc[:, 1][y==1], "bs", label="Iris versicolor") plt.plot(X.iloc[:, 0][y==2], X.iloc[:, 1][y==2], "g^", label="Iris virginica") plt.axis(axes) if iris: plt.xlabel("Petal length", fontsize=14) plt.ylabel("Petal width", fontsize=14) else: plt.xlabel(r"$x_1$", fontsize=18) plt.ylabel(r"$x_2$", fontsize=18, rotation=0) if legend: plt.legend(loc="lower right", fontsize=14) plt.figure(figsize=(8, 4)) plot_decision_boundary(tree_clf, X, y) plt.plot([2.45, 2.45], [0, 3], "k-", linewidth=2) plt.plot([2.45, 7.5], [1.75, 1.75], "k--", linewidth=2) plt.plot([4.95, 4.95], [0, 1.75], "k:", linewidth=2) plt.plot([4.85, 4.85], [1.75, 3], "k:", linewidth=2) plt.text(1.40, 1.0, "Depth=0", fontsize=15) plt.text(3.2, 1.80, "Depth=1", fontsize=13) plt.text(4.05, 0.5, "(Depth=2)", fontsize=11)