ML Lab

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.tree import DecisionTreeClassifier, plot_tree

# Load the data

data = pd.read_csv("/PlayTennisData.csv")

# Prepare the data

X = data.drop('PlayTennis', axis=1)

y = data['PlayTennis']

X = pd.get_dummies(X)

y = y.map({'yes': 1, 'no': 0})

# Fit the Decision Tree Classifier

classifier = DecisionTreeClassifier(criterion='entropy')

classifier.fit(X, y)

# Plot the tree

plt.figure(figsize=(20, 10))  # Adjust the size as needed

plot_tree(classifier, filled=True, feature_names=X.columns, class_names=['no', 'yes'], rounded=True, fontsize=12)

plt.show()

#prog 9

import numpy as np

import matplotlib.pyplot as plt

from statsmodels.nonparametric.smoothers_lowess import lowess

from math import pi

# Generate sample data

n = 100

x = np.linspace(0, 2 * pi, n)

y = np.sin(x) + 0.3 * np.random.randn(n)

# Apply LOWESS

smoothed = lowess(y, x, frac=0.25, it=3)

# Plot results

plt.plot(x, y, 'r.', label='Data Points')

plt.plot(smoothed[:, 0], smoothed[:, 1], 'b-', label='LOWESS Fit')

plt.legend()

plt.show()

#3 program 

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.tree import DecisionTreeClassifier, plot_tree

# Load the data

data = pd.read_csv("/PlayTennisData.csv")

# Prepare the data

X = data.drop('PlayTennis', axis=1)

y = data['PlayTennis']

X = pd.get_dummies(X)

y = y.map({'yes': 1, 'no': 0})

# Fit the Decision Tree Classifier

classifier = DecisionTreeClassifier(criterion='entropy')

classifier.fit(X, y)

# Plot the tree

plt.figure(figsize=(20, 10))  # Adjust the size as needed

plot_tree(classifier, filled=True, feature_names=X.columns, class_names=['no', 'yes'], rounded=True, fontsize=12)

plt.show()