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ML Expt 2

The document demonstrates the use of various Python libraries for data manipulation and visualization, including NumPy for vector operations, SciPy for plotting a normal distribution, and Scikit-learn for training a decision tree classifier on the Iris dataset. It also includes examples of creating and filtering a DataFrame using Pandas, as well as generating a simple line plot with Matplotlib. Each section provides code snippets and outputs to illustrate the functionality of the libraries.

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16 views5 pages

ML Expt 2

The document demonstrates the use of various Python libraries for data manipulation and visualization, including NumPy for vector operations, SciPy for plotting a normal distribution, and Scikit-learn for training a decision tree classifier on the Iris dataset. It also includes examples of creating and filtering a DataFrame using Pandas, as well as generating a simple line plot with Matplotlib. Each section provides code snippets and outputs to illustrate the functionality of the libraries.

Uploaded by

vaibhavigirkar
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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ML EXPT 2

# Using the NumPy library


import numpy as np # numpy

# Creating two vectors (1D arrays)


vector1 = np.array([1, 2, 3])
vector2 = np.array([4, 5, 6])

# Display the vectors


print("Vector 1:", vector1)
print("Vector 2:", vector2)

# Vector addition
add_result = vector1 + vector2
print("Addition:", add_result)

# Scalar multiplication
scalar_mult = 3 * vector1
print("Scalar Multiplication (3 * vector1):", scalar_mult)

# Dot product
dot_product = np.dot(vector1, vector2)
print("Dot Product:", dot_product)

# Mean and standard deviation of vector1


mean_v1 = np.mean(vector1)
std_v1 = np.std(vector1)

print("Mean of vector1:", mean_v1)


print("Standard Deviation of vector1:", std_v1)

OUTPUT
Vector 1: [1 2 3]
Vector 2: [4 5 6]
Addition: [5 7 9]
Scalar Multiplication (3 * vector1): [3 6 9]
Dot Product: 32
Mean of vector1: 2.0
Standard Deviation of vector1: 0.816496580927726

2]
# Using The Scipy Library
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm # scipy

# Generate data for a normal distribution


x = np.linspace(-5, 5, 100)
y = norm.pdf(x, loc=0, scale=1) # mean=0, std=1

# Plot the normal distribution


plt.plot(x, y, label='Normal Distribution')
plt.title("SciPy Normal Distribution")
plt.xlabel("x")
plt.ylabel("Probability Density")
plt.legend()
plt.grid(True)
plt.show()
3]
# Using The Sklearn Library
from sklearn.tree import DecisionTreeClassifier # scikit-learn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import pandas as pd

# Load dataset
X, y = load_iris(return_X_y=True)
names = load_iris().target_names

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
random_state=42)

# Model training
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

# Prediction
y_pred = clf.predict(X_test)

# Show 5 rows of results


df = pd.DataFrame(X_test[:5], columns=load_iris().feature_names)
df["True Label"] = [names[i] for i in y_test[:5]]
df["Predicted"] = [names[i] for i in y_pred[:5]]
print(df)

# Accuracy
print("\nAccuracy:", accuracy_score(y_test, y_pred))
OUTPUT:
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \
0 6.1 2.8 4.7 1.2
1 5.7 3.8 1.7 0.3
2 7.7 2.6 6.9 2.3
3 6.0 2.9 4.5 1.5
4 6.8 2.8 4.8 1.4

True Label Predicted


0 versicolor versicolor
1 setosa setosa
2 virginica virginica
3 versicolor versicolor
4 versicolor versicolor

Accuracy: 1.0

4]
import pandas as pd # pandas

# Create a smaller DataFrame


data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)

# Display the DataFrame


print("DataFrame:\n", df)

# Filter example: People older than 25


print("\nPeople older than 25:\n", df[df['Age'] > 25])
OUTPUT
DataFrame:
Name Age
0 Alice 25
1 Bob 30

People older than 25:


Name Age
1 Bob 30

5]
import matplotlib.pyplot as plt # matplotlib

# Data for plotting


x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Plot the data


plt.plot(x, y)

# Add titles and labels


plt.title('Simple Line Plot')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')

# Show the plot


plt.show()
OUTPUT

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