1.

Develop a program to create histograms for all numerical features and

analyze the distribution of each feature. Generate box plots for all numerical
features and identify any outliers. Use California Housing dataset.

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing

# Step 1: Load the California Housing dataset

data = fetch_california_housing(as_frame=True)
housing_df = data.frame

# Step 2: Create histograms for numerical features

numerical_features = housing_df.select_dtypes(include=[np.number]).columns

# Plot histograms
plt.figure(figsize=(15, 10))
for i, feature in enumerate(numerical_features):
plt.subplot(3, 3, i + 1)
sns.histplot(housing_df[feature], kde=True, bins=30, color='blue')
plt.title(f'Distribution of {feature}')
plt.tight_layout()
plt.show()

# Step 3: Generate box plots for numerical features

plt.figure(figsize=(15, 10))
for i, feature in enumerate(numerical_features):
plt.subplot(3, 3, i + 1)
sns.boxplot(x=housing_df[feature], color='orange')
plt.title(f'Box Plot of {feature}')
plt.tight_layout()
plt.show()

# Step 4: Identify outliers using the IQR method

print("Outliers Detection:")
outliers_summary = {}
for feature in numerical_features:
Q1 = housing_df[feature].quantile(0.25)
Q3 = housing_df[feature].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
outliers = housing_df[(housing_df[feature] < lower_bound) |
(housing_df[feature] > upper_bound)]
outliers_summary[feature] = len(outliers)
print(f"{feature}: {len(outliers)} outliers")

# Optional: Print a summary of the dataset

print("\nDataset Summary:")
print(housing_df.describe())
2. Develop a program to Compute the correlation matrix to understand the
relationships between pairs of features. Visualize the correlation matrix using a
heatmap to know which variables have strong positive/negative correlations.
Create a pair plot to visualize pairwise relationships between features. Use
California Housing dataset.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
# Step 1: Load the California Housing Dataset
california_data = fetch_california_housing(as_frame=True)
data = california_data.frame
# Step 2: Compute the correlation matrix
correlation_matrix = data.corr()
# Step 3: Visualize the correlation matrix using a heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f',
linewidths=0.5)
plt.title('Correlation Matrix of California Housing Features')
plt.show()
# Step 4: Create a pair plot to visualize pairwise relationships
sns.pairplot(data, diag_kind='kde', plot_kws={'alpha': 0.5})
plt.suptitle('Pair Plot of California Housing Features', y=1.02)
plt.show()

3. Develop a program to implement Principal Component Analysis (PCA) for

reducing the dimensionality of the Iris dataset from 4 features to 2.
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
# Load the Iris dataset
iris = load_iris()
data = iris.data
labels = iris.target
label_names = iris.target_names
# Convert to a DataFrame for better visualization
iris_df = pd.DataFrame(data, columns=iris.feature_names)
# Perform PCA to reduce dimensionality to 2
pca = PCA(n_components=2)
data_reduced = pca.fit_transform(data)
# Create a DataFrame for the reduced data
reduced_df = pd.DataFrame(data_reduced, columns=['Principal Component 1',
'Principal Component 2'])
reduced_df['Label'] = labels
# Plot the reduced data
plt.figure(figsize=(8, 6))
colors = ['r', 'g', 'b']
for i, label in enumerate(np.unique(labels)):
plt.scatter(
reduced_df[reduced_df['Label'] == label]['Principal Component 1'],
reduced_df[reduced_df['Label'] == label]['Principal Component 2'],
label=label_names[label],
 color=colors[i]
)

plt.title('PCA on Iris Dataset')

plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend()
plt.grid()
plt.show()

4. For a given set of training data examples stored in a .CSV file, implement and
demonstrate the Find-S algorithm to output a description of the set of all
hypotheses consistent with the training examples.

import pandas as pd
def find_s_algorithm(file_path):
data = pd.read_csv(file_path)
print("Training data:")
print(data)
attributes = data.columns[:-1]
class_label = data.columns[-1]
hypothesis = ['?' for _ in attributes]
for index, row in data.iterrows():
if row[class_label] == 'Yes':
for i, value in enumerate(row[attributes]):
if hypothesis[i] == '?' or hypothesis[i] == value:
hypothesis[i] = value
 else:
hypothesis[i] = '?'
return hypothesis
file_path = 'training_data.csv'
hypothesis = find_s_algorithm(file_path)
print("\nThe final hypothesis is:", hypothesis)

5. Develop a program to implement k-Nearest Neighbour algorithm to classify

the randomly generated 100 values of x in the range of [0,1]. Perform the
following based on dataset generated.
a) Label the first 50 points {x1,……,x50} as follows: if (xi ≤ 0.5), then xi ∊ Class1,
else xi ∊ Class1
b) Classify the remaining points, x51,……,x100 using KNN. Perform this for
k=1,2,3,4,5,20,30

import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
data = np.random.rand(100)
labels = ["Class1" if x <= 0.5 else "Class2" for x in data[:50]]
def euclidean_distance(x1, x2):
return abs(x1 - x2)
def knn_classifier(train_data, train_labels, test_point, k):
distances = [(euclidean_distance(test_point, train_data[i]), train_labels[i]) for
i in range(len(train_data))]
distances.sort(key=lambda x: x[0])
k_nearest_neighbors = distances[:k]
k_nearest_labels = [label for _, label in k_nearest_neighbors]
 return Counter(k_nearest_labels).most_common(1)[0][0]
train_data = data[:50]
train_labels = labels
test_data = data[50:]
k_values = [1, 2, 3, 4, 5, 20, 30]
print("--- k-Nearest Neighbors Classification ---")
print("Training dataset: First 50 points labeled based on the rule (x <= 0.5 ->
Class1, x > 0.5 -> Class2)")
print("Testing dataset: Remaining 50 points to be classified\n")
results = {}
for k in k_values:
print(f"Results for k = {k}:")
classified_labels = [knn_classifier(train_data, train_labels, test_point, k) for
test_point in test_data]
results[k] = classified_labels
for i, label in enumerate(classified_labels, start=51):
print(f"Point x{i} (value: {test_data[i - 51]:.4f}) is classified as {label}")
print("\n")
print("Classification complete.\n")
for k in k_values:
classified_labels = results[k]
class1_points = [test_data[i] for i in range(len(test_data)) if
classified_labels[i] == "Class1"]
class2_points = [test_data[i] for i in range(len(test_data)) if
classified_labels[i] == "Class2"]
plt.figure(figsize=(10, 6))
plt.scatter(train_data, [0] * len(train_data), c=["blue" if label == "Class1" else
"red" for label in train_labels], label="Training Data", marker="o")
 plt.scatter(class1_points, [1] * len(class1_points), c="blue", label="Class1
(Test)", marker="x")
plt.scatter(class2_points, [1] * len(class2_points), c="red", label="Class2
(Test)", marker="x")
plt.title(f"k-NN Classification Results for k = {k}")
plt.xlabel("Data Points")
plt.ylabel("Classification Level")
plt.legend()
plt.grid(True)
plt.show()

6. Implement the non-parametric Locally Weighted Regression algorithm in

order to fit data points. Select appropriate data set for your experiment and
draw graphs.

import numpy as np
import matplotlib.pyplot as plt
def gaussian_kernel(x, xi, tau):
return np.exp(-np.sum((x - xi) ** 2) / (2 * tau ** 2))
def locally_weighted_regression(x, X, y, tau):
m = X.shape[0]
weights = np.array([gaussian_kernel(x, X[i], tau) for i in range(m)])
W = np.diag(weights)
X_transpose_W = X.T @ W
theta = np.linalg.inv(X_transpose_W @ X) @ X_transpose_W @ y
return x @ theta
np.random.seed(42)
X = np.linspace(0, 2 * np.pi, 100)
y = np.sin(X) + 0.1 * np.random.randn(100)
X_bias = np.c_[np.ones(X.shape), X]
x_test = np.linspace(0, 2 * np.pi, 200)
x_test_bias = np.c_[np.ones(x_test.shape), x_test]
tau = 0.5
y_pred = np.array([locally_weighted_regression(xi, X_bias, y, tau) for xi in
x_test_bias])
plt.figure(figsize=(10, 6))
plt.scatter(X, y, color='red', label='Training Data', alpha=0.7)
plt.plot(x_test, y_pred, color='blue', label=f'LWR Fit (tau={tau})', linewidth=2)
plt.xlabel('X', fontsize=12)
plt.ylabel('y', fontsize=12)
plt.title('Locally Weighted Regression', fontsize=14)
plt.legend(fontsize=10)
plt.grid(alpha=0.3)
plt.show()

7. Develop a program to demonstrate the working of Linear Regression and

Polynomial Regression. Use Boston Housing Dataset for Linear Regression and
Auto MPG Dataset (for vehicle fuel efficiency prediction) for Polynomial
Regression.
PROGRAM:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.metrics import mean_squared_error, r2_score
def linear_regression_california():
housing = fetch_california_housing(as_frame=True)
X = housing.data[["AveRooms"]]
y = housing.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
plt.scatter(X_test, y_test, color="blue", label="Actual")
plt.plot(X_test, y_pred, color="red", label="Predicted")
plt.xlabel("Average number of rooms (AveRooms)")
plt.ylabel("Median value of homes ($100,000)")
plt.title("Linear Regression - California Housing Dataset")
plt.legend()
plt.show()

print("Linear Regression - California Housing Dataset")

print("Mean Squared Error:", mean_squared_error(y_test, y_pred))
print("R^2 Score:", r2_score(y_test, y_pred))
def polynomial_regression_auto_mpg():
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/auto-
mpg/auto-mpg.data"
column_names = ["mpg", "cylinders", "displacement", "horsepower",
"weight", "acceleration", "model_year", "origin"]
data = pd.read_csv(url, sep='\s+', names=column_names, na_values="?")
data = data.dropna()
X = data["displacement"].values.reshape(-1, 1)
y = data["mpg"].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,

random_state=42)

poly_model = make_pipeline(PolynomialFeatures(degree=2),
StandardScaler(), LinearRegression())
poly_model.fit(X_train, y_train)

y_pred = poly_model.predict(X_test)

plt.scatter(X_test, y_test, color="blue", label="Actual")

plt.scatter(X_test, y_pred, color="red", label="Predicted")
plt.xlabel("Displacement")
plt.ylabel("Miles per gallon (mpg)")
plt.title("Polynomial Regression - Auto MPG Dataset")
plt.legend()
plt.show()
 print("Polynomial Regression - Auto MPG Dataset")
print("Mean Squared Error:", mean_squared_error(y_test, y_pred))
print("R^2 Score:", r2_score(y_test, y_pred))

if __name__ == "__main__":
print("Demonstrating Linear Regression and Polynomial Regression\n")
linear_regression_california()
polynomial_regression_auto_mpg()

8. Develop a program to demonstrate the working of the decision tree

algorithm. Use Breast Cancer Data set for building the decision tree and apply
this knowledge to classify a new sample.
PROGRAM:
# Importing necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn import tree

data = load_breast_cancer()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
clf = DecisionTreeClassifier(random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)

print(f"Model Accuracy: {accuracy * 100:.2f}%")
new_sample = np.array([X_test[0]])
prediction = clf.predict(new_sample)

prediction_class = "Benign" if prediction == 1 else "Malignant"

print(f"Predicted Class for the new sample: {prediction_class}")

plt.figure(figsize=(12,8))
tree.plot_tree(clf, filled=True, feature_names=data.feature_names,
class_names=data.target_names)
plt.title("Decision Tree - Breast Cancer Dataset")
plt.show()

9. Develop a program to implement the Naive Bayesian classifier considering

Olivetti Face Data set for training. Compute the accuracy of the classifier,
considering a few test data sets.
PROGRAM:
import numpy as np
from sklearn.datasets import fetch_olivetti_faces
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, classification_report,
confusion_matrix
import matplotlib.pyplot as plt

data = fetch_olivetti_faces(shuffle=True, random_state=42)

X = data.data
y = data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,

random_state=42)

gnb = GaussianNB()
gnb.fit(X_train, y_train)
y_pred = gnb.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)

print(f'Accuracy: {accuracy * 100:.2f}%')

print("\nClassification Report:")
print(classification_report(y_test, y_pred, zero_division=1))

print("\nConfusion Matrix:")
print(confusion_matrix(y_test, y_pred))

cross_val_accuracy = cross_val_score(gnb, X, y, cv=5, scoring='accuracy')

print(f'\nCross-validation accuracy: {cross_val_accuracy.mean() * 100:.2f}%')

fig, axes = plt.subplots(3, 5, figsize=(12, 8))

for ax, image, label, prediction in zip(axes.ravel(), X_test, y_test, y_pred):
ax.imshow(image.reshape(64, 64), cmap=plt.cm.gray)
ax.set_title(f"True: {label}, Pred: {prediction}")
ax.axis('off')

plt.show()

0. Develop a program to implement k-means clustering using Wisconsin Breast

Cancer data set and visualize the clustering result.
PROGRAM:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_breast_cancer
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.metrics import confusion_matrix, classification_report

data = load_breast_cancer()
X = data.data
y = data.target

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

kmeans = KMeans(n_clusters=2, random_state=42)

y_kmeans = kmeans.fit_predict(X_scaled)

print("Confusion Matrix:")
print(confusion_matrix(y, y_kmeans))
print("\nClassification Report:")
print(classification_report(y, y_kmeans))

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)

df = pd.DataFrame(X_pca, columns=['PC1', 'PC2'])

df['Cluster'] = y_kmeans
df['True Label'] = y

plt.figure(figsize=(8, 6))
sns.scatterplot(data=df, x='PC1', y='PC2', hue='Cluster', palette='Set1', s=100,
edgecolor='black', alpha=0.7)
plt.title('K-Means Clustering of Breast Cancer Dataset')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend(title="Cluster")
plt.show()

plt.figure(figsize=(8, 6))
sns.scatterplot(data=df, x='PC1', y='PC2', hue='True Label', palette='coolwarm',
s=100, edgecolor='black', alpha=0.7)
plt.title('True Labels of Breast Cancer Dataset')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend(title="True Label")
plt.show()

plt.figure(figsize=(8, 6))
sns.scatterplot(data=df, x='PC1', y='PC2', hue='Cluster', palette='Set1', s=100,
edgecolor='black', alpha=0.7)
centers = pca.transform(kmeans.cluster_centers_)
plt.scatter(centers[:, 0], centers[:, 1], s=200, c='red', marker='X',
label='Centroids')
plt.title('K-Means Clustering with Centroids')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend(title="Cluster")
plt.show()