Question 4 (25 marks)

4.1 Write a pseudo code/ algorithm for the K-means clustering algorithm.(10)

4.2   Explain why accuracy is not always the ideal metric for model evaluation (5)

 4.3 Use Table 4.3 to answer the following questions:  

     Table 4.3

·         age: Patient age (years).

·         blood_pressure: Systolic blood pressure (mmHg)

·         cholesterol: Serum cholesterol level (mg/dL)

·         has_disease: Binary target indicating if the patient has the disease (1 = yes, 0 = no)

Complete the following code to show a Python code snippet to train the decision tree and logistic regression models for making predictions.

import pandas as pd

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

from sklearn.metrics import classification_report, confusion_matrix

# Load the data in the dataframe

data = {

    "age": [40, 55, 60, 45, 50, 35, 70, 65, 54, 75],

    "blood_pressure": [120, 140, 130, 135, 142, 110, 160, 150, 138, 170],

    "cholesterol": [200, 210, 250, 240, 190, 180, 260, 210, 225, 280],

    "has_disease": [0, 1, 0, 1, 0, 0, 1, 1, 0, 1]

}

df = pd.DataFrame(data)

# Split into features (X) and target (y)

X = df[["age", "blood_pressure", "cholesterol"]]

y = df["has_disease"]

# Dataset split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

 # Code for training the Decision tree and make predictions (5)

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# Code for training the Logistic regression  and make predictions                                                 (5)

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