Solution :

Logistic regression:

Logistic regression is one of the most popular Machine Learning algorithms, which comes under the Supervised Learning technique. It is used for predicting the categorical dependent variable using a given set of independent variables. Logistic regression predicts the output of a categorical dependent variable. Therefore the outcome must be a categorical or discrete value. It can be either Yes or No, 0 or 1, true or False, etc. but instead of giving the exact value as 0 and 1, it gives the probabilistic values which lie between 0 and 1.

In this implementation, we use "Study Hours" as the independent variable and "Exam Results (0 or 1)" as the dependent variable.

Steps :

Step 1. Importing Required Libraries

• pandas: For loading and processing the dataset.
• numpy: For numerical computations, such as generating a range of values for plotting.
• matplotlib.pyplot: For creating the graph and visualizing the results.
• sklearn.model_selection: For splitting the dataset into training and testing subsets.
• sklearn.linear_model: For implementing the logistic regression model.
• sklearn.metrics: For evaluating the model’s performance using metrics like accuracy, confusion matrix, and classification report.

Step 2. Reading the Dataset

• pd.read_csv() reads the CSV file into a Pandas DataFrame.
• The file should contain columns for "Study Hours" and "Exam Result".

Step 3. Defining Features (X) and Target (y)

• X stores the independent variable (Study Hours).
• y stores the dependent variable (Exam Result).

Step 4. Splitting the Dataset

• train_test_split() is used to split the data into training (80%) and testing (20%) sets.
• random_state=42 ensures the split is reproducible.

Step 5. Training the Logistic Regression Model

• LogisticRegression() initializes the logistic regression model.
• model.fit(X_train, y_train) trains the model on the training data.

Step 6. Making Predictions

• model.predict(X_test) uses the trained model to predict the exam results for the test set.

Step 7. Evaluating the Model

• Accuracy: The percentage of correct predictions, calculated by accuracy_score().
• Confusion Matrix: Shows TP, TN, FP, FN for insight into performance.
• Classification Report: Provides precision, recall, F1-score, and support for each class.

Step 8. Plotting the Data Points and Decision Boundary

• Scatter Plot: Displays training data in blue and test data in green (x-shaped).
• Decision Boundary: Red line showing model's predicted probabilities for class 1 across study hours.

Library Installation:

To install required libraries, Open Command Prompt or Terminal and execute the following commands

$ pip install scikit-learn

$ pip install numpy

$ pip install pandas

$ pip install matplotlib

CSV file : "study_hours.csv"

Study Hours,Exam Result
1,0
2,0
3,0
4,0
5,1
6,1
7,1
8,1
9,1
10,1

  To Download above CSV file : Click Here

Source Code:

File Name: Logistic_Regression.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# Step 1: Load the dataset
data = pd.read_csv("study_hours.csv")  # Ensure this file exists in the same directory
X = data[['Study Hours']].values
y = data['Exam Result'].values

# Step 2: Train-test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42)

# Step 3: Train logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)

# Step 4: Predict and evaluate
y_pred = model.predict(X_test)

# Step 5: Print results
print(f"Accuracy: {accuracy_score(y_test, y_pred):.1f}")
print("\nConfusion Matrix:")
print(confusion_matrix(y_test, y_pred))

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

# Step 6: Plotting decision boundary
X_range = np.linspace(X.min() - 1, X.max() + 1, 300).reshape(-1, 1)
y_prob = model.predict_proba(X_range)[:, 1]

plt.figure(figsize=(8, 6))
plt.scatter(X_train, y_train, color='blue', label='Training Data')
plt.scatter(X_test, y_test, color='green', marker='x', s=100, label='Testing Data')
plt.plot(X_range, y_prob, color='red', linewidth=2, label='Decision Boundary')
plt.xlabel("Study Hours")
plt.ylabel("Exam Result")
plt.title("Logistic Regression - Study Hours vs Exam Result")
plt.legend()
plt.grid(True)
plt.show()

Output:

Sample Run:
--------------
$ python3 Logistic_Regression.py
Accuracy: 1.0

Confusion Matrix:
[[1 0]
 [0 1]]

Classification Report:
              precision    recall  f1-score   support

           0       1.00      1.00      1.00         1
           1       1.00      1.00      1.00         1

    accuracy                           1.00         2
   macro avg       1.00      1.00      1.00         2
weighted avg       1.00      1.00      1.00         2