Predicting student performance based on study habits is a practical application of **regression analysis** in education. By examining the relationship between the number of hours a student studies and their exam scores, you can model how changes in study time might affect outcomes. **Linear regression** is a statistical technique that fits a straight line to data points, allowing you to make predictions about one variable based on another. In this context, you will use Python's `scikit-learn` library to build a simple linear regression model, visualize the results using `matplotlib`, and predict the exam score for a student who studies a specific number of hours.

import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression

# Example data: hours studied and corresponding exam scores
hours = [2, 3, 5, 6, 8, 10]
scores = [50, 55, 65, 70, 80, 90]

# Prepare the data for regression (reshape for sklearn)
X = np.array(hours).reshape(-1, 1)
y = np.array(scores)

# Fit the linear regression model
model = LinearRegression()
model.fit(X, y)

# Predict scores for the training data
predicted_scores = model.predict(X)

# Plot the data and the regression line
plt.scatter(hours, scores, color='blue', label='Actual Scores')
plt.plot(hours, predicted_scores, color='red', label='Regression Line')
plt.xlabel('Hours Studied')
plt.ylabel('Exam Score')
plt.title('Student Scores vs Study Hours')
plt.legend()
plt.show()


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import io
from contextlib import redirect_stdout
import matplotlib
matplotlib.use('Agg')

class TestTask(unittest.TestCase):
    def test_model_fits_and_predicts(self):
        import user_code
        importlib.reload(user_code)
        hours = [1, 2, 3, 4.5, 5, 6, 7.5, 8, 9]
        scores = [35, 45, 50, 60, 62, 70, 78, 80, 88]
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.analyze_and_predict(hours, scores, 7)
        output = f.getvalue()
        try:
            predicted = float(output.strip().split('\\n')[-1])
        except Exception:
            predicted = None
        # The expected value is close to 75.0 based on the data
        _dynamic_test(
            self,
            predicted is not None and abs(predicted - 75.0) < 3.0,
            "Prediction for 7 hours is within expected range.",
            f"Predicted value for 7 hours is not within expected range: {predicted}",
        )

    def test_plot_generated(self):
        import user_code
        importlib.reload(user_code)
        hours = [1, 2, 3, 4.5, 5, 6, 7.5, 8, 9]
        scores = [35, 45, 50, 60, 62, 70, 78, 80, 88]
        import matplotlib.pyplot as plt
        plt.close('all')
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.analyze_and_predict(hours, scores, 7)
        # Check that a figure was created
        figs = [fig for fig in map(plt.figure, plt.get_fignums())]
        _dynamic_test(
            self,
            len(figs) > 0,
            "Plot is generated by the function.",
            "No plot was generated.",
        )

    def test_function_signature(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'analyze_and_predict', None)
        _dynamic_test(
            self,
            callable(func),
            "Function 'analyze_and_predict' is defined and callable.",
            "Function 'analyze_and_predict' is not defined or not callable.",
        )
        import inspect
        sig = inspect.signature(func)
        params = list(sig.parameters.keys())
        _dynamic_test(
            self,
            params == ['hours', 'scores', 'predict_hours'],
            "Function signature matches required parameters.",
            f"Expected parameters ['hours', 'scores', 'predict_hours'], got {params}",
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

This beginner-level course focuses on the core mathematical operations in Python, including arithmetic, order of operations, and using built-in math functions. It provides hands-on practice and targeted instruction for learners new to programming or seeking to strengthen their Python math skills.

Challenge: Predict Student Scores Based on Study Hours

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