Linear regression function in Python that you could use to predict your final grade in a course based on the grades you have received on various assessments:

linear-regression-example

import numpy as np

def linear_regression(X, y):
  # Add a column of ones to X to represent the bias term
  X = np.concatenate([np.ones((X.shape[0], 1)), X], axis=1)

  # Compute the transpose of X
  X_t = np.transpose(X)

  # Compute the dot product of X_t and X
  X_t_X = np.dot(X_t, X)

  # Compute the inverse of X_t_X
  X_t_X_inv = np.linalg.inv(X_t_X)

  # Compute the dot product of X_t and y
  X_t_y = np.dot(X_t, y)

  # Compute the weights of the linear regression model
  weights = np.dot(X_t_X_inv, X_t_y)

  return weights

# Define the input data
X = np.array([
  [10], # grade on first assessment
  [20], # grade on second assessment
  [30]  # grade on third assessment
])

# Define the target variable
y = np.array([
  [80]  # final grade in the course
])

# Compute the weights of the linear regression model
weights = linear_regression(X, y)

# Define the input data for which we want to make a prediction
X_test = np.array([
  [15], # grade on fourth assessment
  [25], # grade on fifth assessment
  [35]  # grade on sixth assessment
])

# Add a column of ones to X_test to represent the bias term
X_test = np.concatenate([np.ones((X_test.shape[0], 1)), X_test], axis=1)

# Compute the dot product of X_test and weights
predictions = np.dot(X_test, weights)

# Print the predictions
print(predictions)