In this tutorial, you will learn how to implement a simple linear regression algorithm from scratch in Python. It will cover everything from preparing the data to implementing the machine learning algorithm and evaluating the model.  ### Step 1: Data Preparation 1. Import Libraries: Start by importing the necessary libraries. ```py import numpy as np import matplotlib.pyplot as plt ``` 2. Create Training Data: Generate some example data to train the model. For instance, let's create a simple linear relationship with some noise. ```py np.random.seed(0) X = 2 * np.random.rand(100, 1) # Independent variable y = 4 + 3 * X + np.random.randn(100, 1) # Dependent variable (target) ``` 3. Visualize Data: It's always helpful to visualize the data to better understand the relationship between the variables. ```py plt.scatter(X, y) plt.xlabel('Independent Variable (X)') plt.ylabel('Dependent Variable (y)') plt.title('Training Data') plt.show() ``` ### Step 2: Implementation of Linear Regression 1. Parameter Initialization: Define the initial parameters of the model. ```py theta = np.random.randn(2,1) # Random initialization of parameters (including the interception term) ``` 2. Add Intercept Term: Add a column of 1s to handle the interception term. ```py X_b = np.c_[np.ones((100, 1)), X] # Add x0 = 1 to each instance ``` 3. Define Cost Function: Implement the cost function (Mean Squared Error). ```py def calc_cost(theta, X, y): m = len(y) predictions = X.dot(theta) cost = (1/2*m) * np.sum(np.square(predictions - y)) return cost ``` 4. Gradient Descent: Implement the Gradient Descent algorithm to minimize the cost function and find the optimal parameters. ```py def gradient_descent(X, y, theta, learning_rate=0.01, iterations=1000): m = len(y) cost_history = np.zeros(iterations) for it in range(iterations): predictions = np.dot(X, theta) errors = predictions - y gradient = np.dot(X.T, errors) theta -= (1/m) * learning_rate * gradient cost_history[it] = calc_cost(theta, X, y) return theta, cost_history ``` ### Step 3: Model Training Now, it's time to train the model with the data we prepared. ```py theta_final, cost_history = gradient_descent(X_b, y, theta) ``` ### Step 4: Model Evaluation Finally, let's visualize the training results and evaluate the model's performance. ```py plt.plot(range(1, iterations + 1), cost_history, color='blue') plt.xlabel('Number of Iterations') plt.ylabel('Cost J') plt.title('Convergence of Gradient Descent') plt.show() # Plot of the data and regression line plt.scatter(X, y) plt.plot(X, X_b.dot(theta_final), color='red', label='Linear Regression') plt.xlabel('Independent Variable (X)') plt.ylabel('Dependent Variable (y)') plt.title('Resulting Linear Regression') plt.legend() plt.show() ``` help me analyze this code and let me know in the comments what flaws, errors and even good practices the code shows.