From e0bbc1213152c166d177ca79cadeff8d46c3033d Mon Sep 17 00:00:00 2001 From: Omar Santos Date: Tue, 5 Sep 2023 00:01:24 -0400 Subject: [PATCH] Create linear_regression.py --- .../ML_Fundamentals/linear_regression.py | 39 +++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 ai_research/ML_Fundamentals/linear_regression.py diff --git a/ai_research/ML_Fundamentals/linear_regression.py b/ai_research/ML_Fundamentals/linear_regression.py new file mode 100644 index 0000000..632ad2f --- /dev/null +++ b/ai_research/ML_Fundamentals/linear_regression.py @@ -0,0 +1,39 @@ +import numpy as np +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +from sklearn.linear_model import LinearRegression + +# Generate synthetic data +np.random.seed(42) +X = 2 * np.random.rand(100, 1) +y = 4 + 3 * X + np.random.randn(100, 1) + +# Split the data into training and testing sets +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# Create a linear regression model +lin_reg = LinearRegression() + +# Fit the model to the training data +lin_reg.fit(X_train, y_train) + +# Get the slope (coef_) and the intercept (intercept_) +slope = lin_reg.coef_ +intercept = lin_reg.intercept_ + +# Print the slope and intercept +print("Slope (Coefficient): ", slope) +print("Intercept: ", intercept) + +# Predict y values for the test set +y_pred = lin_reg.predict(X_test) + +# Visualize the training data and the regression line +plt.scatter(X_train, y_train, color='blue', label='Training Data') +plt.scatter(X_test, y_test, color='green', label='Testing Data') +plt.plot(X_test, y_pred, color='red', linewidth=2, label='Regression Line') +plt.xlabel('X') +plt.ylabel('y') +plt.title('Linear Regression Demonstration') +plt.legend() +plt.show()