Supervised Learning with scikit-learn

import numpy as np

# Create X from the radio column's values
X = sales_df["radio"].values

# Create y from the sales column's values
y = sales_df["sales"].values

# Reshape X
X = X.reshape(-1,1)

# Check the shape of the features and targets
print(X.shape, y.shape)

# Import LinearRegression
from sklearn.linear_model import LinearRegression

# Create the model
reg = LinearRegression()

# Fit the model to the data
reg.fit(X, y)

# Make predictions
predictions = reg.predict(X)

print(predictions[:5])

# Import matplotlib.pyplot
import matplotlib.pyplot as plt

# Create scatter plot
plt.scatter(X, y, color="blue")

# Create line plot
plt.plot(X, predictions, color="red")
plt.xlabel("Radio Expenditure ($)")
plt.ylabel("Sales ($)")

# Display the plot
plt.show()

# Create X and y arrays
X = sales_df.drop("sales", axis=1).values
y = sales_df["sales"].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Instantiate the model
reg = LinearRegression()

# Fit the model to the data
reg.fit(X_train, y_train)

# Make predictions
y_pred = reg.predict(X_test)
print("Predictions: {}, Actual Values: {}".format(y_pred[:2], y_test[:2]))

# Import mean_squared_error
from sklearn.metrics import mean_squared_error

# Compute R-squared
r_squared = reg.score(X_test, y_test)

# Compute RMSE
rmse = mean_squared_error(y_test, y_pred, squared=False)

# Print the metrics
print("R^2: {}".format(r_squared))
print("RMSE: {}".format(rmse))

# Import the necessary modules
from sklearn.model_selection import KFold, cross_val_score

# Create a KFold object
kf = KFold(n_splits=6, shuffle=True, random_state=5)

reg = LinearRegression()

# Compute 6-fold cross-validation scores
cv_scores = cross_val_score(reg, X, y, cv=kf)

# Print scores
print(cv_scores)

# Print the mean
print(np.mean(cv_results))

# Print the standard deviation
print(np.std(cv_results))

# Print the 95% confidence interval
print(np.quantile(cv_results, [0.025, 0.975]))

# Import Ridge
from sklearn.linear_model import Ridge
alphas = [0.1, 1.0, 10.0, 100.0, 1000.0, 10000.0]
ridge_scores = []
for alpha in alphas:
  
  # Create a Ridge regression model
  ridge = Ridge(alpha=alpha)
  
  # Fit the data
  ridge.fit(X_train, y_train)
  
  # Obtain R-squared
  score = ridge.score(X_test, y_test)
  ridge_scores.append(score)
print(ridge_scores)

# Import Lasso
from sklearn.linear_model import Lasso

# Instantiate a lasso regression model
lasso = Lasso(alpha = 0.3)

# Fit the model to the data
lasso.fit(X, y)

# Compute and print the coefficients
lasso_coef = lasso.coef_
print(lasso_coef)
plt.bar(sales_columns, lasso_coef)
plt.xticks(rotation=45)
plt.show()