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MLSEO Podcast ft. Isaías Blanco

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A Trusted Machine Learning SEO podcast

The following code provides a complete example of how to create a machine-learning model using synthetic data to predict the number of podcast plays based on positive comments.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Step 1: Generate synthetic data
np.random.seed(42)  # For reproducibility
n_samples = 100

# Generate random positive comments
positive_comments = np.random.randint(0, 100, size=n_samples)

# Generate podcast plays based on a linear relationship with some noise
# Let's assume that each positive comment contributes to an average of 10 plays
plays = positive_comments * 10 + np.random.normal(0, 20, size=n_samples)  # Adding some noise

# Create a DataFrame
data = {
    'positive_comments': positive_comments,
    'podcast_plays': plays
}
df = pd.DataFrame(data)

# Step 2: Data preprocessing
X = df[['positive_comments']]
y = df['podcast_plays']

# Splitting the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 3: Model training
model = LinearRegression()
model.fit(X_train, y_train)

# Step 4: Model evaluation
y_pred = model.predict(X_test)

# Calculate metrics
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f'Mean Squared Error: {mse:.2f}')
print(f'R^2 Score: {r2:.2f}')

# Step 5: Visualization
plt.scatter(df['positive_comments'], df['podcast_plays'], color='blue', label='Actual Data')
plt.scatter(X_test, y_pred, color='red', label='Predicted Data')
plt.plot(X_test, y_pred, color='green', linewidth=2, label='Regression Line')
plt.title('Podcast Plays vs Positive Comments')
plt.xlabel('Number of Positive Comments')
plt.ylabel('Number of Podcast Plays')
plt.legend()
plt.show()