Predicting Ticket Sales with Random Forests in Python

In this Python machine learning tutorial, we will delve into Random Forests, a powerful machine learning algorithm, to predict ticket sales for events. We’ll generate a synthetic dataset where ticket sales are influenced by lower ticket prices, higher marketing spend, and lower opponent ranking. We’ll then create a predictive model using this dataset. Additionally, we’ll explain two important evaluation metrics, Mean Squared Error (MSE) and R-squared (R2), during the testing phase.

Table of Contents

  1. Introduction to Random Forests
  2. Generating a Synthetic Dataset
  3. Data Preprocessing
  4. Splitting the Dataset
  5. Building and Training The Model
  6. Testing and Evaluation
  7. Making Predictions
  8. Conclusion

Introduction to Random Forests

Random Forests are like a team of decision makers. Imagine you have a big decision to make, and you’re not sure what the best choice is. So, you gather a group of people with different backgrounds and experiences to help you. Each person gives their opinion, and you make your decision based on the collective wisdom of the group.

In the world of machine learning, Random Forests work in a similar way. They are a group of decision trees that work together to make predictions. Each decision tree is like one person in the group, and they all vote on the best prediction. This teamwork often leads to better and more accurate decisions.

Key Ideas

Now, let’s introduce some key ideas:

  • Ensemble Learning: This is just a fancy term for teamwork. Random Forests are an example of ensemble learning because they involve a team of decision trees working together.
  • Decision Trees: Think of these as simple flowcharts that help make decisions. They ask questions about the data and follow paths until they reach a conclusion. Random Forests use many decision trees to make predictions.
  • Randomness: Randomness is like adding a bit of spice to the decision-making process. In Random Forests, each decision tree sees only part of the data, and they might ask different questions. This randomness makes the group of trees more diverse and clever in solving problems.
  • Aggregation: After all the decision trees have their say, we combine their opinions to make a final decision. It’s like taking a vote among friends – the most popular choice wins.

Random Forests are incredibly useful because they are good at handling many different types of problems. They can predict things like ticket sales, medical diagnoses, or even which movies you might enjoy.

In the rest of this tutorial, we’ll use Random Forests to predict ticket sales based on factors like opponent ranking, marketing spend, and ticket price. But don’t worry if it sounds complicated – we’ll walk through each step together. Let’s get started!

Generating a Synthetic Dataset

To simulate ticket sales influenced by lower ticket prices, higher marketing spend, and lower opponent ranking, we’ll generate a synthetic dataset with these relationships. Here’s the code to create this dataset:

import pandas as pd
import numpy as np

# Set a random seed for reproducibility

# Generate synthetic data with ticket sales influenced by lower prices, higher spend, and lower ranking
n_samples = 1000
opponent_ranking = np.random.randint(1, 11, size=n_samples)
marketing_spend = np.random.uniform(1000, 10000, size=n_samples)
ticket_price = np.random.uniform(20, 100, size=n_samples)

# Define a ticket sales formula with desired relationships
ticket_sales = 2000 - 10 * opponent_ranking + 0.5 * marketing_spend - 15 * ticket_price + np.random.normal(0, 200, size=n_samples)

# Ensure that ticket sales are non-negative
ticket_sales = np.maximum(ticket_sales, 0)

# Create a DataFrame
data = pd.DataFrame({
    'Opponent_Ranking': opponent_ranking,
    'Marketing_Spend': marketing_spend,
    'Ticket_Price': ticket_price,
    'Ticket_Sales': ticket_sales

In this synthetic dataset, we’ve formulated ticket sales to have a positive relationship with lower ticket prices, higher marketing spend, and lower opponent ranking. The noise introduced by np.random.normal adds some variability to the dataset.

Data Preprocessing

Now that we have our synthetic dataset, let’s preprocess it by separating features (X) and the target variable (y):

# Features (X) and target variable (y)
X = data[['Opponent_Ranking', 'Marketing_Spend', 'Ticket_Price']]
y = data['Ticket_Sales']

Splitting the Dataset

To evaluate our model’s performance, we need to split the dataset into a training set and a testing set. We’ll allocate 80% of the data for training and 20% for testing:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Building and Training the Random Forest Model

Let’s proceed to create and train our Random Forest regression model:

from sklearn.ensemble import RandomForestRegressor

# Create the Random Forest model
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)

# Train the model, y_train)

Testing and Evaluation

Testing and evaluation are essential steps in machine learning model development. They allow us to gauge how well our model generalizes to unseen data. In this tutorial, we’ll use two important evaluation metrics: Mean Squared Error (MSE) and R-squared (R2).

  • Mean Squared Error (MSE): MSE measures the average squared difference between predicted values and actual values. Lower MSE indicates better model performance.
  • R-squared (R2) Score: R2 measures the proportion of the variance in the target variable that is predictable from the independent variables. It ranges from 0 to 1, with higher values indicating a better fit. An R2 score of 1 means a perfect fit, while 0 suggests that the model does not explain any variance.
from sklearn.metrics import mean_squared_error, r2_score

# Predict on the test set
y_pred = rf_model.predict(X_test)

# Calculate MSE
mse = mean_squared_error(y_test, y_pred)

# Calculate R2 score
r2 = r2_score(y_test, y_pred)

print(f'Mean Squared Error (MSE): {mse}')
print(f'R-squared (R2) Score: {r2}')

Making Predictions

With our trained model, we can now make predictions for new data. Here’s an example:

# Create a new data point
new_data = pd.DataFrame({
    'Opponent_Ranking': [5],
    'Marketing_Spend': [5000],
    'Ticket_Price': [50]

# Make a prediction
predicted_sales = rf_model.predict(new_data)
print(f'Predicted Ticket Sales: {predicted_sales[0]}')


In this tutorial, we explored the concept of Random Forests, a versatile machine learning algorithm, and used it to predict ticket sales based on lower ticket prices, higher marketing spend, and lower opponent ranking. We also created a synthetic dataset with these relationships and tested our model’s performance using MSE and R2 score. Remember that while synthetic data can be informative for model testing, real-world data should be used for practical applications to ensure accurate predictions.