Data Storytelling through Visualizations in Python/

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Visualizing Machine Learning Predictions

Visualizing Machine Learning Predictions

Break down the components of visualizing machine learning predictions.

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As part of business intelligence efforts, machine learning (ML) models are often used for forecasting and better identifying the factors behind certain relationships.

Let's take a look at a brief example of how to visualize machine learning predictions using the Tips dataset from the Plotly package. Here, we're training a simple linear regression model on the Tips dataset to predict the tips of a restaurant from the total bill that groups paid. We print the R-squared score, also known as the coefficient of determination, to identify the performance of the model on the dataset.

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import numpy as np
import plotly.express as px
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
#Load the data
df = px.data.tips()
#Reshape the x-variable to use for training
X = df.total_bill.values.reshape(-1, 1)
#Define the Linear Reg model
model = LinearRegression()
#Train the model on the Total Bills variable to predict Tips
model.fit(X, df.tip)
#Print the R-squared socre of the model
print(model.score(X, df.tip))

Let's now take a look at two fundamental data visualizations that ...