How to Plot Line of Best Fit in Python (With Examples)

In statistics, the “line of best fit” refers to a line through a scatter plot of data points that best expresses the relationship between those points. Analysts use this line to predict future trends. Here’s how to plot a line of best fit in Python.

Understanding the Concept: The line of best fit is usually calculated using a method called linear regression. This method finds the line that minimizes the sum of the squares of the vertical distances from each data point to the line.Here is a diagram illustrating a scatter plot with a line of best fit:

The line of best fit is usually calculated using a method called linear regression. This method finds the line that minimizes the sum of the squares of the vertical distances from each data point to the line.

Here is a diagram illustrating a scatter plot with a line of best fit:

Libraries Used

• NumPy: For numerical operations, especially handling arrays.
• Matplotlib: For plotting and visualizing the data.
• Scikit-learn: For the linear regression model.

Example 1: Basic Line of Best Fit

import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.linear_model import LinearRegression
 

 # Sample Data
 x = np.array([1, 2, 3, 4, 5]).reshape((-1, 1))
 y = np.array([2, 3.6, 5, 7, 9])
 

 # Create and Fit Model
 model = LinearRegression()
 model.fit(x, y)
 

 # Predict
 y_pred = model.predict(x)
 

 # Plotting
 plt.scatter(x, y, label='Data Points')
 plt.plot(x, y_pred, color='red', label='Line of Best Fit')
 plt.xlabel('X Axis')
 plt.ylabel('Y Axis')
 plt.title('Line of Best Fit Example 1')
 plt.legend()
 plt.show()

Resulting Plot:

In this example:

• Sample data x and y are defined.
• A linear regression model is created and fitted to the data.
• The predict method generates the y-values for the line of best fit.
• Matplotlib is used to plot the scatter plot and the line of best fit.

Example 2: Using Real-world Data

import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.linear_model import LinearRegression
 import pandas as pd
 

 # Load data (replace 'your_data.csv' with your file path)
 data = pd.read_csv('your_data.csv')
 x = data['X'].values.reshape((-1, 1)) # Ensure X and Y are the correct column names
 y = data['Y'].values
 

 # Model
 model = LinearRegression()
 model.fit(x, y)
 y_pred = model.predict(x)
 

 # Plotting
 plt.scatter(x, y, label='Data Points')
 plt.plot(x, y_pred, color='red', label='Line of Best Fit')
 plt.xlabel('X Axis')
 plt.ylabel('Y Axis')
 plt.title('Line of Best Fit Example 2')
 plt.legend()
 plt.show()

Resulting Plot:

This example extends the first one by:

• Loading data from a CSV file using pandas.
• Reshaping the input x to fit the model’s requirements.

Example 3: Advanced Plot Customization

import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.linear_model import LinearRegression
 

 # Sample Data
 x = np.array([1, 2, 3, 4, 5]).reshape((-1, 1))
 y = np.array([2, 3.6, 5, 7, 9])
 

 # Model
 model = LinearRegression()
 model.fit(x, y)
 y_pred = model.predict(x)
 

 # Plotting with customizations
 plt.figure(figsize=(10, 6)) # Adjust figure size
 plt.scatter(x, y, color='blue', marker='o', label='Data Points', s=50) # Customize scatter plot
 plt.plot(x, y_pred, color='green', linestyle='--', linewidth=2, label='Line of Best Fit') # Customize line
 plt.xlabel('Independent Variable', fontsize=12)
 plt.ylabel('Dependent Variable', fontsize=12)
 plt.title('Line of Best Fit Example 3', fontsize=14)
 plt.grid(True) # Show grid
 plt.legend()
 plt.xticks(fontsize=10) # Customize ticks
 plt.yticks(fontsize=10)
 plt.show()

Resulting Plot:

Here, the plot is customized further:

• Figure size, marker style, line style, and colors are modified.
• Labels and titles have adjusted font sizes.
• A grid is added for better readability.

Conclusion

Plotting a line of best fit in Python is straightforward with libraries like NumPy, Matplotlib, and scikit-learn. These examples provide a solid foundation for understanding and implementing linear regression in your data analysis projects.