Linear Regression — Residual plot for Radio spend data

In our last article, we said we will evaluate performance metrics and analyze Residual plot for Radio spend data. So let’s get going

Radio

Import our libraries and load our data

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

file_path = r'/Users/Downloads/advertising.csv'
df_ad_data = pd.read_csv(file_path)
df_ad_data.head()

Creating X matrix for Radio data

X_radio = df_ad_data[["Radio"]]
X_radio.head()

Get Y Vector Column

y_radio = df_ad_data['Sales']
y_radio.head()

Separate out our training data and test data

from sklearn.model_selection import train_test_split
X_train_radio, X_test_radio, y_train_radio, y_test_radio = train_test_split(X_radio, y_radio, test_size=0.3, random_state=101)

Apply LinearRegression

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train_radio, y_train_radio)
test_predictions_radio = model.predict(X_test_radio)

Calculate mean_absolute_error and root_mean_squared_error

from sklearn.metrics import mean_squared_error, mean_absolute_error…