Sales Prediction using Linear Regression in Python
Introduction
In this blog, we have discussed how to implement the linear regression algorithm to predict sales. From the previous blog, we know that “linear regression” finds the linear relationship between the dependent and independent variables by determining the best fit linear line between them. From the statsmodel library and sklearn library we use the inbuilt linear regression function for prediction.
Libraries Required
 Numpy
 Pandas
 Matplotlib
 Seaborn
 sklearn

statsmodels
Implementation of Sales Prediction using Linear Regression
Step1: Importing Libraries and dataset
Firstly, we need to import the required libraries and datasets. Dataset can be downloaded at Advertising.csv.
# Import the numpy and pandas package import numpy as np import pandas as pd # Data Visualisation import matplotlib.pyplot as plt import seaborn as sns advertising = pd.DataFrame(pd.read_csv(" path for the dataset")) # displays the first 5 rows of the dataset advertising.head()
Step2: Preprocessing the data
In this step, we check for any missing values and outliers and deal with them.
# Checking Null values advertising.isnull().sum()*100/advertising.shape[0] # Outlier Analysis fig, axs = plt.subplots(3, figsize = (5,5)) plt1 = sns.boxplot(advertising['TV'], ax = axs[0]) plt2 = sns.boxplot(advertising['Newspaper'], ax = axs[1]) plt3 = sns.boxplot(advertising['Radio'], ax = axs[2]) plt.tight_layout()
Step3: Exploratory Data Analysis
In this step, we explore the data. Exploratory data analysis (EDA) is a method of data analysis that allows for a precise understanding of the data. Basically, it means being aware of the contents of the data we’re working with.
# with this inbuilt function we can get mean, median, mode etc for the given dataset. advertising.describe() # check for any outliers in target variable sns.boxplot(advertising['Sales']) plt.show()
Step4: Splitting the dataset
The feature variable TV is first assigned to variable X, and the response variable Sales is assigned to variable Y and split our variable into training and testing sets.
# assigning the variables X = advertising['TV'] y = advertising['Sales'] # we split our variable into training and testing sets. It is usually a good practice to keep 70% of the data in your train dataset and the rest 30% in your test dataset from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.7, test_size = 0.3, random_state = 100)
Step5: Building the model
Using statsmodel.api library
First import statsmodel library. But in order to have an intercept, you need to manually use the add constant attribute of statsmodel. And once you’ve added the constant to your X_Train dataset, you can go ahead and fit a regression line using the OLS.
# importing the library import statsmodels.api as sm # Add a constant to get an intercept X_train_sm = sm.add_constant(X_train) # Fit the resgression line using 'OLS' lr = sm.OLS(y_train, X_train_sm).fit() # Performing a summary operation lists out all the different parameters of the regression line fitted print(lr.summary())
Using sklearn Library
We use the sklearn library to perform Linear regression. First, we import the Linear Regression module from sklearn and fit the data.
from sklearn import linear_model # create linear regression object reg = linear_model.LinearRegression() X_train= X_train.values.reshape(1, 1) y_train= y_train.values.reshape(1, 1) # train the model using the training sets reg.fit(X_train, y_train)
Let’s visualize how well the model fits the data. From the parameters that we get, our linear regression equation becomes Sales=6.948+0.054×TV.
plt.scatter(X_train, y_train) plt.plot(X_train, 6.948 + 0.054*X_train, 'r') plt.show()
Step6: Predictions on the Test Set
Making predictions on the test data is the next step after fitting a regression line to your train dataset. To do this, you must first add a constant to the X test data, just as you did for the X train data, and then you can proceed to predict the y values corresponding to the X test using the predicted attribute of the fitted regression line.
# Add a constant to X_test X_test_sm = sm.add_constant(X_test) # Predict the y values corresponding to X_test_sm y_pred = lr.predict(X_test_sm)
Let us visualize the fit on the test set
Step7: Evaluation Metrics
Evaluation metrics show how well your model is performing. We use MAE, MSE, RMSE, and R squared error as our evaluation metrics.
from sklearn.metrics import mean_absolute_error,mean_squared_error mae = mean_absolute_error(y_true=y_test,y_pred=y_pred) mse = mean_squared_error(y_true=y_test,y_pred=y_pred) #default=True rmse = mean_squared_error(y_true=y_test,y_pred=y_pred,squared=False) r_squared = r2_score(y_test, y_pred) print("MAE:",mae) print("MSE:",mse) print("RMSE:",rmse) print("r_squared",r_squared)
Also, Read – Linear Regression
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