The classification of social media ads is all about analyzing the ads for classifying whether your target audience will buy the product or not. It’s a great use case for data science in marketing. So, if you want to learn how to analyze social media ads to classify your target audience, then this article is for you. In this article, I will walk you through the task of social media ads classification with machine learning using Python.
Social Media Ads Classification
Classifying social media ads means analyzing your social media ads for finding the most profitable customers for your product who are more likely to buy the product. Sometimes the product you are offering is not suitable for all people when it comes to age and income. For example, a person between the ages of 20 and 25 may like to spend more on smartphone covers than a person between the ages of 40 and 45.
Likewise, a high-income person can afford to spend more on luxury goods than a low-income person. So this is how a business can determine whether a person will buy their product or not by classifying their social media ads. In the section below, I will walk you through social media ads classification with Machine Learning using Python.
Social Media Ads Classification using Python
The dataset I am using for the task of Social Media Ads Classification is downloaded from Kaggle. It contains data about a product’s social media advertising campaign. It contains features like:
- the age of the target audience
- the estimated salary of the target audience
- and whether the target audience has purchased the product or not
So let’s import the dataset and necessary Python libraries to start this task:
Age EstimatedSalary Purchased 0 19 19000 0 1 35 20000 0 2 26 43000 0 3 27 57000 0 4 19 76000 0
Now let’s take a look at some of the insights from the data to see if we need to make any changes to the dataset:
print(data.describe()) print(data.isnull().sum())
Age EstimatedSalary Purchased count 400.000000 400.000000 400.000000 mean 37.655000 69742.500000 0.357500 std 10.482877 34096.960282 0.479864 min 18.000000 15000.000000 0.000000 25% 29.750000 43000.000000 0.000000 50% 37.000000 70000.000000 0.000000 75% 46.000000 88000.000000 1.000000 max 60.000000 150000.000000 1.000000 Age 0 EstimatedSalary 0 Purchased 0 dtype: int64
Now let’s explore some of the important patterns in the dataset. The first thing I want to explore is the ages of the people who responded to the social media ads and bought the product:
The visualization above shows that people over 45 among the target audience are more interested in purchasing the product. Now let’s take a look at the income group of people who responded to social media ads and purchased the product:
The visualization above shows that people with a monthly income of over 90,000 among the target audience are more interested in purchasing the product.
Training a Social Media Ads Classification Model
Now let’s train a model to classify social media ads. First I’ll set the “Purchased” column in the dataset as the target variable and the other two columns as the features we need to train a model:
x = np.array(data[["Age", "EstimatedSalary"]]) y = np.array(data[["Purchased"]])
Now let’s split the data and train a social media ads classification model using the decision tree classifier:
At last, let’s have a look at the classification report of the model:
print(classification_report(ytest, predictions))
precision recall f1-score support
0 0.88 0.85 0.87 27
1 0.71 0.77 0.74 13
accuracy 0.82 40
macro avg 0.80 0.81 0.80 40
weighted avg 0.83 0.82 0.83 40
Summary
So this is how you can analyze and classify social media ads about the marketing campaign of a product. Classifying social media ads means analyzing your social media ads for finding the most profitable customers for your product who are more likely to buy the product. I hope you liked this article on classifying Social Media Ads with Machine Learning using Python. Feel free to ask your valuable questions in the comments section below.
