The effects of artificial intelligence and e-commerce can be clearly seen in people's daily and business lives. Information processing is one of the main applications of artificial intelligence and machine learning algorithms. The main goal of information processing by artificial intelligence is to reduce the time of information extraction and obtain key insights. For example, the ranking of web pages allows us to get the information we need by spending less time, while in the past we had to spend hours to get quality content. The role of artificial intelligence in business can be effective in marketing, sales, and research and development. For example, AI has the capacity to help improve business marketing strategies.

The impact of artificial intelligence on business

Artificial intelligence, as an advanced technology, has had a significant impact on the business world and has made companies achieve good profitability in this field. Among the important effects of this technology on the business world, the following should be mentioned:

Improving business processes: By using artificial intelligence, business processes can be improved. For example, AI can help improve manufacturing processes, supply chain management, inventory management, human resource management, and customer management.

Improved customer experience: Artificial intelligence can help improve customer experience. For example, using artificial intelligence can improve customer service systems and gain a better understanding of customer needs.

Optimizing decision-making: Artificial intelligence can be helpful in optimizing business decisions. For example, AI can be used for market forecasting, demand forecasting and pricing.

Increased productivity: Companies can use artificial intelligence to improve production processes, which leads to improved quality and reduced costs.

Support for macro decisions: by using artificial intelligence algorithms, more accurate decisions can be applied for pricing, production planning and inventory management.

Using artificial intelligence in marketing: using artificial intelligence algorithms can help define the goal and marketing strategy and identify more optimal methods for advertising and marketing.

Using artificial intelligence in customer service: using artificial intelligence algorithms, customer service systems can be improved and a better understanding of customer needs can be obtained. Also, the above technology can be used to analyze customer behavior. For example, by using artificial intelligence algorithms, customers' buying patterns can be identified and their shopping experience can be improved.

How can artificial intelligence be implemented in business?

One of the important questions that some Iranian CEOs raise is how to implement artificial intelligence in the business world. In a situation where some believe that its implementation in the field of business is associated with challenges, but as a strategic technology, artificial intelligence can be used in many industries and businesses. To implement artificial intelligence in the world of business, you must first collect the required data and then use artificial intelligence algorithms to analyze and use this data. Also, to implement artificial intelligence in the field of business activities, there is a need to develop an artificial intelligence model or use models that have already been developed, including support vector machines (SVM), neural networks, and decision trees. In general, building a commercial AI model requires large teams of experts due to its complexity and challenges. But in general, to build a commercial artificial intelligence model, you can take the following steps:

Data collection: First, data related to the business area should be collected. This data may include customer information, orders, products, prices, inventory, etc.

Data pre-processing: After the data is collected, it needs to be pre-processed so that it can be used as input for the AI ​​model. Data preprocessing includes data screening, replacing wrong and incomplete data, using analytical methods to extract important features from the data, etc.

Choosing artificial intelligence algorithms: To build a commercial artificial intelligence model, you need to choose the right algorithms for training the model. These algorithms may include decision tree, support vector machine (SVM), etc.

Model training: After choosing artificial intelligence algorithms, the model should be trained using training data. At this stage, you can use supervised, unsupervised or augmented machine learning algorithms.

Evaluation of the model: After training the model, it should be evaluated to ensure its quality and accuracy. To evaluate the model, you can use criteria such as accuracy, correctness, improvement of business criteria, etc.

Using the model: After evaluating the model and ensuring its accuracy and quality, you can use the model to solve your business problems. To use the model, it is necessary to design suitable interfaces to communicate with the data and outputs of the model. Also, you should note that the AI ​​model needs to be continuously updated and optimized so that it can provide the best performance according to the changes in the data and the business world.

A practical example of implementing a decision tree about the probability of buying a product

A practical example of how artificial intelligence can be used in the business world is the implementation of a decision tree with machine learning to predict the likelihood of a customer buying a product. Suppose the sales department of a company is faced with customer data and their purchases in a certain period of time and wants to predict the probability of purchasing a product for each customer based on customer characteristics. Here, the decision tree can be used to predict the probability of purchase. The steps to implement this example are as follows:

Data collection: To collect data, you need to collect information about customers and their purchases over a certain period of time. Customer information can include age, gender, marital status, education, occupation, etc. Purchase information can include product name, price, purchase date, etc.

Data Preprocessing: After collecting the data, it must be preprocessed. This includes data refinement, replacing wrong and incomplete data, using analytical methods to extract important features from the data, etc.

Designing the structure of the decision tree: To design the structure of the decision tree, you must first identify the important features that are important for deciding whether to buy the product. Then, for each attribute, you need to determine the different values ​​it can have. For example, the age attribute can contain the values ​​"under 25", "between 25 and 35", and "over 35".

Decision tree training: After designing the decision tree structure, it should be trained using training data. At this stage, criteria such as accuracy, correctness and other evaluation criteria should be used to check the performance of the decision tree. To train the decision tree, you need to divide the training data into "purchased" and "not purchased" categories, and then train the decision tree using various algorithms such as ID3, C4.5, and CART.

Decision Tree Evaluation: After training the decision tree, you should evaluate it to check its accuracy and efficiency. For this purpose, you can use test data and check accuracy and other evaluation criteria.

Using the decision tree: after training and evaluating the decision tree, you can use it to predict the probability of customers buying the product. For each customer, put its related features in the decision tree and predict the probability of buying the product for that customer.

For example, suppose you have designed a decision tree to predict the likelihood that customers will purchase a product. If the age of the customer is less than 25 years and the marital status is single, the decision tree can predict that this customer is not likely to buy the product. Also, if the customer is older than 35 and married, the decision tree can predict that this customer is likely to buy the product. To implement the above scenario, you can use libraries like pandas, scikit-learn, etc., which provide different modules for machine learning. The Python code for this example is something like the following pseudocode:

import pandas as pd

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# load the dataset

data = pd.read_csv('hamidreza_taebi.csv')

# preprocess the data

#clean the data, handle missing values, encode categorical variables, etc.

# define the features and target variable

X = data.drop('purchase', axis=1)

y = data['purchase']

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# create the decision tree classifier

clf = DecisionTreeClassifier()

# fit the classifier to the training data

clf.fit(X_train, y_train)

# make predictions on the testing data

y_pred = clf.predict(X_test)

# evaluate the accuracy of the classifier

accuracy = accuracy_score(y_test, y_pred)

print('Accuracy:', accuracy)

In this code, first the data is loaded from a CSV file using the pandas library and after preprocessing, the features and the dependent variable are defined. Then, the data is divided into "training" and "testing" parts. Decision tree using the DecisionTreeClassifier class from scikit-learn

is created and adapted to the training data by calling fit. Then, using predict, the dependence of the dependent variable on the test characteristics is predicted, and finally the accuracy of the model is calculated using the accuracy_score criterion.

How can we change the decision tree parameters?

To apply changes in the parameters of the decision tree mentioned in the example above, we can use different parameters, some of which are mentioned here:

criterion: This parameter specifies the criteria on which the tree should be built. By default, this parameter is equal to 'gini', which means to use the Gini index. You can set this parameter to use another index like entropy.

max_depth: This parameter specifies the depth of the decision tree. By default, this parameter is set to None, which means no tree depth limit. You can set this parameter to limit the depth of the decision tree.

min_samples_split: This parameter specifies the minimum number of samples a node must have in order to split that node into two separate nodes. By default, this parameter is equal to 2. You can adjust this parameter to reduce the number of nodes that split into two separate nodes.

min_samples_leaf: This parameter specifies how many minimum samples should be in a tree leaf. By default, this parameter is equal to 1. You can adjust this parameter to avoid creating fewer leaves.

To adjust these parameters, you can set them when building the classifier. For example, to set the max_depth value to 5, you can add the following code to the previous code:

clf = DecisionTreeClassifier(max_depth=5)

Similarly, other parameters can be set which we will mention below. Of course, note that some of them may not be present in some versions of scikit-learn, but in newer versions of this library, the following parameters can be used in the decision tree of the example above:

max_features: This parameter specifies the maximum number of features (random features) to be selected for examination at each node. By default, this parameter is equal to the total number of features.

max_leaf_nodes: This parameter specifies the maximum number of leaves there should be in the decision tree. This parameter is set to None by default, which means that there is no limit on the number of leaves.

min_impurity_split: This parameter specifies the minimum value based on which a node can be split into two nodes. This parameter is equal to zero by default.

min_weight_fraction_leaf: This parameter specifies how many training samples should be in a leaf. By default, this parameter is equal to zero, but you can adjust it as needed to improve the performance of the model.

random_state: This parameter specifies whether the selection of features and samples should be done randomly or not. By setting this parameter to a constant value, you can get the same results every time you run it.

To adjust any of these parameters, you can set them at classifier build time. For example, to set the max_features value to 3, you can add the following code to the previous code:

clf = DecisionTreeClassifier(max_features=3)

In addition to the above, other parameters can also be adjusted. In general, the adjustable parameters in the decision tree are divided into three categories: parameters related to the tree structure, parameters related to the optimal search of the feature set, and parameters related to the behavior of the model during training.

splitter: This parameter specifies which method to use to check each node. By default, this parameter is equal to 'best', which means to use the best split based on the specified criteria. You can set this parameter to use the random method.

presort: This parameter specifies whether the data should be sorted when building the tree. By default, this parameter is not used for datasets smaller than 10,000. If your dataset is larger than this value, you can adjust this parameter to improve the speed of tree building.

class_weight: This parameter specifies whether the weight should be different for each class. By default, no weights are assigned to classes. You can adjust this parameter to fix the roughness of the class.

To adjust any of these parameters, you can set them at classifier build time. For example, to set the splitter value to random, you can add the following code to the above example:

clf = DecisionTreeClassifier(splitter='random')

If you complete the code snippet mentioned in the example above, you can use it to build a predictive model of product purchases in online stores.

last words

In this article, we tried to introduce you to the important and significant applications of artificial intelligence in the world of business, and we examined how to implement artificial intelligence in the world of e-commerce along with a practical example in this field. As you may have guessed, artificial intelligence as one of the advanced technologies has many applications in e-commerce. Other important and key applications of artificial intelligence in the business world include recommender systems, systems for checking the fakeness of goods, optimization systems for transportation routes, customer service systems, fraud detection systems, and systems for predicting customer needs (similar to the example we mentioned). ) etc. pointed out. In general, artificial intelligence can provide companies with strategic recommendations in improving the shopping experience and increasing sales.