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Predict Order Cancellation Using SVM Classifier & Accuracy Score ?

Introduction

In this article, we will explore how to use a Support Vector Machine (SVM) classifier to predict order cancellations based on several features. We will walk through the necessary steps including data preparation, model training, and evaluation using accuracy scores.

Step 1: Import Libraries

First, we need to begin by importing the required libraries for our Python project, particularly from the scikit-learn library (commonly imported as sklearn) which contains the implementations for machine learning models, including the SVM classifier.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

Step 2: Create & Prepare the Dataset

Next, we will define our dataset. The dataset will consist of the following features: order value, customer satisfaction score, shipping priority, and our label, which indicates whether the order was canceled (1 for canceled and 0 for not canceled).

data = (
    'order_value': [150, 200, 70, 300, 450],
    'satisfaction_score': [5, 4, 3, 2, 1],
    'shipping_priority': [1, 0, 1, 0, 1],
    'order_cancellation': [0, 1, 1, 0, 1]
)

df = pd.DataFrame(data)

Step 3: Define Features and Labels

After preparing our data, we need to separate our features from our labels. The features will include order value, satisfaction score, and shipping priority, while the label will be the order cancellation status.

X = df[['order_value', 'satisfaction_score', 'shipping_priority']]
y = df['order_cancellation']

Step 4: Split the Data into Training and Test Sets

To evaluate the classifier’s performance, we divide our dataset into a training set and a test set, using 30% of the data for testing.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

Step 5: Train the SVM Classifier Model

With our data prepared, we can now proceed to train the SVM classifier model on the training dataset.

model = SVC()
model.fit(X_train, y_train)

Step 6: Make Predictions

After training the model, we can utilize it to make predictions on the test set.

predictions = model.predict(X_test)

Step 7: Evaluate the Model using Accuracy Score

To evaluate the performance of our model, we will calculate the accuracy score which measures how often the classifier is correct.

accuracy = accuracy_score(y_test, predictions)

Step 8: Output Predictions and Accuracy

Finally, we will print out the predictions made by our model along with the accuracy score.

print("Predictions:", predictions)
print("Accuracy:", accuracy)

By following these steps, we can effectively build an SVM classifier to predict whether orders will be canceled based on the defined features.

Keyword

  • Support Vector Machine (SVM)
  • Classifier
  • Order Cancellation
  • Dataset
  • Features
  • Labels
  • Accuracy Score
  • Prediction
  • Machine Learning

FAQ

Q1: What is an SVM classifier?
A1: An SVM classifier is a type of supervised machine learning algorithm that is used for classification tasks. It works by finding the hyperplane that best separates the classes in the feature space.

Q2: How do I prepare my dataset for SVM?
A2: You prepare your dataset by defining the features (independent variables) and labels (dependent variable), converting the data into a DataFrame format, and then splitting it into training and test sets.

Q3: What does the accuracy score represent?
A3: The accuracy score represents the proportion of correctly classified instances compared to the total number of instances, providing a measure of the classifier's performance.

Q4: Can I use SVM for other types of prediction tasks?
A4: Yes, SVM can be used for both classification and regression tasks, making it a versatile algorithm for various machine learning problems.

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