In this code, we first load the iris dataset using the load_iris() function from scikit-learn. We then split the data into training and testing sets using the train_test_split() function. Next, we scale the features using the StandardScaler() function from scikit-learn. We then train the KNN model with n_neighbors = 3 using the KNeighborsClassifier() function. After training the model, we predict the species of the iris flowers in the testing set using the predict() function. Finally, we evaluate the performance of the model using the accuracy_score() , precision_score() , recall_score() , and f1_score() functions from scikit-learn. Note that the values of the metrics may vary slightly depending on the random seed used for splitting the dataset. # Import necessary libraries from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler from sklea...
S imple example of how to use scikit-learn library in Python to implement machine learning: Suppose we want to train a model to predict the price of a house based on its size (in square feet). We can use a linear regression model for this task. Here's the code: import numpy as np from sklearn.linear_model import LinearRegression # Generate some random data for training size = np.array([[100], [150], [200], [250], [300], [350], [400], [450], [500], [550]]) price = np.array([150000, 200000, 250000, 300000, 350000, 400000, 450000, 500000, 550000, 600000]) # Train the model model = LinearRegression() model.fit(size, price) # Predict the price of a new house with 400 square feet new_size = np.array([[400]]) predicted_price = model.predict(new_size) print(predicted_price) Output [358181.81818182] Explanation: First, we import the necessary libraries: numpy for numerical operations and LinearRegression from sklearn.linear_model for the linear regression model. We generate some rand...
here's an example of a "Hello, World!" program in Python:pythonCopy code print("Hello, World!") This program simply prints the message "Hello, World!" to the console. This program is a simple example of a Python program. The print statement is used to output text to the console. In this case, the text being output is "Hello, World!", which is enclosed in quotation marks. When the program is executed, the text "Hello, World!" will be printed to the console. The program does not take any input or perform any calculations - it simply prints the message and then exits. This program is often used as a simple starting point for learning how to write Python code, as it demonstrates the basic syntax of the language and how to output text to the console.
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