Before using Scikit-learn for data preprocessing, the following preparations need to be made: 1. Environment setup: Ensure that Python and Scikit learn have been installed. It can be installed through Anaconda or pip. 2. Dependent class libraries: In addition to Scikit learn, class libraries such as NumPy and Pandas may also be used. 3. Dataset: Select the appropriate dataset and download it from Scikit learn's official website or data science competition websites such as Kaggle. The following sample data uses the Iris Iris dataset. After the environment is established, the steps for data preprocessing can be carried out: 1. Data cleansing: deal with missing values, Outlier, duplicate values and other issues to ensure the integrity and accuracy of data. 2. Feature selection: select features with high correlation with target variables from the original data set, and use correlation coefficient matrix, statistical test and other methods to select. 3. Feature extraction: extract more useful information from original features. Common methods include principal component analysis (PCA), Linear discriminant analysis (LDA), etc. 4. Data conversion: standardize and normalize the original data to make the data meet the requirements of the model, such as Feature scaling, normalization, and unique heat coding. Next, take the Iris Iris dataset as an example to implement a complete data preprocessing example. 1. Preparation work: ```python #Import the required class library from sklearn.datasets import load_iris from sklearn.preprocessing import StandardScaler from sklearn.feature_selection import SelectKBest, chi2 from sklearn.decomposition import PCA from sklearn.model_selection import train_test_split #Load Dataset iris = load_iris() X = iris.data y = iris.target ``` 2. Data cleansing: For Iris data sets, Data cleansing is not necessary because the data sets have been processed. 3. Feature selection: ```python #Feature selection using Chi-squared test selector = SelectKBest(chi2, k=2) X_new = selector.fit_transform(X, y) ``` 4. Feature extraction: ```python #Using Principal Component Analysis for Feature Extraction pca = PCA(n_components=2) X_new = pca.fit_transform(X_new) ``` 5. Data conversion: ```python #Using standardization for data conversion scaler = StandardScaler() X_new = scaler.fit_transform(X_new) ``` 6. Divide training and testing sets: ```python #Divide training and testing sets X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.2, random_state=0) ``` 7. Complete code: ```python #Import the required class library from sklearn.datasets import load_iris from sklearn.preprocessing import StandardScaler from sklearn.feature_selection import SelectKBest, chi2 from sklearn.decomposition import PCA from sklearn.model_selection import train_test_split #Load Dataset iris = load_iris() X = iris.data y = iris.target #Feature selection using Chi-squared test selector = SelectKBest(chi2, k=2) X_new = selector.fit_transform(X, y) #Using Principal Component Analysis for Feature Extraction pca = PCA(n_components=2) X_new = pca.fit_transform(X_new) #Using standardization for data conversion scaler = StandardScaler() X_new = scaler.fit_transform(X_new) #Divide training and testing sets X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.2, random_state=0) ``` Finally, through the above steps, data can be preprocessed, unnecessary data can be cleaned, the most relevant features can be selected, more useful information can be extracted, and the data can be converted into a form acceptable to the model. For different datasets and tasks, corresponding processing can be performed as needed.

Preparation work: 1. Install Python: on the official website（ https://www.python.org/downloads/ ）Download and install the latest version of Python. 2. Install Scikit learn: Open a command line window and run the following command: ``` pip install scikit-learn ``` Dependent class libraries: -Pandas: For data processing and analysis, the installation command is' pip install Pandas'` -NumPy: used for numerical calculations and array operations, with the installation command 'pip install numpy'` -Matplotlib: For visualization, the installation command is' pip install matplotlib '` -Seaborn: Matplotlib based Data and information visualization library. The installation command is ` pip install seaborn` Dataset introduction: The actual battle used a dataset of Titanic passengers, which includes characteristic information of the passengers (such as age, gender, ticket level, etc.) and survival labels. The dataset contains two files: the training set (train. csv) and the test set (test. csv), which can be downloaded from the Kaggle website（ https://www.kaggle.com/c/titanic/data ）. Sample data: Some of the data in the training set are as follows: ``` PassengerId Survived Pclass ... Fare Cabin Embarked 0 1 0 3 ... 7.2500 NaN S 1 2 1 1 ... 71.2833 C85 C 2 3 1 3 ... 7.9250 NaN S 3 4 1 1 ... 53.1000 C123 S 4 5 0 3 ... 8.0500 NaN S ``` The complete sample code is as follows: ```python import pandas as pd from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score #Read Training Set train_data = pd.read_csv('train.csv') #Data preprocessing train_data = train_data[['Survived', 'Pclass', 'Sex', 'Age', 'Fare']] train_data = train_data.dropna() train_data['Sex'] = train_data['Sex'].map({'female': 0, 'male': 1}) #Partition features and labels X = train_data[['Pclass', 'Sex', 'Age', 'Fare']] y = train_data['Survived'] #Divide 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 Logistic regression model model = LogisticRegression() #Model training model.fit(X_train, y_train) #Model prediction y_pred = model.predict(X_test) #Calculation accuracy accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy) ``` Code Description: Firstly, import the required libraries and classes. 2. Use the Pandas library to read the CSV file of the training set, preprocess the data, select the required feature columns, and delete rows containing null values. At the same time, convert the value of the gender column to a numerical representation. 3. Divide features and labels. 4. Use train_ Test_ Split divides the dataset into training and testing sets. 5. Create a LogisticRegression object, that is, a Logistic regression model. 6. Train the model. 7. Use the trained model to predict the test set. 8. Use accuracy_ Calculate the prediction accuracy based on score. 9. Printing accuracy. Summary: The Logistic regression model in the Scikit-learn database was used to predict the passenger data of the Titanic in this actual battle, and the prediction accuracy was calculated. Through this example, we can learn how to use Scikit learn to model and predict machine learning tasks, and to preprocess data and Feature selection.

Preparation work: 1. Install Python. You can download and install the latest version of Python from the official website. 2. Install Scikit learn. You can use the pip command (pip install scikit learn) or use Anaconda for installation (conda install scikit learn). 3. Download the dataset. This sample uses the Iris dataset, which can be downloaded from the following website: https://archive.ics.uci.edu/ml/datasets/iris Dependent class libraries: 1. sklearn. svm. SVC: SVM algorithm implementation class in Scikit learn. 2. sklearn.datasets.load_ Iris: Iris dataset loading function in Scikit learn. Sample data description: The Iris dataset contains 150 samples, each with 4 features (sepal length, sepal width, petal length, and petal width) and their corresponding category labels (Setosa, Versicolor, Virginia). The Python code implementation is as follows: ```python from sklearn import svm from sklearn.datasets import load_iris #Import Dataset iris = load_iris() #Create an SVM classifier object clf = svm.SVC() #Training SVM models using datasets clf.fit(iris.data, iris.target) #Predict the category of new samples new_sample = [[5.0, 3.6, 1.3, 0.25]] predicted_class = clf.predict(new_sample) #Print prediction results print("Predicted class:", predicted_class) ``` Program output result: ``` Predicted class: [0] ``` Summary: This example demonstrates how to use the SVM algorithm in Scikit learn to classify iris datasets. Firstly, we need to install Python and Scikit learn libraries and download the dataset. Then, we import the necessary class libraries and datasets, and create an SVM classifier object. Next, use the dataset to train the SVM model and use the trained model to predict the category of new samples. Finally, print the predicted results. Through this example, we can see that the SVM algorithm using Scikit learn is very simple and flexible. With the appropriate environment setup and preparation work, we can quickly use the SVM algorithm in Scikit learn to complete practical tasks.

Environmental construction and preparation work: 1. Ensure that Python and pip are installed, and it is recommended to use Python version 3. x. 2. Install the Scikit learn library: You can use the following command to install: ``` pip install -U scikit-learn ``` 3. Import the required class library: ``` import pandas as pd from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn import metrics ``` Dataset introduction and download website: This example uses the Iris (Iris) dataset, which is a commonly used classification experiment dataset that includes 150 samples divided into 3 categories, with 50 samples in each category. Each sample contains 4 features: calyx length, calyx width, petal length, and petal width. Dataset download website: https://archive.ics.uci.edu/ml/datasets/iris Sample code implementation: ```python #Read Dataset url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class'] dataset = pd.read_csv(url, names=names) #The dataset is divided into features and target variables X = dataset.iloc[:, :-1] y = dataset.iloc[:, -1] #The dataset is divided into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) #Create Decision Tree Classifier clf = DecisionTreeClassifier() #Using training sets to train models clf.fit(X_train, y_train) #Predictive Test Set y_pred = clf.predict(X_test) #Calculation accuracy accuracy = metrics.accuracy_score(y_test, y_pred) Print ("Accuracy:", accuracy) ``` Summary: This article introduces the practical application of Python's decision tree algorithm in the Scikit learn library for classification tasks. Firstly, the environment was built and prepared, including installing Scikit learn and importing the required class libraries. Then it introduced the dataset Iris used and provided a website for downloading the dataset. Subsequently, a complete sample code was provided, including steps such as dataset reading, feature and target variable partitioning, model training, prediction, and calculation accuracy. Finally, a summary of the entire process was provided.

Preparation work: 1. Environment setup: Install Python and Scikit learn libraries. 2. Dependent class libraries: numpy, pandas, matplotlib. Dataset introduction: We will use a built-in Iris classification dataset (IRIS dataset) from the Scikit learn library. This dataset contains 150 records, each with 4 features: sepal length, sepal width, petal length, and petal width. Each record belongs to one of three categories: Setosa, Versicolor, and Virginia. Code implementation: #Import the required class libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier from sklearn import datasets #Load Iris Dataset iris = datasets.load_iris() X = iris.data y = iris.target #Divide the dataset into training and testing sets np.random.seed(0) indices = np.random.permutation(len(X)) X_train = X[indices[:-30]] y_train = y[indices[:-30]] X_test = X[indices[-30:]] y_test = y[indices[-30:]] #Create Random forest Classifier Model model = RandomForestClassifier(n_estimators=10) #Training model model.fit(X_train, y_train) #Prediction predicted = model.predict(X_test) #Print prediction results Print ("Predicted result:", predicted) #Print real results Print ("Real result:", y_test) #Printing accuracy accuracy = np.mean(predicted == y_test) Print ("Accuracy:", accuracy) Code description: Firstly, import the necessary class libraries. 2. Load the Iris dataset, store the features in variable X, and store the target variable in variable y. 3. Use the random. mutation function of the numpy library to randomly shuffle the dataset and divide it into training and testing sets. 4. Create a Random forest classifier model and set n_ The estimators parameter is 10, indicating the use of 10 decision trees. 5. Use training set data for model training. 6. Use test set data for prediction. 7. Print predicted and actual results. 8. Calculate and print the accuracy rate, which represents the proportion of the predicted results that are consistent with the actual results. Summary: This example uses the Random forest classifier in the Scikit-learn library to classify the iris dataset. Firstly, import the necessary class libraries, load the dataset, and divide it into training and testing sets. Then create and train the Random forest classifier model, use the test set data to predict, and calculate the accuracy. Finally, print the predicted results, true results, and accuracy. Random forest classifier is a powerful machine learning model, which is suitable for classification and regression problems. Its advantages include the ability to process large amounts of data, good accuracy, and the ability to process high-dimensional data.

Preparation work and environmental setup: 1. Install Python: On the Python official website（ https://www.python.org/downloads/ ）Download the Python version suitable for your operating system and install it. 2. Install Scikit learn: Open a command prompt and enter the following command to install Scikit learn: ``` pip install -U scikit-learn ``` 3. Install other necessary class libraries: In this practice, we also need to use the numpy, pandas, and matplotlib class libraries. Enter the following command to install: ``` pip install numpy pandas matplotlib ``` Dependent class libraries: 1. numpy: Used for numerical calculations and array operations. 2. Pandas: Used for data preprocessing and analysis. 3. matplotlib: used for Data and information visualization. 4. Scikit learn: used to construct and train machine learning models. Dataset introduction: The actual combat used this time is the Boston Housing Dataset, which comes with Scikit-learn and is a classic dataset used for regression problems. This dataset contains 506 samples, each with 13 features such as crime rate, average number of rooms, etc. The target variable is the median price of houses in the region. Dataset download website: The dataset provided by Scikit learn can be downloaded directly from its server without the need for additional download links. Sample data and code: The following is a complete sample code for linear regression using Scikit-learn: ```python import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_boston from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error #Load Boston House Price Dataset boston = load_boston() df = pd.DataFrame(boston.data, columns=boston.feature_names) df['PRICE'] = boston.target #Extract features and target variables X = df.drop('PRICE', axis=1).values y = df['PRICE'].values #Divide training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) #Building a linear regression model model = LinearRegression() #Training model model.fit(X_train, y_train) #Prediction y_pred = model.predict(X_test) #Evaluation mse = mean_squared_error(y_test, y_pred) print('Mean Squared Error:', mse) #Visualization results plt.scatter(y_test, y_pred) plt.plot([y.min(), y.max()], [y.min(), y.max()], '--', color='red', linewidth=2) plt.xlabel('True Price') plt.ylabel('Predicted Price') plt.title('Boston Housing Dataset - Linear Regression') plt.show() ``` Run the above code to perform linear regression and obtain a visual display of the results. Summary: This practical exercise introduced how to use Scikit-learn for linear regression, and used the Boston housing price dataset as an example to train, predict, and evaluate the model. Through this example, we can learn the basic process of building machine learning models using Scikit learn, as well as using relevant class libraries for data processing and visualization.

Environmental construction and preparation work: 1. Install Python environment: Scikit learn is a machine learning library based on Python, and Python needs to be installed first. You can access it from the official website（ https://www.python.org/ ）Download the latest version of Python and install it. 2. Install the Scikit learn library: Use the pip command to install the Scikit learn library, as follows: pip install scikit-learn Dependent class libraries: In this example, only the Scikit learn library is used. Dataset introduction: This example takes the Iris Iris dataset as an example, which is one of the very classic datasets in the field of machine learning, used for multi classification problems. The dataset contains 150 samples, each with 4 features, divided into 3 categories. Dataset download website: The Iris dataset is an example dataset that comes with the Scikit learn library and can be loaded using the following code: ```python from sklearn.datasets import load_iris iris = load_iris() ``` Sample data: The samples in the Iris dataset contain four features, namely calyx length, calyx width, petal length, and petal width. Each sample comes with a label, which represents the variety of flowers. The complete sample code is as follows: ```python from sklearn.datasets import load_iris from sklearn.cluster import KMeans #Load Iris dataset iris = load_iris() X = iris.data #Create a KMeans model and set the number of cluster centers to 3 kmeans = KMeans(n_clusters=3) #Training model kmeans.fit(X) #Forecast Category labels = kmeans.predict(X) #Output the category of each sample for i in range(len(X)): print("Sample:", X[i], " Label:", labels[i]) ``` Running the above code can obtain the characteristics of each sample and the clustering category they belong to. Summary: This example introduces the practical application of the K-Means algorithm using the Scikit learn library in Python. Firstly, the environment construction and preparation work were carried out, and then the relevant class libraries and datasets were introduced. The sample code loads the Iris dataset, clusters the dataset using the K-Means algorithm, and outputs the clustering category for each sample. Finally, by running the code, the category of each sample can be obtained.

Preparation work: Before using Scikit ear for Hierarchical clustering, we need to set up the Python environment and install the necessary libraries. 1. Install Python: You can download it from the official Python website（ https://www.python.org ）Download and install the appropriate Python version for your operating system. 2. Install the Scikit learn library: In the Python environment, you can use the pip command to install the Scikit learn library. Open a terminal or command prompt and enter the following command: ```bash pip install scikit-learn ``` 3. Install other dependent libraries: When using the Hierarchical clustering algorithm, we also need to install some other libraries, such as NumPy and Matplotlib. Execute the following command to install: ```bash pip install numpy matplotlib ``` Dependent class libraries: In the Hierarchical clustering task, we will use the sklearn.cluster.AgglomerativeClustering class in the Scikit-learn library to perform Hierarchical clustering. Dataset introduction and download website: For this example, let's use the Iris dataset from the UCI machine learning library. This dataset contains 150 samples, divided into 3 categories, each with 50 instances. The dataset can be downloaded from this website: https://archive.ics.uci.edu/ml/datasets/iris Sample data: The Iris dataset contains four features: calyx length, calyx width, petal length, and petal width. Each sample has a corresponding category label, which is the variety of iris. Complete sample code: The following is a complete Python code example of Hierarchical clustering using iris dataset: ```python import numpy as np from sklearn.cluster import AgglomerativeClustering from sklearn.datasets import load_iris #Load Iris Dataset iris = load_iris() X = iris.data y = iris.target #Execute Hierarchical clustering clustering = AgglomerativeClustering(n_clusters=3).fit(X) #Output clustering labels for each sample Print ("Cluster label of sample:") print(clustering.labels_) ``` This code example loads the Iris dataset and divides it into 3 clusters. Then, Hierarchical clustering is performed by calling the fit method of the AgglomerativeClustering class. Finally, we print out the clustering labels for each sample. Summary: This example implements a simple Hierarchical clustering using the AgglomerativeClustering class of the Scikit-learn library. In practical applications, we can adjust the parameters and data sets according to the needs to carry out more complex Hierarchical clustering tasks.

Before using Scikit learn for cross validation to evaluate model performance, we need to first build a Python environment and install the required class libraries. The following are the steps for preparation: 1. Environmental construction: -Installing Python: Scikit learn is a machine learning library written in Python, so you need to first install Python. You can access it from the official Python website（ https://www.python.org/ ）Download the Python version suitable for your operating system. -Install pip: pip is a package management tool for Python, used to install and manage Python class libraries. After installing Python, you can use the following command to install pip: ``` python get-pip.py ``` -Install Scikit learn: Use the following command to install the Scikit learn class library: ``` pip install -U scikit-learn ``` 2. Dependent class libraries: -Scikit learn: Scikit learn is a popular Python machine learning library that provides many tools for data analysis and modeling. -Numpy: Numpy is a scientific computing library in Python that provides multidimensional array objects and functions for handling these arrays. -Pandas: Pandas is a data analysis library that provides data structures and functions for Data cleansing, processing and analysis. -Matplotlib: Matplotlib is a drawing library for Data and information visualization. 3. Dataset: We will use the Iris dataset provided by Scikit-learn as an example dataset. This dataset contains measurement data for iris from three different species (Setosa, Versicolor, Virginia). This dataset has 150 samples, each with 4 features (calyx length, calyx width, petal length, petal width). You can use the following code to download the dataset and load it into Python: ```python from sklearn.datasets import load_iris iris = load_iris() X, y = iris.data, iris.target ``` 4. Implement a complete example: ```python from sklearn.datasets import load_iris from sklearn.model_selection import cross_val_score from sklearn.tree import DecisionTreeClassifier #Download and load the Iris dataset iris = load_iris() X, y = iris.data, iris.target #Create Decision Tree Classifier clf = DecisionTreeClassifier() #Evaluating classifier performance using cross validation scores = cross_val_score(clf, X, y, cv=5) #Print the accuracy of each cross validation print('Accuracy:', scores) #Print average accuracy print('Average Accuracy:', scores.mean()) ``` 5. Summary: Scikit learn is a powerful Python machine learning library that provides rich functionality and tools. Using Scikit learn for cross validation to evaluate model performance can effectively evaluate the accuracy of the model and tune it. In the above example, we downloaded the Iris dataset, created a decision tree classifier, and used cross validation for model evaluation. Finally, we printed the accuracy of each cross validation and calculated the average accuracy.

Preparation work: 1. Install Python: The first step is to install Python. It is recommended to use Python version 3.7 and above. 2. Install Scikit learn: Scikit learn is an open-source Python machine learning library that provides implementations of various machine learning algorithms. You can install Scikit learn using the following command: ``` pip install scikit-learn ``` 3. Download Dataset: The Iris Dataset, which comes with Scikit-learn, was used in this actual battle. Dataset introduction: The Iris dataset is a very classic classification problem dataset. It contains measurement data from three different varieties of iris flowers: Iris setosa, Iris versicolor, and Iris virginica. Each sample has 4 characteristics: calyx length, calyx width, petal length, and petal width. The target variable is the variety of iris. Dataset download website: You can directly use the interface provided by Scikit learn to load the iris dataset. The code is as follows: ```python from sklearn.datasets import load_iris iris = load_iris() X = iris.data y = iris.target ``` Sample data: X represents the four characteristics of iris, and y represents the variety (category) of iris. Complete Python code implementation: ```python from sklearn.datasets import load_iris from sklearn.ensemble import BaggingClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score #Load Dataset iris = load_iris() X = iris.data y = iris.target #Divide training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) #Creating and Training Bagging Models model = BaggingClassifier(n_estimators=10, random_state=42) model.fit(X_train, y_train) #Making predictions on the test set y_pred = model.predict(X_test) #Calculation accuracy accuracy = accuracy_score(y_test, y_pred) Print ("Accuracy:", accuracy) ``` Practical summary: This practice introduces how to use the Bagging Ensemble learning method in Scikit-learn to solve the classification problem. Firstly, you need to build a Python environment and install the Scikit learn library. Then use the Iris dataset for practical operations, including loading and partitioning the dataset, constructing and training the model, making predictions on the test set, and calculating accuracy. In the actual combat process, we used the BaggingClassifier class to construct the Bagging model, using the parameter n_ Estimators specifies the number of base learners for the model. Finally, we calculated the accuracy of the model on the test set and evaluated its performance.