Use logistic regression LogisticRegression to classify our own data excel or csv data----python program code, which can be run directly

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1. LogisticRegressionWhat is logistic regression?

Logistic regression is a machine learning algorithm used for binary classification problems. It is based on a weighted summation of input features and then passing this summation result into a sigmoid function to predict the probability of the output label. During the training process, we need to use maximum likelihood estimation to update the model parameters so that the model's prediction results are most consistent with the actual situation.

2. LogisticRegressionSpecific steps for classification using logistic regression

Logistic regression is a classification algorithm commonly used for binary classification problems. For a binary classification problem, when applying the logistic regression algorithm for classification, there are usually the following steps:

1. Data preprocessing: First, the training data and test data need to be preprocessed, including missing value filling, outlier processing, data normalization, feature selection, feature engineering, etc.

2. Feature extraction: Before classification, useful features need to be extracted from the original input data, which can affect the results. Usually this step requires the cooperation of experience and artificial intelligence algorithms.

3. Set up a logistic regression model: We need to define a logistic regression model and decide which activation functions and regularization methods to use.

4. Define loss function: In order to train the model and optimize parameters, we need to define a loss function. Usually we use cross entropy as the loss function.

5. Optimize model parameters: We need to use gradient descent algorithm or other optimization algorithms to update model parameters in order to minimize the loss function.

6. Model evaluation: After we train the model, we need to evaluate the model to determine whether the model's performance meets the requirements. Usually we use indicators such as accuracy, precision, recall, and F1 value to evaluate model performance.

7. Predicting unknown data: Once the model is trained, we can use it to make predictions and explain the possible interpretation problems encountered during the prediction process.

2. LogisticRegressionDetailed code for binary classification using logistic regression

In Python, we can implement the logistic regression algorithm using classes scikit-learnin the library . LogisticRegressionHere is an example of a Python program that performs binary classification on the data you provide:

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 读取数据文件
data = pd.read_csv('data.csv', header=None, names=['feature', 'label'])

# 准备训练数据和测试数据
X_train, X_test, y_train, y_test = train_test_split(data['feature'], data['label'], test_size=0.2, random_state=42)

# 构建并训练逻辑回归模型
model = LogisticRegression()
model.fit(X_train.to_numpy().reshape(-1, 1), y_train.to_numpy())

# 在测试数据上进行预测，并计算准确率
y_pred = model.predict(X_test.to_numpy().reshape(-1, 1))
accuracy = accuracy_score(y_test, y_pred)

print(f"Accuracy: {
      
      accuracy:.2f}")

3. LogisticRegressionWide range of uses of logistic regression

Logistic Regression is a machine learning algorithm suitable for classification problems. Its main function is to predict which category the output label of an input variable belongs to. Specific applications of logistic regression include but are not limited to the following aspects:

1. Financial risk control: Logistic regression can be used to predict whether a user will default, or to determine whether a certain investment is risky.

2. Disease prediction: Logistic regression can be used to predict the probability of a person getting sick, or to determine whether a patient needs a certain examination or surgery.

3. Spam identification: Logistic regression can be used to determine whether an email is spam.

4. Recommendation system: Logistic regression can use the user's historical behavior and preferences to predict whether the user is interested in a certain product.

5. Natural language processing: Logistic regression can be used for text classification, such as determining whether an article is news, sports or technology.

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Summarize

In this example program, we first read the data file through the Pandas library, and then used the train_test_split function to divide the data set into a training set and a test set. Next, we instantiated the LogisticRegression class and passed the training set data and labels into the fit method for model training. Finally, we used the test set data to make predictions and calculated the accuracy of the prediction results.