logistic regression python in python

To perform logistic regression in Python, you can use the scikit-learn library which provides a quick and easy way to implement many machine learning models, including logistic regression.

Here's a step-by-step guide:

1. Import the necessary libraries:

main.py
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
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2. Load the data you want to use for logistic regression. You can use the Pandas library to read in data from a CSV or any other standard data format. For example:

main.py
data = pd.read_csv('data.csv')
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3. Split the data into training and testing sets:

main.py
X = data.drop('target_variable', axis=1)
y = data['target_variable']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
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4. Create an instance of the LogisticRegression class:

main.py
log_reg = LogisticRegression()
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5. Fit the model to the training data:

main.py
log_reg.fit(X_train, y_train)
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6. Make predictions on the testing data:

main.py
y_pred = log_reg.predict(X_test)
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7. Evaluate the performance of your model:

main.py
from sklearn.metrics import confusion_matrix, accuracy_score
print(confusion_matrix(y_test, y_pred))
print(accuracy_score(y_test, y_pred))
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This will give you the confusion matrix and accuracy score of your logistic regression model.

Please note that you need to replace 'data.csv' with your actual dataset file name and 'target_variable' with the name of your target variable to drop.