This is a simple implementation of Logistic Regression using Python and the scikit-learn library. Logistic Regression is a classification algorithm commonly used for binary classification problems.
The provided code demonstrates the use of Logistic Regression on a dataset named "Social_Network_Ads.csv". The dataset contains information about users, including their age, estimated salary, and whether they purchased a product (target variable).
Make sure you have the following libraries installed before running the code:
pip install numpy matplotlib pandas scikit-learn-
Importing Libraries: Import necessary libraries - NumPy for numerical operations, Matplotlib for plotting, and Pandas for handling datasets.
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Importing Dataset: Load the dataset using Pandas, where
Xcontains features (age and estimated salary), andycontains the target variable (purchase status). -
Splitting the Dataset: Split the dataset into training and testing sets using the
train_test_splitfunction from scikit-learn. -
Feature Scaling: Standardize the features using
StandardScalerto ensure that all features contribute equally to the model. -
Training the Model: Create and train the Logistic Regression model using the training set.
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Making Predictions: Use the trained model to make predictions, and print the result for a new input (e.g., age=30, salary=87000).
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Confusion Matrix: Evaluate the model performance using a confusion matrix and calculate the accuracy.
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Visualizing Results: Plot decision boundaries and visualize the results on both the training and test sets.
- Ensure the dataset file ("Social_Network_Ads.csv") is in the same directory as the notebook or script.
- Run the code cell by cell in a Python environment (e.g., Jupyter Notebook, Google Colab).
- The provided code assumes that the dataset has a specific structure, with the last column representing the target variable.
- Customize the code according to your dataset and requirements.
Feel free to explore and modify the code to suit your needs.