Logistic Regression Classification

This project implements Logistic Regression for a binary classification task using a dataset related to advertising.

Table of Contents

• Introduction
• Installation
• Usage
• Data Exploration
• Model Training
• Model Evaluation
• Conclusion

Introduction

This project aims to classify whether a user clicked on an advertisement based on various features. Logistic Regression serves as the primary algorithm for this classification task.

Installation

To run this project, install the following packages:

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

Usage

1. Clone the repository.
2. Navigate to the project directory.
3. Open the Jupyter Notebook and run the cells to perform data analysis, training, and evaluation.

Data Exploration

The dataset advertising.csv includes features such as:

• Age
• Gender
• Estimated Salary
• Purchased (target variable)

Visualizations

The notebook includes several visualizations to analyze the data:

• Age Distribution
• Income vs Age
• Daily Internet Usage by Gender:

Explore the dataset using Pandas and visualize it with Matplotlib and Seaborn.

Model Training and Evaluation

Logistic Regression is used to classify whether a user will click on an ad. The notebook includes:

• Feature selection
• Data splitting
• Model training
• Predictions and evaluations

Model Evaluation

Evaluate the model using accuracy, confusion matrix, and other relevant metrics.

Hyperparameter Tuning

The notebook also performs hyperparameter tuning using GridSearchCV to optimize the model. After tuning the hyperparameters, the accuracy of the model improved.

Conclusion

The Logistic Regression model was successfully trained and evaluated. Future work could involve experimenting with different models and hyperparameters.

License

This project is licensed under the MIT License - see the LICENSE file for details.