This project focuses on building a classification model to predict whether users on a social network are likely to make a purchase based on their demographic information. The dataset used in this project contains user information such as gender, age, and estimated salary.
The dataset used in this project, "Social Network Ads," is available on Kaggle. It provides information about users' demographics and purchase behavior. The dataset consists of features like user ID, gender, age, estimated salary, and the target variable indicating whether a user made a purchase or not.
Link to the dataset: Social Network Ads Dataset
- Clone the GitHub repository:
git clone https://github.com/[your-username]/social-network-ads-classifier.git - Navigate to the project directory:
cd social-network-ads-classifier - Install the required dependencies:
pip install -r requirements.txt
- Open the notebook
social_network_ads_classifier.ipynbusing Jupyter Notebook or any compatible IDE. - Run the notebook cells to preprocess the data, train the classifier, and evaluate the model's performance.
- Use the trained model to predict whether new users are likely to make a purchase based on their demographic information.
Note: Make sure to have the necessary dependencies installed before running the notebook.
This project is licensed under the MIT License.
This notebook was inspired by the project titled "KNN, SVM & SVM with Kernel & Hyperparameter" on Kaggle. We would like to acknowledge the contribution of the author, Sandhya Krishnan, for their work, which provided valuable insights and guidance for developing this classification model.