Fashion Blog Predictor Model This repository contains the Fashion Blog Predictor Model project, which aims to predict the success of fashion blog posts using machine learning techniques.

Project Overview The project includes:

Logistic Regression Baseline Model: Establishes a starting point for prediction accuracy. Support Vector Machine (SVM) Model: Serves as the target model for improved accuracy. Hyperparameter Tuning: Utilizes GridSearchCV to fine-tune the model parameters for better performance. Components Logistic Regression Model: A baseline model to predict the success of blog posts. Support Vector Machine (SVM) Model: A more advanced model aimed at achieving higher prediction accuracy. GridSearchCV: Used for hyperparameter tuning to optimize the performance of the SVM model. Contributions The logistic regression baseline model, support vector machine model, and hyperparameter tuning were completed by Sean W. Ryan. The remainder of the project was collaboratively completed with my partner, Janna Foy. Requirements To run the project, you need the following Python libraries:

pandas numpy scikit-learn matplotlib (if there are visualizations) You can install the required libraries using the following command:

bash Copy code pip install pandas numpy scikit-learn matplotlib How to Run Clone the repository: bash Copy code git clone [repository link] Navigate to the project directory: bash Copy code cd FashionBlogPredictorModel Open the Jupyter Notebook: bash Copy code jupyter notebook FashionBlogPredictorModel.ipynb Usage Follow the steps in the Jupyter Notebook to load the dataset, preprocess the data, build and evaluate the models, and perform hyperparameter tuning.

Results The project demonstrates the improvement in prediction accuracy achieved through hyperparameter tuning using GridSearchCV.

Acknowledgements Sean W. Ryan: Developed the logistic regression baseline model, support vector machine model, and performed hyperparameter tuning. Janna Foy: Contributed to the other aspects of the project.