A machine learning model that can predict the likelihood of success for online seminars and lectures This project aims to build a machine learning model that can predict the likelihood of success for online seminars and lectures. The model will be trained on a dataset containing information about a variety of online events, including the content, the participants, and any other relevant factors.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
In order to run this project, you will need to have the following software installed on your machine:
- Python 3.6 or higher
- NumPy
- Pandas
- Scikit-learn
- You will also need to have a dataset containing information about online seminars and lectures.
- Clone or download the repository to your local machine.
- Navigate to the root directory of the project.
- Install the required packages by running the following command:
pip install -r requirements.txt
- To train and evaluate the machine learning model, run the following command from the root directory of the project:
python main.py - This will train the model on the training data, and evaluate its performance on the testing data. The evaluation metrics will be printed to the console.
This project is licensed under the MIT License - see the LICENSE file for details.