Algorithm

Chooses an appropriate machine learning algorithm, such as a decision tree, random forest, or neural network.

Fine tuning

Fine-tunes the model parameters to optimize performance.

Prediction

Uses the model to make predictions on new data and provides a score indicating the potential success of the company.

Training

Splits the data into training and testing sets.

Evaluation

Trains the model on the training set and evaluates its performance on the testing set