Data Preparation: Begin by preparing your dataset, handling missing values, encoding categorical features, and splitting the data into training and testing sets.
Model Initialization: Initialize the Random Forest classifier, specifying parameters such as the number of trees in the forest (n_estimators) and other optional settings.
Training the model and Make Prediction: By train the model on Training set and by use the train model to predict the test data set.
Model Evaluation: Evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score.
Tuning Hyperparameters: Fine-tune the model by adjusting hyperparameters using techniques like Grid Search or Randomized Search.