Deploying Prediction Models

Binary Classification with Random Forest Classifier We will walk through the process of deploying a Random Forest Classifier model. We'll use a model trained on the Titanic dataset to predict passenger survival and explain each step along the way. Step 1: Model Selection and Training A Random Forest Classifier was chosen as the model for this binary classification problem. The model was trained on the preprocessed dataset with a random seed for reproducibility. The goal was to predict whether a passenger survived or not based on various features. Step 2: Data Preprocessing Before training the model, I preprocessed the data to clean and prepare it for analysis. This included: Normalizing passenger names. Extracting ticket numbers and ticket items. Handling missing data in age and embarked columns. One-hot encoding categorical variables like "Sex" and "Embarked. Step 3: Feature Selection The model used a combination of numerical features like "Pclass," ...