Random Forest Classifier

The Random Forest Classifier was employed to predict the values in the "pred" column of a dataset consisting of two categorical columns (pc, ma) and sixteen numerical columns (m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14). To handle missing values, the columns ('ld', 'm0', 'm1', 'm2', 'm3', 'm4', 'm5', 'm6', 'm7') were imputed with the median value, while the remaining columns ('m8', 'm9', 'm10', 'm11', 'm12', 'm13', 'm14') were imputed with the mean value using a simple imputer. The Random Forest Classifier is an ensemble learning method that combines multiple decision trees. Each decision tree is trained on a random subset of the data, and the final prediction is made by aggregating the predictions of all individual trees. This approach helps to reduce overfitting and improve the model's generalization ability. After training the Random Forest Classifier on the dataset, the model's performance was evaluated using various metrics such as accuracy, precision, recall, or F1 score. These metrics provided insights into the classifier's ability to accurately predict the values in the "pred" column. The predictions generated by the Random Forest Classifier were saved in a CSV file named "submission.csv." This file contains the predicted values for the "pred" column based on the input data and the trained Random Forest Classifier. It is important to note that further improvements and adjustments can be explored in future work, such as fine-tuning the hyperparameters of the Random Forest Classifier or considering different imputation techniques. Additionally, feature selection or engineering techniques can be applied to enhance the model's predictive performance.