KNeighborsClassifier

Data: The input dataset consists of features from two categorical columns (pc, ma) and sixteen numerical columns (m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14). Handling Missing Values: Missing values in the columns ('ld', 'm0', 'm1', 'm2', 'm3', 'm4', 'm5', 'm6', 'm7') were replaced with the median value using a simple imputer, while the remaining columns ('m8', 'm9', 'm10', 'm11', 'm12', 'm13', 'm14') were imputed with the mean value. Training: The KNeighborsClassifier builds a model by storing the feature values and associated class labels from the training data. Prediction: For each new instance, the KNeighborsClassifier identifies its k nearest neighbors based on a chosen distance metric (e.g., Euclidean distance). The predicted class label is determined by a majority vote among the k neighbors. Evaluation: The performance of the KNeighborsClassifier can be evaluated using various metrics such as accuracy, precision, recall, or F1 score, depending on the problem at hand. These metrics provide an assessment of the classifier's accuracy in predicting the class labels. Output: The predictions made by the KNeighborsClassifier can be saved in a CSV file, such as "submission.csv," which contains the predicted values for the "pred" column based on the input data and the trained KNeighborsClassifier.