Health Classification

n my project, I encountered a dataset with categorical data that needed to be preprocessed before further analysis. To convert the categorical data into a numeric format, I applied the ordinal encoder technique. This encoding method assigns numerical labels to different categories, allowing machine learning algorithms to process the data effectively. However, the dataset also contained missing values that needed to be addressed. To handle this, I employed K nearest neighbor (KNN) imputation. KNN imputation estimates missing values by considering the values of their neighboring data points. This technique helped to fill in the gaps in the dataset, ensuring a more complete and reliable dataset for subsequent analysis. With the missing values filled, I proceeded to train my dataset using various machine learning algorithms. After experimenting with different models, I found that the xgBoost algorithm yielded the best results. By combining the ordinal encoder, KNN imputation, and the xgBoost algorithm, I was able to achieve improved performance in my project. The categorical data was effectively transformed into a numeric representation, missing values were imputed, and the dataset was successfully trained using a well-performing algorithm. This comprehensive approach enhanced the accuracy and reliability of the results obtained from my analysis.