This experiment is to predict the severity of Autism Spectrum Disorder in kids and the possibility of its dominance in their adolescent years.
The data used in training the model was collected from the UCI Machine Learning Repository. The training data set used in this experiment consist of the following features: - age - gender - ethnicity - born with jaundice - country of residence - severity of autism - age range - test aided by - presence of ASD Two data sources from UCI Machine Learning Repository are used in this experiment, they are : - Autistic Spectrum Disorder Screening Data for Adolescent - Autistic Spectrum Disorder Screening Data for Children **Data Processing** The data set was downloaded and locally imported into the azure machine learning studio. The **Metadata Editor** module was used in selecting data columns from the data set, then **Clean Missing Data** module was used in removing rows with empty data. Renaming the labels was necessary, an **R script** was executed to rename the columns for easy readability. Then the data was **Split** into proportions in which a section was passed into the **Train Model** with a learning algorithm and the other section of the data was passed into the **Score Model**. The scored model is then passed into the **Evaluate Model** for evaluation of the scored data.