{'fraction_non_alphanumeric': 0.01818181818181818, 'fraction_numerical': 0.0, 'mean_word_length': 5.116402116402116, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Parkinson’s Disease Predictor via Voice Analysis

with the increasing integration of automobiles in our daily lives, the number of four-wheelers on the road has seen a substantial jump in the tally. Furthermore, the number of drivers has also increased. Moreover, people nowadays have a slightly higher chance to opt for a taxi for daily commute. With this statistic, a coinciding fact that the number of Parkinson’s Disease cases have also increased cannot be overlooked. Also, the advancement in the technology of machine learning has enabled us to accurately detect Parkinson’s Disease with unorthodox testing techniques like voice analysis. With these things in mind, we have attempted to use machine learning to predict whether a person has Parkinson’s disease or not using their voice samples whilst designing the model to assign higher weights to features that help accurately classify the voice sample. For Example, pitch being a critical factor to determine if the person is showing an excited emotional state. Once the model reaches the desired generalization ability, it can be integrated into the recruiting process of organizations like uber.