Overview / Usage

-> This project aims to analyze the Parkinsons dataset from UCI Machine Learning Repository using oneAPI, a cross-industry, open,standards-based unified programming model that enables developers to write code that can run on a variety of hardware architectures, including CPUs, GPUs, and FPGAs. - ->The dataset is composed of the data used in this study which were gathered from 188 patients with Parkinsons Disease with ages ranging from 33 to 87.

-> The project includes data preprocessing, exploratory data analysis, and model selection and evaluation, all implemented using the OneAPI toolkit .

->The main aim of this project is that how parkinson’s disease could be detected through OneApi and how OneApi could be a gamechanger in the field of open source.

-> The findings of this project could benefit our human race and help in the early detection of parkinson’s disease.

Methodology / Approach

->IMPORTING THE LIBRARIES

->READING DATAS INTO PANDAS DATAFRAME

->EXPLORATORY DATA ANALYSIS

->BALANCING DATASET

->MACHINE LEARNING MODELS

1)SUPPORT VECTOR MACHINE

2)LOGISTIC CLASSIFIER

3)RANDOM FOREST

->CLASSIFICATION REPORT

->CONFUSION MATRIX

Technologies Used

1)PYTHON

2)JUPYTER NOTEBOOK

3)ONE API

4)DEV CLOUD

Repository

https://github.com/reddevilrohith/parkinsons_detectionbysvm-rf-lc