Diabetes and Parkinson's Disease Prediction System

Role: Developer || Researcher

Stages to Develop the System

Phase-1 : To learn the requirement of build the project.
Phase-2 : To develop an Model trained with dataset.
Phase-3 : Integrated with Web Application.
Pahse-4 : To Deploy the model as a result

Requirements attach with a file

cmd: pip install -r requirements.txt

Steps Implemented to train the model

1. Make an x and y label to classify the data for prediction.
2. Using classifier objects to enhance the service of Gradient Boosting, which provides
a prediction for the model.
3. Before classifiers, the labels need to be standardized, which makes
mode to be trained with a scalar object.

To find the accuracy of the given data

# Execute to find the accuracy of model
X_train_prediction = classifier.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)

Following package need to be access

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
from sklearn.ensemble import GradientBoostingClassifier
import warnings

Binary Formating of Model

Architecture of the Project

 1. The model has been trained with the required algorithm and standardized.
make an analysis with label values.
2. Deploy the project using streamlet and spider web applications to 
enhance the dataset with easy access to output.