Parkinson_Disease_Prediction (#18)

This is a python code file for the prediction of Parkinson's disease. It uses the logistic regression algorithm of machine learning to make the prediction.  It also calculated the accuracy of the prediction model.

Author: poorvi-sharma

Parkinson_disease_prediction.py

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+#Logistic Regression
+#importing the required packages
+import numpy as np 
+import pandas as pd 
+from matplotlib import pyplot as plt 
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score 
+
+#loading the data
+dataset = pd.read_csv('/content/drive/MyDrive/TCR/datasets/parkinsons.csv')
+
+dataset.head()
+dataset.shape
+dataset.info()
+
+#checking for null values if any
+dataset.isnull().sum()
+
+dataset.describe()
+
+#EDA 
+dataset.status.value_counts()
+dataset['status'].value_counts()
+dataset.groupby('status').mean()
+
+#dividing the data in features and target
+x = dataset.drop(columns=['name', 'status'], axis=1)
+y = dataset['status']
+
+x.head()
+
+#test-train split
+X_train, X_test, Y_train, Y_test  =  train_test_split(x, y, test_size=0.10, random_state=2)
+print(X_train.shape, X_test.shape, Y_train.shape, Y_test.shape)
+
+#standardising the data for better accuracy
+sclaer = StandardScaler()
+sclaer.fit(X_train)
+X_train = sclaer.transform(X_train)
+X_test = sclaer.transform(X_test)
+
+#logistic regression in training data
+model = LogisticRegression()
+model.fit(X_train, Y_train)
+
+#accuracy check using accuracy score
+Y_pred_train = model.predict(X_train)
+Y_pred_train_accuracy = accuracy_score(Y_train, Y_pred_train)
+print("Accuracy on training Data : ", Y_pred_train_accuracy)
+
+Y_pred_test = model.predict(X_test)
+Y_pred_test_accuracy = accuracy_score(Y_test, Y_pred_test)
+print("Accuracy on testing data : ", Y_pred_test_accuracy)
+
+#testing the model with data
+input_data = x[5:6]
+input_data_as_array = np.asarray(input_data)
+input_data_reshaped = input_data_as_array.reshape(1, -1)
+std_scaler = sclaer.transform(input_data_reshaped)
+prediction = model.predict(std_scaler)
+print(prediction)
+if (prediction[0] == 0):
+  print("The Person doesnt have parkinson diesease")
+else:
+  print("The Person is suffering from Parkinson diesease")
+
+#comparing the predicted value with actual value
+print("The actual value is : ", y[5:6])
+
+#This is the end of file, please ignore this comment.