parkinson's disease detection project.py

#!/usr/bin/env python
# coding: utf-8

# In[2]:


#importing libraries
import pandas as pd
import numpy as np
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split


# In[5]:


#loading the dataset
df=pd.read_csv("C:\\Users\\soumi\\.ipython\\archive (4).zip")


# In[6]:


print(df)


# In[7]:


#selecting the top 5 columns
df.head()


# In[8]:


df.describe()


# In[9]:


df.info()


# In[10]:


df.shape


# In[11]:


#data cleaning
df.isnull().sum()


# In[12]:


df['status'].value_counts()


# In[13]:


df.groupby('status').mean()


# In[14]:


X=df.drop(columns=['name','status'],axis=1)
Y=df['status']


# In[15]:


print(Y)


# In[16]:


print(X)


# In[17]:


X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=2)


# In[18]:


s=StandardScaler()
s.fit(X_train)


# In[19]:


X_train=s.transform(X_train)
X_test=s.transform(X_test)


# In[20]:


print(X_train)


# In[21]:


print(X_test)


# In[23]:


model=svm.SVC(kernel='linear')


# In[25]:


#modelling the training dataset
model.fit(X_train,Y_train)


# In[26]:


#printing the result
X_train_pred=model.predict(X_train)
train_data_accurac=accuracy_score(Y_train,X_train_pred)


# In[27]:


print(train_data_accurac)


# In[28]:


#modeling the test dataset and result
X_test_pred=model.predict(X_test)
test_data_accurac=accuracy_score(Y_test,X_test_pred)
print(test_data_accurac)


# In[35]:


#building the model 
input_data = (138.19,203.522,83.34,0.00704,0.00005,0.00406,0.00398,0.01218,0.04479,0.441,0.02587,0.02567,0.0322,0.07761,0.01968,18.305,0.538016,0.74148,-5.418787,0.160267,2.090438,0.229892)
input_data_np = np.asarray(input_data)
input_data_re = input_data_np.reshape(1,-1)
s_data = s.transform(input_data_re)
pred = model.predict(s_data)
print(pred)

if(pred[0]==0):
    print("the result is neagtive, no parkinsons disease is found")
else:
    print("the result is positive,parkinson disease is detected")


# In[ ]: