Simple_Linear_Regression

 #!/usr/bin/env python

# coding: utf-8

# In[3]:


#importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


# In[4]:


myfile="D:\Sunny115\week2_Salary_Data.csv"
dataset=pd.read_csv(myfile)
X=dataset.iloc[:,:-1].values
y=dataset.iloc[:,-1].values


# In[5]:


from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=1/3,random_state=0)


# In[6]:


from sklearn.linear_model import LinearRegression
regressor=LinearRegression()
regressor.fit(X_train,y_train)


# In[7]:


print(X_train)


# In[9]:


y_pred=regressor.predict(X_test)
print(y_test)
print(y_pred)


# In[10]:


from sklearn.metrics import r2_score
r2_score(y_test,y_pred)


# In[13]:


#Visulising the training set results
plt.scatter(X_train,y_train,color='blue')
plt.plot(X_train,regressor.predict(X_train),color='green')
plt.title('Salary vs Expereince (training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()


# In[14]:


#Visulising the training set results
plt.scatter(X_test,y_test,color='blue')
plt.plot(X_train,regressor.predict(X_train),color='green')
plt.title('Salary vs Expereince (training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()


# In[ ]:





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