Multiple_Linear_Regression

 #!/usr/bin/env python

# coding: utf-8

# In[1]:


import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


# In[2]:


myfile="D:\Sunny115\week2_50_Startups.csv"
dataset=pd.read_csv(myfile)
X=dataset.iloc[:,:-1].values
y=dataset.iloc[:,-1].values


# In[6]:


#encoding categorical data
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct=ColumnTransformer(transformers=[('encoder',OneHotEncoder(),[3])],remainder='passthrough')
X=np.array(ct.fit_transform(X))


# In[8]:


print(X)


# In[13]:


#splitting the dataset
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0)


# In[14]:


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


# In[15]:


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


# In[16]:


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


# In[ ]:





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