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mlr.py
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22 lines (22 loc) · 788 Bytes
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import numpy as np
import pickle
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
data=pd.read_csv('taxi.csv')
#print(data.tail())
#print(type(data))
#print(data.shape)
data_x = data.iloc[:,0:-1].values
data_y = data.iloc[:,-1].values
#print(data_y)
x_train,x_test,y_train,y_test=train_test_split(data_x,data_y,test_size=0.3,random_state=0)
#print(x_train.shape)
reg=LinearRegression()
reg.fit(x_train,y_train)
#print("train score:",reg.score(x_train,y_train))
#print("test score:",reg.score(x_test,y_test))
pickle.dump(reg,open('taxi.pkl','wb'))
model=pickle.load(open('taxi.pkl','rb'))
#print("No of weekly rides are",model.predict([[80,1770000,6000,85]]))