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Create numpy_scipy #4

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24 changes: 24 additions & 0 deletions numpy_scipy
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# Import the required libraries:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model# Read the CSV file:
data = pd.read_csv(“Fuel.csv”)
data.head()# Consider features we want to work on:
X = data[[ ‘ENGINESIZE’, ‘CYLINDERS’, ‘FUELCONSUMPTION_CITY’,’FUELCONSUMPTION_HWY’,
‘FUELCONSUMPTION_COMB’,’FUELCONSUMPTION_COMB_MPG’]]Y = data[“CO2EMISSIONS”]# Generating training and testing data from our data:
# We are using 80% data for training.
train = data[:(int((len(data)*0.8)))]
test = data[(int((len(data)*0.8))):]
#Using sklearn package to model data :
regr = linear_model.LinearRegression()train_x = np.array(train[[ ‘ENGINESIZE’, ‘CYLINDERS’, ‘FUELCONSUMPTION_CITY’,
‘FUELCONSUMPTION_HWY’, ‘FUELCONSUMPTION_COMB’,’FUELCONSUMPTION_COMB_MPG’]])
train_y = np.array(train[“CO2EMISSIONS”])regr.fit(train_x,train_y)test_x = np.array(test[[ ‘ENGINESIZE’, ‘CYLINDERS’, ‘FUELCONSUMPTION_CITY’,
‘FUELCONSUMPTION_HWY’, ‘FUELCONSUMPTION_COMB’,’FUELCONSUMPTION_COMB_MPG’]])
test_y = np.array(test[“CO2EMISSIONS”])# print the coefficient values:
coeff_data = pd.DataFrame(regr.coef_ , X.columns , columns=[“Coefficients”])
coeff_data#Now let’s do prediction of data:
Y_pred = regr.predict(test_x)
from sklearn.metrics import r2_score
R = r2_score(test_y , Y_pred)
print (“R² :”,R)