Simple and Multivariate Linear Regression

#Simple linear regression

# Import 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()
# Let’s select some features to explore more :
data = data[[“ENGINESIZE”,”CO2EMISSIONS”]]
# ENGINESIZE vs CO2EMISSIONS:
plt.scatter(data[“ENGINESIZE”] , data[“CO2EMISSIONS”] , color=”blue”)
plt.xlabel(“ENGINESIZE”)
plt.ylabel(“CO2EMISSIONS”)
plt.show()
# 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))):]
# Modeling:
# Using sklearn package to model data :
regr = linear_model.LinearRegression()
train_x = np.array(train[[“ENGINESIZE”]])
train_y = np.array(train[[“CO2EMISSIONS”]])
regr.fit(train_x,train_y)
# The coefficients:
print (“coefficients : “,regr.coef_) #Slope
print (“Intercept : “,regr.intercept_) #Intercept
# Plotting the regression line:
plt.scatter(train[“ENGINESIZE”], train[“CO2EMISSIONS”], color=’blue’)
plt.plot(train_x, regr.coef_*train_x + regr.intercept_, ‘-r’)
plt.xlabel(“Engine size”)
plt.ylabel(“Emission”)
# Predicting values:
# Function for predicting future values :
def get_regression_predictions(input_features,intercept,slope):
 predicted_values = input_features*slope + intercept
 return predicted_values
# Predicting emission for future car:
my_engine_size = 3.5
estimatd_emission = get_regression_predictions(my_engine_size,regr.intercept_[0],regr.coef_[0][0])
print (“Estimated Emission :”,estimatd_emission)
# Checking various accuracy:
from sklearn.metrics import r2_score
test_x = np.array(test[[‘ENGINESIZE’]])
test_y = np.array(test[[‘CO2EMISSIONS’]])
test_y_ = regr.predict(test_x)
print(“Mean absolute error: %.2f” % np.mean(np.absolute(test_y_ — test_y)))
print(“Mean sum of squares (MSE): %.2f” % np.mean((test_y_ — test_y) ** 2))
print(“R2-score: %.2f” % r2_score(test_y_ , test_y) )

#multivariate Linear Regression

# 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))):]
#Modeling:
#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)
# Check accuracy:
from sklearn.metrics import r2_score
R = r2_score(test_y , Y_pred)
print (“R² :”,R)