Machine Learning code snippets

Creating a Multiple linear regression model for estimating CO2 emission from a car model using independent variables like Engine size, cylinder, fuel consumption of a car. import matplotlib.pyplot as plt import pandas as pd import pylab as pl import numpy as np %matplotlib inline df = pd.read_csv("FuelConsumptionCo2.csv") #df.head() cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']] #cdf.head(9) # Plotting plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS, color = 'blue') plt.xlabel('Engine Size') plt.ylabel('Emissions') #plt.show() # Creating train and test dataset msk = np.random.rand(len(df)) < 0.8 train = cdf[msk] test = cdf[~msk] # train data distribution plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue') plt.xlabel("Engine size") plt.ylabel("Emission") #plt.show() #### MULTIPLE REGRESSION MODEL #### from sklea...

Using the IRIS dataset to estimate the class of the IRIS using different ML Algorithms. import pandas from pandas . plotting import scatter_matrix import matplotlib . pyplot as plt from sklearn import model_selection from sklearn . metrics import classification_report from sklearn . metrics import confusion_matrix from sklearn . metrics import accuracy_score from sklearn . linear_model import LogisticRegression from sklearn . tree import DecisionTreeClassifier from sklearn . neighbors import KNeighborsClassifier from sklearn . discriminant_analysis import LinearDiscriminantAnalysis from sklearn . naive_bayes import GaussianNB from sklearn . svm import SVC url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data' names = [ 'sepal length' , 'sepal width' , 'petal length' , 'petal width' , 'class' ] dataset = pandas . read_csv ( url , names = names ) print...