Machine-Learning-Algorithms

Machine Learning Algorithms

Classification according to the ways of learning:

:black_circle: Supervised learning

:white_circle: Unsupervised learning

:black_circle: Semi-supervised learning

:white_circle: Reinforcement learning

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Popular Machine Learning Algorithms

:one:Linear Regression:

# Import Library
# Import other necessary libraries like panda, numpy...

from sklearn import linear_model

# Load Train and Test datasets
# Identify feature and response variable(s) and 
# values must be numeric and numpy arrays

x_train = input_variables_values_training_datasets
y_train = target_variables_values_training_datasets  
x_test = input_variables_values_test_datasets

# Create linear regression object
linear = linear model.LinearRegression()

#Train the model using the training sets and
#check score 

linear.fit(x train, y_train)
linear.score(x train, y_train)

# Equation coefficient and Intercept

print('Coefficient: \n', linear.coef_)
print('Intercept: \n', linear. intercept_) 

#Predict Output 
predicted = linear.predict(x_test) 

:two:Logistic Regression:

# Import Library 
from sklearn.linear model import LogisticRegression

# Assumed you have, X (predictor) and Y (target) 
# for training data set and x_test(predictor) of test dataset 

# Create logistic regression object 
model = LogisticRegression()

# Train the model using the training sets and check score 
model.fit(X, y)
model.score(X, y)

# Equation coefficient and Intercept 
print('Coefficient: \n', model.coef_) 
print('Intercept: \n', model.intercept_)

# Predict Output
predicted = model. predict(x_test) 

:three:Decision Tree:

# Import Library
# Import other necessary libraries like pandas, numpy...

from sklearn import tree

# Assumed you have, X (predictor) and Y (target) for
# training data set and x_test(predictor) of test dataset 

# Create tree object 
model = tree.DecisionTreeClassifier(criterion='gini') 

# for classification, here you can change the
# algorithm as gini or entropy (information gain) by 
# default it is gini 

model = tree.DecisionTreeRegressor() # for regression

# Train the model using the training sets and check score 
model.fit(X, y)
model.score(X, y) 

# Predict Output 
predicted = model.predict(x_test) 

:four:Support Vector Machine(SVM):

# Import Library
from sklearn import svm

# Assumed you have, X (predictor) and Y (target) for
# training data set and x_test(predictor) of test_dataset 

# Create SVM classification object
model = svm.svc()

# there are various options associated with it, this is simple for classification.

# Train the model using the training sets & check the score
model.fit(X, y)
model.score(X, y)

# Predict Output 
predicted = model.predict(x_test) 

:five:Naive Bayes:

# Import Library
from sklearn.naive bayes import GaussianNB

# Assumed you have, X (predictor) and Y (target) for
# training data set and x_test(predictor) of test_dataset 

# Create SVM classification object 
model = GaussianNB()

# there is other distribution for multinomial classes like Bernoulli Naive Bayes

# Train the model using the training sets and check score
model.fit(X, y)

# Predict Output 
predicted = model.predict(x_test) 

:six:K-Nearest Neighbors(kNN):

# Import Library 
from sklearn.neighbors import KNeighborsClassifier

# Assumed you have, X (predictor) and Y (target) for 
# training data set and x_test(predictor) of test_data