You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
129 lines
4.4 KiB
129 lines
4.4 KiB
#Linear Regression
|
|
|
|
#Import Library
|
|
#Import other necessary libraries like pandas, 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)
|
|
|
|
|
|
|
|
#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)
|
|
|
|
|
|
|
|
|
|
#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)
|
|
|
|
|
|
#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 is various option associated with it, this is simple for classification. You can refer link, for mo# re detail.
|
|
# 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)
|
|
|
|
# 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, Refer link
|
|
# Train the model using the training sets and check score
|
|
model.fit(X, y)
|
|
#Predict Output
|
|
predicted= model.predict(x_test)
|
|
|
|
#kNN (k- Nearest Neighbors)
|
|
|
|
#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_dataset
|
|
# Create KNeighbors classifier object model
|
|
KNeighborsClassifier(n_neighbors=6) # default value for n_neighbors is 5
|
|
# Train the model using the training sets and check score
|
|
model.fit(X, y)
|
|
#Predict Output
|
|
predicted= model.predict(x_test)
|
|
|
|
#K-Means
|
|
#Import Library
|
|
from sklearn.cluster import KMeans
|
|
#Assumed you have, X (attributes) for training data set and x_test(attributes) of test_dataset
|
|
# Create KNeighbors classifier object model
|
|
k_means = KMeans(n_clusters=3, random_state=0)
|
|
# Train the model using the training sets and check score
|
|
model.fit(X)
|
|
#Predict Output
|
|
predicted= model.predict(x_test)
|
|
|
|
#Random Forest
|
|
|
|
#Import Library
|
|
from sklearn.ensemble import RandomForestClassifier
|
|
#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
|
|
# Create Random Forest object
|
|
model= RandomForestClassifier()
|
|
# Train the model using the training sets and check score
|
|
model.fit(X, y)
|
|
#Predict Output
|
|
predicted= model.predict(x_test)
|
|
|
|
#Dimensionality Reduction Algorithms
|
|
|
|
#Import Library
|
|
from sklearn import decomposition
|
|
#Assumed you have training and test data set as train and test
|
|
# Create PCA obeject pca= decomposition.PCA(n_components=k) #default value of k =min(n_sample, n_features)
|
|
# For Factor analysis
|
|
#fa= decomposition.FactorAnalysis()
|
|
# Reduced the dimension of training dataset using PCA
|
|
train_reduced = pca.fit_transform(train)
|
|
#Reduced the dimension of test dataset
|
|
test_reduced = pca.transform(test)
|
|
|