Regresión lineal, regresión logística, árbol de decisión, SVM, Naive Bayes, kNN, k-Means, Random Forest, PCA, GBM, XGBoost, LightGBM, Catboost
#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)
#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)
#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)
#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)
#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)
#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)
#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)
#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)
#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)
#Import Library
from sklearn.ensemble import GradientBoostingClassifier
#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create Gradient Boosting Classifier object
model= GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)
# Train the model using the training sets and check score
model.fit(X, y)
#Predict Output
predicted= model.predict(x_test)
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
X = dataset[:,0:10]
Y = dataset[:,10:]
seed = 1
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=seed)
model = XGBClassifier()
model.fit(X_train, y_train)
#Make predictions for test data
y_pred = model.predict(X_test)
data = np.random.rand(500, 10) # 500 entities, each contains 10 features
label = np.random.randint(2, size=500) # binary target
train_data = lgb.Dataset(data, label=label)
test_data = train_data.create_valid('test.svm')
param = {'num_leaves':31, 'num_trees':100, 'objective':'binary'}
param['metric'] = 'auc'
num_round = 10
bst = lgb.train(param, train_data, num_round, valid_sets=[test_data])
bst.save_model('model.txt')
# 7 entities, each contains 10 features
data = np.random.rand(7, 10)
ypred = bst.predict(data)
import pandas as pd
import numpy as np
from catboost import CatBoostRegressor
#Read training and testing files
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
#Imputing missing values for both train and test
train.fillna(-999, inplace=True)
test.fillna(-999,inplace=True)
#Creating a training set for modeling and validation set to check model performance
X = train.drop(['Item_Outlet_Sales'], axis=1)
y = train.Item_Outlet_Sales
from sklearn.model_selection import train_test_split
X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=0.7, random_state=1234)
categorical_features_indices = np.where(X.dtypes != np.float)[0]
#importing library and building model
from catboost import CatBoostRegressormodel=CatBoostRegressor(iterations=50, depth=3, learning_rate=0.1, loss_function='RMSE')
model.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_validation, y_validation),plot=True)
submission = pd.DataFrame()
submission['Item_Identifier'] = test['Item_Identifier']
submission['Outlet_Identifier'] = test['Outlet_Identifier']
submission['Item_Outlet_Sales'] = model.predict(test)
Referencias: https://www.analyticsvidhya.com/