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# lets plot this pixels so we can see the image
fig =plt.figure(1, (14, 14))

k = 0 
for label in sorted(data.emotion.unique()):
    for j in range(7):
        px = data[data.emotion == label].pixels.iloc[k]
        px = np.array(px.split(' ')).reshape(48, 48).astype('float32')
        k += 1
        ax = plt.subplot(7,7,k)
# Ensemble of Models 
estimator = [] 
estimator.append(('LR',LogisticRegression(solver ='lbfgs',multi_class ='multinomial',max_iter = 200))) 
estimator.append(('SVC', SVC(gamma ='auto', probability = True))) 
estimator.append(('DTC', DecisionTreeClassifier())) 

# Voting Classifier with hard voting 
hard_voting = VotingClassifier(estimators = estimator, voting ='hard'), y_train) 
y_pred = hard_voting.predict(X_test)   

# accuracy_score metric to predict Accuracy 
score = accuracy_score(y_test, y_pred) 
print("Hard Voting Score % d" % score)

# Voting Classifier with soft voting 
soft_voting = VotingClassifier(estimators = estimator, voting ='soft'), y_train) 
y_pred = soft_voting.predict(X_test) 

# Using accuracy_score 
score = accuracy_score(y_test, y_pred) 
print("Soft Voting Score % d" % score)
# importing libraries 
from sklearn.ensemble import VotingClassifier ,BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier,GradientBoostingClassifier
from sklearn.metrics import accuracy_score 
from numpy import mean,std
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score,RepeatedStratifiedKFold,train_test_split
from sklearn.linear_model import LogisticRegression,RidgeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from matplotlib import pyplot
from sklearn.datasets import load_wine,load_iris
from matplotlib.pyplot import figure
figure(num=2, figsize=(16, 12), dpi=80, facecolor='w', edgecolor='k')
import xgboost as xgb
from sklearn.feature_selection import SelectKBest,f_regression
from sklearn.linear_model import LinearRegression,BayesianRidge,ElasticNet,Lasso,SGDRegressor,Ridge
from sklearn.kernel_ridge import KernelRidge
from sklearn.preprocessing import LabelEncoder,OneHotEncoder,RobustScaler,StandardScaler
from sklearn.pipeline import make_pipeline,Pipeline
from sklearn.metrics import mean_squared_error
from sklearn.decomposition import PCA,KernelPCA
from sklearn.ensemble import ExtraTreesRegressor,GradientBoostingRegressor,RandomForestRegressor,VotingClassifier
from sklearn.model_selection import cross_val_score,KFold,GridSearchCV,RandomizedSearchCV,StratifiedKFold,train_test_split
from sklearn.base import BaseEstimator,clone,TransformerMixin,RegressorMixin
from sklearn.svm import LinearSVR,SVR
#import xgboost 
from xgboost import XGBRegressor
#Import Pandas
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import warnings
from scipy.stats import skew
from scipy.stats.stats import pearsonr
%matplotlib inline
seed = 1075

Tue Jun 01 2021 12:23:33 GMT+0000 (Coordinated Universal Time)

#ensemble #voting #classifier

Mon Feb 01 2021 11:48:14 GMT+0000 (Coordinated Universal Time)

#ensemble #voting #classifier

Mon Feb 01 2021 11:45:31 GMT+0000 (Coordinated Universal Time)

#matplotlib #pandas #seaborn #ensemble #voting #classifier #xgb #boosting

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