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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)
        ax.imshow(px,cmap='gray')
        ax.set_xticks([])
        ax.set_yticks([])
        ax.set_title(emotion_label_to_text[label])
        plt.tight_layout()
# 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') 
hard_voting.fit(X_train, 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') 
soft_voting.fit(X_train, 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
warnings.filterwarnings('ignore')
from scipy.stats import skew
from scipy.stats.stats import pearsonr
%matplotlib inline
seed = 1075
np.random.seed(seed)

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