notebook.community

Edit and run



In [4]:

    
%matplotlib inline



In [2]:

    
import pandas as pd
import numpy as np

data = pd.read_excel('../../bd/BANCO_FINAL.xlsx')



In [3]:

    
# TODO: Calculate number of students
n_students = data.shape[0]

# TODO: Calculate number of features
n_features = len(data.columns[:-2])

# TODO: Calculate passing students
n_curso = data[data.DESISTENTE == 'SIM'].shape[0]

# TODO: Calculate failing students
n_desistentes = data[data.DESISTENTE == 'NÃO'].shape[0]

# TODO: Calculate graduation rate
grad_rate = 1 - n_desistentes/n_students

# Print the results
print ("Número total de estudantes: {}".format(n_students))
print ("Número de características: {}".format(n_features))
print ("Número de estudantes em curso: {}".format(n_curso))
print ("Número de estudantes desistentes: {}".format(n_desistentes))
print ("Taxa de desistentes: {:.2f}%".format(grad_rate*100))









    



Número total de estudantes: 7156
Número de características: 26
Número de estudantes em curso: 2137
Número de estudantes desistentes: 5019
Taxa de desistentes: 29.86%



In [5]:

    
print(list(data.columns.values))









    



['INGRESSOCPF', 'CAMPUS', 'ANO', 'DESISTENTE', 'SITUAÇÃO', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13', 'Q14', 'Q15', 'Q16', 'Q17', 'Q18', 'Q19', 'CR', 'renda sm', 'Pessoas que vivem com essa renda', 'renda per']



In [6]:

    
# Extract feature columns
feature_cols = ['CAMPUS', 'ANO', 'Q1', 'Q2', 'Q3', 
                'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9',
                'Q10', 'Q11', 'Q12', 'Q13', 'Q14',
                'Q15', 'Q16', 'Q17', 'Q18', 'Q19',
                'CR', 'renda sm',
                'Pessoas que vivem com essa renda', 'renda per']

# Extract target column 'passed'
target_col = ['DESISTENTE']

# Show the list of columns
print ("Feature columns:\n{}".format(feature_cols))
print ("\nTarget column: {}".format(target_col))









    



Feature columns:
['CAMPUS', 'ANO', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13', 'Q14', 'Q15', 'Q16', 'Q17', 'Q18', 'Q19', 'CR', 'renda sm', 'Pessoas que vivem com essa renda', 'renda per']

Target column: ['DESISTENTE']



In [7]:

    
# Separate the data into feature data and target data (X_all and y_all, respectively)
X_all = data[feature_cols]
y_all = data[target_col]



In [20]:

    
X_all.groupby(['ANO']).count()









    Out[20]:






  
    
      
      CAMPUS
      Q1
      Q2
      Q3
      Q4
      Q5
      Q6
      Q7
      Q8
      Q9
      ...
      Q14
      Q15
      Q16
      Q17
      Q18
      Q19
      CR
      renda sm
      Pessoas que vivem com essa renda
      renda per
    
    
      ANO
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      2012
      1753
      1753
      1753
      1753
      1753
      1753
      1753
      1753
      1753
      1753
      ...
      1753
      1753
      1753
      1753
      1753
      1753
      1753
      1753
      1753
      1753
    
    
      2013
      2577
      2577
      2577
      2577
      2577
      2577
      2577
      2577
      2577
      2577
      ...
      2577
      2577
      2577
      2577
      2577
      2577
      2577
      2577
      2577
      2577
    
    
      2014
      2826
      2826
      2826
      2826
      2826
      2826
      2826
      2826
      2826
      2826
      ...
      2826
      2826
      2826
      2826
      2826
      2826
      2826
      2826
      2826
      2826
    
  

3 rows × 24 columns



In [7]:

    
if True:
    def preprocess_features(X):
        ''' Preprocesses the student data and converts non-numeric binary variables into
            binary (0/1) variables. Converts categorical variables into dummy variables. '''

        # Initialize new output DataFrame
        output = pd.DataFrame(index = X.index)

        # Investigate each feature column for the data
        for col, col_data in X.iteritems():

            # If data type is non-numeric, replace all yes/no values with 1/0
            if col_data.dtype == object:
                col_data = col_data.replace(['SIM', 'NÃO'], [1, 0])

            # If data type is categorical, convert to dummy variables
            if col_data.dtype == object:
                # Example: 'school' => 'school_GP' and 'school_MS'
                col_data = pd.get_dummies(col_data, prefix = col)  

            # Collect the revised columns
            output = output.join(col_data)

        return output

    X_all = preprocess_features(X_all)
    #y_all = preprocess_features(y_all)
print ("Processed feature columns ({} total features):\n".format(len(X_all.columns)))









    



Processed feature columns (75 total features):



In [8]:

    
X_norm = (X_all - X_all.mean()) / (X_all.max() - X_all.min())



In [9]:

    
from sklearn.decomposition import PCA
pca = PCA(2, random_state=42).fit(X_norm)
reduced_data = pca.transform(X_norm)
reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])

# Generate PCA results plot
import render as rs
pca_results = rs.pca_results(X_norm, pca)



In [10]:

    
# TODO: Apply your clustering algorithm of choice to the reduced data 
from sklearn.cluster import KMeans
from sklearn import mixture
from sklearn.metrics import silhouette_score

for n in range(2,10):
    clusterer = KMeans(n_clusters=n, random_state=42).fit(X_all)

# TODO: Predict the cluster for each data point
    preds = clusterer.predict(X_all)

# TODO: Find the cluster centers
    centers = clusterer.cluster_centers_

# TODO: Predict the cluster for each transformed sample data point
    sample_preds = clusterer.predict(X_all)

# TODO: Calculate the mean silhouette coefficient for the number of clusters chosen
    score = silhouette_score(X_all, preds)
    print ("Silhouette Score for {0} clusters is {1:.4f}.".format(n, score))









    



Silhouette Score for 2 clusters is 0.5141.
Silhouette Score for 3 clusters is 0.3136.
Silhouette Score for 4 clusters is 0.2761.
Silhouette Score for 5 clusters is 0.2768.
Silhouette Score for 6 clusters is 0.2251.
Silhouette Score for 7 clusters is 0.2235.
Silhouette Score for 8 clusters is 0.2226.
Silhouette Score for 9 clusters is 0.2049.



In [11]:

    
# TODO: Apply your clustering algorithm of choice to the reduced data 
from sklearn.cluster import KMeans
from sklearn import mixture
from sklearn.metrics import silhouette_score

for n in range(2,10):
    clusterer = KMeans(n_clusters=n, random_state=42).fit(reduced_data)

# TODO: Predict the cluster for each data point
    preds = clusterer.predict(reduced_data)

# TODO: Find the cluster centers
    centers = clusterer.cluster_centers_

# TODO: Predict the cluster for each transformed sample data point
    sample_preds = clusterer.predict(reduced_data)

# TODO: Calculate the mean silhouette coefficient for the number of clusters chosen
    score = silhouette_score(reduced_data, preds)
    print ("Silhouette Score for {0} clusters is {1:.4f}.".format(n, score))









    



Silhouette Score for 2 clusters is 0.5769.
Silhouette Score for 3 clusters is 0.5541.
Silhouette Score for 4 clusters is 0.5439.
Silhouette Score for 5 clusters is 0.5260.
Silhouette Score for 6 clusters is 0.5023.
Silhouette Score for 7 clusters is 0.4664.
Silhouette Score for 8 clusters is 0.4424.
Silhouette Score for 9 clusters is 0.4380.



In [12]:

    
# Display the results of the clustering from implementation
clusterer = KMeans(n_clusters=2, random_state=5).fit(reduced_data)
preds = clusterer.predict(reduced_data)
centers = clusterer.cluster_centers_
sample_preds = clusterer.predict(reduced_data)

rs.cluster_results(reduced_data, preds, centers)



In [13]:

    
centers









    Out[13]:





array([[ 1.4546029 , -0.20806215],
       [-1.2116723 ,  0.17331407]])



In [14]:

    
y_clu = []
for row in reduced_data.iterrows():
    if np.linalg.norm(row[1]-centers[0]) > np.linalg.norm(row[1]-centers[1]):
        y_clu.append(0)
    else:
        y_clu.append(1)
print(y_clu)









    



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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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In [15]:

    
X_all['clusters'] = y_clu
X_all.head()









    Out[15]:






  
    
      
      CAMPUS_B. SANTISTA
      CAMPUS_DIADEMA
      CAMPUS_GUARULHOS
      CAMPUS_OSASCO
      CAMPUS_S.JOSÉ DOS CAMPOS
      CAMPUS_S.PAULO
      ANO
      Q1_Casado(a) / mora com um(a) companheiro(a) / união estável
      Q1_Separado(a) / divorciado(a) / desquitado(a)
      Q1_Solteiro(a)
      ...
      Q18_Nunca trabalhei
      Q18_Sem jornada fixa, até 10 horas semanais
      Q19_Antes dos 18 anos
      Q19_Após os 18 anos
      Q19_Nunca trabalhei enquanto estudava
      CR
      renda sm
      Pessoas que vivem com essa renda
      renda per
      clusters
    
  
  
    
      0
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      2012
      0.0
      0.0
      1.0
      ...
      1.0
      0.0
      0.0
      0.0
      1.0
      8.621
      15.000000
      4
      3.750000
      0
    
    
      1
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      2014
      0.0
      0.0
      1.0
      ...
      1.0
      0.0
      0.0
      0.0
      1.0
      0.000
      7.983425
      4
      1.995856
      0
    
    
      2
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      2012
      0.0
      0.0
      1.0
      ...
      1.0
      0.0
      0.0
      0.0
      1.0
      8.168
      25.000000
      5
      5.000000
      0
    
    
      3
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      2013
      0.0
      0.0
      1.0
      ...
      1.0
      0.0
      0.0
      0.0
      1.0
      7.151
      22.123894
      4
      5.530973
      0
    
    
      4
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      2012
      0.0
      0.0
      1.0
      ...
      1.0
      0.0
      0.0
      0.0
      1.0
      8.342
      8.500000
      5
      1.700000
      0
    
  

5 rows × 76 columns



In [16]:

    
pd.set_option('display.max_rows', len(X_all))



In [17]:

    
y_clu.count(0)









    Out[17]:





3904



In [18]:

    
from IPython.display import display, HTML



In [19]:

    
HTML(X_all[X_all.clusters == 0].mode().to_html())









    Out[19]:





  
    
      
      CAMPUS_B. SANTISTA
      CAMPUS_DIADEMA
      CAMPUS_GUARULHOS
      CAMPUS_OSASCO
      CAMPUS_S.JOSÉ DOS CAMPOS
      CAMPUS_S.PAULO
      ANO
      Q1_Casado(a) / mora com um(a) companheiro(a) / união estável
      Q1_Separado(a) / divorciado(a) / desquitado(a)
      Q1_Solteiro(a)
      Q2_Não tenho filhos (as)
      Q2_Tenho filhos (as)
      Q3_Amarelo (a)
      Q3_Branco (a)
      Q3_Indígena
      Q3_Pardo (a)
      Q3_Preto (a)
      Q4_Diurno
      Q4_Noturno
      Q5_Escola Particular
      Q5_Escola Pública
      Q5_Exterior
      Q6_Ensino Fundamental Completo/Ensino Médio Incompleto
      Q6_Ensino Médio Completo/Superior Incompleto
      Q6_Ensino Superior Completo
      Q6_Ensino fundamental Incompleto
      Q6_Não Sei
      Q6_Sem instrução / Não Alfabetizado
      Q7_Ensino fundamental Completo/Ensino médio incompleto
      Q7_Ensino fundamental Incompleto
      Q7_Ensino médio completo/Superior Incompleto
      Q7_Ensino superior Completo
      Q7_Não sei
      Q7_Sem instrução/Não alfabetizado
      Q8_Eu
      Q8_Meu (minha) Cônjuge ou Companheiro
      Q8_Meu filho ou outra pessoa
      Q8_Meus Pais (Meu pai, Minha Mãe, Meus irmãos)
      Q9
      Q10_Companheiro(a) / Cônjuge
      Q10_Outra pessoa ou familiar
      Q10_Seus pais
      Q10_Você mesmo (a)
      Q11_Desempregado
      Q11_Empregado (a)
      Q11_Outra situação
      Q12_Nunca trabalhei
      Q12_Trabalho com ou sem carteira assinada
      Q13_Com a família /companheiro(o)/cônjuge
      Q13_Com outros estudantes, em residência alugada
      Q13_Outro
      Q13_Sozinho
      Q14_Entre 30 e 60 minutos
      Q14_Mais de 1 hora
      Q14_Menos de 30 minutos
      Q14_Não sei responder
      Q15_No estado de São Paulo
      Q15_Outro
      Q16_Com o cônjuge ou companheiro (a)
      Q16_Com os pais
      Q16_Outra situação
      Q16_República, pensão, habitação coletiva, etc.
      Q16_Sozinho (a)
      Q17_Não
      Q17_Sim
      Q18_Com jornada fixa
      Q18_Nunca trabalhei
      Q18_Sem jornada fixa, até 10 horas semanais
      Q19_Antes dos 18 anos
      Q19_Após os 18 anos
      Q19_Nunca trabalhei enquanto estudava
      CR
      renda sm
      Pessoas que vivem com essa renda
      renda per
      clusters
    
  
  
    
      0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      2014
      0.0
      0.0
      1.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      2
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      8.5
      4
      1.474926
      0



In [20]:

    
y_clu.count(1)









    Out[20]:





3252



In [21]:

    
HTML(X_all[X_all.clusters == 1].mode().to_html())









    Out[21]:





  
    
      
      CAMPUS_B. SANTISTA
      CAMPUS_DIADEMA
      CAMPUS_GUARULHOS
      CAMPUS_OSASCO
      CAMPUS_S.JOSÉ DOS CAMPOS
      CAMPUS_S.PAULO
      ANO
      Q1_Casado(a) / mora com um(a) companheiro(a) / união estável
      Q1_Separado(a) / divorciado(a) / desquitado(a)
      Q1_Solteiro(a)
      Q2_Não tenho filhos (as)
      Q2_Tenho filhos (as)
      Q3_Amarelo (a)
      Q3_Branco (a)
      Q3_Indígena
      Q3_Pardo (a)
      Q3_Preto (a)
      Q4_Diurno
      Q4_Noturno
      Q5_Escola Particular
      Q5_Escola Pública
      Q5_Exterior
      Q6_Ensino Fundamental Completo/Ensino Médio Incompleto
      Q6_Ensino Médio Completo/Superior Incompleto
      Q6_Ensino Superior Completo
      Q6_Ensino fundamental Incompleto
      Q6_Não Sei
      Q6_Sem instrução / Não Alfabetizado
      Q7_Ensino fundamental Completo/Ensino médio incompleto
      Q7_Ensino fundamental Incompleto
      Q7_Ensino médio completo/Superior Incompleto
      Q7_Ensino superior Completo
      Q7_Não sei
      Q7_Sem instrução/Não alfabetizado
      Q8_Eu
      Q8_Meu (minha) Cônjuge ou Companheiro
      Q8_Meu filho ou outra pessoa
      Q8_Meus Pais (Meu pai, Minha Mãe, Meus irmãos)
      Q9
      Q10_Companheiro(a) / Cônjuge
      Q10_Outra pessoa ou familiar
      Q10_Seus pais
      Q10_Você mesmo (a)
      Q11_Desempregado
      Q11_Empregado (a)
      Q11_Outra situação
      Q12_Nunca trabalhei
      Q12_Trabalho com ou sem carteira assinada
      Q13_Com a família /companheiro(o)/cônjuge
      Q13_Com outros estudantes, em residência alugada
      Q13_Outro
      Q13_Sozinho
      Q14_Entre 30 e 60 minutos
      Q14_Mais de 1 hora
      Q14_Menos de 30 minutos
      Q14_Não sei responder
      Q15_No estado de São Paulo
      Q15_Outro
      Q16_Com o cônjuge ou companheiro (a)
      Q16_Com os pais
      Q16_Outra situação
      Q16_República, pensão, habitação coletiva, etc.
      Q16_Sozinho (a)
      Q17_Não
      Q17_Sim
      Q18_Com jornada fixa
      Q18_Nunca trabalhei
      Q18_Sem jornada fixa, até 10 horas semanais
      Q19_Antes dos 18 anos
      Q19_Após os 18 anos
      Q19_Nunca trabalhei enquanto estudava
      CR
      renda sm
      Pessoas que vivem com essa renda
      renda per
      clusters
    
  
  
    
      0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      2014
      0.0
      0.0
      1.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      2
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      3.5
      4
      1.381215
      1



In [22]:

    
pd.reset_option('display.max_rows')



In [23]:

    
X_all['evasao'] = y_all
X_all.head()









    Out[23]:






  
    
      
      CAMPUS_B. SANTISTA
      CAMPUS_DIADEMA
      CAMPUS_GUARULHOS
      CAMPUS_OSASCO
      CAMPUS_S.JOSÉ DOS CAMPOS
      CAMPUS_S.PAULO
      ANO
      Q1_Casado(a) / mora com um(a) companheiro(a) / união estável
      Q1_Separado(a) / divorciado(a) / desquitado(a)
      Q1_Solteiro(a)
      ...
      Q18_Sem jornada fixa, até 10 horas semanais
      Q19_Antes dos 18 anos
      Q19_Após os 18 anos
      Q19_Nunca trabalhei enquanto estudava
      CR
      renda sm
      Pessoas que vivem com essa renda
      renda per
      clusters
      evasao
    
  
  
    
      0
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      2012
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      1.0
      8.621
      15.000000
      4
      3.750000
      0
      NÃO
    
    
      1
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      2014
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      1.0
      0.000
      7.983425
      4
      1.995856
      0
      SIM
    
    
      2
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      2012
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      1.0
      8.168
      25.000000
      5
      5.000000
      0
      NÃO
    
    
      3
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      2013
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      1.0
      7.151
      22.123894
      4
      5.530973
      0
      NÃO
    
    
      4
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      2012
      0.0
      0.0
      1.0
      ...
      0.0
      0.0
      0.0
      1.0
      8.342
      8.500000
      5
      1.700000
      0
      NÃO
    
  

5 rows × 77 columns



In [31]:

    
sum(X_all[X_all.clusters == 1]['evasao'] == 'SIM') / X_all[X_all.clusters == 0]['evasao'].count()









    Out[31]:





0.31634221311475408



In [30]:

    
sum(X_all[X_all.clusters == 1]['evasao'] == 'SIM') / X_all[X_all.clusters == 1]['evasao'].count()









    Out[30]:





0.37976629766297665

	CAMPUS	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	...	Q14	Q15	Q16	Q17	Q18	Q19	CR	renda sm	Pessoas que vivem com essa renda	renda per
ANO
2012	1753	1753	1753	1753	1753	1753	1753	1753	1753	1753	...	1753	1753	1753	1753	1753	1753	1753	1753	1753	1753
2013	2577	2577	2577	2577	2577	2577	2577	2577	2577	2577	...	2577	2577	2577	2577	2577	2577	2577	2577	2577	2577
2014	2826	2826	2826	2826	2826	2826	2826	2826	2826	2826	...	2826	2826	2826	2826	2826	2826	2826	2826	2826	2826

	CAMPUS_B. SANTISTA	CAMPUS_DIADEMA	CAMPUS_S.PAULO	ANO	Q1_Solteiro(a)	...	Q18_Nunca trabalhei	Q19_Nunca trabalhei enquanto estudava	CR	renda sm	Pessoas que vivem com essa renda	renda per
0	0.0	0.0	1.0	2012	1.0	...	1.0	1.0	8.621	15.000000	4	3.750000
1	0.0	1.0	0.0	2014	1.0	...	1.0	1.0	0.000	7.983425	4	1.995856
2	0.0	0.0	1.0	2012	1.0	...	1.0	1.0	8.168	25.000000	5	5.000000
3	0.0	0.0	1.0	2013	1.0	...	1.0	1.0	7.151	22.123894	4	5.530973
4	1.0	0.0	0.0	2012	1.0	...	1.0	1.0	8.342	8.500000	5	1.700000