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##### Author : Amir Shokri
##### github link : https://github.com/amirshnll/Covertype
##### dataset link : http://archive.ics.uci.edu/ml/datasets/Covertype
##### email : amirsh.nll@gmail.com
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error as MSE
from sklearn.metrics import classification_report
df = pd.read_csv('covtype_data.csv', header=None)
df
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0 1 2 3 4 5 6 7 8 9 ... 45 46 47 48 49 50 51 52 53 54
0 2596 51 3 258 0 510 221 232 148 6279 ... 0 0 0 0 0 0 0 0 0 5
1 2590 56 2 212 -6 390 220 235 151 6225 ... 0 0 0 0 0 0 0 0 0 5
2 2804 139 9 268 65 3180 234 238 135 6121 ... 0 0 0 0 0 0 0 0 0 2
3 2785 155 18 242 118 3090 238 238 122 6211 ... 0 0 0 0 0 0 0 0 0 2
4 2595 45 2 153 -1 391 220 234 150 6172 ... 0 0 0 0 0 0 0 0 0 5
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
581007 2396 153 20 85 17 108 240 237 118 837 ... 0 0 0 0 0 0 0 0 0 3
581008 2391 152 19 67 12 95 240 237 119 845 ... 0 0 0 0 0 0 0 0 0 3
581009 2386 159 17 60 7 90 236 241 130 854 ... 0 0 0 0 0 0 0 0 0 3
581010 2384 170 15 60 5 90 230 245 143 864 ... 0 0 0 0 0 0 0 0 0 3
581011 2383 165 13 60 4 67 231 244 141 875 ... 0 0 0 0 0 0 0 0 0 3

581012 rows × 55 columns

df.describe()
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0 1 2 3 4 5 6 7 8 9 ... 45 46 47 48 49 50 51 52 53 54
count 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 ... 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000 581012.000000
mean 2959.365301 155.656807 14.103704 269.428217 46.418855 2350.146611 212.146049 223.318716 142.528263 1980.291226 ... 0.090392 0.077716 0.002773 0.003255 0.000205 0.000513 0.026803 0.023762 0.015060 2.051471
std 279.984734 111.913721 7.488242 212.549356 58.295232 1559.254870 26.769889 19.768697 38.274529 1324.195210 ... 0.286743 0.267725 0.052584 0.056957 0.014310 0.022641 0.161508 0.152307 0.121791 1.396504
min 1859.000000 0.000000 0.000000 0.000000 -173.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000
25% 2809.000000 58.000000 9.000000 108.000000 7.000000 1106.000000 198.000000 213.000000 119.000000 1024.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000
50% 2996.000000 127.000000 13.000000 218.000000 30.000000 1997.000000 218.000000 226.000000 143.000000 1710.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000
75% 3163.000000 260.000000 18.000000 384.000000 69.000000 3328.000000 231.000000 237.000000 168.000000 2550.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000
max 3858.000000 360.000000 66.000000 1397.000000 601.000000 7117.000000 254.000000 254.000000 254.000000 7173.000000 ... 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 7.000000

8 rows × 55 columns

x = df[df.columns[:54]]
y = df[df.columns[54]]
scaler = MinMaxScaler()
scaled_x = scaler.fit_transform(x)
y.value_counts().plot.pie()
<matplotlib.axes._subplots.AxesSubplot at 0x1c9c8d1c488>

svg

#Dimentionality reduction
pca = PCA(n_components=15)
reduced_x = pca.fit_transform(scaled_x)
#Choose whether reduces or not
X = scaled_x
X = reduced_x
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
#Now we run algorithms and evaluate
from sklearn.naive_bayes import CategoricalNB
cnb = CategoricalNB()
cnb.fit(X_train, y_train)
predicted = cnb.predict(X_test)

print('MSE:', MSE(y_test, predicted))
print(classification_report(y_test, predicted))
MSE: 1.8519196346612814
              precision    recall  f1-score   support

           1       0.67      0.03      0.06     63498
           2       0.51      0.98      0.67     85198
           3       0.58      0.33      0.42     10581
           4       0.00      0.00      0.00       822
           5       0.00      0.00      0.00      2850
           6       0.00      0.00      0.00      5229
           7       0.00      0.00      0.00      6126

    accuracy                           0.51    174304
   macro avg       0.25      0.19      0.16    174304
weighted avg       0.52      0.51      0.37    174304

from sklearn.neural_network import MLPClassifier
mlp = MLPClassifier(hidden_layer_sizes=(100, 100), activation='relu', solver='adam', alpha=0.0001)
mlp.fit(X_train, y_train)
predicted = mlp.predict(X_test)

print('MSE:', MSE(y_test, predicted))
print(classification_report(y_test, predicted))
MSE: 0.96
              precision    recall  f1-score   support

           1       0.66      0.58      0.62        71
           2       0.81      0.82      0.81       168
           5       0.78      0.87      0.82        61

    accuracy                           0.77       300
   macro avg       0.75      0.75      0.75       300
weighted avg       0.77      0.77      0.77       300

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
predicted = knn.predict(X_test)

print('MSE:', MSE(y_test, predicted))
print(classification_report(y_test, predicted))
MSE: 0.35506930420414906
              precision    recall  f1-score   support

           1       0.93      0.92      0.93     63498
           2       0.94      0.95      0.94     85198
           3       0.92      0.93      0.92     10581
           4       0.86      0.71      0.78       822
           5       0.84      0.77      0.80      2850
           6       0.87      0.86      0.87      5229
           7       0.94      0.93      0.93      6126

    accuracy                           0.93    174304
   macro avg       0.90      0.87      0.88    174304
weighted avg       0.93      0.93      0.93    174304

from sklearn.tree import DecisionTreeClassifier
Dtree = DecisionTreeClassifier()
Dtree.fit(X_train, y_train)
predicted = Dtree.predict(X_test)

print('MSE:', MSE(y_test, predicted))
print(classification_report(y_test, predicted))
MSE: 0.4798799798053975
              precision    recall  f1-score   support

           1       0.90      0.90      0.90     63498
           2       0.91      0.91      0.91     85198
           3       0.88      0.88      0.88     10581
           4       0.74      0.73      0.74       822
           5       0.73      0.74      0.74      2850
           6       0.80      0.80      0.80      5229
           7       0.92      0.92      0.92      6126

    accuracy                           0.90    174304
   macro avg       0.84      0.84      0.84    174304
weighted avg       0.90      0.90      0.90    174304

from sklearn.linear_model import LogisticRegression
lreg = LogisticRegression()
lreg.fit(X_train, y_train)
predicted = lreg.predict(X_test)

print('MSE:', MSE(y_test, predicted))
print(classification_report(y_test, predicted))
MSE: 1.7821335138608407
              precision    recall  f1-score   support

           1       0.66      0.57      0.61     63498
           2       0.67      0.80      0.73     85198
           3       0.60      0.76      0.67     10581
           4       0.11      0.00      0.00       822
           5       1.00      0.00      0.00      2850
           6       0.37      0.10      0.15      5229
           7       0.66      0.37      0.47      6126

    accuracy                           0.66    174304
   macro avg       0.58      0.37      0.38    174304
weighted avg       0.66      0.66      0.64    174304



   




      


  
      

      

        

          

            

  
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        Covertype Data Set
      

      

        
        
          archive.ics.uci.edu/ml/datasets/Covertype
        
      


    
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