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from sklearn.datasets import load_iris | |
import pandas as pd | |
import os | |
from sklearn.tree import DecisionTreeClassifier,export_graphviz | |
from sklearn.metrics import confusion_matrix,accuracy_score,classification_report | |
from io import StringIO | |
import pydotplus | |
from sklearn.model_selection import train_test_split | |
from sklearn.ensemble import RandomForestClassifier | |
from IPython.display import Image | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
df = pd.read_csv('./flowers.csv') | |
X = df[list(df.columns)[:-1]] | |
y = df['Flower'] | |
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0) | |
a = DecisionTreeClassifier(criterion = "entropy", random_state = 100,max_depth=3, min_samples_leaf=5) # gini | |
a.fit(X_train, y_train) | |
y_pred = a.predict(X_test) | |
print("Confusion Matrix: ", confusion_matrix(y_test, y_pred)) | |
print ("Accuracy : ", accuracy_score(y_test,y_pred)*100) | |
print("Report : ", classification_report(y_test, y_pred)) | |
dot_data = StringIO() | |
export_graphviz(a, out_file=dot_data,filled=True, rounded=True,special_characters=True) | |
graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) | |
Image(graph.create_png()) | |
graph.write_png("decisiontree.png") | |
b = RandomForestClassifier(max_depth = None, n_estimators=100) | |
b.fit(X_train,y_train) | |
y_pred = b.predict(X_test) | |
print("Confusion Matrix: ", confusion_matrix(y_test, y_pred)) | |
print ("Accuracy : ", accuracy_score(y_test,y_pred)*100) | |
print("Report : ", classification_report(y_test, y_pred)) | |
export_graphviz(b.estimators_[5], out_file='tree.dot', feature_names = X_train.columns.tolist(), | |
class_names = ['Lotus', 'Jasmin', 'Rose'], | |
rounded = True, proportion = False, precision = 2, filled = True) | |
os.system ("dot -Tpng tree.dot -o randomforest.png -Gdpi=600") | |
Image(filename = 'randomforest.png') | |
f = pd.Series(b.feature_importances_,index=X_train.columns.tolist()).sort_values(ascending=False) | |
sns.barplot(x=f, y=f.index) | |
plt.xlabel('Feature Importance Score') | |
plt.ylabel('Features') | |
plt.legend() | |
plt.show() |
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thank you sharing the code..