Data Analysis Using Python > Classifications > Random Forest

Random Forest

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import matplotlib.pyplot as plt

data = {
'Age': ['Youth', 'Youth', 'Middle', 'Senior', 'Senior', 'Senior', 'Middle', 'Youth', 'Youth', 'Senior'],
'Income': ['High', 'High', 'High', 'Medium', 'Low', 'Low', 'Low', 'Medium', 'Low', 'Medium'],
'Student': ['No', 'No', 'No', 'No', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'Yes'],
'Credit_rating': ['Fair', 'Excellent', 'Fair', 'Fair', 'Fair', 'Excellent', 'Excellent', 'Fair', 'Excellent', 'Fair'],
'Buys_Computer': ['No', 'No', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes']
}

df = pd.DataFrame(data)
df

	Age	Income	Student	Credit_rating	Buys_Computer
0	Youth	High	No	Fair	No
1	Youth	High	No	Excellent	No
2	Middle	High	No	Fair	Yes
3	Senior	Medium	No	Fair	Yes
4	Senior	Low	Yes	Fair	Yes
5	Senior	Low	Yes	Excellent	No
6	Middle	Low	Yes	Excellent	Yes
7	Youth	Medium	No	Fair	No
8	Youth	Low	Yes	Excellent	Yes
9	Senior	Medium	Yes	Fair	Yes

from sklearn.preprocessing import LabelEncoder

for col in df.columns:
 le = LabelEncoder()
 df[col] = le.fit_transform(df[col])
 df

	Age	Income	Student	Credit_rating	Buys_Computer
0	2	0	0	1	0
1	2	0	0	0	0
2	0	0	0	1	1
3	1	2	0	1	1
4	1	1	1	1	1
5	1	1	1	0	0
6	0	1	1	0	1
7	2	2	0	1	0
8	2	1	1	0	1
9	1	2	1	1	1

X = df.drop('Buys_Computer', axis=1)
y = df['Buys_Computer']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

y_pred_rf = rf_model.predict(X_test)

print("Random Forest Accuracy:", accuracy_score(y_test, y_pred_rf))
print(confusion_matrix(y_test, y_pred_rf))
print(classification_report(y_test, y_pred_rf))

Random Forest Accuracy: 0.3333333333333333
[[1 1]
 [1 0]]
              precision    recall  f1-score   support

           0       0.50      0.50      0.50         2
           1       0.00      0.00      0.00         1

    accuracy                           0.33         3
   macro avg       0.25      0.25      0.25         3
weighted avg       0.33      0.33      0.33         3

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