Random Forest - Python Code

 import pandas as pd

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score,classification_report

data = {

    'CGPA': ['>=9', '<9', '>=9', '<9', '>=9'],

    'Interactivity': ['Yes', 'No', 'No', 'No', 'Yes'],

    'Comm': ['Good', 'Moderate', 'Moderate', 'Moderate', 'Moderate'],

    'Practical': ['Good', 'Good', 'Average', 'Average', 'Average'],

    'JobOffer': ['Yes', 'No', 'Yes', 'No', 'Yes']

}

df=pd.DataFrame(data)

df.replace({'>=9':1,'<9':0,'Yes':1,'No':0,'Good':1,'Moderate':0,'Average':0},inplace=True)

X=df.drop('JobOffer',axis=1)

y=df['JobOffer']

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)

model=RandomForestClassifier()

model.fit(X_train,y_train)

y_pred=model.predict(X_test)

print("Accuracy:", accuracy_score(y_test,y_pred))

print("Report:", classification_report(y_test,y_pred))