Random_forest.py

#Random Forest Regression

#importing the libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#importing the dataset
dataset = pd.read_csv("Dataset.csv")
from sklearn.preprocessing import LabelEncoder 
le = LabelEncoder() 
  
dataset['Date']= le.fit_transform(dataset['Date'])


X = dataset.iloc[:, 2:6].values
y = dataset.iloc[:, 6:7].values

#splitting into Train and Test data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

#Featuring Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)

#Fitting the Random Forest Regression
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = 300, random_state = 0)
regressor.fit(X_train,y_train)

#Predicting the Results
y_pred = regressor.predict(X_test)

if(y_pred.all()<2.5):
    y_pred=np.round(y_pred-0.5)
    
else:
    y_pred=np.round(y_pred+0.5)

#Calculationg the error and accuracy

df1=(y_pred-y_test)/y_test
df1=round(df1.mean()*100,2)
print("Error = ",df1,"%") 
a=100-df1
print("Accuracy= ",a,"%")