Random Forest Regression in Python – Step 5.) Visualize Results with Random Forest Regression Model

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#Importing Libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv(‘Random Forest Data.csv’)
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)

# Training Random Forest Regression Model
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)
regressor.fit(X, y)

# Predict Result from Random Forest Regression Model
y_pred = regressor.predict(6.5)

# Visualising the Random Forest Regression results (higher resolution)
X_grid = np.arange(min(X), max(X), 0.01)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = ‘red’)
plt.plot(X_grid, regressor.predict(X_grid), color = ‘blue’)
plt.title(‘Random Forest Regression Model’)
plt.ylabel(‘Account Balance’)

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