decisiontree.py

# -*- coding: utf-8 -*-
"""DecisionTree.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1kyQF8I-NpsW7lTBD9dfhxHZmeYEPTjJX

## Importing Libraries
"""

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

dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values

print(X)

print(y)

"""## Training Decision Tree Regression model on the whole dataset"""

from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = 0)
regressor.fit(X, y)

"""## Predicting New Result"""

regressor.predict([[6.5]])

"""## Visualizing the descision tree regression results"""

plt.scatter(X, y, color ='red')
plt.plot(X, regressor.predict(X), color = 'blue')
plt.title('Truth or Bluff (Decision Tree Regression)')
plt.xlabel('Position Level')
plt.ylabel('Salary')
plt.show()

"""## High 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 ="blue")
plt.plot(X_grid, regressor.predict(X_grid), color ='red')
plt.title('Truth or Bluff (Decision Tree Regression)')
plt.xlabel('Position Level')
plt.ylabel('Salary')
plt.show()