Decision tree.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Apr  4 16:28:56 2020

@author: thulas
"""
# Decision Tree Regression

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

# Importing the dataset
dataset = pd.read_csv('leads1.csv',delimiter=';')
X = dataset.iloc[:, [3, 4]].values
y = dataset.iloc[:, 5].values

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

 #taking care of missing values

from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values ="NaN", strategy= 'mean',axis =0)# Play around with strategy
imputer=imputer.fit(X[:, 0:1])
X[:, 0:1]=imputer.transform(X[:, 0:1])

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train)

# Fitting Decision Tree Regression to the dataset
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = 0)
regressor.fit(X, y)

# Predicting a new result
y_pred = regressor.predict(1, -1)

# Visualising the Decision Tree 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('Truth or Bluff (Decision Tree Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()