In [1]:
import pandas as pd
import numpy as np

Load data

Using petrol consumption: https://www.kaggle.com/arpikr/petrol-consumption

In [2]:
df = pd.read_csv('petrol_consumption.csv')
df.head()
Out[2]:
Petrol_tax Average_income Paved_Highways Population_Driver_licence(%) Petrol_Consumption
0 9.0 3571 1976 0.525 541
1 9.0 4092 1250 0.572 524
2 9.0 3865 1586 0.580 561
3 7.5 4870 2351 0.529 414
4 8.0 4399 431 0.544 410
In [3]:
df.shape
Out[3]:
(48, 5)
In [4]:
df.dtypes
Out[4]:
Petrol_tax                      float64
Average_income                    int64
Paved_Highways                    int64
Population_Driver_licence(%)    float64
Petrol_Consumption                int64
dtype: object
In [6]:
X = df.drop('Petrol_Consumption', axis=1)
y = df['Petrol_Consumption']

print(X.shape, y.shape)

(48, 4) (48,)
In [7]:
from sklearn.model_selection import train_test_split
In [9]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.10, random_state=0)

print(X_train.shape, X_test.shape)

(43, 4) (5, 4)

Using sklearn

In [5]:
from sklearn.tree import DecisionTreeRegressor
In [10]:
regressor = DecisionTreeRegressor()
regressor.fit(X_train, y_train)
Out[10]:
DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,
                      max_leaf_nodes=None, min_impurity_decrease=0.0,
                      min_impurity_split=None, min_samples_leaf=1,
                      min_samples_split=2, min_weight_fraction_leaf=0.0,
                      presort=False, random_state=None, splitter='best')
In [12]:
y_pred = regressor.predict(X_test)

pd.DataFrame({
    'Actual': y_test,
    'Predicted': y_pred,
    'Diff': y_test - y_pred
})
Out[12]:
Actual Predicted Diff
29 534 487.0 47.0
4 410 524.0 -114.0
26 577 574.0 3.0
30 571 554.0 17.0
32 577 574.0 3.0
In [14]:
from sklearn.metrics import mean_squared_error
In [16]:
np.sqrt(mean_squared_error(y_test, y_pred))
Out[16]:
55.69919209467943
In [20]:
from IPython.display import Image  
from sklearn.externals.six import StringIO
from sklearn.tree import export_graphviz
import pydotplus

dot_data = StringIO()  
export_graphviz(regressor, out_file=dot_data,  
                         feature_names=X.columns)  
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
Image(graph.create_png())
Out[20]: