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Predict Gas Consumptions Using Decision Tree for Regression

Project Objective: Is to predict the gas consumption on the bases of data provided of US states which can help in many decision making for climate change, people, goverment policies and many more thing.The process of solving regression problem with decision tree using Scikit Learn is very similar to that of classification. However for regression we use DecisionTreeRegressor class of the tree library. Also the evaluation matrics for regression differ from those of classification. The rest of the process is almost same like other regression models.

Importing the libraries

Firstly, we import necessary library(numpy, matplotlib and pandas) for this model.

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

Importing the dataset

Now we read CSV file name petrol_consumption.csv. We will use this dataset to try and predict gas consumptions (in millions of gallons) in 48 US states based upon gas tax (in cents), per capita income (dollars), paved highways (in miles) and the proportion of population with a drivers license.

In [2]:
dataset = pd.read_csv('petrol_consumption.csv')

It contain 48 Column and 5 Rows containg imformation about US satates information related to petrol consumption prediction. We will again use the head function of the dataframe to see what our data actually looks like

In [3]:
(48, 5)

To see statistical details of the dataset, execute the following command:

In [4]:
Petrol_tax Average_income Paved_Highways Population_Driver_licence(%) Petrol_Consumption
count 48.000000 48.000000 48.000000 48.000000 48.000000
mean 7.668333 4241.833333 5565.416667 0.570333 576.770833
std 0.950770 573.623768 3491.507166 0.055470 111.885816
min 5.000000 3063.000000 431.000000 0.451000 344.000000
25% 7.000000 3739.000000 3110.250000 0.529750 509.500000
50% 7.500000 4298.000000 4735.500000 0.564500 568.500000
75% 8.125000 4578.750000 7156.000000 0.595250 632.750000
max 10.000000 5342.000000 17782.000000 0.724000 968.000000

Preparing the Data

As with the classification task, in this section we will divide our data into attributes and labels and consequently into training and test sets. Execute the following commands to divide data into labels and attributes:

In [5]:
X = dataset.drop('Petrol_Consumption', axis=1)
y = dataset['Petrol_Consumption']

Here the X variable contains all the columns from the dataset, except 'Petrol_Consumption' column, which is the label. The y variable contains values from the 'Petrol_Consumption' column, which means that the X variable contains the attribute set and y variable contains the corresponding labels.

Execute the following code to divide our data into training and test sets:

In [6]:
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)

Training and Making Predictions

As mentioned earlier, for a regression task we'll use a different sklearn class than we did for the classification task. The class we'll be using here is the DecisionTreeRegressor class, as opposed to the DecisionTreeClassifier from before.

To train the tree, we'll instantiate the DecisionTreeRegressor class and call the fit method

In [7]:
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor()
regressor.fit(X_train, y_train)
DecisionTreeRegressor(ccp_alpha=0.0, 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='deprecated',
                      random_state=None, splitter='best')

To make predictions on the test set, ues the predict method:

In [8]:
y_pred = regressor.predict(X_test)

Now let's compare some of our predicted values with the actual values and see how accurate we were:

In [9]:
df=pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})
Actual Predicted
29 534 547.0
4 410 414.0
26 577 574.0
30 571 554.0
32 577 631.0
37 704 644.0
34 487 628.0
40 587 540.0
7 467 414.0
10 580 464.0

Remember that in your case the records compared may be different, depending upon the training and testing split. Since the train_test_split method randomly splits the data we likely won't have the same training and test sets.

Evaluating the Algorithm

To evaluate performance of the regression algorithm, the commonly used metrics are mean absolute error, mean squared error, and root mean squared error. The Scikit-Learn library contains functions that can help calculate these values for us. To do so, use this code from the metrics package:

In [10]:
from sklearn import metrics
print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))
print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
Mean Absolute Error: 50.8
Mean Squared Error: 4535.4
Root Mean Squared Error: 67.34537846058926

The mean absolute error for our algorithm is 54.7, which is less than 10 percent of the mean of all the values in the 'Petrol_Consumption' column. This means that our algorithm did a fine prediction job.

Conclusion: We showed how we can use Python's Scikit-Learn library to use decision trees for regression tasks. While being a fairly simple algorithm in itself, implementing decision trees with regression is even easier. Eventhough it can be perform by both classification and regression.

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