Key Driver Analysis

Key Driver Analysis also known as Importance Analysis and Relative Importance Analysis. The goal of this analysis is to quantify the relative importance of each of the predictor variables in predicting the target variable. Each of the predictors is commonly referred to as a driver.

For more information on key driver analysis refer to this blog post

Installation

Using pip

You can install using the pip package manager by running

pip install key-driver-analysis

Alternatively, you could install the latest version directly from Github:

pip install https://github.com/TVS-Motor-Company/key-driver-analysis/key-driver-analysis/archive/master.zip

Using conda

You can install using the conda package manager by running

conda install -c conda-forge key-driver-analysis

From source

Download the source code by cloning the repository or by pressing 'Download ZIP' on this page.

Install by navigating to the proper directory and running:

python setup.py install

Usage

import pandas as pd
import key_driver_analysis as kda

df = pd.DataFrame(data={
        'age': [40, 50, 60, 10, 20, 30, 7, 80, 90],
        'salary': [123, 4423, 56563, 75545, 2345, 2346, 5534, 775, 34345],
        'no_of_cars_owned': [1, 3, 4, 2, 1, 3, 5, 3, 2],
        'no_of_mobiles_purchased': [10, 3, 5, 65, 34, 6, 21, 76, 9]
    })
    print(df)
    target = 'no_of_mobiles_purchased'
    features=set(df.columns.tolist()).difference(set([target]))
    print(f'target --> {target}')
    print(f'features --> {features}')
    rw_df = kda.relative_importance(df,
                                target=target,
                                features=features,
                                verbose=True)
    print(rw_df)


   age  salary  no_of_cars_owned  no_of_mobiles_purchased
0   40     123                 1                       10
1   50    4423                 3                        3
2   60   56563                 4                        5
3   10   75545                 2                       65
4   20    2345                 1                       34
5   30    2346                 3                        6
6    7    5534                 5                       21
7   80     775                 3                       76
8   90   34345                 2                        9
target --> no_of_mobiles_purchased
features --> {'salary', 'no_of_cars_owned', 'age'}
(9, 4)
Dataset size before dropping nulls --> (9, 4)
Dataset size after dropping nulls --> (9, 4)
r2 score --> 0.05963122389990851
            feature  raw_rel_imp  norm_rel_imp
0            salary     0.035140     58.928857
1  no_of_cars_owned     0.019415     32.558853
2               age     0.005076      8.512289