Purchase Predictive Analytics#

Imports#

import pandas as pd
import scipy as sp
import seaborn as sns
import matplotlib.pyplot as plt 
import sklearn
import numpy as np
import pickle
import joblib
import itertools
from sklearn.linear_model import LogisticRegression

sns.set()

Read and Prepare Dataset#

df = pd.read_csv("purchase data.csv"); df.head()

	ID	Day	Price_1	Price_2	Price_3	...	Age	Education	Income	Occupation
0	200000001	1	1.59	1.87	2.01	...	47	1	110866	1
1	200000001	11	1.51	1.89	1.99	...	47	1	110866	1
2	200000001	12	1.51	1.89	1.99	...	47	1	110866	1
3	200000001	16	1.52	1.89	1.98	...	47	1	110866	1
4	200000001	18	1.52	1.89	1.99	...	47	1	110866	1

5 rows × 24 columns

def do_clustering(df, pipeline, drop_cols=None, sel_cols=None, do_fit=False):
    y = None
    df_new = df.copy()
    if drop_cols: df_new = df_new.drop(columns=drop_cols, axis=1)
    df_filter = df_new.copy()    
    if sel_cols: df_filter = df_new[sel_cols]
    if do_fit:y = pipeline.fit_predict(df_filter)
    else: y = pipeline.predict(df_filter)
    if 'pca' in pipeline.named_steps:
        m = pipeline.named_steps['pca']
        comp_names = [f"PCA{i+1}" for i in range(m.n_components)]
        transform_df = df_filter.copy()
        for step in pipeline.named_steps:
            transform_df = pipeline.named_steps[step].transform(transform_df)
            if step == "pca": break
        pca_df = pd.DataFrame(transform_df, 
                              columns=comp_names, 
                              index=df_filter.index)
        df_new = pd.concat([df_new, pca_df], axis=1)
    df_new['y'] = y+1
    return df_new, pipeline

pipeline = joblib.load("cluster_pipeline.pkl"); pipeline
sel_cols = ['Sex','Marital status','Age','Education','Income','Occupation','Settlement size']
df_segments, _ = do_clustering(df, pipeline, sel_cols=sel_cols)
names = {1:"Standard",
         2:"Career-Focussed",
         3:"Fewer-Opportunities",
         4:"Well-off"}

df_segments['labels'] = df_segments['y'].map(names)
df_segments.head().T
## May be we might include pca names later

	0	1	2	3	4
ID	200000001	200000001	200000001	200000001	200000001
Day	1	11	12	16	18
Incidence	0	0	0	0	0
Brand	0	0	0	0	0
Quantity	0	0	0	0	0
Last_Inc_Brand	0	0	0	0	0
Last_Inc_Quantity	0	0	0	0	0
Price_1	1.59	1.51	1.51	1.52	1.52
Price_2	1.87	1.89	1.89	1.89	1.89
Price_3	2.01	1.99	1.99	1.98	1.99
Price_4	2.09	2.09	2.09	2.09	2.09
Price_5	2.66	2.66	2.66	2.66	2.66
Promotion_1	0	0	0	0	0
Promotion_2	1	0	0	0	0
Promotion_3	0	0	0	0	0
Promotion_4	0	0	0	0	0
Promotion_5	0	0	0	0	0
Sex	0	0	0	0	0
Marital status	0	0	0	0	0
Age	47	47	47	47	47
Education	1	1	1	1	1
Income	110866	110866	110866	110866	110866
Occupation	1	1	1	1	1
Settlement size	0	0	0	0	0
PCA1	0.362152	0.362152	0.362152	0.362152	0.362152
PCA2	-0.639557	-0.639557	-0.639557	-0.639557	-0.639557
PCA3	1.462706	1.462706	1.462706	1.462706	1.462706
PCA4	-0.593242	-0.593242	-0.593242	-0.593242	-0.593242
y	3	3	3	3	3
labels	Fewer-Opportunities	Fewer-Opportunities	Fewer-Opportunities	Fewer-Opportunities	Fewer-Opportunities

df_segments_dummies = pd.get_dummies(df_segments['labels']); df_segments_dummies.head()

	Career-Focussed	Fewer-Opportunities	Standard	Well-off
0	0	1	0	0
1	0	1	0	0
2	0	1	0	0
3	0	1	0	0
4	0	1	0	0

df_segments = pd.concat([df_segments, df_segments_dummies], axis=1); df_segments.head()

	ID	Day	Price_1	Price_2	Price_3	...	PCA1	PCA2	PCA3	PCA4	y	labels	Fewer-Opportunities
0	200000001	1	1.59	1.87	2.01	...	0.362152	-0.639557	1.462706	-0.593242	3	Fewer-Opportunities	1
1	200000001	11	1.51	1.89	1.99	...	0.362152	-0.639557	1.462706	-0.593242	3	Fewer-Opportunities	1
2	200000001	12	1.51	1.89	1.99	...	0.362152	-0.639557	1.462706	-0.593242	3	Fewer-Opportunities	1
3	200000001	16	1.52	1.89	1.98	...	0.362152	-0.639557	1.462706	-0.593242	3	Fewer-Opportunities	1
4	200000001	18	1.52	1.89	1.99	...	0.362152	-0.639557	1.462706	-0.593242	3	Fewer-Opportunities	1

5 rows × 34 columns

Purchase Probability#

Note

We can estimate purchase probability by a simple binomial/ logistics regression model with average price as X. Since we are only interested in probabilities we might not even need to estimate train-test split ? I think this intuition might be weird. My thinking is we should do a test-train split

df_segments['Avg_Price'] = df_segments.filter(regex='Price*').mean(axis=1)

df_segments.head()

	ID	Day	Price_1	Price_2	Price_3	...	PCA2	PCA3	PCA4	y	labels	Fewer-Opportunities	Avg_Price
0	200000001	1	1.59	1.87	2.01	...	-0.639557	1.462706	-0.593242	3	Fewer-Opportunities	1	2.044
1	200000001	11	1.51	1.89	1.99	...	-0.639557	1.462706	-0.593242	3	Fewer-Opportunities	1	2.028
2	200000001	12	1.51	1.89	1.99	...	-0.639557	1.462706	-0.593242	3	Fewer-Opportunities	1	2.028
3	200000001	16	1.52	1.89	1.98	...	-0.639557	1.462706	-0.593242	3	Fewer-Opportunities	1	2.028
4	200000001	18	1.52	1.89	1.99	...	-0.639557	1.462706	-0.593242	3	Fewer-Opportunities	1	2.030

5 rows × 35 columns

model_purchase = LogisticRegression()

df_segments['Avg_Price'].values, df_segments['Incidence'].values

(array([2.044, 2.028, 2.028, ..., 2.086, 2.092, 2.092]),
 array([0, 0, 0, ..., 0, 1, 0]))

model_purchase.fit(df_segments.Avg_Price.values[:, np.newaxis],df_segments.Incidence.values )

LogisticRegression()

model_purchase.coef_[0][0]

-2.3480548048384446

Price Elasticity#

Determining Price Ranges#

df_segments.filter(regex='Price*').min().min(),df_segments.filter(regex='Price*').max().max()

(1.1, 2.8)

Based on this we can choose the price to be between (0.5, 3.5). Will give us some space if we want to increase of decrease price

prices = np.arange(0.5, 3.5,0.01); prices.shape, prices

((300,),
 array([0.5 , 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ,
        0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 , 0.71,
        0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 , 0.81, 0.82,
        0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 , 0.91, 0.92, 0.93,
        0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.  , 1.01, 1.02, 1.03, 1.04,
        1.05, 1.06, 1.07, 1.08, 1.09, 1.1 , 1.11, 1.12, 1.13, 1.14, 1.15,
        1.16, 1.17, 1.18, 1.19, 1.2 , 1.21, 1.22, 1.23, 1.24, 1.25, 1.26,
        1.27, 1.28, 1.29, 1.3 , 1.31, 1.32, 1.33, 1.34, 1.35, 1.36, 1.37,
        1.38, 1.39, 1.4 , 1.41, 1.42, 1.43, 1.44, 1.45, 1.46, 1.47, 1.48,
        1.49, 1.5 , 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59,
        1.6 , 1.61, 1.62, 1.63, 1.64, 1.65, 1.66, 1.67, 1.68, 1.69, 1.7 ,
        1.71, 1.72, 1.73, 1.74, 1.75, 1.76, 1.77, 1.78, 1.79, 1.8 , 1.81,
        1.82, 1.83, 1.84, 1.85, 1.86, 1.87, 1.88, 1.89, 1.9 , 1.91, 1.92,
        1.93, 1.94, 1.95, 1.96, 1.97, 1.98, 1.99, 2.  , 2.01, 2.02, 2.03,
        2.04, 2.05, 2.06, 2.07, 2.08, 2.09, 2.1 , 2.11, 2.12, 2.13, 2.14,
        2.15, 2.16, 2.17, 2.18, 2.19, 2.2 , 2.21, 2.22, 2.23, 2.24, 2.25,
        2.26, 2.27, 2.28, 2.29, 2.3 , 2.31, 2.32, 2.33, 2.34, 2.35, 2.36,
        2.37, 2.38, 2.39, 2.4 , 2.41, 2.42, 2.43, 2.44, 2.45, 2.46, 2.47,
        2.48, 2.49, 2.5 , 2.51, 2.52, 2.53, 2.54, 2.55, 2.56, 2.57, 2.58,
        2.59, 2.6 , 2.61, 2.62, 2.63, 2.64, 2.65, 2.66, 2.67, 2.68, 2.69,
        2.7 , 2.71, 2.72, 2.73, 2.74, 2.75, 2.76, 2.77, 2.78, 2.79, 2.8 ,
        2.81, 2.82, 2.83, 2.84, 2.85, 2.86, 2.87, 2.88, 2.89, 2.9 , 2.91,
        2.92, 2.93, 2.94, 2.95, 2.96, 2.97, 2.98, 2.99, 3.  , 3.01, 3.02,
        3.03, 3.04, 3.05, 3.06, 3.07, 3.08, 3.09, 3.1 , 3.11, 3.12, 3.13,
        3.14, 3.15, 3.16, 3.17, 3.18, 3.19, 3.2 , 3.21, 3.22, 3.23, 3.24,
        3.25, 3.26, 3.27, 3.28, 3.29, 3.3 , 3.31, 3.32, 3.33, 3.34, 3.35,
        3.36, 3.37, 3.38, 3.39, 3.4 , 3.41, 3.42, 3.43, 3.44, 3.45, 3.46,
        3.47, 3.48, 3.49]))

model_purchase.predict_proba(prices[:, np.newaxis])[:,1]

array([0.91789303, 0.91610596, 0.91428362, 0.91242548, 0.910531  ,
       0.90859965, 0.90663088, 0.90462415, 0.90257893, 0.90049467,
       0.89837085, 0.89620692, 0.89400234, 0.8917566 , 0.88946917,
       0.88713951, 0.88476711, 0.88235145, 0.87989203, 0.87738834,
       0.87483988, 0.87224617, 0.86960672, 0.86692105, 0.86418871,
       0.86140924, 0.85858219, 0.85570714, 0.85278366, 0.84981134,
       0.84678979, 0.84371863, 0.84059749, 0.83742604, 0.83420393,
       0.83093086, 0.82760652, 0.82423064, 0.82080297, 0.81732327,
       0.81379133, 0.81020696, 0.80657   , 0.80288029, 0.79913773,
       0.79534223, 0.79149372, 0.78759217, 0.78363757, 0.77962994,
       0.77556934, 0.77145585, 0.76728959, 0.76307071, 0.75879938,
       0.75447583, 0.7501003 , 0.74567308, 0.74119449, 0.73666488,
       0.73208466, 0.72745424, 0.7227741 , 0.71804474, 0.71326671,
       0.70844058, 0.70356699, 0.69864658, 0.69368006, 0.68866816,
       0.68361166, 0.67851138, 0.67336816, 0.66818289, 0.66295652,
       0.65768999, 0.65238432, 0.64704055, 0.64165976, 0.63624305,
       0.63079157, 0.62530651, 0.61978908, 0.61424052, 0.60866212,
       0.60305518, 0.59742104, 0.59176106, 0.58607665, 0.58036921,
       0.57464019, 0.56889106, 0.56312329, 0.55733841, 0.55153792,
       0.54572339, 0.53989635, 0.53405839, 0.52821108, 0.52235602,
       0.51649481, 0.51062906, 0.50476038, 0.49889039, 0.4930207 ,
       0.48715294, 0.48128872, 0.47542965, 0.46957733, 0.46373337,
       0.45789935, 0.45207685, 0.44626745, 0.44047268, 0.43469409,
       0.42893319, 0.42319149, 0.41747046, 0.41177155, 0.40609621,
       0.40044584, 0.39482183, 0.38922552, 0.38365825, 0.37812131,
       0.37261597, 0.36714347, 0.361705  , 0.35630173, 0.35093481,
       0.34560531, 0.34031433, 0.33506286, 0.32985192, 0.32468244,
       0.31955535, 0.31447152, 0.30943179, 0.30443696, 0.29948778,
       0.29458499, 0.28972927, 0.28492126, 0.28016156, 0.27545074,
       0.27078934, 0.26617785, 0.26161671, 0.25710634, 0.25264713,
       0.24823942, 0.24388351, 0.23957968, 0.23532815, 0.23112914,
       0.22698282, 0.22288931, 0.21884873, 0.21486115, 0.2109266 ,
       0.2070451 , 0.20321664, 0.19944116, 0.19571859, 0.19204884,
       0.18843178, 0.18486725, 0.18135509, 0.17789508, 0.17448702,
       0.17113066, 0.16782574, 0.16457196, 0.16136904, 0.15821665,
       0.15511445, 0.15206209, 0.1490592 , 0.14610539, 0.14320027,
       0.14034341, 0.1375344 , 0.1347728 , 0.13205816, 0.12939002,
       0.12676792, 0.12419137, 0.12165989, 0.119173  , 0.11673018,
       0.11433094, 0.11197476, 0.10966112, 0.10738951, 0.1051594 ,
       0.10297025, 0.10082155, 0.09871274, 0.09664331, 0.0946127 ,
       0.09262039, 0.09066583, 0.08874848, 0.0868678 , 0.08502326,
       0.08321431, 0.08144043, 0.07970108, 0.07799571, 0.07632382,
       0.07468485, 0.0730783 , 0.07150363, 0.06996034, 0.0684479 ,
       0.0669658 , 0.06551354, 0.0640906 , 0.0626965 , 0.06133074,
       0.05999282, 0.05868227, 0.0573986 , 0.05614133, 0.05491   ,
       0.05370413, 0.05252328, 0.05136698, 0.05023479, 0.04912626,
       0.04804096, 0.04697845, 0.0459383 , 0.0449201 , 0.04392342,
       0.04294787, 0.04199303, 0.04105851, 0.04014392, 0.03924886,
       0.03837296, 0.03751585, 0.03667715, 0.0358565 , 0.03505355,
       0.03426794, 0.03349932, 0.03274736, 0.03201172, 0.03129207,
       0.03058809, 0.02989945, 0.02922585, 0.02856698, 0.02792254,
       0.02729223, 0.02667575, 0.02607283, 0.02548317, 0.02490651,
       0.02434258, 0.0237911 , 0.02325182, 0.02272447, 0.02220882,
       0.0217046 , 0.02121159, 0.02072954, 0.02025821, 0.01979739,
       0.01934684, 0.01890634, 0.01847569, 0.01805466, 0.01764306,
       0.01724068, 0.01684731, 0.01646277, 0.01608687, 0.01571941,
       0.01536021, 0.0150091 , 0.01466589, 0.01433041, 0.0140025 ,
       0.01368199, 0.01336872, 0.01306252, 0.01276325, 0.01247075,
       0.01218487, 0.01190546, 0.01163238, 0.0113655 , 0.01110467,
       0.01084976, 0.01060063, 0.01035717, 0.01011925, 0.00988673])

\[Elasiticity = E = \beta*price*(1-Pr)\]

df_price_elasticity = pd.DataFrame()
df_price_elasticity["Price"]= prices
df_price_elasticity["Probabilities"]= model_purchase.predict_proba(prices[:, np.newaxis])[:,1]
df_price_elasticity["E"] = model_purchase.coef_[0][0]*df_price_elasticity["Price"]*(1-df_price_elasticity["Probabilities"])
df_price_elasticity["Is_Elastic"] = df_price_elasticity["E"].abs() >1 
df_price_elasticity

	Price	Probabilities	E	Is_Elastic
0	0.50	0.917893	-0.096396	False
1	0.51	0.916106	-0.100464	False
2	0.52	0.914284	-0.104659	False
3	0.53	0.912425	-0.108984	False
4	0.54	0.910531	-0.113442	False
...	...	...	...	...
295	3.45	0.010850	-8.012897	True
296	3.46	0.010601	-8.038147	True
297	3.47	0.010357	-8.063363	True
298	3.48	0.010119	-8.088544	True
299	3.49	0.009887	-8.113692	True

300 rows × 4 columns

ax = sns.lineplot(data=df_price_elasticity, x='Price', y='E', hue='Is_Elastic')
ax2 = ax.twinx()
sns.lineplot(data=df_price_elasticity, x='Price', y='Probabilities', hue='Is_Elastic', ax=ax2)

<AxesSubplot:xlabel='Price', ylabel='Probabilities'>

../../_images/05_purchase_predictive_analytics_27_1.png

df_price_elasticity[['Price','Probabilities', 'Is_Elastic']][df_price_elasticity['Is_Elastic']][:1] #Price Point where demand becomes elastic -> We can increase the price below this to increase revenue

	Price	Probabilities	Is_Elastic
75	1.25	0.65769	True

-2.3*2*(1-0.3)

-3.2199999999999998

0.02/2.56*100 * 1.22

0.953125

Price Elasticity By Segments#

segments = df_segments['labels'].unique().tolist(); segments
segments.insert(0, None)
segments

[None, 'Fewer-Opportunities', 'Well-off', 'Career-Focussed', 'Standard']

def get_elasticity_df(df_segments, segment=None):
    label = "Aggregate"
    df = df_segments.copy()
    if segment: 
        label = segment
        df = df_segments[df_segments['labels'] == segment].copy()
    df['Avg_Price'] = df.filter(regex='Price*').mean(axis=1)
    model_purchase = LogisticRegression()
    model_purchase.fit(df.Avg_Price.values[:, np.newaxis],df.Incidence.values)
    df_price_elasticity = pd.DataFrame()
    df_price_elasticity["Price"]= np.arange(0.5, 3.5,0.01)
    df_price_elasticity["Probabilities"]= model_purchase.predict_proba(df_price_elasticity["Price"].values[:, np.newaxis])[:,1]
    df_price_elasticity["E"] = model_purchase.coef_[0][0]*df_price_elasticity["Price"]*(1-df_price_elasticity["Probabilities"])
    df_price_elasticity["Is_Elastic"] = df_price_elasticity["E"].abs() >1 
    df_price_elasticity['label'] = label
    return df_price_elasticity
    
get_elasticity_df(df_segments,segment=segments[0]).head()

	Price	Probabilities	E	Is_Elastic	label
0	0.50	0.917893	-0.096396	False	Aggregate
1	0.51	0.916106	-0.100464	False	Aggregate
2	0.52	0.914284	-0.104659	False	Aggregate
3	0.53	0.912425	-0.108984	False	Aggregate
4	0.54	0.910531	-0.113442	False	Aggregate

df_price_elasticity_all = pd.concat([get_elasticity_df(df_segments,segment=s) for s in segments]).reset_index(drop=True)

sns.lineplot(data=df_price_elasticity_all, x='Price', y='E', hue='label')
# df_price_elasticity_all.reset_index(drop=t)

<AxesSubplot:xlabel='Price', ylabel='E'>

../../_images/05_purchase_predictive_analytics_35_1.png

df_price_elasticity_all[df_price_elasticity_all['Is_Elastic']].groupby('label').first()

	Price	Probabilities	E	Is_Elastic
label
Aggregate	1.25	0.657690	-1.004703	True
Career-Focussed	1.42	0.568479	-1.012470	True
Fewer-Opportunities	1.26	0.778088	-1.003297	True
Standard	1.22	0.450234	-1.012326	True
Well-off	1.46	0.450117	-1.000156	True

Price Elasticity & Promotion#

def get_elasticity_promo_df(df_segments, segment=None, promo=1):
    label = "Aggregate"
    df = df_segments.copy()
    if segment: 
        label = segment
        df = df_segments[df_segments['labels'] == segment].copy()
    df['Avg_Price'] = df.filter(regex='Price*').mean(axis=1)
    df['Avg_Promo'] = df.filter(regex='Promotion*').mean(axis=1)
    model_purchase = LogisticRegression()
    model_purchase.fit(df[['Avg_Price', 'Avg_Promo']].values, df.Incidence.values)
    df_price_elasticity = pd.DataFrame()
    df_price_elasticity["Price"]= np.arange(0.5, 3.5,0.01)
    df_price_elasticity["Promo"] = promo
    df_price_elasticity["Probabilities"]= model_purchase.predict_proba(df_price_elasticity[['Price', 'Promo']].values)[:,1]
    df_price_elasticity["E"] = model_purchase.coef_[0][0]*df_price_elasticity["Price"]*(1-df_price_elasticity["Probabilities"])
    df_price_elasticity["Is_Elastic"] = df_price_elasticity["E"].abs() >1 
    df_price_elasticity['label'] = label+ f"_promo_{promo}"
    return df_price_elasticity
    # return df

df = get_elasticity_promo_df(df_segments)
df

	Price	Promo	Probabilities	E	Is_Elastic	label
0	0.50	1	0.831687	-0.125732	False	Aggregate_promo_1
1	0.51	1	0.829586	-0.129848	False	Aggregate_promo_1
2	0.52	1	0.827463	-0.134043	False	Aggregate_promo_1
3	0.53	1	0.825320	-0.138318	False	Aggregate_promo_1
4	0.54	1	0.823155	-0.142674	False	Aggregate_promo_1
...	...	...	...	...	...	...
295	3.45	1	0.056800	-4.861622	True	Aggregate_promo_1
296	3.46	1	0.056005	-4.879824	True	Aggregate_promo_1
297	3.47	1	0.055220	-4.897996	True	Aggregate_promo_1
298	3.48	1	0.054446	-4.916137	True	Aggregate_promo_1
299	3.49	1	0.053682	-4.934247	True	Aggregate_promo_1

300 rows × 6 columns

segments

[None, 'Fewer-Opportunities', 'Well-off', 'Career-Focussed', 'Standard']

promo = [0,1]

list(itertools.product(segments, promo))

[(None, 0),
 (None, 1),
 ('Fewer-Opportunities', 0),
 ('Fewer-Opportunities', 1),
 ('Well-off', 0),
 ('Well-off', 1),
 ('Career-Focussed', 0),
 ('Career-Focussed', 1),
 ('Standard', 0),
 ('Standard', 1)]

df_price_elasticity_promo_all = pd.concat([get_elasticity_promo_df(df_segments, segment=s, promo=p) for s,p in itertools.product(segments, promo)]).reset_index(drop=True)

df_price_elasticity_promo_all

	Price	Promo	Probabilities	E	Is_Elastic	label
0	0.50	0	0.738098	-0.195644	False	Aggregate_promo_0
1	0.51	0	0.735200	-0.201765	False	Aggregate_promo_0
2	0.52	0	0.732281	-0.207989	False	Aggregate_promo_0
3	0.53	0	0.729342	-0.214316	False	Aggregate_promo_0
4	0.54	0	0.726383	-0.220747	False	Aggregate_promo_0
...	...	...	...	...	...	...
2995	3.45	1	0.230267	-1.011516	True	Standard_promo_1
2996	3.46	1	0.229592	-1.015337	True	Standard_promo_1
2997	3.47	1	0.228919	-1.019161	True	Standard_promo_1
2998	3.48	1	0.228248	-1.022988	True	Standard_promo_1
2999	3.49	1	0.227577	-1.026819	True	Standard_promo_1

3000 rows × 6 columns

fig, ax = plt.subplots(figsize=(12, 12))
sns.lineplot(data=df_price_elasticity_promo_all, x='Price', y='E', hue='label',ax=ax ).set(title='Price Elasticity with & without promotion')

[Text(0.5, 1.0, 'Price Elasticity with & without promotion')]

../../_images/05_purchase_predictive_analytics_45_1.png

df_price_elasticity_promo_all[df_price_elasticity_promo_all['Is_Elastic']].groupby('label').first()

	Price	Promo	Probabilities	E	Is_Elastic
label
Aggregate_promo_0	1.27	0	0.471458	-1.002863	True
Aggregate_promo_1	1.46	1	0.540752	-1.001749	True
Career-Focussed_promo_0	1.66	0	0.397008	-1.008915	True
Career-Focussed_promo_1	1.85	1	0.463423	-1.000549	True
Fewer-Opportunities_promo_0	1.16	0	0.666050	-1.018239	True
Fewer-Opportunities_promo_1	1.33	1	0.712542	-1.004932	True
Standard_promo_0	3.02	0	0.127973	-1.003114	True
Standard_promo_1	3.42	1	0.232298	-1.000073	True
Well-off_promo_0	2.16	0	0.258809	-1.006010	True
Well-off_promo_1	2.37	1	0.325245	-1.004877	True

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Purchase Predictive Analytics

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