Telco Churn¶

This example uses the Telco Churn dataset to predict which Telco user is likely to churn; that is, customers that will likely stop using Telco. You can download the Jupyter Notebook of the study here.

Churn - customers that left within the last month
Services - services of each customer (phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies)
Customer account information - how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
Customer demographics - gender, age range, and if they have partners and dependents

We will follow the data science cycle (Data Exploration - Data Preparation - Data Modeling - Model Evaluation - Model Deployment) to solve this problem.

Initialization¶

This example uses the following version of VerticaPy:

In [1]:

import verticapy as vp
vp.__version__

Out[1]:

'0.9.0'

Connect to Vertica. This example uses an existing connection called "VerticaDSN." For details on how to create a connection, use see the connection tutorial.

In [2]:

vp.connect("VerticaDSN")

Let's create a Virtual DataFrame of the dataset. The dataset is available here.

In [3]:

churn = vp.read_csv('data/churn.csv')
display(churn)

	Abc customerID Varchar(20)	Abc gender Varchar(20)	123 SeniorCitizen Int	010 Partner Boolean	010 Dependents Boolean	123 tenure Int	010 PhoneService Boolean	Abc MultipleLines Varchar(100)	Abc InternetService Varchar(22)	Abc OnlineSecurity Varchar(38)	Abc OnlineBackup Varchar(38)	Abc DeviceProtection Varchar(38)	Abc TechSupport Varchar(38)	Abc StreamingTV Varchar(38)	Abc StreamingMovies Varchar(38)	Abc Contract Varchar(28)	010 PaperlessBilling Boolean	Abc PaymentMethod Varchar(50)	123 MonthlyCharges Numeric(8,3)	123 TotalCharges Numeric(9,3)	010 Churn Boolean
1	0002-ORFBO	Female	0	✅	✅	9	✅	No	DSL	No	Yes	No	Yes	Yes	No	One year	✅	Mailed check	65.6	593.3	❌
2	0003-MKNFE	Male	0	❌	❌	9	✅	Yes	DSL	No	No	No	No	No	Yes	Month-to-month	❌	Mailed check	59.9	542.4	❌
3	0004-TLHLJ	Male	0	❌	❌	4	✅	No	Fiber optic	No	No	Yes	No	No	No	Month-to-month	✅	Electronic check	73.9	280.85	✅
4	0011-IGKFF	Male	1	✅	❌	13	✅	No	Fiber optic	No	Yes	Yes	No	Yes	Yes	Month-to-month	✅	Electronic check	98.0	1237.85	✅
5	0013-EXCHZ	Female	1	✅	❌	3	✅	No	Fiber optic	No	No	No	Yes	Yes	No	Month-to-month	✅	Mailed check	83.9	267.4	✅
6	0013-MHZWF	Female	0	❌	✅	9	✅	No	DSL	No	No	No	Yes	Yes	Yes	Month-to-month	✅	Credit card (automatic)	69.4	571.45	❌
7	0013-SMEOE	Female	1	✅	❌	71	✅	No	Fiber optic	Yes	Yes	Yes	Yes	Yes	Yes	Two year	✅	Bank transfer (automatic)	109.7	7904.25	❌
8	0014-BMAQU	Male	0	✅	❌	63	✅	Yes	Fiber optic	Yes	No	No	Yes	No	No	Two year	✅	Credit card (automatic)	84.65	5377.8	❌
9	0015-UOCOJ	Female	1	❌	❌	7	✅	No	DSL	Yes	No	No	No	No	No	Month-to-month	✅	Electronic check	48.2	340.35	❌
10	0016-QLJIS	Female	0	✅	✅	65	✅	Yes	DSL	Yes	Yes	Yes	Yes	Yes	Yes	Two year	✅	Mailed check	90.45	5957.9	❌
11	0017-DINOC	Male	0	❌	❌	54	❌	No phone service	DSL	Yes	No	No	Yes	Yes	No	Two year	❌	Credit card (automatic)	45.2	2460.55	❌
12	0017-IUDMW	Female	0	✅	✅	72	✅	Yes	Fiber optic	Yes	Yes	Yes	Yes	Yes	Yes	Two year	✅	Credit card (automatic)	116.8	8456.75	❌
13	0018-NYROU	Female	0	✅	❌	5	✅	No	Fiber optic	No	No	No	No	No	No	Month-to-month	✅	Electronic check	68.95	351.5	❌
14	0019-EFAEP	Female	0	❌	❌	72	✅	Yes	Fiber optic	Yes	Yes	Yes	No	Yes	No	Two year	✅	Bank transfer (automatic)	101.3	7261.25	❌
15	0019-GFNTW	Female	0	❌	❌	56	❌	No phone service	DSL	Yes	Yes	Yes	Yes	No	No	Two year	❌	Bank transfer (automatic)	45.05	2560.1	❌
16	0020-INWCK	Female	0	✅	✅	71	✅	Yes	Fiber optic	No	Yes	Yes	No	No	Yes	Two year	✅	Credit card (automatic)	95.75	6849.4	❌
17	0020-JDNXP	Female	0	✅	✅	34	❌	No phone service	DSL	Yes	No	Yes	Yes	Yes	Yes	One year	❌	Mailed check	61.25	1993.2	❌
18	0021-IKXGC	Female	1	❌	❌	1	✅	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	✅	Electronic check	72.1	72.1	❌
19	0022-TCJCI	Male	1	❌	❌	45	✅	No	DSL	Yes	No	Yes	No	No	Yes	One year	❌	Credit card (automatic)	62.7	2791.5	✅
20	0023-HGHWL	Male	1	❌	❌	1	❌	No phone service	DSL	No	No	No	No	No	No	Month-to-month	✅	Electronic check	25.1	25.1	✅
21	0023-UYUPN	Female	1	✅	❌	50	✅	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	❌	Electronic check	25.2	1306.3	❌
22	0023-XUOPT	Female	0	✅	❌	13	✅	Yes	Fiber optic	No	Yes	Yes	No	Yes	No	Month-to-month	❌	Electronic check	94.1	1215.6	✅
23	0027-KWYKW	Female	0	✅	✅	23	✅	Yes	Fiber optic	No	No	No	No	Yes	No	Month-to-month	✅	Electronic check	83.75	1849.95	❌
24	0030-FNXPP	Female	0	❌	❌	3	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	❌	Mailed check	19.85	57.2	❌
25	0031-PVLZI	Female	0	✅	✅	4	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	❌	Mailed check	20.35	76.35	✅
26	0032-PGELS	Female	0	✅	✅	1	❌	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	❌	Bank transfer (automatic)	30.5	30.5	✅
27	0036-IHMOT	Female	0	✅	✅	55	✅	No	Fiber optic	No	Yes	Yes	Yes	Yes	Yes	One year	✅	Bank transfer (automatic)	103.7	5656.75	❌
28	0040-HALCW	Male	0	✅	✅	54	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	❌	Credit card (automatic)	20.4	1090.6	❌
29	0042-JVWOJ	Male	0	❌	❌	26	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	✅	Bank transfer (automatic)	19.6	471.85	❌
30	0042-RLHYP	Female	0	✅	✅	69	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	❌	Bank transfer (automatic)	19.7	1396.9	❌
31	0048-LUMLS	Male	0	✅	✅	37	✅	No	Fiber optic	No	No	No	No	Yes	Yes	One year	❌	Credit card (automatic)	91.2	3247.55	❌
32	0048-PIHNL	Female	0	✅	❌	49	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	❌	Bank transfer (automatic)	20.45	900.9	❌
33	0052-DCKON	Male	0	✅	❌	66	✅	Yes	Fiber optic	Yes	Yes	Yes	Yes	Yes	Yes	One year	❌	Bank transfer (automatic)	115.8	7942.15	❌
34	0052-YNYOT	Female	0	❌	❌	67	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	❌	Electronic check	20.55	1343.4	❌
35	0056-EPFBG	Male	0	✅	✅	20	❌	No phone service	DSL	Yes	No	Yes	Yes	No	No	Two year	✅	Credit card (automatic)	39.4	825.4	❌
36	0057-QBUQH	Female	0	❌	✅	43	✅	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	✅	Electronic check	25.1	1070.15	❌
37	0058-EVZWM	Female	0	✅	❌	55	✅	Yes	Fiber optic	Yes	No	No	No	Yes	No	Month-to-month	✅	Bank transfer (automatic)	89.8	4959.6	❌
38	0060-FUALY	Female	0	✅	❌	59	✅	Yes	Fiber optic	Yes	Yes	No	No	Yes	No	Month-to-month	✅	Electronic check	94.75	5597.65	❌
39	0064-SUDOG	Female	0	✅	✅	12	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	❌	Bank transfer (automatic)	20.3	224.5	❌
40	0064-YIJGF	Male	0	✅	✅	27	✅	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	✅	Bank transfer (automatic)	75.75	1929.0	❌
41	0067-DKWBL	Male	1	❌	❌	2	✅	No	DSL	Yes	No	No	No	No	No	Month-to-month	✅	Electronic check	49.25	91.1	✅
42	0068-FIGTF	Female	0	❌	❌	27	✅	No	DSL	No	Yes	Yes	Yes	Yes	Yes	One year	❌	Mailed check	78.2	2078.95	❌
43	0071-NDAFP	Male	0	✅	✅	25	✅	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	❌	Credit card (automatic)	25.5	630.6	❌
44	0074-HDKDG	Male	0	✅	✅	25	✅	No	DSL	Yes	Yes	Yes	No	No	No	One year	✅	Bank transfer (automatic)	61.6	1611.0	❌
45	0076-LVEPS	Male	0	❌	✅	29	❌	No phone service	DSL	Yes	Yes	Yes	Yes	No	No	Month-to-month	✅	Mailed check	45.0	1242.45	❌
46	0078-XZMHT	Male	0	✅	❌	72	✅	Yes	DSL	No	Yes	Yes	Yes	Yes	Yes	Two year	✅	Bank transfer (automatic)	85.15	6316.2	❌
47	0080-EMYVY	Female	0	❌	❌	14	✅	No	DSL	No	Yes	No	No	No	No	One year	❌	Credit card (automatic)	51.45	727.85	❌
48	0080-OROZO	Female	0	❌	❌	35	✅	No	Fiber optic	No	No	Yes	Yes	Yes	Yes	One year	✅	Electronic check	99.25	3532.0	❌
49	0082-LDZUE	Male	0	❌	❌	1	✅	No	DSL	No	No	No	No	No	No	Month-to-month	✅	Mailed check	44.3	44.3	❌
50	0082-OQIQY	Male	0	❌	❌	29	✅	No	Fiber optic	No	No	No	Yes	Yes	Yes	Month-to-month	✅	Electronic check	94.2	2607.6	❌
51	0083-PIVIK	Male	0	❌	❌	64	✅	Yes	DSL	Yes	Yes	Yes	Yes	Yes	No	One year	❌	Electronic check	81.25	5567.55	❌
52	0089-IIQKO	Female	0	✅	✅	39	✅	Yes	Fiber optic	No	No	No	Yes	Yes	Yes	Month-to-month	✅	Credit card (automatic)	99.95	3767.4	❌
53	0093-EXYQL	Female	1	❌	❌	40	✅	No	Fiber optic	No	No	No	No	Yes	Yes	Month-to-month	✅	Electronic check	91.55	3673.6	❌
54	0093-XWZFY	Male	0	❌	❌	40	✅	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	Month-to-month	✅	Credit card (automatic)	104.5	4036.85	✅
55	0094-OIFMO	Female	1	❌	❌	11	✅	No	Fiber optic	No	Yes	No	No	Yes	Yes	Month-to-month	✅	Electronic check	95.0	1120.3	✅
56	0096-BXERS	Female	0	✅	❌	6	✅	Yes	DSL	No	No	No	No	No	No	Month-to-month	❌	Electronic check	50.35	314.55	❌
57	0096-FCPUF	Male	0	❌	❌	30	✅	Yes	DSL	Yes	No	No	No	No	Yes	Month-to-month	✅	Mailed check	64.5	1888.45	❌
58	0098-BOWSO	Male	0	❌	❌	27	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	✅	Electronic check	19.4	529.8	❌
59	0100-DUVFC	Male	1	✅	❌	70	✅	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	One year	✅	Electronic check	104.8	7308.95	❌
60	0103-CSITQ	Female	0	✅	❌	57	✅	Yes	Fiber optic	No	Yes	Yes	Yes	Yes	Yes	One year	✅	Bank transfer (automatic)	109.4	6252.7	❌
61	0104-PPXDV	Male	0	✅	❌	58	✅	No	DSL	No	No	Yes	No	No	No	One year	❌	Credit card (automatic)	50.3	2878.55	❌
62	0106-GHRQR	Male	0	❌	❌	16	✅	Yes	DSL	No	Yes	Yes	No	No	Yes	Month-to-month	✅	Bank transfer (automatic)	71.4	1212.1	❌
63	0106-UGRDO	Female	0	✅	❌	69	✅	Yes	Fiber optic	Yes	Yes	Yes	Yes	Yes	Yes	Two year	✅	Electronic check	116.0	8182.85	❌
64	0107-WESLM	Male	0	❌	❌	1	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	✅	Electronic check	19.85	19.85	✅
65	0107-YHINA	Male	0	❌	✅	1	✅	Yes	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	✅	Electronic check	99.75	99.75	✅
66	0111-KLBQG	Male	1	✅	✅	32	✅	No	Fiber optic	No	Yes	No	No	Yes	Yes	Month-to-month	✅	Mailed check	93.95	2861.45	❌
67	0112-QAWRZ	Male	0	✅	✅	16	✅	Yes	Fiber optic	No	No	Yes	No	Yes	No	Month-to-month	✅	Bank transfer (automatic)	90.8	1442.2	❌
68	0112-QWPNC	Male	0	✅	❌	49	✅	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	One year	❌	Electronic check	84.35	4059.35	✅
69	0114-IGABW	Female	0	✅	❌	71	❌	No phone service	DSL	No	Yes	Yes	Yes	Yes	Yes	Two year	❌	Bank transfer (automatic)	58.25	4145.9	❌
70	0114-PEGZZ	Female	0	❌	❌	33	✅	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	Month-to-month	❌	Electronic check	107.55	3645.5	❌
71	0114-RSRRW	Female	0	✅	❌	10	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	✅	Bank transfer (automatic)	19.95	187.75	❌
72	0115-TFERT	Male	0	✅	❌	21	✅	Yes	Fiber optic	No	Yes	Yes	Yes	Yes	Yes	Month-to-month	✅	Electronic check	111.2	2317.1	✅
73	0117-LFRMW	Male	0	✅	✅	37	❌	No phone service	DSL	Yes	Yes	Yes	No	No	No	Month-to-month	❌	Bank transfer (automatic)	40.2	1448.8	✅
74	0118-JPNOY	Female	1	❌	❌	26	✅	No	Fiber optic	Yes	No	No	No	Yes	No	Month-to-month	✅	Credit card (automatic)	85.8	2193.65	❌
75	0121-SNYRK	Male	0	❌	❌	50	❌	No phone service	DSL	Yes	No	No	Yes	No	No	One year	✅	Mailed check	35.4	1748.9	❌
76	0122-OAHPZ	Female	0	❌	❌	7	✅	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	✅	Electronic check	73.85	511.25	✅
77	0123-CRBRT	Female	0	✅	✅	61	✅	Yes	DSL	Yes	Yes	Yes	Yes	Yes	Yes	Two year	❌	Mailed check	88.1	5526.75	❌
78	0125-LZQXK	Male	0	❌	❌	15	✅	No	Fiber optic	No	No	Yes	Yes	Yes	Yes	Month-to-month	✅	Electronic check	101.35	1553.95	✅
79	0128-MKWSG	Female	0	❌	✅	26	❌	No phone service	DSL	Yes	No	No	Yes	No	Yes	Month-to-month	❌	Mailed check	45.8	1147.0	❌
80	0129-KPTWJ	Male	0	✅	❌	72	✅	No	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	✅	Electronic check	94.65	6747.35	❌
81	0129-QMPDR	Male	0	✅	✅	44	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	✅	Bank transfer (automatic)	20.5	865.05	❌
82	0130-SXOUN	Male	0	❌	❌	66	✅	Yes	Fiber optic	No	Yes	No	No	No	Yes	Month-to-month	❌	Credit card (automatic)	89.4	5976.9	❌
83	0133-BMFZO	Female	0	❌	❌	2	✅	No	Fiber optic	Yes	Yes	No	Yes	No	No	Month-to-month	✅	Electronic check	86.25	181.65	✅
84	0134-XWXCE	Female	1	❌	❌	44	✅	No	DSL	No	No	Yes	Yes	Yes	Yes	One year	❌	Bank transfer (automatic)	74.85	3268.05	❌
85	0135-NMXAP	Female	0	❌	❌	12	✅	Yes	Fiber optic	No	Yes	No	No	Yes	No	Month-to-month	✅	Bank transfer (automatic)	89.75	1052.4	✅
86	0136-IFMYD	Male	1	✅	❌	69	✅	Yes	Fiber optic	No	Yes	Yes	Yes	Yes	Yes	Two year	✅	Electronic check	109.95	7634.25	❌
87	0137-OCGAB	Female	0	❌	❌	1	✅	Yes	Fiber optic	No	Yes	No	No	No	No	Month-to-month	✅	Mailed check	80.2	80.2	✅
88	0137-UDEUO	Female	0	❌	❌	3	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	❌	Mailed check	19.85	63.75	✅
89	0139-IVFJG	Female	0	✅	❌	2	✅	No	Fiber optic	Yes	Yes	No	No	Yes	No	Month-to-month	❌	Electronic check	90.35	190.5	❌
90	0141-YEAYS	Female	1	❌	❌	27	✅	Yes	Fiber optic	No	Yes	Yes	No	No	No	Month-to-month	✅	Bank transfer (automatic)	86.45	2401.05	❌
91	0142-GVYSN	Male	0	❌	❌	26	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	✅	Electronic check	20.3	511.25	❌
92	0147-ESWWR	Female	1	✅	❌	39	✅	Yes	Fiber optic	No	No	No	Yes	Yes	Yes	Month-to-month	✅	Electronic check	101.25	3949.15	❌
93	0148-DCDOS	Male	0	❌	❌	25	✅	Yes	Fiber optic	No	No	No	No	Yes	Yes	Month-to-month	✅	Bank transfer (automatic)	94.7	2362.1	✅
94	0151-ONTOV	Female	0	❌	❌	1	✅	No	Fiber optic	No	No	No	No	No	No	Month-to-month	❌	Mailed check	70.9	70.9	✅
95	0156-FVPTA	Male	0	✅	❌	22	✅	No	DSL	Yes	No	No	Yes	No	No	Month-to-month	✅	Electronic check	54.2	1152.7	✅
96	0164-APGRB	Female	0	❌	❌	72	✅	Yes	Fiber optic	Yes	Yes	Yes	Yes	Yes	Yes	Two year	✅	Electronic check	114.9	8496.7	❌
97	0164-XAIRP	Female	0	❌	❌	24	✅	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	✅	Bank transfer (automatic)	19.55	470.2	❌
98	0168-XZKBB	Female	0	✅	❌	19	✅	Yes	Fiber optic	No	No	No	No	Yes	No	Month-to-month	✅	Electronic check	86.85	1564.4	❌
99	0174-QRVVY	Male	0	✅	✅	71	✅	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	❌	Credit card (automatic)	25.35	1847.55	❌
100	0177-PXBAT	Male	1	✅	❌	33	✅	Yes	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Month-to-month	✅	Bank transfer (automatic)	109.9	3694.7	❌

Rows: 1-100 | Columns: 21

Data Exploration and Preparation¶

Let's examine our data.

In [6]:

churn.describe(method = "categorical", unique = True)

Out[6]:

	dtype	count	top	top_percent	unique
"customerID"	varchar(20)	7043	0002-ORFBO	0.014	7043.0
"gender"	varchar(20)	7043	Male	50.476	2.0
"SeniorCitizen"	int	7043	0	83.785	2.0
"Partner"	boolean	7043	0	51.697	2.0
"Dependents"	boolean	7043	0	70.041	2.0
"tenure"	int	7043	1	8.704	73.0
"PhoneService"	boolean	7043	1	90.317	2.0
"MultipleLines"	varchar(100)	7043	No	48.133	3.0
"InternetService"	varchar(22)	7043	Fiber optic	43.959	3.0
"OnlineSecurity"	varchar(38)	7043	No	49.666	3.0
"OnlineBackup"	varchar(38)	7043	No	43.845	3.0
"DeviceProtection"	varchar(38)	7043	No	43.944	3.0
"TechSupport"	varchar(38)	7043	No	49.311	3.0
"StreamingTV"	varchar(38)	7043	No	39.898	3.0
"StreamingMovies"	varchar(38)	7043	No	39.543	3.0
"Contract"	varchar(28)	7043	Month-to-month	55.019	3.0
"PaperlessBilling"	boolean	7043	1	59.222	2.0
"PaymentMethod"	varchar(50)	7043	Electronic check	33.579	4.0
"MonthlyCharges"	numeric(8,3)	7043	20.05	0.866	1585.0
"TotalCharges"	numeric(9,3)	7032	[null]	0.156	6530.0
"Churn"	boolean	7043	0	73.463	2.0

Rows: 1-21 | Columns: 6

Several variables are categorical, and since they all have low cardinalities, we can compute their dummies. We can also convert all booleans to numeric.

In [7]:

for column in ["DeviceProtection", 
               "MultipleLines",
               "PaperlessBilling",
               "Churn",
               "TechSupport",
               "Partner",
               "StreamingTV",
               "OnlineBackup",
               "Dependents",
               "OnlineSecurity",
               "PhoneService",
               "StreamingMovies"]:
    churn[column].decode("Yes", 1, 0)
churn.one_hot_encode().drop(["customerID", 
                             "gender", 
                             "Contract", 
                             "PaymentMethod", 
                             "InternetService"])

Out[7]:

	123 SeniorCitizen Int	123 Partner Integer	123 Dependents Integer	123 tenure Int	123 PhoneService Integer	123 MultipleLines Integer	123 OnlineSecurity Integer	123 OnlineBackup Integer	123 DeviceProtection Integer	123 TechSupport Integer	123 StreamingTV Integer	123 StreamingMovies Integer	123 PaperlessBilling Integer	123 MonthlyCharges Numeric(8,3)	123 TotalCharges Numeric(9,3)	123 Churn Integer	123 gender_Female Bool	123 InternetService_DSL Bool	123 InternetService_Fiber_optic Bool	123 Contract_Month-to-month Bool	123 Contract_One_year Bool	123 Bool	123 Bool	123 PaymentMethod_Electronic_check Bool
1	0	1	1	9	1	0	0	1	0	1	1	0	1	65.6	593.3	0	1	1	0	0	1			0
2	0	0	0	9	1	1	0	0	0	0	0	1	0	59.9	542.4	0	0	1	0	1	0			0
3	0	0	0	4	1	0	0	0	1	0	0	0	1	73.9	280.85	1	0	0	1	1	0			1
4	1	1	0	13	1	0	0	1	1	0	1	1	1	98.0	1237.85	1	0	0	1	1	0			1
5	1	1	0	3	1	0	0	0	0	1	1	0	1	83.9	267.4	1	1	0	1	1	0			0
6	0	0	1	9	1	0	0	0	0	1	1	1	1	69.4	571.45	0	1	1	0	1	0			0
7	1	1	0	71	1	0	1	1	1	1	1	1	1	109.7	7904.25	0	1	0	1	0	0			0
8	0	1	0	63	1	1	1	0	0	1	0	0	1	84.65	5377.8	0	0	0	1	0	0			0
9	1	0	0	7	1	0	1	0	0	0	0	0	1	48.2	340.35	0	1	1	0	1	0			1
10	0	1	1	65	1	1	1	1	1	1	1	1	1	90.45	5957.9	0	1	1	0	0	0			0
11	0	0	0	54	0	0	1	0	0	1	1	0	0	45.2	2460.55	0	0	1	0	0	0			0
12	0	1	1	72	1	1	1	1	1	1	1	1	1	116.8	8456.75	0	1	0	1	0	0			0
13	0	1	0	5	1	0	0	0	0	0	0	0	1	68.95	351.5	0	1	0	1	1	0			1
14	0	0	0	72	1	1	1	1	1	0	1	0	1	101.3	7261.25	0	1	0	1	0	0			0
15	0	0	0	56	0	0	1	1	1	1	0	0	0	45.05	2560.1	0	1	1	0	0	0			0
16	0	1	1	71	1	1	0	1	1	0	0	1	1	95.75	6849.4	0	1	0	1	0	0			0
17	0	1	1	34	0	0	1	0	1	1	1	1	0	61.25	1993.2	0	1	1	0	0	1			0
18	1	0	0	1	1	1	0	0	0	0	0	0	1	72.1	72.1	0	1	0	1	1	0			1
19	1	0	0	45	1	0	1	0	1	0	0	1	0	62.7	2791.5	1	0	1	0	0	1			0
20	1	0	0	1	0	0	0	0	0	0	0	0	1	25.1	25.1	1	0	1	0	1	0			1
21	1	1	0	50	1	1	0	0	0	0	0	0	0	25.2	1306.3	0	1	0	0	0	1			1
22	0	1	0	13	1	1	0	1	1	0	1	0	0	94.1	1215.6	1	1	0	1	1	0			1
23	0	1	1	23	1	1	0	0	0	0	1	0	1	83.75	1849.95	0	1	0	1	1	0			1
24	0	0	0	3	1	0	0	0	0	0	0	0	0	19.85	57.2	0	1	0	0	1	0			0
25	0	1	1	4	1	0	0	0	0	0	0	0	0	20.35	76.35	1	1	0	0	1	0			0
26	0	1	1	1	0	0	1	0	0	0	0	0	0	30.5	30.5	1	1	1	0	1	0			0
27	0	1	1	55	1	0	0	1	1	1	1	1	1	103.7	5656.75	0	1	0	1	0	1			0
28	0	1	1	54	1	0	0	0	0	0	0	0	0	20.4	1090.6	0	0	0	0	0	0			0
29	0	0	0	26	1	0	0	0	0	0	0	0	1	19.6	471.85	0	0	0	0	0	1			0
30	0	1	1	69	1	0	0	0	0	0	0	0	0	19.7	1396.9	0	1	0	0	0	0			0
31	0	1	1	37	1	0	0	0	0	0	1	1	0	91.2	3247.55	0	0	0	1	0	1			0
32	0	1	0	49	1	0	0	0	0	0	0	0	0	20.45	900.9	0	1	0	0	0	1			0
33	0	1	0	66	1	1	1	1	1	1	1	1	0	115.8	7942.15	0	0	0	1	0	1			0
34	0	0	0	67	1	0	0	0	0	0	0	0	0	20.55	1343.4	0	1	0	0	0	1			1
35	0	1	1	20	0	0	1	0	1	1	0	0	1	39.4	825.4	0	0	1	0	0	0			0
36	0	0	1	43	1	1	0	0	0	0	0	0	1	25.1	1070.15	0	1	0	0	0	0			1
37	0	1	0	55	1	1	1	0	0	0	1	0	1	89.8	4959.6	0	1	0	1	1	0			0
38	0	1	0	59	1	1	1	1	0	0	1	0	1	94.75	5597.65	0	1	0	1	1	0			1
39	0	1	1	12	1	0	0	0	0	0	0	0	0	20.3	224.5	0	1	0	0	0	0			0
40	0	1	1	27	1	1	0	0	0	0	0	0	1	75.75	1929.0	0	0	0	1	1	0			0
41	1	0	0	2	1	0	1	0	0	0	0	0	1	49.25	91.1	1	0	1	0	1	0			1
42	0	0	0	27	1	0	0	1	1	1	1	1	0	78.2	2078.95	0	1	1	0	0	1			0
43	0	1	1	25	1	1	0	0	0	0	0	0	0	25.5	630.6	0	0	0	0	0	0			0
44	0	1	1	25	1	0	1	1	1	0	0	0	1	61.6	1611.0	0	0	1	0	0	1			0
45	0	0	1	29	0	0	1	1	1	1	0	0	1	45.0	1242.45	0	0	1	0	1	0			0
46	0	1	0	72	1	1	0	1	1	1	1	1	1	85.15	6316.2	0	0	1	0	0	0			0
47	0	0	0	14	1	0	0	1	0	0	0	0	0	51.45	727.85	0	1	1	0	0	1			0
48	0	0	0	35	1	0	0	0	1	1	1	1	1	99.25	3532.0	0	1	0	1	0	1			1
49	0	0	0	1	1	0	0	0	0	0	0	0	1	44.3	44.3	0	0	1	0	1	0			0
50	0	0	0	29	1	0	0	0	0	1	1	1	1	94.2	2607.6	0	0	0	1	1	0			1
51	0	0	0	64	1	1	1	1	1	1	1	0	0	81.25	5567.55	0	0	1	0	0	1			1
52	0	1	1	39	1	1	0	0	0	1	1	1	1	99.95	3767.4	0	1	0	1	1	0			0
53	1	0	0	40	1	0	0	0	0	0	1	1	1	91.55	3673.6	0	1	0	1	1	0			1
54	0	0	0	40	1	1	0	1	1	0	1	1	1	104.5	4036.85	1	0	0	1	1	0			0
55	1	0	0	11	1	0	0	1	0	0	1	1	1	95.0	1120.3	1	1	0	1	1	0			1
56	0	1	0	6	1	1	0	0	0	0	0	0	0	50.35	314.55	0	1	1	0	1	0			1
57	0	0	0	30	1	1	1	0	0	0	0	1	1	64.5	1888.45	0	0	1	0	1	0			0
58	0	0	0	27	1	0	0	0	0	0	0	0	1	19.4	529.8	0	0	0	0	1	0			1
59	1	1	0	70	1	1	0	1	1	0	1	1	1	104.8	7308.95	0	0	0	1	0	1			1
60	0	1	0	57	1	1	0	1	1	1	1	1	1	109.4	6252.7	0	1	0	1	0	1			0
61	0	1	0	58	1	0	0	0	1	0	0	0	0	50.3	2878.55	0	0	1	0	0	1			0
62	0	0	0	16	1	1	0	1	1	0	0	1	1	71.4	1212.1	0	0	1	0	1	0			0
63	0	1	0	69	1	1	1	1	1	1	1	1	1	116.0	8182.85	0	1	0	1	0	0			1
64	0	0	0	1	1	0	0	0	0	0	0	0	1	19.85	19.85	1	0	0	0	1	0			1
65	0	0	1	1	1	1	0	0	1	0	1	1	1	99.75	99.75	1	0	0	1	1	0			1
66	1	1	1	32	1	0	0	1	0	0	1	1	1	93.95	2861.45	0	0	0	1	1	0			0
67	0	1	1	16	1	1	0	0	1	0	1	0	1	90.8	1442.2	0	0	0	1	1	0			0
68	0	1	0	49	1	1	1	0	1	1	1	1	0	84.35	4059.35	1	0	1	0	0	1			1
69	0	1	0	71	0	0	0	1	1	1	1	1	0	58.25	4145.9	0	1	1	0	0	0			0
70	0	0	0	33	1	1	0	1	1	0	1	1	0	107.55	3645.5	0	1	0	1	1	0			1
71	0	1	0	10	1	0	0	0	0	0	0	0	1	19.95	187.75	0	1	0	0	1	0			0
72	0	1	0	21	1	1	0	1	1	1	1	1	1	111.2	2317.1	1	0	0	1	1	0			1
73	0	1	1	37	0	0	1	1	1	0	0	0	0	40.2	1448.8	1	0	1	0	1	0			0
74	1	0	0	26	1	0	1	0	0	0	1	0	1	85.8	2193.65	0	1	0	1	1	0			0
75	0	0	0	50	0	0	1	0	0	1	0	0	1	35.4	1748.9	0	0	1	0	0	1			0
76	0	0	0	7	1	1	0	0	0	0	0	0	1	73.85	511.25	1	1	0	1	1	0			1
77	0	1	1	61	1	1	1	1	1	1	1	1	0	88.1	5526.75	0	1	1	0	0	0			0
78	0	0	0	15	1	0	0	0	1	1	1	1	1	101.35	1553.95	1	0	0	1	1	0			1
79	0	0	1	26	0	0	1	0	0	1	0	1	0	45.8	1147.0	0	1	1	0	1	0			0
80	0	1	0	72	1	0	0	0	1	0	1	1	1	94.65	6747.35	0	0	0	1	1	0			1
81	0	1	1	44	1	0	0	0	0	0	0	0	1	20.5	865.05	0	0	0	0	0	1			0
82	0	0	0	66	1	1	0	1	0	0	0	1	0	89.4	5976.9	0	0	0	1	1	0			0
83	0	0	0	2	1	0	1	1	0	1	0	0	1	86.25	181.65	1	1	0	1	1	0			1
84	1	0	0	44	1	0	0	0	1	1	1	1	0	74.85	3268.05	0	1	1	0	0	1			0
85	0	0	0	12	1	1	0	1	0	0	1	0	1	89.75	1052.4	1	1	0	1	1	0			0
86	1	1	0	69	1	1	0	1	1	1	1	1	1	109.95	7634.25	0	0	0	1	0	0			1
87	0	0	0	1	1	1	0	1	0	0	0	0	1	80.2	80.2	1	1	0	1	1	0			0
88	0	0	0	3	1	0	0	0	0	0	0	0	0	19.85	63.75	1	1	0	0	1	0			0
89	0	1	0	2	1	0	1	1	0	0	1	0	0	90.35	190.5	0	1	0	1	1	0			1
90	1	0	0	27	1	1	0	1	1	0	0	0	1	86.45	2401.05	0	1	0	1	1	0			0
91	0	0	0	26	1	0	0	0	0	0	0	0	1	20.3	511.25	0	0	0	0	1	0			1
92	1	1	0	39	1	1	0	0	0	1	1	1	1	101.25	3949.15	0	1	0	1	1	0			1
93	0	0	0	25	1	1	0	0	0	0	1	1	1	94.7	2362.1	1	0	0	1	1	0			0
94	0	0	0	1	1	0	0	0	0	0	0	0	0	70.9	70.9	1	1	0	1	1	0			0
95	0	1	0	22	1	0	1	0	0	1	0	0	1	54.2	1152.7	1	0	1	0	1	0			1
96	0	0	0	72	1	1	1	1	1	1	1	1	1	114.9	8496.7	0	1	0	1	0	0			1
97	0	0	0	24	1	0	0	0	0	0	0	0	1	19.55	470.2	0	1	0	0	0	0			0
98	0	1	0	19	1	1	0	0	0	0	1	0	1	86.85	1564.4	0	1	0	1	1	0			1
99	0	1	1	71	1	1	0	0	0	0	0	0	0	25.35	1847.55	0	0	0	0	0	0			0
100	1	1	0	33	1	1	1	0	1	1	1	1	1	109.9	3694.7	0	0	0	1	1	0			0

Rows: 1-100 of 7043 | Columns: 24

Let's compute the correlations between the different variables and the response column.

In [8]:

%matplotlib inline
churn.corr(focus = "Churn")

Out[8]:

	"Churn"
	1.0
	0.405
	-0.352
	0.308
	0.302
	-0.199
	0.193
	0.192
	-0.178
	-0.171
	-0.165
	-0.164
	0.151
	-0.15
	-0.134
	-0.124
	-0.118
	-0.082
	-0.066
	0.063
	0.061
	0.04
	0.012
	0.009

Rows: 1-24 | Columns: 2

Many features have a strong correlation with the 'Churn' variable. For example, the customers that have a 'Month to Month' contract are more likely to churn. Having this type of contract gives customers a lot of flexibility and allows them to leave at any time. New customers are also likely to churn.

In [9]:

# No lock-in = Churn
churn.hist(["Contract_Month-to-month", "tenure"], method = "avg", of = "Churn")

Out[9]:

<AxesSubplot:xlabel='"Contract_Month-to-month"', ylabel='avg("Churn")'>

The following scatter plot shows that providing better tariff plans can prevent churning. Indeed, customers having high total charges are more likely to churn even if they've been with the company for a long time.

In [10]:

churn.scatter(["TotalCharges", "tenure"], catcol = "Churn")

Out[10]:

<AxesSubplot:xlabel='"TotalCharges"', ylabel='"tenure"'>

Let's move on to machine learning.

Machine Learning¶

Logistic regression is a very powerful algorithm and we can use it to detect churns. Let's split our vDataFrame into training and testing set to evaluate our model.

In [11]:

train, test = churn.train_test_split(test_size = 0.2, 
                                     random_state = 0)

Let's train and evaluate our model.

In [12]:

from verticapy.learn.linear_model import LogisticRegression
model = LogisticRegression("churn_model", 
                           penalty = 'L2', 
                           tol = 1e-6, 
                           max_iter = 1000, 
                           solver = "BFGS")
model.fit(train, 
          churn.get_columns(exclude_columns = ["churn"]), 
          'churn',
          test)
model.classification_report()

Out[12]:

	value
auc	0.8554368006422792
prc_auc	0.6822415600545241
accuracy	0.8031161473087819
log_loss	0.1717601946823
precision	0.6761006289308176
recall	0.5512820512820513
f1_score	0.6073446327683616
mcc	0.48220268504473107
informedness	0.45049927241708065
markedness	0.5161371920021156
csi	0.43610547667342797
cutoff	0.5

Rows: 1-12 | Columns: 2

The model is excellent! Let's run some machine learning on the entire dataset and compute the importance of each feature.

In [13]:

model.fit(churn, 
          churn.get_columns(exclude_columns = ["churn"]), 
          'churn')
model.features_importance()

Out[13]:

	importance	sign
	28.58	-1
	18.43	1
	8.66	1
	7.0	1
	5.79	1
	5.26	-1
	4.31	1
	3.58	1
	2.93	-1
	2.81	-1
	2.4	1
	2.29	1
	1.45	1
	1.4	-1
	1.34	1
	0.97	-1
	0.73	1
	0.68	1
	0.63	-1
	0.39	1
	0.21	-1
	0.17	1
	0.0	-1

Rows: 1-23 | Columns: 3

Based on our model, most churning customers are at least one of the following:

Paying higher bills
New Telco customers
Have a monthly contract

Notice that customers have a 'Fiber Optic' option are also likely to churn. Let's check if this relationship is causal by computing some aggregations.

In [15]:

import verticapy.stats as st
# Is Fiber optic a Bad Option? - VerticaPy
churn.groupby(["InternetService_Fiber_optic"], 
              [st.avg(churn["tenure"])._as("tenure"),
               st.avg(churn["totalcharges"])._as("totalcharges"),
               st.avg(churn["contract_month-to-month"])._as("contract_month_to_month"),
               st.avg(churn["monthlycharges"])._as("monthlycharges")])

Out[15]:

	123 InternetService_Fiber_optic Integer	123 tenure Float	123 totalcharges Float	123 contract_month_to_month Float	123 monthlycharges Float
1	0	31.9422346085635	1558.06548526423	0.442614644033443	43.7882442361287
2	1	32.9179586563307	3205.30457041344	0.68733850129199	91.5001291989664

Rows: 1-2 | Columns: 5

It seems like the 'Fiber Optic' option in and of itself doesn't lead to churning, but customers that have this option tend to churn because their contract puts them into one of the three categories we listed before: they're paying more.

To retain these customers, we'll need to make some changes to what types of contracts we offer.

We'll use a lift chart to help us identify which of our customers are likely to churn.

In [16]:

model.lift_chart()

Out[16]:

	decision_boundary	positive_prediction_ratio	lift
1	0.0	0.0	3.76243980738363
2	0.001	0.0	3.76243980738363
3	0.002	0.0	3.76243980738363
4	0.003	0.0	3.76243980738363
5	0.004	0.0	3.76243980738363
6	0.005	0.0	3.76243980738363
7	0.006	0.0	3.76243980738363
8	0.007	0.0	3.76243980738363
9	0.008	0.0	3.76243980738363
10	0.009	0.0	3.76243980738363
11	0.01	0.0	3.76243980738363
12	0.011	0.0	3.76243980738363
13	0.012	0.0	3.76243980738363
14	0.013	0.0	3.76243980738363
15	0.014	0.0	3.76243980738363
16	0.015	0.0	3.76243980738363
17	0.016	0.0	3.76243980738363
18	0.017	0.0	3.76243980738363
19	0.018	0.0	3.76243980738363
20	0.019	0.0	3.76243980738363
21	0.02	0.0	3.76243980738363
22	0.021	0.0	3.76243980738363
23	0.022	0.0	3.76243980738363
24	0.023	0.0	3.76243980738363
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999	0.998	1.0	1.01136200201352
1000	0.999	1.0	1.0
1001	1.0	1.0	1.0

Rows: 1-1001 | Columns: 3

By targeting less than 30% of the entire distribution, our predictions will be more than three times more accurate than the other 70%.

Conclusion¶

We've solved our problem in a Pandas-like way, all without ever loading data into memory!

VerticaPy

Python API for Vertica Data Science at Scale

About the Author

Badr Ouali
Head of Data Science

Badr Ouali works as a Lead Data Scientist for Vertica worldwide. He can embrace data projects end to end through a clear understanding of the “big picture” as well as attention to details, resulting in achieving great business outcomes – a distinctive differentiator in his role. Badr enjoys sharing knowledge and insights related to data analytics with colleagues & peers and has a sweet spot for Python. He loves helping customers finding the best value from their data and empower them to solve their use-cases.