Chance of paying after 20 videos

Train the same model (all rows, videos_watched → paid_flag).

Store the probability of paying for a user who watched 20 videos, rounded to 3 decimals, in result.

Already run for you

import io
import pandas as pd

trials = pd.read_csv(io.StringIO("""user_id,videos_watched,quiz_score,days_active,city_tier,paid
1,4,59,3,3,No
2,11,62,10,2,No
3,7,45,4,3,No
4,5,68,5,2,No
5,10,92,6,3,No
6,3,74,5,2,No
7,6,75,2,2,No
8,19,61,10,2,Yes
9,19,61,14,2,Yes
10,9,44,6,1,No
11,10,58,8,1,No
12,3,26,2,1,No
13,12,64,8,3,No
14,9,56,9,3,No
15,17,36,9,1,No
16,13,57,10,2,No
17,8,56,9,3,No
18,5,54,4,3,No
19,18,46,13,2,No
20,13,34,7,1,Yes
21,2,50,2,2,Yes
22,9,15,8,1,No
23,3,64,1,1,No
24,13,48,12,1,No
25,10,52,14,1,No
26,0,76,1,1,No
27,13,42,12,1,Yes
28,5,62,4,1,No
29,20,46,14,2,Yes
30,1,60,1,2,No
31,14,66,11,1,No
32,6,41,11,3,No
33,13,56,12,1,Yes
34,1,52,1,1,No
35,6,85,9,3,No
36,6,60,5,1,No
37,14,33,10,2,No
38,1,43,4,3,No
39,15,48,13,1,No
40,7,38,6,3,No
41,14,43,13,2,No
42,4,37,4,2,No
43,7,64,11,1,No
44,20,47,11,2,No
45,2,41,1,1,No
46,12,83,12,2,No
47,3,56,2,2,No
48,15,41,11,3,No
49,9,39,9,2,No
50,19,50,13,1,Yes
51,9,53,14,1,Yes
52,7,95,13,1,Yes
53,16,29,8,2,No
54,8,71,9,1,No
55,13,38,11,2,No
56,2,67,1,1,No
57,6,71,6,2,No
58,6,40,4,3,No
59,2,65,2,1,No
60,14,58,11,1,No
61,2,47,3,2,No
62,2,37,1,1,No
63,5,84,4,2,No
64,2,67,2,1,No
65,0,27,2,1,No
66,9,49,8,2,No
67,6,73,13,1,No
68,8,55,9,1,No
69,4,94,3,2,No
70,6,62,6,1,No
71,5,73,1,1,No
72,14,37,12,3,No
73,9,78,7,2,No
74,20,53,13,2,Yes
75,12,67,7,1,Yes
76,11,57,9,2,No
77,10,64,6,1,Yes
78,4,39,8,3,No
79,5,64,4,1,No
80,2,56,3,2,No
81,3,68,5,1,No
82,15,49,8,3,No
83,10,58,6,2,No
84,22,35,14,2,No
85,6,33,8,3,No
86,3,60,2,2,No
87,2,100,10,2,No
88,12,54,10,1,No
89,11,54,12,2,Yes
90,3,46,5,3,No
91,16,96,14,1,Yes
92,5,68,9,3,No
93,16,49,10,1,Yes
94,14,26,12,3,No
95,17,79,11,2,No
96,6,52,6,2,No
97,2,38,1,3,No
98,11,42,10,1,No
99,6,57,8,1,Yes
100,7,58,9,2,No
101,19,63,14,2,Yes
102,11,46,9,2,Yes
103,1,48,5,1,No
104,4,26,2,1,No
105,0,38,1,3,No
106,12,65,13,1,No
107,6,63,5,2,No
108,8,23,12,3,No
109,10,43,10,2,No
110,12,45,7,1,No
111,16,88,11,2,Yes
112,10,63,14,1,No
113,17,72,14,3,Yes
114,11,57,7,3,No
115,12,61,10,1,No
116,22,45,14,3,Yes
117,6,82,9,1,No
118,21,58,12,1,Yes
119,5,80,9,3,No
120,1,59,6,1,No
121,9,48,9,2,No
122,2,44,9,1,No
123,10,57,10,2,No
124,2,78,2,3,No
125,3,88,2,2,No
126,13,93,14,1,Yes
127,18,42,12,2,Yes
128,7,80,7,3,No
129,14,66,13,2,No
130,5,36,7,2,No
131,10,81,6,1,No
132,8,37,9,2,No
133,10,70,7,1,No
134,11,53,7,2,No
135,8,72,10,3,No
136,13,79,8,2,No
137,14,42,12,3,No
138,16,55,9,1,No
139,9,38,13,1,No
140,14,84,9,1,Yes
141,6,48,4,3,No
142,9,11,7,3,No
143,7,62,10,2,No
144,4,74,3,2,No
145,3,47,11,3,No
146,5,36,3,2,No
147,8,62,10,1,No
148,15,31,10,2,No
149,8,46,5,2,Yes
150,4,91,3,2,No
151,12,84,8,1,Yes
152,1,56,5,1,No
153,16,26,8,3,No
154,7,60,11,3,No
155,8,38,8,1,No
156,19,57,14,1,No
157,4,68,5,2,No
158,9,53,14,1,No
159,2,54,5,2,No
160,8,88,6,1,No
161,8,76,14,2,Yes
162,16,61,13,2,No
163,2,40,3,3,No
164,4,70,1,1,No
165,23,48,14,1,Yes
166,7,48,5,2,No
167,5,54,1,1,No
168,10,60,13,2,No
169,14,56,11,1,No
170,7,59,6,3,No
171,0,80,1,1,No
172,3,40,1,1,No
173,5,50,2,2,No
174,20,63,13,1,Yes
175,5,26,6,2,No
176,7,47,14,1,Yes
177,8,74,10,1,No
178,2,14,11,1,No
179,20,90,11,1,Yes
180,6,67,7,1,No
181,13,66,10,2,Yes
182,5,39,6,2,No
183,25,67,14,1,Yes
184,13,44,13,1,No
185,8,47,6,1,Yes
186,3,51,1,2,No
187,8,72,6,3,No
188,12,77,6,1,No
189,6,52,8,1,No
190,10,32,12,1,No
191,14,47,8,1,No
192,2,44,1,1,No
193,6,71,7,1,No
194,8,46,11,2,No
195,20,70,12,1,Yes
196,14,13,11,2,Yes
197,3,36,12,2,No
198,3,72,2,1,No
199,15,51,10,2,No
200,1,66,4,1,No"""))

Store your final answer in a variable named result.

Hints(0/2)

Each hint gives a little more help. Try on your own first.

Solution

Unlocks after you solve it or after 3 submissions

Notebook · scikit-learnRun and Submit check all cells from the top

Shift + Enter30 seconds limit per cell

[ ]

Run your code to see the output here.

Already run for you

import io import pandas as pd trials = pd.read_csv(io.StringIO("""user_id,videos_watched,quiz_score,days_active,city_tier,paid 1,4,59,3,3,No 2,11,62,10,2,No 3,7,45,4,3,No 4,5,68,5,2,No 5,10,92,6,3,No 6,3,74,5,2,No 7,6,75,2,2,No 8,19,61,10,2,Yes 9,19,61,14,2,Yes 10,9,44,6,1,No 11,10,58,8,1,No 12,3,26,2,1,No 13,12,64,8,3,No 14,9,56,9,3,No 15,17,36,9,1,No 16,13,57,10,2,No 17,8,56,9,3,No 18,5,54,4,3,No 19,18,46,13,2,No 20,13,34,7,1,Yes 21,2,50,2,2,Yes 22,9,15,8,1,No 23,3,64,1,1,No 24,13,48,12,1,No 25,10,52,14,1,No 26,0,76,1,1,No 27,13,42,12,1,Yes 28,5,62,4,1,No 29,20,46,14,2,Yes 30,1,60,1,2,No 31,14,66,11,1,No 32,6,41,11,3,No 33,13,56,12,1,Yes 34,1,52,1,1,No 35,6,85,9,3,No 36,6,60,5,1,No 37,14,33,10,2,No 38,1,43,4,3,No 39,15,48,13,1,No 40,7,38,6,3,No 41,14,43,13,2,No 42,4,37,4,2,No 43,7,64,11,1,No 44,20,47,11,2,No 45,2,41,1,1,No 46,12,83,12,2,No 47,3,56,2,2,No 48,15,41,11,3,No 49,9,39,9,2,No 50,19,50,13,1,Yes 51,9,53,14,1,Yes 52,7,95,13,1,Yes 53,16,29,8,2,No 54,8,71,9,1,No 55,13,38,11,2,No 56,2,67,1,1,No 57,6,71,6,2,No 58,6,40,4,3,No 59,2,65,2,1,No 60,14,58,11,1,No 61,2,47,3,2,No 62,2,37,1,1,No 63,5,84,4,2,No 64,2,67,2,1,No 65,0,27,2,1,No 66,9,49,8,2,No 67,6,73,13,1,No 68,8,55,9,1,No 69,4,94,3,2,No 70,6,62,6,1,No 71,5,73,1,1,No 72,14,37,12,3,No 73,9,78,7,2,No 74,20,53,13,2,Yes 75,12,67,7,1,Yes 76,11,57,9,2,No 77,10,64,6,1,Yes 78,4,39,8,3,No 79,5,64,4,1,No 80,2,56,3,2,No 81,3,68,5,1,No 82,15,49,8,3,No 83,10,58,6,2,No 84,22,35,14,2,No 85,6,33,8,3,No 86,3,60,2,2,No 87,2,100,10,2,No 88,12,54,10,1,No 89,11,54,12,2,Yes 90,3,46,5,3,No 91,16,96,14,1,Yes 92,5,68,9,3,No 93,16,49,10,1,Yes 94,14,26,12,3,No 95,17,79,11,2,No 96,6,52,6,2,No 97,2,38,1,3,No 98,11,42,10,1,No 99,6,57,8,1,Yes 100,7,58,9,2,No 101,19,63,14,2,Yes 102,11,46,9,2,Yes 103,1,48,5,1,No 104,4,26,2,1,No 105,0,38,1,3,No 106,12,65,13,1,No 107,6,63,5,2,No 108,8,23,12,3,No 109,10,43,10,2,No 110,12,45,7,1,No 111,16,88,11,2,Yes 112,10,63,14,1,No 113,17,72,14,3,Yes 114,11,57,7,3,No 115,12,61,10,1,No 116,22,45,14,3,Yes 117,6,82,9,1,No 118,21,58,12,1,Yes 119,5,80,9,3,No 120,1,59,6,1,No 121,9,48,9,2,No 122,2,44,9,1,No 123,10,57,10,2,No 124,2,78,2,3,No 125,3,88,2,2,No 126,13,93,14,1,Yes 127,18,42,12,2,Yes 128,7,80,7,3,No 129,14,66,13,2,No 130,5,36,7,2,No 131,10,81,6,1,No 132,8,37,9,2,No 133,10,70,7,1,No 134,11,53,7,2,No 135,8,72,10,3,No 136,13,79,8,2,No 137,14,42,12,3,No 138,16,55,9,1,No 139,9,38,13,1,No 140,14,84,9,1,Yes 141,6,48,4,3,No 142,9,11,7,3,No 143,7,62,10,2,No 144,4,74,3,2,No 145,3,47,11,3,No 146,5,36,3,2,No 147,8,62,10,1,No 148,15,31,10,2,No 149,8,46,5,2,Yes 150,4,91,3,2,No 151,12,84,8,1,Yes 152,1,56,5,1,No 153,16,26,8,3,No 154,7,60,11,3,No 155,8,38,8,1,No 156,19,57,14,1,No 157,4,68,5,2,No 158,9,53,14,1,No 159,2,54,5,2,No 160,8,88,6,1,No 161,8,76,14,2,Yes 162,16,61,13,2,No 163,2,40,3,3,No 164,4,70,1,1,No 165,23,48,14,1,Yes 166,7,48,5,2,No 167,5,54,1,1,No 168,10,60,13,2,No 169,14,56,11,1,No 170,7,59,6,3,No 171,0,80,1,1,No 172,3,40,1,1,No 173,5,50,2,2,No 174,20,63,13,1,Yes 175,5,26,6,2,No 176,7,47,14,1,Yes 177,8,74,10,1,No 178,2,14,11,1,No 179,20,90,11,1,Yes 180,6,67,7,1,No 181,13,66,10,2,Yes 182,5,39,6,2,No 183,25,67,14,1,Yes 184,13,44,13,1,No 185,8,47,6,1,Yes 186,3,51,1,2,No 187,8,72,6,3,No 188,12,77,6,1,No 189,6,52,8,1,No 190,10,32,12,1,No 191,14,47,8,1,No 192,2,44,1,1,No 193,6,71,7,1,No 194,8,46,11,2,No 195,20,70,12,1,Yes 196,14,13,11,2,Yes 197,3,36,12,2,No 198,3,72,2,1,No 199,15,51,10,2,No 200,1,66,4,1,No"""))