Michael Rey
commited on
Commit
·
759a794
1
Parent(s):
07e65eb
initial commit
Browse files- Social_Network_Ads.csv +401 -0
- app.py +73 -0
- requirements.txt +4 -0
Social_Network_Ads.csv
ADDED
@@ -0,0 +1,401 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
User ID,Gender,Age,EstimatedSalary,Purchased
|
2 |
+
15624510,Male,19,19000,0
|
3 |
+
15810944,Male,35,20000,0
|
4 |
+
15668575,Female,26,43000,0
|
5 |
+
15603246,Female,27,57000,0
|
6 |
+
15804002,Male,19,76000,0
|
7 |
+
15728773,Male,27,58000,0
|
8 |
+
15598044,Female,27,84000,0
|
9 |
+
15694829,Female,32,150000,1
|
10 |
+
15600575,Male,25,33000,0
|
11 |
+
15727311,Female,35,65000,0
|
12 |
+
15570769,Female,26,80000,0
|
13 |
+
15606274,Female,26,52000,0
|
14 |
+
15746139,Male,20,86000,0
|
15 |
+
15704987,Male,32,18000,0
|
16 |
+
15628972,Male,18,82000,0
|
17 |
+
15697686,Male,29,80000,0
|
18 |
+
15733883,Male,47,25000,1
|
19 |
+
15617482,Male,45,26000,1
|
20 |
+
15704583,Male,46,28000,1
|
21 |
+
15621083,Female,48,29000,1
|
22 |
+
15649487,Male,45,22000,1
|
23 |
+
15736760,Female,47,49000,1
|
24 |
+
15714658,Male,48,41000,1
|
25 |
+
15599081,Female,45,22000,1
|
26 |
+
15705113,Male,46,23000,1
|
27 |
+
15631159,Male,47,20000,1
|
28 |
+
15792818,Male,49,28000,1
|
29 |
+
15633531,Female,47,30000,1
|
30 |
+
15744529,Male,29,43000,0
|
31 |
+
15669656,Male,31,18000,0
|
32 |
+
15581198,Male,31,74000,0
|
33 |
+
15729054,Female,27,137000,1
|
34 |
+
15573452,Female,21,16000,0
|
35 |
+
15776733,Female,28,44000,0
|
36 |
+
15724858,Male,27,90000,0
|
37 |
+
15713144,Male,35,27000,0
|
38 |
+
15690188,Female,33,28000,0
|
39 |
+
15689425,Male,30,49000,0
|
40 |
+
15671766,Female,26,72000,0
|
41 |
+
15782806,Female,27,31000,0
|
42 |
+
15764419,Female,27,17000,0
|
43 |
+
15591915,Female,33,51000,0
|
44 |
+
15772798,Male,35,108000,0
|
45 |
+
15792008,Male,30,15000,0
|
46 |
+
15715541,Female,28,84000,0
|
47 |
+
15639277,Male,23,20000,0
|
48 |
+
15798850,Male,25,79000,0
|
49 |
+
15776348,Female,27,54000,0
|
50 |
+
15727696,Male,30,135000,1
|
51 |
+
15793813,Female,31,89000,0
|
52 |
+
15694395,Female,24,32000,0
|
53 |
+
15764195,Female,18,44000,0
|
54 |
+
15744919,Female,29,83000,0
|
55 |
+
15671655,Female,35,23000,0
|
56 |
+
15654901,Female,27,58000,0
|
57 |
+
15649136,Female,24,55000,0
|
58 |
+
15775562,Female,23,48000,0
|
59 |
+
15807481,Male,28,79000,0
|
60 |
+
15642885,Male,22,18000,0
|
61 |
+
15789109,Female,32,117000,0
|
62 |
+
15814004,Male,27,20000,0
|
63 |
+
15673619,Male,25,87000,0
|
64 |
+
15595135,Female,23,66000,0
|
65 |
+
15583681,Male,32,120000,1
|
66 |
+
15605000,Female,59,83000,0
|
67 |
+
15718071,Male,24,58000,0
|
68 |
+
15679760,Male,24,19000,0
|
69 |
+
15654574,Female,23,82000,0
|
70 |
+
15577178,Female,22,63000,0
|
71 |
+
15595324,Female,31,68000,0
|
72 |
+
15756932,Male,25,80000,0
|
73 |
+
15726358,Female,24,27000,0
|
74 |
+
15595228,Female,20,23000,0
|
75 |
+
15782530,Female,33,113000,0
|
76 |
+
15592877,Male,32,18000,0
|
77 |
+
15651983,Male,34,112000,1
|
78 |
+
15746737,Male,18,52000,0
|
79 |
+
15774179,Female,22,27000,0
|
80 |
+
15667265,Female,28,87000,0
|
81 |
+
15655123,Female,26,17000,0
|
82 |
+
15595917,Male,30,80000,0
|
83 |
+
15668385,Male,39,42000,0
|
84 |
+
15709476,Male,20,49000,0
|
85 |
+
15711218,Male,35,88000,0
|
86 |
+
15798659,Female,30,62000,0
|
87 |
+
15663939,Female,31,118000,1
|
88 |
+
15694946,Male,24,55000,0
|
89 |
+
15631912,Female,28,85000,0
|
90 |
+
15768816,Male,26,81000,0
|
91 |
+
15682268,Male,35,50000,0
|
92 |
+
15684801,Male,22,81000,0
|
93 |
+
15636428,Female,30,116000,0
|
94 |
+
15809823,Male,26,15000,0
|
95 |
+
15699284,Female,29,28000,0
|
96 |
+
15786993,Female,29,83000,0
|
97 |
+
15709441,Female,35,44000,0
|
98 |
+
15710257,Female,35,25000,0
|
99 |
+
15582492,Male,28,123000,1
|
100 |
+
15575694,Male,35,73000,0
|
101 |
+
15756820,Female,28,37000,0
|
102 |
+
15766289,Male,27,88000,0
|
103 |
+
15593014,Male,28,59000,0
|
104 |
+
15584545,Female,32,86000,0
|
105 |
+
15675949,Female,33,149000,1
|
106 |
+
15672091,Female,19,21000,0
|
107 |
+
15801658,Male,21,72000,0
|
108 |
+
15706185,Female,26,35000,0
|
109 |
+
15789863,Male,27,89000,0
|
110 |
+
15720943,Male,26,86000,0
|
111 |
+
15697997,Female,38,80000,0
|
112 |
+
15665416,Female,39,71000,0
|
113 |
+
15660200,Female,37,71000,0
|
114 |
+
15619653,Male,38,61000,0
|
115 |
+
15773447,Male,37,55000,0
|
116 |
+
15739160,Male,42,80000,0
|
117 |
+
15689237,Male,40,57000,0
|
118 |
+
15679297,Male,35,75000,0
|
119 |
+
15591433,Male,36,52000,0
|
120 |
+
15642725,Male,40,59000,0
|
121 |
+
15701962,Male,41,59000,0
|
122 |
+
15811613,Female,36,75000,0
|
123 |
+
15741049,Male,37,72000,0
|
124 |
+
15724423,Female,40,75000,0
|
125 |
+
15574305,Male,35,53000,0
|
126 |
+
15678168,Female,41,51000,0
|
127 |
+
15697020,Female,39,61000,0
|
128 |
+
15610801,Male,42,65000,0
|
129 |
+
15745232,Male,26,32000,0
|
130 |
+
15722758,Male,30,17000,0
|
131 |
+
15792102,Female,26,84000,0
|
132 |
+
15675185,Male,31,58000,0
|
133 |
+
15801247,Male,33,31000,0
|
134 |
+
15725660,Male,30,87000,0
|
135 |
+
15638963,Female,21,68000,0
|
136 |
+
15800061,Female,28,55000,0
|
137 |
+
15578006,Male,23,63000,0
|
138 |
+
15668504,Female,20,82000,0
|
139 |
+
15687491,Male,30,107000,1
|
140 |
+
15610403,Female,28,59000,0
|
141 |
+
15741094,Male,19,25000,0
|
142 |
+
15807909,Male,19,85000,0
|
143 |
+
15666141,Female,18,68000,0
|
144 |
+
15617134,Male,35,59000,0
|
145 |
+
15783029,Male,30,89000,0
|
146 |
+
15622833,Female,34,25000,0
|
147 |
+
15746422,Female,24,89000,0
|
148 |
+
15750839,Female,27,96000,1
|
149 |
+
15749130,Female,41,30000,0
|
150 |
+
15779862,Male,29,61000,0
|
151 |
+
15767871,Male,20,74000,0
|
152 |
+
15679651,Female,26,15000,0
|
153 |
+
15576219,Male,41,45000,0
|
154 |
+
15699247,Male,31,76000,0
|
155 |
+
15619087,Female,36,50000,0
|
156 |
+
15605327,Male,40,47000,0
|
157 |
+
15610140,Female,31,15000,0
|
158 |
+
15791174,Male,46,59000,0
|
159 |
+
15602373,Male,29,75000,0
|
160 |
+
15762605,Male,26,30000,0
|
161 |
+
15598840,Female,32,135000,1
|
162 |
+
15744279,Male,32,100000,1
|
163 |
+
15670619,Male,25,90000,0
|
164 |
+
15599533,Female,37,33000,0
|
165 |
+
15757837,Male,35,38000,0
|
166 |
+
15697574,Female,33,69000,0
|
167 |
+
15578738,Female,18,86000,0
|
168 |
+
15762228,Female,22,55000,0
|
169 |
+
15614827,Female,35,71000,0
|
170 |
+
15789815,Male,29,148000,1
|
171 |
+
15579781,Female,29,47000,0
|
172 |
+
15587013,Male,21,88000,0
|
173 |
+
15570932,Male,34,115000,0
|
174 |
+
15794661,Female,26,118000,0
|
175 |
+
15581654,Female,34,43000,0
|
176 |
+
15644296,Female,34,72000,0
|
177 |
+
15614420,Female,23,28000,0
|
178 |
+
15609653,Female,35,47000,0
|
179 |
+
15594577,Male,25,22000,0
|
180 |
+
15584114,Male,24,23000,0
|
181 |
+
15673367,Female,31,34000,0
|
182 |
+
15685576,Male,26,16000,0
|
183 |
+
15774727,Female,31,71000,0
|
184 |
+
15694288,Female,32,117000,1
|
185 |
+
15603319,Male,33,43000,0
|
186 |
+
15759066,Female,33,60000,0
|
187 |
+
15814816,Male,31,66000,0
|
188 |
+
15724402,Female,20,82000,0
|
189 |
+
15571059,Female,33,41000,0
|
190 |
+
15674206,Male,35,72000,0
|
191 |
+
15715160,Male,28,32000,0
|
192 |
+
15730448,Male,24,84000,0
|
193 |
+
15662067,Female,19,26000,0
|
194 |
+
15779581,Male,29,43000,0
|
195 |
+
15662901,Male,19,70000,0
|
196 |
+
15689751,Male,28,89000,0
|
197 |
+
15667742,Male,34,43000,0
|
198 |
+
15738448,Female,30,79000,0
|
199 |
+
15680243,Female,20,36000,0
|
200 |
+
15745083,Male,26,80000,0
|
201 |
+
15708228,Male,35,22000,0
|
202 |
+
15628523,Male,35,39000,0
|
203 |
+
15708196,Male,49,74000,0
|
204 |
+
15735549,Female,39,134000,1
|
205 |
+
15809347,Female,41,71000,0
|
206 |
+
15660866,Female,58,101000,1
|
207 |
+
15766609,Female,47,47000,0
|
208 |
+
15654230,Female,55,130000,1
|
209 |
+
15794566,Female,52,114000,0
|
210 |
+
15800890,Female,40,142000,1
|
211 |
+
15697424,Female,46,22000,0
|
212 |
+
15724536,Female,48,96000,1
|
213 |
+
15735878,Male,52,150000,1
|
214 |
+
15707596,Female,59,42000,0
|
215 |
+
15657163,Male,35,58000,0
|
216 |
+
15622478,Male,47,43000,0
|
217 |
+
15779529,Female,60,108000,1
|
218 |
+
15636023,Male,49,65000,0
|
219 |
+
15582066,Male,40,78000,0
|
220 |
+
15666675,Female,46,96000,0
|
221 |
+
15732987,Male,59,143000,1
|
222 |
+
15789432,Female,41,80000,0
|
223 |
+
15663161,Male,35,91000,1
|
224 |
+
15694879,Male,37,144000,1
|
225 |
+
15593715,Male,60,102000,1
|
226 |
+
15575002,Female,35,60000,0
|
227 |
+
15622171,Male,37,53000,0
|
228 |
+
15795224,Female,36,126000,1
|
229 |
+
15685346,Male,56,133000,1
|
230 |
+
15691808,Female,40,72000,0
|
231 |
+
15721007,Female,42,80000,1
|
232 |
+
15794253,Female,35,147000,1
|
233 |
+
15694453,Male,39,42000,0
|
234 |
+
15813113,Male,40,107000,1
|
235 |
+
15614187,Male,49,86000,1
|
236 |
+
15619407,Female,38,112000,0
|
237 |
+
15646227,Male,46,79000,1
|
238 |
+
15660541,Male,40,57000,0
|
239 |
+
15753874,Female,37,80000,0
|
240 |
+
15617877,Female,46,82000,0
|
241 |
+
15772073,Female,53,143000,1
|
242 |
+
15701537,Male,42,149000,1
|
243 |
+
15736228,Male,38,59000,0
|
244 |
+
15780572,Female,50,88000,1
|
245 |
+
15769596,Female,56,104000,1
|
246 |
+
15586996,Female,41,72000,0
|
247 |
+
15722061,Female,51,146000,1
|
248 |
+
15638003,Female,35,50000,0
|
249 |
+
15775590,Female,57,122000,1
|
250 |
+
15730688,Male,41,52000,0
|
251 |
+
15753102,Female,35,97000,1
|
252 |
+
15810075,Female,44,39000,0
|
253 |
+
15723373,Male,37,52000,0
|
254 |
+
15795298,Female,48,134000,1
|
255 |
+
15584320,Female,37,146000,1
|
256 |
+
15724161,Female,50,44000,0
|
257 |
+
15750056,Female,52,90000,1
|
258 |
+
15609637,Female,41,72000,0
|
259 |
+
15794493,Male,40,57000,0
|
260 |
+
15569641,Female,58,95000,1
|
261 |
+
15815236,Female,45,131000,1
|
262 |
+
15811177,Female,35,77000,0
|
263 |
+
15680587,Male,36,144000,1
|
264 |
+
15672821,Female,55,125000,1
|
265 |
+
15767681,Female,35,72000,0
|
266 |
+
15600379,Male,48,90000,1
|
267 |
+
15801336,Female,42,108000,1
|
268 |
+
15721592,Male,40,75000,0
|
269 |
+
15581282,Male,37,74000,0
|
270 |
+
15746203,Female,47,144000,1
|
271 |
+
15583137,Male,40,61000,0
|
272 |
+
15680752,Female,43,133000,0
|
273 |
+
15688172,Female,59,76000,1
|
274 |
+
15791373,Male,60,42000,1
|
275 |
+
15589449,Male,39,106000,1
|
276 |
+
15692819,Female,57,26000,1
|
277 |
+
15727467,Male,57,74000,1
|
278 |
+
15734312,Male,38,71000,0
|
279 |
+
15764604,Male,49,88000,1
|
280 |
+
15613014,Female,52,38000,1
|
281 |
+
15759684,Female,50,36000,1
|
282 |
+
15609669,Female,59,88000,1
|
283 |
+
15685536,Male,35,61000,0
|
284 |
+
15750447,Male,37,70000,1
|
285 |
+
15663249,Female,52,21000,1
|
286 |
+
15638646,Male,48,141000,0
|
287 |
+
15734161,Female,37,93000,1
|
288 |
+
15631070,Female,37,62000,0
|
289 |
+
15761950,Female,48,138000,1
|
290 |
+
15649668,Male,41,79000,0
|
291 |
+
15713912,Female,37,78000,1
|
292 |
+
15586757,Male,39,134000,1
|
293 |
+
15596522,Male,49,89000,1
|
294 |
+
15625395,Male,55,39000,1
|
295 |
+
15760570,Male,37,77000,0
|
296 |
+
15566689,Female,35,57000,0
|
297 |
+
15725794,Female,36,63000,0
|
298 |
+
15673539,Male,42,73000,1
|
299 |
+
15705298,Female,43,112000,1
|
300 |
+
15675791,Male,45,79000,0
|
301 |
+
15747043,Male,46,117000,1
|
302 |
+
15736397,Female,58,38000,1
|
303 |
+
15678201,Male,48,74000,1
|
304 |
+
15720745,Female,37,137000,1
|
305 |
+
15637593,Male,37,79000,1
|
306 |
+
15598070,Female,40,60000,0
|
307 |
+
15787550,Male,42,54000,0
|
308 |
+
15603942,Female,51,134000,0
|
309 |
+
15733973,Female,47,113000,1
|
310 |
+
15596761,Male,36,125000,1
|
311 |
+
15652400,Female,38,50000,0
|
312 |
+
15717893,Female,42,70000,0
|
313 |
+
15622585,Male,39,96000,1
|
314 |
+
15733964,Female,38,50000,0
|
315 |
+
15753861,Female,49,141000,1
|
316 |
+
15747097,Female,39,79000,0
|
317 |
+
15594762,Female,39,75000,1
|
318 |
+
15667417,Female,54,104000,1
|
319 |
+
15684861,Male,35,55000,0
|
320 |
+
15742204,Male,45,32000,1
|
321 |
+
15623502,Male,36,60000,0
|
322 |
+
15774872,Female,52,138000,1
|
323 |
+
15611191,Female,53,82000,1
|
324 |
+
15674331,Male,41,52000,0
|
325 |
+
15619465,Female,48,30000,1
|
326 |
+
15575247,Female,48,131000,1
|
327 |
+
15695679,Female,41,60000,0
|
328 |
+
15713463,Male,41,72000,0
|
329 |
+
15785170,Female,42,75000,0
|
330 |
+
15796351,Male,36,118000,1
|
331 |
+
15639576,Female,47,107000,1
|
332 |
+
15693264,Male,38,51000,0
|
333 |
+
15589715,Female,48,119000,1
|
334 |
+
15769902,Male,42,65000,0
|
335 |
+
15587177,Male,40,65000,0
|
336 |
+
15814553,Male,57,60000,1
|
337 |
+
15601550,Female,36,54000,0
|
338 |
+
15664907,Male,58,144000,1
|
339 |
+
15612465,Male,35,79000,0
|
340 |
+
15810800,Female,38,55000,0
|
341 |
+
15665760,Male,39,122000,1
|
342 |
+
15588080,Female,53,104000,1
|
343 |
+
15776844,Male,35,75000,0
|
344 |
+
15717560,Female,38,65000,0
|
345 |
+
15629739,Female,47,51000,1
|
346 |
+
15729908,Male,47,105000,1
|
347 |
+
15716781,Female,41,63000,0
|
348 |
+
15646936,Male,53,72000,1
|
349 |
+
15768151,Female,54,108000,1
|
350 |
+
15579212,Male,39,77000,0
|
351 |
+
15721835,Male,38,61000,0
|
352 |
+
15800515,Female,38,113000,1
|
353 |
+
15591279,Male,37,75000,0
|
354 |
+
15587419,Female,42,90000,1
|
355 |
+
15750335,Female,37,57000,0
|
356 |
+
15699619,Male,36,99000,1
|
357 |
+
15606472,Male,60,34000,1
|
358 |
+
15778368,Male,54,70000,1
|
359 |
+
15671387,Female,41,72000,0
|
360 |
+
15573926,Male,40,71000,1
|
361 |
+
15709183,Male,42,54000,0
|
362 |
+
15577514,Male,43,129000,1
|
363 |
+
15778830,Female,53,34000,1
|
364 |
+
15768072,Female,47,50000,1
|
365 |
+
15768293,Female,42,79000,0
|
366 |
+
15654456,Male,42,104000,1
|
367 |
+
15807525,Female,59,29000,1
|
368 |
+
15574372,Female,58,47000,1
|
369 |
+
15671249,Male,46,88000,1
|
370 |
+
15779744,Male,38,71000,0
|
371 |
+
15624755,Female,54,26000,1
|
372 |
+
15611430,Female,60,46000,1
|
373 |
+
15774744,Male,60,83000,1
|
374 |
+
15629885,Female,39,73000,0
|
375 |
+
15708791,Male,59,130000,1
|
376 |
+
15793890,Female,37,80000,0
|
377 |
+
15646091,Female,46,32000,1
|
378 |
+
15596984,Female,46,74000,0
|
379 |
+
15800215,Female,42,53000,0
|
380 |
+
15577806,Male,41,87000,1
|
381 |
+
15749381,Female,58,23000,1
|
382 |
+
15683758,Male,42,64000,0
|
383 |
+
15670615,Male,48,33000,1
|
384 |
+
15715622,Female,44,139000,1
|
385 |
+
15707634,Male,49,28000,1
|
386 |
+
15806901,Female,57,33000,1
|
387 |
+
15775335,Male,56,60000,1
|
388 |
+
15724150,Female,49,39000,1
|
389 |
+
15627220,Male,39,71000,0
|
390 |
+
15672330,Male,47,34000,1
|
391 |
+
15668521,Female,48,35000,1
|
392 |
+
15807837,Male,48,33000,1
|
393 |
+
15592570,Male,47,23000,1
|
394 |
+
15748589,Female,45,45000,1
|
395 |
+
15635893,Male,60,42000,1
|
396 |
+
15757632,Female,39,59000,0
|
397 |
+
15691863,Female,46,41000,1
|
398 |
+
15706071,Male,51,23000,1
|
399 |
+
15654296,Female,50,20000,1
|
400 |
+
15755018,Male,36,33000,0
|
401 |
+
15594041,Female,49,36000,1
|
app.py
ADDED
@@ -0,0 +1,73 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import streamlit as st
|
2 |
+
import pandas as pd
|
3 |
+
import numpy as np
|
4 |
+
import matplotlib.pyplot as plt
|
5 |
+
import seaborn as sns
|
6 |
+
from sklearn.model_selection import train_test_split
|
7 |
+
from sklearn.preprocessing import StandardScaler
|
8 |
+
from sklearn.linear_model import LogisticRegression
|
9 |
+
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
|
10 |
+
|
11 |
+
# Load dataset
|
12 |
+
df = pd.read_csv("Social_Network_Ads.csv")
|
13 |
+
df = df.drop(columns=["User ID"]) # Remove User ID
|
14 |
+
|
15 |
+
# App Title
|
16 |
+
st.title("💼 Social Network Ads - Customer Purchase Prediction")
|
17 |
+
st.write("### Predict if a user will purchase a product based on Age & Salary.")
|
18 |
+
|
19 |
+
# Dataset Preview
|
20 |
+
st.write("#### Dataset Preview:")
|
21 |
+
st.dataframe(df.head())
|
22 |
+
|
23 |
+
# Data Distribution
|
24 |
+
st.write("#### Data Distribution")
|
25 |
+
fig, ax = plt.subplots(1, 2, figsize=(12, 5))
|
26 |
+
sns.histplot(df["Age"], bins=20, kde=True, ax=ax[0], color="blue")
|
27 |
+
ax[0].set_title("Age Distribution")
|
28 |
+
sns.histplot(df["EstimatedSalary"], bins=20, kde=True, ax=ax[1], color="green")
|
29 |
+
ax[1].set_title("Salary Distribution")
|
30 |
+
st.pyplot(fig)
|
31 |
+
|
32 |
+
# Preprocessing
|
33 |
+
X = df[["Age", "EstimatedSalary"]]
|
34 |
+
y = df["Purchased"]
|
35 |
+
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
|
36 |
+
scaler = StandardScaler()
|
37 |
+
X_train = scaler.fit_transform(X_train)
|
38 |
+
X_test = scaler.transform(X_test)
|
39 |
+
|
40 |
+
# Train Logistic Regression Model
|
41 |
+
model = LogisticRegression()
|
42 |
+
model.fit(X_train, y_train)
|
43 |
+
y_pred = model.predict(X_test)
|
44 |
+
accuracy = accuracy_score(y_test, y_pred)
|
45 |
+
conf_matrix = confusion_matrix(y_test, y_pred)
|
46 |
+
|
47 |
+
# Model Performance
|
48 |
+
st.write("### 📊 Model Performance")
|
49 |
+
st.write(f"**Model Accuracy:** {accuracy:.2f}")
|
50 |
+
st.write("#### Classification Report:")
|
51 |
+
st.text(classification_report(y_test, y_pred))
|
52 |
+
|
53 |
+
st.write("#### Confusion Matrix:")
|
54 |
+
fig, ax = plt.subplots()
|
55 |
+
sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues", xticklabels=["Not Purchased", "Purchased"], yticklabels=["Not Purchased", "Purchased"])
|
56 |
+
st.pyplot(fig)
|
57 |
+
|
58 |
+
# Prediction
|
59 |
+
st.write("### 🤖 Try the Model")
|
60 |
+
st.write("Enter details to check if a customer will purchase.")
|
61 |
+
|
62 |
+
age = st.slider("Select Age", min_value=int(X["Age"].min()), max_value=int(X["Age"].max()), value=30)
|
63 |
+
salary = st.slider("Select Estimated Salary", min_value=int(X["EstimatedSalary"].min()), max_value=int(X["EstimatedSalary"].max()), value=50000)
|
64 |
+
|
65 |
+
if st.button("Predict Purchase"):
|
66 |
+
input_data = scaler.transform([[age, salary]])
|
67 |
+
prediction = model.predict(input_data)[0]
|
68 |
+
prediction_proba = model.predict_proba(input_data)[0]
|
69 |
+
|
70 |
+
st.subheader("Prediction Result")
|
71 |
+
result_text = "Yes! The user is likely to purchase." if prediction == 1 else "No, the user is not likely to purchase."
|
72 |
+
st.success(result_text) if prediction == 1 else st.warning(result_text)
|
73 |
+
st.write(f"Confidence: {prediction_proba[prediction]:.2f}")
|
requirements.txt
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
streamlit
|
2 |
+
pandas
|
3 |
+
numpy
|
4 |
+
scikit-learn
|