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Commit 741e8f9

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1 parent be78519 commit 741e8f9

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6 files changed

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‎Datasets/50_Startups.csv

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R&D Spend,Administration,Marketing Spend,State,Profit
2+
165349.2,136897.8,471784.1,New York,192261.83
3+
162597.7,151377.59,443898.53,California,191792.06
4+
153441.51,101145.55,407934.54,Florida,191050.39
5+
144372.41,118671.85,383199.62,New York,182901.99
6+
142107.34,91391.77,366168.42,Florida,166187.94
7+
131876.9,99814.71,362861.36,New York,156991.12
8+
134615.46,147198.87,127716.82,California,156122.51
9+
130298.13,145530.06,323876.68,Florida,155752.6
10+
120542.52,148718.95,311613.29,New York,152211.77
11+
123334.88,108679.17,304981.62,California,149759.96
12+
101913.08,110594.11,229160.95,Florida,146121.95
13+
100671.96,91790.61,249744.55,California,144259.4
14+
93863.75,127320.38,249839.44,Florida,141585.52
15+
91992.39,135495.07,252664.93,California,134307.35
16+
119943.24,156547.42,256512.92,Florida,132602.65
17+
114523.61,122616.84,261776.23,New York,129917.04
18+
78013.11,121597.55,264346.06,California,126992.93
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94657.16,145077.58,282574.31,New York,125370.37
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91749.16,114175.79,294919.57,Florida,124266.9
21+
86419.7,153514.11,0,New York,122776.86
22+
76253.86,113867.3,298664.47,California,118474.03
23+
78389.47,153773.43,299737.29,New York,111313.02
24+
73994.56,122782.75,303319.26,Florida,110352.25
25+
67532.53,105751.03,304768.73,Florida,108733.99
26+
77044.01,99281.34,140574.81,New York,108552.04
27+
64664.71,139553.16,137962.62,California,107404.34
28+
75328.87,144135.98,134050.07,Florida,105733.54
29+
72107.6,127864.55,353183.81,New York,105008.31
30+
66051.52,182645.56,118148.2,Florida,103282.38
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65605.48,153032.06,107138.38,New York,101004.64
32+
61994.48,115641.28,91131.24,Florida,99937.59
33+
61136.38,152701.92,88218.23,New York,97483.56
34+
63408.86,129219.61,46085.25,California,97427.84
35+
55493.95,103057.49,214634.81,Florida,96778.92
36+
46426.07,157693.92,210797.67,California,96712.8
37+
46014.02,85047.44,205517.64,New York,96479.51
38+
28663.76,127056.21,201126.82,Florida,90708.19
39+
44069.95,51283.14,197029.42,California,89949.14
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20229.59,65947.93,185265.1,New York,81229.06
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38558.51,82982.09,174999.3,California,81005.76
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28754.33,118546.05,172795.67,California,78239.91
43+
27892.92,84710.77,164470.71,Florida,77798.83
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23640.93,96189.63,148001.11,California,71498.49
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15505.73,127382.3,35534.17,New York,69758.98
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22177.74,154806.14,28334.72,California,65200.33
47+
1000.23,124153.04,1903.93,New York,64926.08
48+
1315.46,115816.21,297114.46,Florida,49490.75
49+
0,135426.92,0,California,42559.73
50+
542.05,51743.15,0,New York,35673.41
51+
0,116983.8,45173.06,California,14681.4

‎Datasets/Data.csv

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Country,Age,Salary,Purchased
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France,44,72000,No
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Spain,27,48000,Yes
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Germany,30,54000,No
5+
Spain,38,61000,No
6+
Germany,40,,Yes
7+
France,35,58000,Yes
8+
Spain,,52000,No
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France,48,79000,Yes
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Germany,50,83000,No
11+
France,37,67000,Yes

‎Datasets/Mall_Customers.csv

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CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100)
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0001,Male,19,15,39
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0002,Male,21,15,81
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0003,Female,20,16,6
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0004,Female,23,16,77
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0005,Female,31,17,40
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0006,Female,22,17,76
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0007,Female,35,18,6
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0008,Female,23,18,94
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0009,Male,64,19,3
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0010,Female,30,19,72
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0011,Male,67,19,14
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0012,Female,35,19,99
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0013,Female,58,20,15
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0014,Female,24,20,77
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0015,Male,37,20,13
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0016,Male,22,20,79
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0017,Female,35,21,35
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0018,Male,20,21,66
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0019,Male,52,23,29
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0020,Female,35,23,98
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0021,Male,35,24,35
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0022,Male,25,24,73
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0023,Female,46,25,5
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0024,Male,31,25,73
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0025,Female,54,28,14
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0026,Male,29,28,82
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0027,Female,45,28,32
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0028,Male,35,28,61
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0029,Female,40,29,31
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0030,Female,23,29,87
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0031,Male,60,30,4
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0032,Female,21,30,73
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0033,Male,53,33,4
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0034,Male,18,33,92
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0035,Female,49,33,14
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0036,Female,21,33,81
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0037,Female,42,34,17
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0038,Female,30,34,73
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0039,Female,36,37,26
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0040,Female,20,37,75
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0041,Female,65,38,35
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0042,Male,24,38,92
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0043,Male,48,39,36
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0044,Female,31,39,61
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0045,Female,49,39,28
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0046,Female,24,39,65
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0047,Female,50,40,55
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0048,Female,27,40,47
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0049,Female,29,40,42
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0050,Female,31,40,42
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0051,Female,49,42,52
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0052,Male,33,42,60
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0053,Female,31,43,54
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0054,Male,59,43,60
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0055,Female,50,43,45
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0056,Male,47,43,41
58+
0057,Female,51,44,50
59+
0058,Male,69,44,46
60+
0059,Female,27,46,51
61+
0060,Male,53,46,46
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0061,Male,70,46,56
63+
0062,Male,19,46,55
64+
0063,Female,67,47,52
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0064,Female,54,47,59
66+
0065,Male,63,48,51
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0066,Male,18,48,59
68+
0067,Female,43,48,50
69+
0068,Female,68,48,48
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0069,Male,19,48,59
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0070,Female,32,48,47
72+
0071,Male,70,49,55
73+
0072,Female,47,49,42
74+
0073,Female,60,50,49
75+
0074,Female,60,50,56
76+
0075,Male,59,54,47
77+
0076,Male,26,54,54
78+
0077,Female,45,54,53
79+
0078,Male,40,54,48
80+
0079,Female,23,54,52
81+
0080,Female,49,54,42
82+
0081,Male,57,54,51
83+
0082,Male,38,54,55
84+
0083,Male,67,54,41
85+
0084,Female,46,54,44
86+
0085,Female,21,54,57
87+
0086,Male,48,54,46
88+
0087,Female,55,57,58
89+
0088,Female,22,57,55
90+
0089,Female,34,58,60
91+
0090,Female,50,58,46
92+
0091,Female,68,59,55
93+
0092,Male,18,59,41
94+
0093,Male,48,60,49
95+
0094,Female,40,60,40
96+
0095,Female,32,60,42
97+
0096,Male,24,60,52
98+
0097,Female,47,60,47
99+
0098,Female,27,60,50
100+
0099,Male,48,61,42
101+
0100,Male,20,61,49
102+
0101,Female,23,62,41
103+
0102,Female,49,62,48
104+
0103,Male,67,62,59
105+
0104,Male,26,62,55
106+
0105,Male,49,62,56
107+
0106,Female,21,62,42
108+
0107,Female,66,63,50
109+
0108,Male,54,63,46
110+
0109,Male,68,63,43
111+
0110,Male,66,63,48
112+
0111,Male,65,63,52
113+
0112,Female,19,63,54
114+
0113,Female,38,64,42
115+
0114,Male,19,64,46
116+
0115,Female,18,65,48
117+
0116,Female,19,65,50
118+
0117,Female,63,65,43
119+
0118,Female,49,65,59
120+
0119,Female,51,67,43
121+
0120,Female,50,67,57
122+
0121,Male,27,67,56
123+
0122,Female,38,67,40
124+
0123,Female,40,69,58
125+
0124,Male,39,69,91
126+
0125,Female,23,70,29
127+
0126,Female,31,70,77
128+
0127,Male,43,71,35
129+
0128,Male,40,71,95
130+
0129,Male,59,71,11
131+
0130,Male,38,71,75
132+
0131,Male,47,71,9
133+
0132,Male,39,71,75
134+
0133,Female,25,72,34
135+
0134,Female,31,72,71
136+
0135,Male,20,73,5
137+
0136,Female,29,73,88
138+
0137,Female,44,73,7
139+
0138,Male,32,73,73
140+
0139,Male,19,74,10
141+
0140,Female,35,74,72
142+
0141,Female,57,75,5
143+
0142,Male,32,75,93
144+
0143,Female,28,76,40
145+
0144,Female,32,76,87
146+
0145,Male,25,77,12
147+
0146,Male,28,77,97
148+
0147,Male,48,77,36
149+
0148,Female,32,77,74
150+
0149,Female,34,78,22
151+
0150,Male,34,78,90
152+
0151,Male,43,78,17
153+
0152,Male,39,78,88
154+
0153,Female,44,78,20
155+
0154,Female,38,78,76
156+
0155,Female,47,78,16
157+
0156,Female,27,78,89
158+
0157,Male,37,78,1
159+
0158,Female,30,78,78
160+
0159,Male,34,78,1
161+
0160,Female,30,78,73
162+
0161,Female,56,79,35
163+
0162,Female,29,79,83
164+
0163,Male,19,81,5
165+
0164,Female,31,81,93
166+
0165,Male,50,85,26
167+
0166,Female,36,85,75
168+
0167,Male,42,86,20
169+
0168,Female,33,86,95
170+
0169,Female,36,87,27
171+
0170,Male,32,87,63
172+
0171,Male,40,87,13
173+
0172,Male,28,87,75
174+
0173,Male,36,87,10
175+
0174,Male,36,87,92
176+
0175,Female,52,88,13
177+
0176,Female,30,88,86
178+
0177,Male,58,88,15
179+
0178,Male,27,88,69
180+
0179,Male,59,93,14
181+
0180,Male,35,93,90
182+
0181,Female,37,97,32
183+
0182,Female,32,97,86
184+
0183,Male,46,98,15
185+
0184,Female,29,98,88
186+
0185,Female,41,99,39
187+
0186,Male,30,99,97
188+
0187,Female,54,101,24
189+
0188,Male,28,101,68
190+
0189,Female,41,103,17
191+
0190,Female,36,103,85
192+
0191,Female,34,103,23
193+
0192,Female,32,103,69
194+
0193,Male,33,113,8
195+
0194,Female,38,113,91
196+
0195,Female,47,120,16
197+
0196,Female,35,120,79
198+
0197,Female,45,126,28
199+
0198,Male,32,126,74
200+
0199,Male,32,137,18
201+
0200,Male,30,137,83

‎Datasets/Position_Salaries.csv

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Position,Level,Salary
2+
Business Analyst,1,45000
3+
Junior Consultant,2,50000
4+
Senior Consultant,3,60000
5+
Manager,4,80000
6+
Country Manager,5,110000
7+
Region Manager,6,150000
8+
Partner,7,200000
9+
Senior Partner,8,300000
10+
C-level,9,500000
11+
CEO,10,1000000

‎Datasets/Salary_Data.csv

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YearsExperience,Salary
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1.1,39343.00
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1.3,46205.00
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1.5,37731.00
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2.0,43525.00
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2.2,39891.00
7+
2.9,56642.00
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3.0,60150.00
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3.2,54445.00
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3.2,64445.00
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3.7,57189.00
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3.9,63218.00
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4.0,55794.00
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4.0,56957.00
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4.1,57081.00
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4.5,61111.00
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4.9,67938.00
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5.1,66029.00
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5.3,83088.00
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5.9,81363.00
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6.0,93940.00
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6.8,91738.00
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7.1,98273.00
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7.9,101302.00
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8.2,113812.00
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8.7,109431.00
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9.0,105582.00
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9.5,116969.00
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9.6,112635.00
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10.3,122391.00
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10.5,121872.00

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