├── .gitignore
├── Heart.csv
├── LogisticRegressionWithHeartDataset.ipynb
├── NeuralNetworkFinal.ipynb
├── ex1data1.txt
├── ex1data2.txt
├── ex2data1.txt
├── ex3d1.xlsx
├── ex8data1.xlsx
├── k_mean_clustering_final.ipynb
├── kmean.xlsx
├── linearRegressionWithMultipleFeatures.ipynb
├── linearRegressionWithOneVariable.ipynb
├── linearRegressionWithOneVariable1.ipynb
├── logisticRegressionWithOptimizationFunc.ipynb
├── multicall_classification_with_logistic_regression.ipynb
├── multiclass_classification_with_fmin_tnc.ipynb
├── polynomial regression.ipynb
└── position_salaries.csv


/.gitignore:
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130 | 


--------------------------------------------------------------------------------
/Heart.csv:
--------------------------------------------------------------------------------
  1 | ,Age,Sex,ChestPain,RestBP,Chol,Fbs,RestECG,MaxHR,ExAng,Oldpeak,Slope,Ca,Thal,AHD
  2 | 1,63,1,typical,145,233,1,2,150,0,2.3,3,0,fixed,No
  3 | 2,67,1,asymptomatic,160,286,0,2,108,1,1.5,2,3,normal,Yes
  4 | 3,67,1,asymptomatic,120,229,0,2,129,1,2.6,2,2,reversable,Yes
  5 | 4,37,1,nonanginal,130,250,0,0,187,0,3.5,3,0,normal,No
  6 | 5,41,0,nontypical,130,204,0,2,172,0,1.4,1,0,normal,No
  7 | 6,56,1,nontypical,120,236,0,0,178,0,0.8,1,0,normal,No
  8 | 7,62,0,asymptomatic,140,268,0,2,160,0,3.6,3,2,normal,Yes
  9 | 8,57,0,asymptomatic,120,354,0,0,163,1,0.6,1,0,normal,No
 10 | 9,63,1,asymptomatic,130,254,0,2,147,0,1.4,2,1,reversable,Yes
 11 | 10,53,1,asymptomatic,140,203,1,2,155,1,3.1,3,0,reversable,Yes
 12 | 11,57,1,asymptomatic,140,192,0,0,148,0,0.4,2,0,fixed,No
 13 | 12,56,0,nontypical,140,294,0,2,153,0,1.3,2,0,normal,No
 14 | 13,56,1,nonanginal,130,256,1,2,142,1,0.6,2,1,fixed,Yes
 15 | 14,44,1,nontypical,120,263,0,0,173,0,0,1,0,reversable,No
 16 | 15,52,1,nonanginal,172,199,1,0,162,0,0.5,1,0,reversable,No
 17 | 16,57,1,nonanginal,150,168,0,0,174,0,1.6,1,0,normal,No
 18 | 17,48,1,nontypical,110,229,0,0,168,0,1,3,0,reversable,Yes
 19 | 18,54,1,asymptomatic,140,239,0,0,160,0,1.2,1,0,normal,No
 20 | 19,48,0,nonanginal,130,275,0,0,139,0,0.2,1,0,normal,No
 21 | 20,49,1,nontypical,130,266,0,0,171,0,0.6,1,0,normal,No
 22 | 21,64,1,typical,110,211,0,2,144,1,1.8,2,0,normal,No
 23 | 22,58,0,typical,150,283,1,2,162,0,1,1,0,normal,No
 24 | 23,58,1,nontypical,120,284,0,2,160,0,1.8,2,0,normal,Yes
 25 | 24,58,1,nonanginal,132,224,0,2,173,0,3.2,1,2,reversable,Yes
 26 | 25,60,1,asymptomatic,130,206,0,2,132,1,2.4,2,2,reversable,Yes
 27 | 26,50,0,nonanginal,120,219,0,0,158,0,1.6,2,0,normal,No
 28 | 27,58,0,nonanginal,120,340,0,0,172,0,0,1,0,normal,No
 29 | 28,66,0,typical,150,226,0,0,114,0,2.6,3,0,normal,No
 30 | 29,43,1,asymptomatic,150,247,0,0,171,0,1.5,1,0,normal,No
 31 | 30,40,1,asymptomatic,110,167,0,2,114,1,2,2,0,reversable,Yes
 32 | 31,69,0,typical,140,239,0,0,151,0,1.8,1,2,normal,No
 33 | 32,60,1,asymptomatic,117,230,1,0,160,1,1.4,1,2,reversable,Yes
 34 | 33,64,1,nonanginal,140,335,0,0,158,0,0,1,0,normal,Yes
 35 | 34,59,1,asymptomatic,135,234,0,0,161,0,0.5,2,0,reversable,No
 36 | 35,44,1,nonanginal,130,233,0,0,179,1,0.4,1,0,normal,No
 37 | 36,42,1,asymptomatic,140,226,0,0,178,0,0,1,0,normal,No
 38 | 37,43,1,asymptomatic,120,177,0,2,120,1,2.5,2,0,reversable,Yes
 39 | 38,57,1,asymptomatic,150,276,0,2,112,1,0.6,2,1,fixed,Yes
 40 | 39,55,1,asymptomatic,132,353,0,0,132,1,1.2,2,1,reversable,Yes
 41 | 40,61,1,nonanginal,150,243,1,0,137,1,1,2,0,normal,No
 42 | 41,65,0,asymptomatic,150,225,0,2,114,0,1,2,3,reversable,Yes
 43 | 42,40,1,typical,140,199,0,0,178,1,1.4,1,0,reversable,No
 44 | 43,71,0,nontypical,160,302,0,0,162,0,0.4,1,2,normal,No
 45 | 44,59,1,nonanginal,150,212,1,0,157,0,1.6,1,0,normal,No
 46 | 45,61,0,asymptomatic,130,330,0,2,169,0,0,1,0,normal,Yes
 47 | 46,58,1,nonanginal,112,230,0,2,165,0,2.5,2,1,reversable,Yes
 48 | 47,51,1,nonanginal,110,175,0,0,123,0,0.6,1,0,normal,No
 49 | 48,50,1,asymptomatic,150,243,0,2,128,0,2.6,2,0,reversable,Yes
 50 | 49,65,0,nonanginal,140,417,1,2,157,0,0.8,1,1,normal,No
 51 | 50,53,1,nonanginal,130,197,1,2,152,0,1.2,3,0,normal,No
 52 | 51,41,0,nontypical,105,198,0,0,168,0,0,1,1,normal,No
 53 | 52,65,1,asymptomatic,120,177,0,0,140,0,0.4,1,0,reversable,No
 54 | 53,44,1,asymptomatic,112,290,0,2,153,0,0,1,1,normal,Yes
 55 | 54,44,1,nontypical,130,219,0,2,188,0,0,1,0,normal,No
 56 | 55,60,1,asymptomatic,130,253,0,0,144,1,1.4,1,1,reversable,Yes
 57 | 56,54,1,asymptomatic,124,266,0,2,109,1,2.2,2,1,reversable,Yes
 58 | 57,50,1,nonanginal,140,233,0,0,163,0,0.6,2,1,reversable,Yes
 59 | 58,41,1,asymptomatic,110,172,0,2,158,0,0,1,0,reversable,Yes
 60 | 59,54,1,nonanginal,125,273,0,2,152,0,0.5,3,1,normal,No
 61 | 60,51,1,typical,125,213,0,2,125,1,1.4,1,1,normal,No
 62 | 61,51,0,asymptomatic,130,305,0,0,142,1,1.2,2,0,reversable,Yes
 63 | 62,46,0,nonanginal,142,177,0,2,160,1,1.4,3,0,normal,No
 64 | 63,58,1,asymptomatic,128,216,0,2,131,1,2.2,2,3,reversable,Yes
 65 | 64,54,0,nonanginal,135,304,1,0,170,0,0,1,0,normal,No
 66 | 65,54,1,asymptomatic,120,188,0,0,113,0,1.4,2,1,reversable,Yes
 67 | 66,60,1,asymptomatic,145,282,0,2,142,1,2.8,2,2,reversable,Yes
 68 | 67,60,1,nonanginal,140,185,0,2,155,0,3,2,0,normal,Yes
 69 | 68,54,1,nonanginal,150,232,0,2,165,0,1.6,1,0,reversable,No
 70 | 69,59,1,asymptomatic,170,326,0,2,140,1,3.4,3,0,reversable,Yes
 71 | 70,46,1,nonanginal,150,231,0,0,147,0,3.6,2,0,normal,Yes
 72 | 71,65,0,nonanginal,155,269,0,0,148,0,0.8,1,0,normal,No
 73 | 72,67,1,asymptomatic,125,254,1,0,163,0,0.2,2,2,reversable,Yes
 74 | 73,62,1,asymptomatic,120,267,0,0,99,1,1.8,2,2,reversable,Yes
 75 | 74,65,1,asymptomatic,110,248,0,2,158,0,0.6,1,2,fixed,Yes
 76 | 75,44,1,asymptomatic,110,197,0,2,177,0,0,1,1,normal,Yes
 77 | 76,65,0,nonanginal,160,360,0,2,151,0,0.8,1,0,normal,No
 78 | 77,60,1,asymptomatic,125,258,0,2,141,1,2.8,2,1,reversable,Yes
 79 | 78,51,0,nonanginal,140,308,0,2,142,0,1.5,1,1,normal,No
 80 | 79,48,1,nontypical,130,245,0,2,180,0,0.2,2,0,normal,No
 81 | 80,58,1,asymptomatic,150,270,0,2,111,1,0.8,1,0,reversable,Yes
 82 | 81,45,1,asymptomatic,104,208,0,2,148,1,3,2,0,normal,No
 83 | 82,53,0,asymptomatic,130,264,0,2,143,0,0.4,2,0,normal,No
 84 | 83,39,1,nonanginal,140,321,0,2,182,0,0,1,0,normal,No
 85 | 84,68,1,nonanginal,180,274,1,2,150,1,1.6,2,0,reversable,Yes
 86 | 85,52,1,nontypical,120,325,0,0,172,0,0.2,1,0,normal,No
 87 | 86,44,1,nonanginal,140,235,0,2,180,0,0,1,0,normal,No
 88 | 87,47,1,nonanginal,138,257,0,2,156,0,0,1,0,normal,No
 89 | 88,53,0,nonanginal,128,216,0,2,115,0,0,1,0,NA,No
 90 | 89,53,0,asymptomatic,138,234,0,2,160,0,0,1,0,normal,No
 91 | 90,51,0,nonanginal,130,256,0,2,149,0,0.5,1,0,normal,No
 92 | 91,66,1,asymptomatic,120,302,0,2,151,0,0.4,2,0,normal,No
 93 | 92,62,0,asymptomatic,160,164,0,2,145,0,6.2,3,3,reversable,Yes
 94 | 93,62,1,nonanginal,130,231,0,0,146,0,1.8,2,3,reversable,No
 95 | 94,44,0,nonanginal,108,141,0,0,175,0,0.6,2,0,normal,No
 96 | 95,63,0,nonanginal,135,252,0,2,172,0,0,1,0,normal,No
 97 | 96,52,1,asymptomatic,128,255,0,0,161,1,0,1,1,reversable,Yes
 98 | 97,59,1,asymptomatic,110,239,0,2,142,1,1.2,2,1,reversable,Yes
 99 | 98,60,0,asymptomatic,150,258,0,2,157,0,2.6,2,2,reversable,Yes
100 | 99,52,1,nontypical,134,201,0,0,158,0,0.8,1,1,normal,No
101 | 100,48,1,asymptomatic,122,222,0,2,186,0,0,1,0,normal,No
102 | 101,45,1,asymptomatic,115,260,0,2,185,0,0,1,0,normal,No
103 | 102,34,1,typical,118,182,0,2,174,0,0,1,0,normal,No
104 | 103,57,0,asymptomatic,128,303,0,2,159,0,0,1,1,normal,No
105 | 104,71,0,nonanginal,110,265,1,2,130,0,0,1,1,normal,No
106 | 105,49,1,nonanginal,120,188,0,0,139,0,2,2,3,reversable,Yes
107 | 106,54,1,nontypical,108,309,0,0,156,0,0,1,0,reversable,No
108 | 107,59,1,asymptomatic,140,177,0,0,162,1,0,1,1,reversable,Yes
109 | 108,57,1,nonanginal,128,229,0,2,150,0,0.4,2,1,reversable,Yes
110 | 109,61,1,asymptomatic,120,260,0,0,140,1,3.6,2,1,reversable,Yes
111 | 110,39,1,asymptomatic,118,219,0,0,140,0,1.2,2,0,reversable,Yes
112 | 111,61,0,asymptomatic,145,307,0,2,146,1,1,2,0,reversable,Yes
113 | 112,56,1,asymptomatic,125,249,1,2,144,1,1.2,2,1,normal,Yes
114 | 113,52,1,typical,118,186,0,2,190,0,0,2,0,fixed,No
115 | 114,43,0,asymptomatic,132,341,1,2,136,1,3,2,0,reversable,Yes
116 | 115,62,0,nonanginal,130,263,0,0,97,0,1.2,2,1,reversable,Yes
117 | 116,41,1,nontypical,135,203,0,0,132,0,0,2,0,fixed,No
118 | 117,58,1,nonanginal,140,211,1,2,165,0,0,1,0,normal,No
119 | 118,35,0,asymptomatic,138,183,0,0,182,0,1.4,1,0,normal,No
120 | 119,63,1,asymptomatic,130,330,1,2,132,1,1.8,1,3,reversable,Yes
121 | 120,65,1,asymptomatic,135,254,0,2,127,0,2.8,2,1,reversable,Yes
122 | 121,48,1,asymptomatic,130,256,1,2,150,1,0,1,2,reversable,Yes
123 | 122,63,0,asymptomatic,150,407,0,2,154,0,4,2,3,reversable,Yes
124 | 123,51,1,nonanginal,100,222,0,0,143,1,1.2,2,0,normal,No
125 | 124,55,1,asymptomatic,140,217,0,0,111,1,5.6,3,0,reversable,Yes
126 | 125,65,1,typical,138,282,1,2,174,0,1.4,2,1,normal,Yes
127 | 126,45,0,nontypical,130,234,0,2,175,0,0.6,2,0,normal,No
128 | 127,56,0,asymptomatic,200,288,1,2,133,1,4,3,2,reversable,Yes
129 | 128,54,1,asymptomatic,110,239,0,0,126,1,2.8,2,1,reversable,Yes
130 | 129,44,1,nontypical,120,220,0,0,170,0,0,1,0,normal,No
131 | 130,62,0,asymptomatic,124,209,0,0,163,0,0,1,0,normal,No
132 | 131,54,1,nonanginal,120,258,0,2,147,0,0.4,2,0,reversable,No
133 | 132,51,1,nonanginal,94,227,0,0,154,1,0,1,1,reversable,No
134 | 133,29,1,nontypical,130,204,0,2,202,0,0,1,0,normal,No
135 | 134,51,1,asymptomatic,140,261,0,2,186,1,0,1,0,normal,No
136 | 135,43,0,nonanginal,122,213,0,0,165,0,0.2,2,0,normal,No
137 | 136,55,0,nontypical,135,250,0,2,161,0,1.4,2,0,normal,No
138 | 137,70,1,asymptomatic,145,174,0,0,125,1,2.6,3,0,reversable,Yes
139 | 138,62,1,nontypical,120,281,0,2,103,0,1.4,2,1,reversable,Yes
140 | 139,35,1,asymptomatic,120,198,0,0,130,1,1.6,2,0,reversable,Yes
141 | 140,51,1,nonanginal,125,245,1,2,166,0,2.4,2,0,normal,No
142 | 141,59,1,nontypical,140,221,0,0,164,1,0,1,0,normal,No
143 | 142,59,1,typical,170,288,0,2,159,0,0.2,2,0,reversable,Yes
144 | 143,52,1,nontypical,128,205,1,0,184,0,0,1,0,normal,No
145 | 144,64,1,nonanginal,125,309,0,0,131,1,1.8,2,0,reversable,Yes
146 | 145,58,1,nonanginal,105,240,0,2,154,1,0.6,2,0,reversable,No
147 | 146,47,1,nonanginal,108,243,0,0,152,0,0,1,0,normal,Yes
148 | 147,57,1,asymptomatic,165,289,1,2,124,0,1,2,3,reversable,Yes
149 | 148,41,1,nonanginal,112,250,0,0,179,0,0,1,0,normal,No
150 | 149,45,1,nontypical,128,308,0,2,170,0,0,1,0,normal,No
151 | 150,60,0,nonanginal,102,318,0,0,160,0,0,1,1,normal,No
152 | 151,52,1,typical,152,298,1,0,178,0,1.2,2,0,reversable,No
153 | 152,42,0,asymptomatic,102,265,0,2,122,0,0.6,2,0,normal,No
154 | 153,67,0,nonanginal,115,564,0,2,160,0,1.6,2,0,reversable,No
155 | 154,55,1,asymptomatic,160,289,0,2,145,1,0.8,2,1,reversable,Yes
156 | 155,64,1,asymptomatic,120,246,0,2,96,1,2.2,3,1,normal,Yes
157 | 156,70,1,asymptomatic,130,322,0,2,109,0,2.4,2,3,normal,Yes
158 | 157,51,1,asymptomatic,140,299,0,0,173,1,1.6,1,0,reversable,Yes
159 | 158,58,1,asymptomatic,125,300,0,2,171,0,0,1,2,reversable,Yes
160 | 159,60,1,asymptomatic,140,293,0,2,170,0,1.2,2,2,reversable,Yes
161 | 160,68,1,nonanginal,118,277,0,0,151,0,1,1,1,reversable,No
162 | 161,46,1,nontypical,101,197,1,0,156,0,0,1,0,reversable,No
163 | 162,77,1,asymptomatic,125,304,0,2,162,1,0,1,3,normal,Yes
164 | 163,54,0,nonanginal,110,214,0,0,158,0,1.6,2,0,normal,No
165 | 164,58,0,asymptomatic,100,248,0,2,122,0,1,2,0,normal,No
166 | 165,48,1,nonanginal,124,255,1,0,175,0,0,1,2,normal,No
167 | 166,57,1,asymptomatic,132,207,0,0,168,1,0,1,0,reversable,No
168 | 167,52,1,nonanginal,138,223,0,0,169,0,0,1,NA,normal,No
169 | 168,54,0,nontypical,132,288,1,2,159,1,0,1,1,normal,No
170 | 169,35,1,asymptomatic,126,282,0,2,156,1,0,1,0,reversable,Yes
171 | 170,45,0,nontypical,112,160,0,0,138,0,0,2,0,normal,No
172 | 171,70,1,nonanginal,160,269,0,0,112,1,2.9,2,1,reversable,Yes
173 | 172,53,1,asymptomatic,142,226,0,2,111,1,0,1,0,reversable,No
174 | 173,59,0,asymptomatic,174,249,0,0,143,1,0,2,0,normal,Yes
175 | 174,62,0,asymptomatic,140,394,0,2,157,0,1.2,2,0,normal,No
176 | 175,64,1,asymptomatic,145,212,0,2,132,0,2,2,2,fixed,Yes
177 | 176,57,1,asymptomatic,152,274,0,0,88,1,1.2,2,1,reversable,Yes
178 | 177,52,1,asymptomatic,108,233,1,0,147,0,0.1,1,3,reversable,No
179 | 178,56,1,asymptomatic,132,184,0,2,105,1,2.1,2,1,fixed,Yes
180 | 179,43,1,nonanginal,130,315,0,0,162,0,1.9,1,1,normal,No
181 | 180,53,1,nonanginal,130,246,1,2,173,0,0,1,3,normal,No
182 | 181,48,1,asymptomatic,124,274,0,2,166,0,0.5,2,0,reversable,Yes
183 | 182,56,0,asymptomatic,134,409,0,2,150,1,1.9,2,2,reversable,Yes
184 | 183,42,1,typical,148,244,0,2,178,0,0.8,1,2,normal,No
185 | 184,59,1,typical,178,270,0,2,145,0,4.2,3,0,reversable,No
186 | 185,60,0,asymptomatic,158,305,0,2,161,0,0,1,0,normal,Yes
187 | 186,63,0,nontypical,140,195,0,0,179,0,0,1,2,normal,No
188 | 187,42,1,nonanginal,120,240,1,0,194,0,0.8,3,0,reversable,No
189 | 188,66,1,nontypical,160,246,0,0,120,1,0,2,3,fixed,Yes
190 | 189,54,1,nontypical,192,283,0,2,195,0,0,1,1,reversable,Yes
191 | 190,69,1,nonanginal,140,254,0,2,146,0,2,2,3,reversable,Yes
192 | 191,50,1,nonanginal,129,196,0,0,163,0,0,1,0,normal,No
193 | 192,51,1,asymptomatic,140,298,0,0,122,1,4.2,2,3,reversable,Yes
194 | 193,43,1,asymptomatic,132,247,1,2,143,1,0.1,2,NA,reversable,Yes
195 | 194,62,0,asymptomatic,138,294,1,0,106,0,1.9,2,3,normal,Yes
196 | 195,68,0,nonanginal,120,211,0,2,115,0,1.5,2,0,normal,No
197 | 196,67,1,asymptomatic,100,299,0,2,125,1,0.9,2,2,normal,Yes
198 | 197,69,1,typical,160,234,1,2,131,0,0.1,2,1,normal,No
199 | 198,45,0,asymptomatic,138,236,0,2,152,1,0.2,2,0,normal,No
200 | 199,50,0,nontypical,120,244,0,0,162,0,1.1,1,0,normal,No
201 | 200,59,1,typical,160,273,0,2,125,0,0,1,0,normal,Yes
202 | 201,50,0,asymptomatic,110,254,0,2,159,0,0,1,0,normal,No
203 | 202,64,0,asymptomatic,180,325,0,0,154,1,0,1,0,normal,No
204 | 203,57,1,nonanginal,150,126,1,0,173,0,0.2,1,1,reversable,No
205 | 204,64,0,nonanginal,140,313,0,0,133,0,0.2,1,0,reversable,No
206 | 205,43,1,asymptomatic,110,211,0,0,161,0,0,1,0,reversable,No
207 | 206,45,1,asymptomatic,142,309,0,2,147,1,0,2,3,reversable,Yes
208 | 207,58,1,asymptomatic,128,259,0,2,130,1,3,2,2,reversable,Yes
209 | 208,50,1,asymptomatic,144,200,0,2,126,1,0.9,2,0,reversable,Yes
210 | 209,55,1,nontypical,130,262,0,0,155,0,0,1,0,normal,No
211 | 210,62,0,asymptomatic,150,244,0,0,154,1,1.4,2,0,normal,Yes
212 | 211,37,0,nonanginal,120,215,0,0,170,0,0,1,0,normal,No
213 | 212,38,1,typical,120,231,0,0,182,1,3.8,2,0,reversable,Yes
214 | 213,41,1,nonanginal,130,214,0,2,168,0,2,2,0,normal,No
215 | 214,66,0,asymptomatic,178,228,1,0,165,1,1,2,2,reversable,Yes
216 | 215,52,1,asymptomatic,112,230,0,0,160,0,0,1,1,normal,Yes
217 | 216,56,1,typical,120,193,0,2,162,0,1.9,2,0,reversable,No
218 | 217,46,0,nontypical,105,204,0,0,172,0,0,1,0,normal,No
219 | 218,46,0,asymptomatic,138,243,0,2,152,1,0,2,0,normal,No
220 | 219,64,0,asymptomatic,130,303,0,0,122,0,2,2,2,normal,No
221 | 220,59,1,asymptomatic,138,271,0,2,182,0,0,1,0,normal,No
222 | 221,41,0,nonanginal,112,268,0,2,172,1,0,1,0,normal,No
223 | 222,54,0,nonanginal,108,267,0,2,167,0,0,1,0,normal,No
224 | 223,39,0,nonanginal,94,199,0,0,179,0,0,1,0,normal,No
225 | 224,53,1,asymptomatic,123,282,0,0,95,1,2,2,2,reversable,Yes
226 | 225,63,0,asymptomatic,108,269,0,0,169,1,1.8,2,2,normal,Yes
227 | 226,34,0,nontypical,118,210,0,0,192,0,0.7,1,0,normal,No
228 | 227,47,1,asymptomatic,112,204,0,0,143,0,0.1,1,0,normal,No
229 | 228,67,0,nonanginal,152,277,0,0,172,0,0,1,1,normal,No
230 | 229,54,1,asymptomatic,110,206,0,2,108,1,0,2,1,normal,Yes
231 | 230,66,1,asymptomatic,112,212,0,2,132,1,0.1,1,1,normal,Yes
232 | 231,52,0,nonanginal,136,196,0,2,169,0,0.1,2,0,normal,No
233 | 232,55,0,asymptomatic,180,327,0,1,117,1,3.4,2,0,normal,Yes
234 | 233,49,1,nonanginal,118,149,0,2,126,0,0.8,1,3,normal,Yes
235 | 234,74,0,nontypical,120,269,0,2,121,1,0.2,1,1,normal,No
236 | 235,54,0,nonanginal,160,201,0,0,163,0,0,1,1,normal,No
237 | 236,54,1,asymptomatic,122,286,0,2,116,1,3.2,2,2,normal,Yes
238 | 237,56,1,asymptomatic,130,283,1,2,103,1,1.6,3,0,reversable,Yes
239 | 238,46,1,asymptomatic,120,249,0,2,144,0,0.8,1,0,reversable,Yes
240 | 239,49,0,nontypical,134,271,0,0,162,0,0,2,0,normal,No
241 | 240,42,1,nontypical,120,295,0,0,162,0,0,1,0,normal,No
242 | 241,41,1,nontypical,110,235,0,0,153,0,0,1,0,normal,No
243 | 242,41,0,nontypical,126,306,0,0,163,0,0,1,0,normal,No
244 | 243,49,0,asymptomatic,130,269,0,0,163,0,0,1,0,normal,No
245 | 244,61,1,typical,134,234,0,0,145,0,2.6,2,2,normal,Yes
246 | 245,60,0,nonanginal,120,178,1,0,96,0,0,1,0,normal,No
247 | 246,67,1,asymptomatic,120,237,0,0,71,0,1,2,0,normal,Yes
248 | 247,58,1,asymptomatic,100,234,0,0,156,0,0.1,1,1,reversable,Yes
249 | 248,47,1,asymptomatic,110,275,0,2,118,1,1,2,1,normal,Yes
250 | 249,52,1,asymptomatic,125,212,0,0,168,0,1,1,2,reversable,Yes
251 | 250,62,1,nontypical,128,208,1,2,140,0,0,1,0,normal,No
252 | 251,57,1,asymptomatic,110,201,0,0,126,1,1.5,2,0,fixed,No
253 | 252,58,1,asymptomatic,146,218,0,0,105,0,2,2,1,reversable,Yes
254 | 253,64,1,asymptomatic,128,263,0,0,105,1,0.2,2,1,reversable,No
255 | 254,51,0,nonanginal,120,295,0,2,157,0,0.6,1,0,normal,No
256 | 255,43,1,asymptomatic,115,303,0,0,181,0,1.2,2,0,normal,No
257 | 256,42,0,nonanginal,120,209,0,0,173,0,0,2,0,normal,No
258 | 257,67,0,asymptomatic,106,223,0,0,142,0,0.3,1,2,normal,No
259 | 258,76,0,nonanginal,140,197,0,1,116,0,1.1,2,0,normal,No
260 | 259,70,1,nontypical,156,245,0,2,143,0,0,1,0,normal,No
261 | 260,57,1,nontypical,124,261,0,0,141,0,0.3,1,0,reversable,Yes
262 | 261,44,0,nonanginal,118,242,0,0,149,0,0.3,2,1,normal,No
263 | 262,58,0,nontypical,136,319,1,2,152,0,0,1,2,normal,Yes
264 | 263,60,0,typical,150,240,0,0,171,0,0.9,1,0,normal,No
265 | 264,44,1,nonanginal,120,226,0,0,169,0,0,1,0,normal,No
266 | 265,61,1,asymptomatic,138,166,0,2,125,1,3.6,2,1,normal,Yes
267 | 266,42,1,asymptomatic,136,315,0,0,125,1,1.8,2,0,fixed,Yes
268 | 267,52,1,asymptomatic,128,204,1,0,156,1,1,2,0,NA,Yes
269 | 268,59,1,nonanginal,126,218,1,0,134,0,2.2,2,1,fixed,Yes
270 | 269,40,1,asymptomatic,152,223,0,0,181,0,0,1,0,reversable,Yes
271 | 270,42,1,nonanginal,130,180,0,0,150,0,0,1,0,normal,No
272 | 271,61,1,asymptomatic,140,207,0,2,138,1,1.9,1,1,reversable,Yes
273 | 272,66,1,asymptomatic,160,228,0,2,138,0,2.3,1,0,fixed,No
274 | 273,46,1,asymptomatic,140,311,0,0,120,1,1.8,2,2,reversable,Yes
275 | 274,71,0,asymptomatic,112,149,0,0,125,0,1.6,2,0,normal,No
276 | 275,59,1,typical,134,204,0,0,162,0,0.8,1,2,normal,Yes
277 | 276,64,1,typical,170,227,0,2,155,0,0.6,2,0,reversable,No
278 | 277,66,0,nonanginal,146,278,0,2,152,0,0,2,1,normal,No
279 | 278,39,0,nonanginal,138,220,0,0,152,0,0,2,0,normal,No
280 | 279,57,1,nontypical,154,232,0,2,164,0,0,1,1,normal,Yes
281 | 280,58,0,asymptomatic,130,197,0,0,131,0,0.6,2,0,normal,No
282 | 281,57,1,asymptomatic,110,335,0,0,143,1,3,2,1,reversable,Yes
283 | 282,47,1,nonanginal,130,253,0,0,179,0,0,1,0,normal,No
284 | 283,55,0,asymptomatic,128,205,0,1,130,1,2,2,1,reversable,Yes
285 | 284,35,1,nontypical,122,192,0,0,174,0,0,1,0,normal,No
286 | 285,61,1,asymptomatic,148,203,0,0,161,0,0,1,1,reversable,Yes
287 | 286,58,1,asymptomatic,114,318,0,1,140,0,4.4,3,3,fixed,Yes
288 | 287,58,0,asymptomatic,170,225,1,2,146,1,2.8,2,2,fixed,Yes
289 | 288,58,1,nontypical,125,220,0,0,144,0,0.4,2,NA,reversable,No
290 | 289,56,1,nontypical,130,221,0,2,163,0,0,1,0,reversable,No
291 | 290,56,1,nontypical,120,240,0,0,169,0,0,3,0,normal,No
292 | 291,67,1,nonanginal,152,212,0,2,150,0,0.8,2,0,reversable,Yes
293 | 292,55,0,nontypical,132,342,0,0,166,0,1.2,1,0,normal,No
294 | 293,44,1,asymptomatic,120,169,0,0,144,1,2.8,3,0,fixed,Yes
295 | 294,63,1,asymptomatic,140,187,0,2,144,1,4,1,2,reversable,Yes
296 | 295,63,0,asymptomatic,124,197,0,0,136,1,0,2,0,normal,Yes
297 | 296,41,1,nontypical,120,157,0,0,182,0,0,1,0,normal,No
298 | 297,59,1,asymptomatic,164,176,1,2,90,0,1,2,2,fixed,Yes
299 | 298,57,0,asymptomatic,140,241,0,0,123,1,0.2,2,0,reversable,Yes
300 | 299,45,1,typical,110,264,0,0,132,0,1.2,2,0,reversable,Yes
301 | 300,68,1,asymptomatic,144,193,1,0,141,0,3.4,2,2,reversable,Yes
302 | 301,57,1,asymptomatic,130,131,0,0,115,1,1.2,2,1,reversable,Yes
303 | 302,57,0,nontypical,130,236,0,2,174,0,0,2,1,normal,Yes
304 | 303,38,1,nonanginal,138,175,0,0,173,0,0,1,NA,normal,No
305 | 


--------------------------------------------------------------------------------
/ex1data1.txt:
--------------------------------------------------------------------------------
 1 | 6.1101,17.592
 2 | 5.5277,9.1302
 3 | 8.5186,13.662
 4 | 7.0032,11.854
 5 | 5.8598,6.8233
 6 | 8.3829,11.886
 7 | 7.4764,4.3483
 8 | 8.5781,12
 9 | 6.4862,6.5987
10 | 5.0546,3.8166
11 | 5.7107,3.2522
12 | 14.164,15.505
13 | 5.734,3.1551
14 | 8.4084,7.2258
15 | 5.6407,0.71618
16 | 5.3794,3.5129
17 | 6.3654,5.3048
18 | 5.1301,0.56077
19 | 6.4296,3.6518
20 | 7.0708,5.3893
21 | 6.1891,3.1386
22 | 20.27,21.767
23 | 5.4901,4.263
24 | 6.3261,5.1875
25 | 5.5649,3.0825
26 | 18.945,22.638
27 | 12.828,13.501
28 | 10.957,7.0467
29 | 13.176,14.692
30 | 22.203,24.147
31 | 5.2524,-1.22
32 | 6.5894,5.9966
33 | 9.2482,12.134
34 | 5.8918,1.8495
35 | 8.2111,6.5426
36 | 7.9334,4.5623
37 | 8.0959,4.1164
38 | 5.6063,3.3928
39 | 12.836,10.117
40 | 6.3534,5.4974
41 | 5.4069,0.55657
42 | 6.8825,3.9115
43 | 11.708,5.3854
44 | 5.7737,2.4406
45 | 7.8247,6.7318
46 | 7.0931,1.0463
47 | 5.0702,5.1337
48 | 5.8014,1.844
49 | 11.7,8.0043
50 | 5.5416,1.0179
51 | 7.5402,6.7504
52 | 5.3077,1.8396
53 | 7.4239,4.2885
54 | 7.6031,4.9981
55 | 6.3328,1.4233
56 | 6.3589,-1.4211
57 | 6.2742,2.4756
58 | 5.6397,4.6042
59 | 9.3102,3.9624
60 | 9.4536,5.4141
61 | 8.8254,5.1694
62 | 5.1793,-0.74279
63 | 21.279,17.929
64 | 14.908,12.054
65 | 18.959,17.054
66 | 7.2182,4.8852
67 | 8.2951,5.7442
68 | 10.236,7.7754
69 | 5.4994,1.0173
70 | 20.341,20.992
71 | 10.136,6.6799
72 | 7.3345,4.0259
73 | 6.0062,1.2784
74 | 7.2259,3.3411
75 | 5.0269,-2.6807
76 | 6.5479,0.29678
77 | 7.5386,3.8845
78 | 5.0365,5.7014
79 | 10.274,6.7526
80 | 5.1077,2.0576
81 | 5.7292,0.47953
82 | 5.1884,0.20421
83 | 6.3557,0.67861
84 | 9.7687,7.5435
85 | 6.5159,5.3436
86 | 8.5172,4.2415
87 | 9.1802,6.7981
88 | 6.002,0.92695
89 | 5.5204,0.152
90 | 5.0594,2.8214
91 | 5.7077,1.8451
92 | 7.6366,4.2959
93 | 5.8707,7.2029
94 | 5.3054,1.9869
95 | 8.2934,0.14454
96 | 13.394,9.0551
97 | 5.4369,0.61705
98 | 


--------------------------------------------------------------------------------
/ex1data2.txt:
--------------------------------------------------------------------------------
 1 | 2104,3,399900
 2 | 1600,3,329900
 3 | 2400,3,369000
 4 | 1416,2,232000
 5 | 3000,4,539900
 6 | 1985,4,299900
 7 | 1534,3,314900
 8 | 1427,3,198999
 9 | 1380,3,212000
10 | 1494,3,242500
11 | 1940,4,239999
12 | 2000,3,347000
13 | 1890,3,329999
14 | 4478,5,699900
15 | 1268,3,259900
16 | 2300,4,449900
17 | 1320,2,299900
18 | 1236,3,199900
19 | 2609,4,499998
20 | 3031,4,599000
21 | 1767,3,252900
22 | 1888,2,255000
23 | 1604,3,242900
24 | 1962,4,259900
25 | 3890,3,573900
26 | 1100,3,249900
27 | 1458,3,464500
28 | 2526,3,469000
29 | 2200,3,475000
30 | 2637,3,299900
31 | 1839,2,349900
32 | 1000,1,169900
33 | 2040,4,314900
34 | 3137,3,579900
35 | 1811,4,285900
36 | 1437,3,249900
37 | 1239,3,229900
38 | 2132,4,345000
39 | 4215,4,549000
40 | 2162,4,287000
41 | 1664,2,368500
42 | 2238,3,329900
43 | 2567,4,314000
44 | 1200,3,299000
45 | 852,2,179900
46 | 1852,4,299900
47 | 1203,3,239500
48 | 


--------------------------------------------------------------------------------
/ex2data1.txt:
--------------------------------------------------------------------------------
  1 | 34.62365962451697,78.0246928153624,0
  2 | 30.28671076822607,43.89499752400101,0
  3 | 35.84740876993872,72.90219802708364,0
  4 | 60.18259938620976,86.30855209546826,1
  5 | 79.0327360507101,75.3443764369103,1
  6 | 45.08327747668339,56.3163717815305,0
  7 | 61.10666453684766,96.51142588489624,1
  8 | 75.02474556738889,46.55401354116538,1
  9 | 76.09878670226257,87.42056971926803,1
 10 | 84.43281996120035,43.53339331072109,1
 11 | 95.86155507093572,38.22527805795094,0
 12 | 75.01365838958247,30.60326323428011,0
 13 | 82.30705337399482,76.48196330235604,1
 14 | 69.36458875970939,97.71869196188608,1
 15 | 39.53833914367223,76.03681085115882,0
 16 | 53.9710521485623,89.20735013750205,1
 17 | 69.07014406283025,52.74046973016765,1
 18 | 67.94685547711617,46.67857410673128,0
 19 | 70.66150955499435,92.92713789364831,1
 20 | 76.97878372747498,47.57596364975532,1
 21 | 67.37202754570876,42.83843832029179,0
 22 | 89.67677575072079,65.79936592745237,1
 23 | 50.534788289883,48.85581152764205,0
 24 | 34.21206097786789,44.20952859866288,0
 25 | 77.9240914545704,68.9723599933059,1
 26 | 62.27101367004632,69.95445795447587,1
 27 | 80.1901807509566,44.82162893218353,1
 28 | 93.114388797442,38.80067033713209,0
 29 | 61.83020602312595,50.25610789244621,0
 30 | 38.78580379679423,64.99568095539578,0
 31 | 61.379289447425,72.80788731317097,1
 32 | 85.40451939411645,57.05198397627122,1
 33 | 52.10797973193984,63.12762376881715,0
 34 | 52.04540476831827,69.43286012045222,1
 35 | 40.23689373545111,71.16774802184875,0
 36 | 54.63510555424817,52.21388588061123,0
 37 | 33.91550010906887,98.86943574220611,0
 38 | 64.17698887494485,80.90806058670817,1
 39 | 74.78925295941542,41.57341522824434,0
 40 | 34.1836400264419,75.2377203360134,0
 41 | 83.90239366249155,56.30804621605327,1
 42 | 51.54772026906181,46.85629026349976,0
 43 | 94.44336776917852,65.56892160559052,1
 44 | 82.36875375713919,40.61825515970618,0
 45 | 51.04775177128865,45.82270145776001,0
 46 | 62.22267576120188,52.06099194836679,0
 47 | 77.19303492601364,70.45820000180959,1
 48 | 97.77159928000232,86.7278223300282,1
 49 | 62.07306379667647,96.76882412413983,1
 50 | 91.56497449807442,88.69629254546599,1
 51 | 79.94481794066932,74.16311935043758,1
 52 | 99.2725269292572,60.99903099844988,1
 53 | 90.54671411399852,43.39060180650027,1
 54 | 34.52451385320009,60.39634245837173,0
 55 | 50.2864961189907,49.80453881323059,0
 56 | 49.58667721632031,59.80895099453265,0
 57 | 97.64563396007767,68.86157272420604,1
 58 | 32.57720016809309,95.59854761387875,0
 59 | 74.24869136721598,69.82457122657193,1
 60 | 71.79646205863379,78.45356224515052,1
 61 | 75.3956114656803,85.75993667331619,1
 62 | 35.28611281526193,47.02051394723416,0
 63 | 56.25381749711624,39.26147251058019,0
 64 | 30.05882244669796,49.59297386723685,0
 65 | 44.66826172480893,66.45008614558913,0
 66 | 66.56089447242954,41.09209807936973,0
 67 | 40.45755098375164,97.53518548909936,1
 68 | 49.07256321908844,51.88321182073966,0
 69 | 80.27957401466998,92.11606081344084,1
 70 | 66.74671856944039,60.99139402740988,1
 71 | 32.72283304060323,43.30717306430063,0
 72 | 64.0393204150601,78.03168802018232,1
 73 | 72.34649422579923,96.22759296761404,1
 74 | 60.45788573918959,73.09499809758037,1
 75 | 58.84095621726802,75.85844831279042,1
 76 | 99.82785779692128,72.36925193383885,1
 77 | 47.26426910848174,88.47586499559782,1
 78 | 50.45815980285988,75.80985952982456,1
 79 | 60.45555629271532,42.50840943572217,0
 80 | 82.22666157785568,42.71987853716458,0
 81 | 88.9138964166533,69.80378889835472,1
 82 | 94.83450672430196,45.69430680250754,1
 83 | 67.31925746917527,66.58935317747915,1
 84 | 57.23870631569862,59.51428198012956,1
 85 | 80.36675600171273,90.96014789746954,1
 86 | 68.46852178591112,85.59430710452014,1
 87 | 42.0754545384731,78.84478600148043,0
 88 | 75.47770200533905,90.42453899753964,1
 89 | 78.63542434898018,96.64742716885644,1
 90 | 52.34800398794107,60.76950525602592,0
 91 | 94.09433112516793,77.15910509073893,1
 92 | 90.44855097096364,87.50879176484702,1
 93 | 55.48216114069585,35.57070347228866,0
 94 | 74.49269241843041,84.84513684930135,1
 95 | 89.84580670720979,45.35828361091658,1
 96 | 83.48916274498238,48.38028579728175,1
 97 | 42.2617008099817,87.10385094025457,1
 98 | 99.31500880510394,68.77540947206617,1
 99 | 55.34001756003703,64.9319380069486,1
100 | 74.77589300092767,89.52981289513276,1
101 | 


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/linearRegressionWithMultipleFeatures.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "data": {
 10 |       "text/html": [
 11 |        "<div>\n",
 12 |        "<style scoped>\n",
 13 |        "    .dataframe tbody tr th:only-of-type {\n",
 14 |        "        vertical-align: middle;\n",
 15 |        "    }\n",
 16 |        "\n",
 17 |        "    .dataframe tbody tr th {\n",
 18 |        "        vertical-align: top;\n",
 19 |        "    }\n",
 20 |        "\n",
 21 |        "    .dataframe thead th {\n",
 22 |        "        text-align: right;\n",
 23 |        "    }\n",
 24 |        "</style>\n",
 25 |        "<table border=\"1\" class=\"dataframe\">\n",
 26 |        "  <thead>\n",
 27 |        "    <tr style=\"text-align: right;\">\n",
 28 |        "      <th></th>\n",
 29 |        "      <th>0</th>\n",
 30 |        "      <th>1</th>\n",
 31 |        "      <th>2</th>\n",
 32 |        "    </tr>\n",
 33 |        "  </thead>\n",
 34 |        "  <tbody>\n",
 35 |        "    <tr>\n",
 36 |        "      <th>0</th>\n",
 37 |        "      <td>2104</td>\n",
 38 |        "      <td>3</td>\n",
 39 |        "      <td>399900</td>\n",
 40 |        "    </tr>\n",
 41 |        "    <tr>\n",
 42 |        "      <th>1</th>\n",
 43 |        "      <td>1600</td>\n",
 44 |        "      <td>3</td>\n",
 45 |        "      <td>329900</td>\n",
 46 |        "    </tr>\n",
 47 |        "    <tr>\n",
 48 |        "      <th>2</th>\n",
 49 |        "      <td>2400</td>\n",
 50 |        "      <td>3</td>\n",
 51 |        "      <td>369000</td>\n",
 52 |        "    </tr>\n",
 53 |        "    <tr>\n",
 54 |        "      <th>3</th>\n",
 55 |        "      <td>1416</td>\n",
 56 |        "      <td>2</td>\n",
 57 |        "      <td>232000</td>\n",
 58 |        "    </tr>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>4</th>\n",
 61 |        "      <td>3000</td>\n",
 62 |        "      <td>4</td>\n",
 63 |        "      <td>539900</td>\n",
 64 |        "    </tr>\n",
 65 |        "  </tbody>\n",
 66 |        "</table>\n",
 67 |        "</div>"
 68 |       ],
 69 |       "text/plain": [
 70 |        "      0  1       2\n",
 71 |        "0  2104  3  399900\n",
 72 |        "1  1600  3  329900\n",
 73 |        "2  2400  3  369000\n",
 74 |        "3  1416  2  232000\n",
 75 |        "4  3000  4  539900"
 76 |       ]
 77 |      },
 78 |      "execution_count": 1,
 79 |      "metadata": {},
 80 |      "output_type": "execute_result"
 81 |     }
 82 |    ],
 83 |    "source": [
 84 |     "import pandas as pd\n",
 85 |     "df = pd.read_csv('ex1data2.txt', header = None)\n",
 86 |     "df.head()"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 2,
 92 |    "metadata": {},
 93 |    "outputs": [],
 94 |    "source": [
 95 |     "m = len(df)"
 96 |    ]
 97 |   },
 98 |   {
 99 |    "cell_type": "code",
100 |    "execution_count": 3,
101 |    "metadata": {},
102 |    "outputs": [
103 |     {
104 |      "data": {
105 |       "text/plain": [
106 |        "47"
107 |       ]
108 |      },
109 |      "execution_count": 3,
110 |      "metadata": {},
111 |      "output_type": "execute_result"
112 |     }
113 |    ],
114 |    "source": [
115 |     "m"
116 |    ]
117 |   },
118 |   {
119 |    "cell_type": "code",
120 |    "execution_count": 4,
121 |    "metadata": {},
122 |    "outputs": [
123 |     {
124 |      "data": {
125 |       "text/html": [
126 |        "<div>\n",
127 |        "<style scoped>\n",
128 |        "    .dataframe tbody tr th:only-of-type {\n",
129 |        "        vertical-align: middle;\n",
130 |        "    }\n",
131 |        "\n",
132 |        "    .dataframe tbody tr th {\n",
133 |        "        vertical-align: top;\n",
134 |        "    }\n",
135 |        "\n",
136 |        "    .dataframe thead th {\n",
137 |        "        text-align: right;\n",
138 |        "    }\n",
139 |        "</style>\n",
140 |        "<table border=\"1\" class=\"dataframe\">\n",
141 |        "  <thead>\n",
142 |        "    <tr style=\"text-align: right;\">\n",
143 |        "      <th></th>\n",
144 |        "      <th>00</th>\n",
145 |        "      <th>0</th>\n",
146 |        "      <th>1</th>\n",
147 |        "      <th>2</th>\n",
148 |        "    </tr>\n",
149 |        "  </thead>\n",
150 |        "  <tbody>\n",
151 |        "    <tr>\n",
152 |        "      <th>0</th>\n",
153 |        "      <td>1</td>\n",
154 |        "      <td>2104</td>\n",
155 |        "      <td>3</td>\n",
156 |        "      <td>399900</td>\n",
157 |        "    </tr>\n",
158 |        "    <tr>\n",
159 |        "      <th>1</th>\n",
160 |        "      <td>1</td>\n",
161 |        "      <td>1600</td>\n",
162 |        "      <td>3</td>\n",
163 |        "      <td>329900</td>\n",
164 |        "    </tr>\n",
165 |        "    <tr>\n",
166 |        "      <th>2</th>\n",
167 |        "      <td>1</td>\n",
168 |        "      <td>2400</td>\n",
169 |        "      <td>3</td>\n",
170 |        "      <td>369000</td>\n",
171 |        "    </tr>\n",
172 |        "    <tr>\n",
173 |        "      <th>3</th>\n",
174 |        "      <td>1</td>\n",
175 |        "      <td>1416</td>\n",
176 |        "      <td>2</td>\n",
177 |        "      <td>232000</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <th>4</th>\n",
181 |        "      <td>1</td>\n",
182 |        "      <td>3000</td>\n",
183 |        "      <td>4</td>\n",
184 |        "      <td>539900</td>\n",
185 |        "    </tr>\n",
186 |        "  </tbody>\n",
187 |        "</table>\n",
188 |        "</div>"
189 |       ],
190 |       "text/plain": [
191 |        "   00     0  1       2\n",
192 |        "0   1  2104  3  399900\n",
193 |        "1   1  1600  3  329900\n",
194 |        "2   1  2400  3  369000\n",
195 |        "3   1  1416  2  232000\n",
196 |        "4   1  3000  4  539900"
197 |       ]
198 |      },
199 |      "execution_count": 4,
200 |      "metadata": {},
201 |      "output_type": "execute_result"
202 |     }
203 |    ],
204 |    "source": [
205 |     "df = pd.concat([pd.Series(1, index=df.index, name='00'), df], axis=1)\n",
206 |     "df.head()"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "code",
211 |    "execution_count": 6,
212 |    "metadata": {},
213 |    "outputs": [
214 |     {
215 |      "data": {
216 |       "text/plain": [
217 |        "0    402008\n",
218 |        "1    331504\n",
219 |        "2    371404\n",
220 |        "3    233419\n",
221 |        "4    542905\n",
222 |        "dtype: int64"
223 |       ]
224 |      },
225 |      "execution_count": 6,
226 |      "metadata": {},
227 |      "output_type": "execute_result"
228 |     }
229 |    ],
230 |    "source": [
231 |     "import numpy as np\n",
232 |     "np.sum(df, axis=1).head()"
233 |    ]
234 |   },
235 |   {
236 |    "cell_type": "code",
237 |    "execution_count": 7,
238 |    "metadata": {},
239 |    "outputs": [],
240 |    "source": [
241 |     "y = df.iloc[:, 3]"
242 |    ]
243 |   },
244 |   {
245 |    "cell_type": "code",
246 |    "execution_count": 8,
247 |    "metadata": {},
248 |    "outputs": [
249 |     {
250 |      "data": {
251 |       "text/html": [
252 |        "<div>\n",
253 |        "<style scoped>\n",
254 |        "    .dataframe tbody tr th:only-of-type {\n",
255 |        "        vertical-align: middle;\n",
256 |        "    }\n",
257 |        "\n",
258 |        "    .dataframe tbody tr th {\n",
259 |        "        vertical-align: top;\n",
260 |        "    }\n",
261 |        "\n",
262 |        "    .dataframe thead th {\n",
263 |        "        text-align: right;\n",
264 |        "    }\n",
265 |        "</style>\n",
266 |        "<table border=\"1\" class=\"dataframe\">\n",
267 |        "  <thead>\n",
268 |        "    <tr style=\"text-align: right;\">\n",
269 |        "      <th></th>\n",
270 |        "      <th>00</th>\n",
271 |        "      <th>0</th>\n",
272 |        "      <th>1</th>\n",
273 |        "    </tr>\n",
274 |        "  </thead>\n",
275 |        "  <tbody>\n",
276 |        "    <tr>\n",
277 |        "      <th>0</th>\n",
278 |        "      <td>1</td>\n",
279 |        "      <td>2104</td>\n",
280 |        "      <td>3</td>\n",
281 |        "    </tr>\n",
282 |        "    <tr>\n",
283 |        "      <th>1</th>\n",
284 |        "      <td>1</td>\n",
285 |        "      <td>1600</td>\n",
286 |        "      <td>3</td>\n",
287 |        "    </tr>\n",
288 |        "    <tr>\n",
289 |        "      <th>2</th>\n",
290 |        "      <td>1</td>\n",
291 |        "      <td>2400</td>\n",
292 |        "      <td>3</td>\n",
293 |        "    </tr>\n",
294 |        "    <tr>\n",
295 |        "      <th>3</th>\n",
296 |        "      <td>1</td>\n",
297 |        "      <td>1416</td>\n",
298 |        "      <td>2</td>\n",
299 |        "    </tr>\n",
300 |        "    <tr>\n",
301 |        "      <th>4</th>\n",
302 |        "      <td>1</td>\n",
303 |        "      <td>3000</td>\n",
304 |        "      <td>4</td>\n",
305 |        "    </tr>\n",
306 |        "  </tbody>\n",
307 |        "</table>\n",
308 |        "</div>"
309 |       ],
310 |       "text/plain": [
311 |        "   00     0  1\n",
312 |        "0   1  2104  3\n",
313 |        "1   1  1600  3\n",
314 |        "2   1  2400  3\n",
315 |        "3   1  1416  2\n",
316 |        "4   1  3000  4"
317 |       ]
318 |      },
319 |      "execution_count": 8,
320 |      "metadata": {},
321 |      "output_type": "execute_result"
322 |     }
323 |    ],
324 |    "source": [
325 |     "X = df.drop(columns=2)\n",
326 |     "X.head()"
327 |    ]
328 |   },
329 |   {
330 |    "cell_type": "code",
331 |    "execution_count": 9,
332 |    "metadata": {},
333 |    "outputs": [],
334 |    "source": [
335 |     "for i in range(1, len(X.columns)):\n",
336 |     "    X[i-1] = X[i-1]/np.max(X[i-1])"
337 |    ]
338 |   },
339 |   {
340 |    "cell_type": "code",
341 |    "execution_count": 10,
342 |    "metadata": {},
343 |    "outputs": [
344 |     {
345 |      "data": {
346 |       "text/html": [
347 |        "<div>\n",
348 |        "<style scoped>\n",
349 |        "    .dataframe tbody tr th:only-of-type {\n",
350 |        "        vertical-align: middle;\n",
351 |        "    }\n",
352 |        "\n",
353 |        "    .dataframe tbody tr th {\n",
354 |        "        vertical-align: top;\n",
355 |        "    }\n",
356 |        "\n",
357 |        "    .dataframe thead th {\n",
358 |        "        text-align: right;\n",
359 |        "    }\n",
360 |        "</style>\n",
361 |        "<table border=\"1\" class=\"dataframe\">\n",
362 |        "  <thead>\n",
363 |        "    <tr style=\"text-align: right;\">\n",
364 |        "      <th></th>\n",
365 |        "      <th>00</th>\n",
366 |        "      <th>0</th>\n",
367 |        "      <th>1</th>\n",
368 |        "    </tr>\n",
369 |        "  </thead>\n",
370 |        "  <tbody>\n",
371 |        "    <tr>\n",
372 |        "      <th>0</th>\n",
373 |        "      <td>1</td>\n",
374 |        "      <td>0.469853</td>\n",
375 |        "      <td>0.6</td>\n",
376 |        "    </tr>\n",
377 |        "    <tr>\n",
378 |        "      <th>1</th>\n",
379 |        "      <td>1</td>\n",
380 |        "      <td>0.357302</td>\n",
381 |        "      <td>0.6</td>\n",
382 |        "    </tr>\n",
383 |        "    <tr>\n",
384 |        "      <th>2</th>\n",
385 |        "      <td>1</td>\n",
386 |        "      <td>0.535954</td>\n",
387 |        "      <td>0.6</td>\n",
388 |        "    </tr>\n",
389 |        "    <tr>\n",
390 |        "      <th>3</th>\n",
391 |        "      <td>1</td>\n",
392 |        "      <td>0.316213</td>\n",
393 |        "      <td>0.4</td>\n",
394 |        "    </tr>\n",
395 |        "    <tr>\n",
396 |        "      <th>4</th>\n",
397 |        "      <td>1</td>\n",
398 |        "      <td>0.669942</td>\n",
399 |        "      <td>0.8</td>\n",
400 |        "    </tr>\n",
401 |        "  </tbody>\n",
402 |        "</table>\n",
403 |        "</div>"
404 |       ],
405 |       "text/plain": [
406 |        "   00         0    1\n",
407 |        "0   1  0.469853  0.6\n",
408 |        "1   1  0.357302  0.6\n",
409 |        "2   1  0.535954  0.6\n",
410 |        "3   1  0.316213  0.4\n",
411 |        "4   1  0.669942  0.8"
412 |       ]
413 |      },
414 |      "execution_count": 10,
415 |      "metadata": {},
416 |      "output_type": "execute_result"
417 |     }
418 |    ],
419 |    "source": [
420 |     "X.head()"
421 |    ]
422 |   },
423 |   {
424 |    "cell_type": "code",
425 |    "execution_count": 11,
426 |    "metadata": {},
427 |    "outputs": [
428 |     {
429 |      "data": {
430 |       "text/plain": [
431 |        "array([0, 0, 0])"
432 |       ]
433 |      },
434 |      "execution_count": 11,
435 |      "metadata": {},
436 |      "output_type": "execute_result"
437 |     }
438 |    ],
439 |    "source": [
440 |     "import numpy as np\n",
441 |     "np.array([0]*len(X.columns))"
442 |    ]
443 |   },
444 |   {
445 |    "cell_type": "code",
446 |    "execution_count": 12,
447 |    "metadata": {},
448 |    "outputs": [],
449 |    "source": [
450 |     "theta = np.array([0]*len(X.columns))"
451 |    ]
452 |   },
453 |   {
454 |    "cell_type": "code",
455 |    "execution_count": 13,
456 |    "metadata": {},
457 |    "outputs": [],
458 |    "source": [
459 |     "y1 = theta*X\n",
460 |     "y1=np.sum(y1, axis=1)"
461 |    ]
462 |   },
463 |   {
464 |    "cell_type": "code",
465 |    "execution_count": 14,
466 |    "metadata": {},
467 |    "outputs": [
468 |     {
469 |      "data": {
470 |       "text/plain": [
471 |        "170206.32978723405"
472 |       ]
473 |      },
474 |      "execution_count": 14,
475 |      "metadata": {},
476 |      "output_type": "execute_result"
477 |     }
478 |    ],
479 |    "source": [
480 |     "cost = sum(np.sqrt((y1-y)**2))/(2*len(df))\n",
481 |     "cost"
482 |    ]
483 |   },
484 |   {
485 |    "cell_type": "code",
486 |    "execution_count": 15,
487 |    "metadata": {},
488 |    "outputs": [
489 |     {
490 |      "data": {
491 |       "text/plain": [
492 |        "3404.1265957446813"
493 |       ]
494 |      },
495 |      "execution_count": 15,
496 |      "metadata": {},
497 |      "output_type": "execute_result"
498 |     }
499 |    ],
500 |    "source": [
501 |     "alpha=0.01\n",
502 |     "0-alpha*(sum((y1-y)*X.iloc[:,0])/47) "
503 |    ]
504 |   },
505 |   {
506 |    "cell_type": "code",
507 |    "execution_count": 16,
508 |    "metadata": {},
509 |    "outputs": [],
510 |    "source": [
511 |     "def hypothesis(theta, X):\n",
512 |     "    return theta*X"
513 |    ]
514 |   },
515 |   {
516 |    "cell_type": "code",
517 |    "execution_count": 17,
518 |    "metadata": {},
519 |    "outputs": [],
520 |    "source": [
521 |     "def computeCost(X, y, theta):\n",
522 |     "    y1 = hypothesis(theta, X)\n",
523 |     "    y1=np.sum(y1, axis=1)\n",
524 |     "    return sum(np.sqrt((y1-y)**2))/(2*47)"
525 |    ]
526 |   },
527 |   {
528 |    "cell_type": "code",
529 |    "execution_count": 18,
530 |    "metadata": {},
531 |    "outputs": [],
532 |    "source": [
533 |     "alpha=0.05\n",
534 |     "i=1500\n",
535 |     "theta = np.array([0.0]*len(X.columns))\n",
536 |     "def gradientDescent(X, y, theta, alpha, i):\n",
537 |     "    #theta = np.array([0]*len(X.columns))\n",
538 |     "    J = []\n",
539 |     "    k = 0\n",
540 |     "    while k < i:        \n",
541 |     "        y1 = hypothesis(theta, X)\n",
542 |     "        y1 = np.sum(y1, axis=1)\n",
543 |     "        for c in range(0, len(X.columns)):\n",
544 |     "            theta[c] = theta[c] - alpha*(sum((y1-y)*X.iloc[:,c])/len(X))\n",
545 |     "        j = computeCost(X, y, theta)\n",
546 |     "        J.append(j)\n",
547 |     "        k += 1\n",
548 |     "    return J, j, theta"
549 |    ]
550 |   },
551 |   {
552 |    "cell_type": "code",
553 |    "execution_count": 67,
554 |    "metadata": {},
555 |    "outputs": [],
556 |    "source": [
557 |     "J, j, theta = gradientDescent(X, y, theta, 0.05, 10000)"
558 |    ]
559 |   },
560 |   {
561 |    "cell_type": "code",
562 |    "execution_count": 68,
563 |    "metadata": {},
564 |    "outputs": [],
565 |    "source": [
566 |     "y_hat = hypothesis(theta, X)\n",
567 |     "y_hat = np.sum(y_hat, axis=1)"
568 |    ]
569 |   },
570 |   {
571 |    "cell_type": "code",
572 |    "execution_count": 61,
573 |    "metadata": {},
574 |    "outputs": [
575 |     {
576 |      "data": {
577 |       "text/plain": [
578 |        "range(0, 47)"
579 |       ]
580 |      },
581 |      "execution_count": 61,
582 |      "metadata": {},
583 |      "output_type": "execute_result"
584 |     }
585 |    ],
586 |    "source": [
587 |     "range(len(y))"
588 |    ]
589 |   },
590 |   {
591 |    "cell_type": "code",
592 |    "execution_count": 69,
593 |    "metadata": {},
594 |    "outputs": [
595 |     {
596 |      "data": {
597 |       "text/plain": [
598 |        "0     356283.051438\n",
599 |        "1     286120.953677\n",
600 |        "2     397489.362821\n",
601 |        "3     269244.015544\n",
602 |        "4     472277.873709\n",
603 |        "5     330979.204608\n",
604 |        "6     276933.059923\n",
605 |        "7     262037.535200\n",
606 |        "8     255494.641163\n",
607 |        "9     271364.639466\n",
608 |        "10    324714.731594\n",
609 |        "11    341805.158249\n",
610 |        "12    326492.001992\n",
611 |        "13    669293.213632\n",
612 |        "14    239903.063883\n",
613 |        "15    374830.515708\n",
614 |        "16    255879.806447\n",
615 |        "17    235448.327517\n",
616 |        "18    417846.563740\n",
617 |        "19    476593.399563\n",
618 |        "20    309369.109086\n",
619 |        "21    334951.376939\n",
620 |        "22    286677.795723\n",
621 |        "23    327777.362845\n",
622 |        "24    604913.024851\n",
623 |        "25    216515.697962\n",
624 |        "26    266353.061054\n",
625 |        "27    415029.887261\n",
626 |        "28    369647.260535\n",
627 |        "29    430482.254030\n",
628 |        "30    328130.061879\n",
629 |        "31    220070.238759\n",
630 |        "32    338635.782737\n",
631 |        "33    500087.509745\n",
632 |        "34    306756.575619\n",
633 |        "35    263429.640314\n",
634 |        "36    235865.959051\n",
635 |        "37    351443.149788\n",
636 |        "38    641418.645096\n",
637 |        "39    355619.465131\n",
638 |        "40    303768.222379\n",
639 |        "41    374937.259969\n",
640 |        "42    411999.722260\n",
641 |        "43    230436.749105\n",
642 |        "44    190729.287098\n",
643 |        "45    312464.206588\n",
644 |        "46    230854.380640\n",
645 |        "dtype: float64"
646 |       ]
647 |      },
648 |      "execution_count": 69,
649 |      "metadata": {},
650 |      "output_type": "execute_result"
651 |     }
652 |    ],
653 |    "source": [
654 |     "y_hat"
655 |    ]
656 |   },
657 |   {
658 |    "cell_type": "code",
659 |    "execution_count": 23,
660 |    "metadata": {},
661 |    "outputs": [
662 |     {
663 |      "data": {
664 |       "text/plain": [
665 |        "47"
666 |       ]
667 |      },
668 |      "execution_count": 23,
669 |      "metadata": {},
670 |      "output_type": "execute_result"
671 |     }
672 |    ],
673 |    "source": [
674 |     "len(y)"
675 |    ]
676 |   },
677 |   {
678 |    "cell_type": "code",
679 |    "execution_count": 33,
680 |    "metadata": {},
681 |    "outputs": [
682 |     {
683 |      "data": {
684 |       "text/plain": [
685 |        "47"
686 |       ]
687 |      },
688 |      "execution_count": 33,
689 |      "metadata": {},
690 |      "output_type": "execute_result"
691 |     }
692 |    ],
693 |    "source": [
694 |     "len(y_hat)"
695 |    ]
696 |   },
697 |   {
698 |    "cell_type": "code",
699 |    "execution_count": 70,
700 |    "metadata": {},
701 |    "outputs": [
702 |     {
703 |      "data": {
704 |       "image/png": "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\n",
705 |       "text/plain": [
706 |        "<Figure size 432x288 with 1 Axes>"
707 |       ]
708 |      },
709 |      "metadata": {
710 |       "needs_background": "light"
711 |      },
712 |      "output_type": "display_data"
713 |     }
714 |    ],
715 |    "source": [
716 |     "%matplotlib inline\n",
717 |     "import matplotlib.pyplot as plt\n",
718 |     "plt.figure()\n",
719 |     "plt.scatter(x=list(range(0, 47)),y= y, color='blue')         \n",
720 |     "plt.scatter(x=list(range(0, 47)), y=y_hat, color='red')\n",
721 |     "plt.show()"
722 |    ]
723 |   },
724 |   {
725 |    "cell_type": "code",
726 |    "execution_count": 172,
727 |    "metadata": {},
728 |    "outputs": [],
729 |    "source": [
730 |     "X2 = df.drop(columns=2)"
731 |    ]
732 |   },
733 |   {
734 |    "cell_type": "code",
735 |    "execution_count": 173,
736 |    "metadata": {},
737 |    "outputs": [],
738 |    "source": [
739 |     "X1 = np.sum(X, axis=1)"
740 |    ]
741 |   },
742 |   {
743 |    "cell_type": "code",
744 |    "execution_count": 174,
745 |    "metadata": {},
746 |    "outputs": [],
747 |    "source": [
748 |     "X2_sum = np.sum(X2, axis=1)"
749 |    ]
750 |   },
751 |   {
752 |    "cell_type": "code",
753 |    "execution_count": 72,
754 |    "metadata": {},
755 |    "outputs": [
756 |     {
757 |      "data": {
758 |       "text/plain": [
759 |        "25751.391715683636"
760 |       ]
761 |      },
762 |      "execution_count": 72,
763 |      "metadata": {},
764 |      "output_type": "execute_result"
765 |     }
766 |    ],
767 |    "source": [
768 |     "cost = sum(np.sqrt((y_hat-y)**2))/(2*len(df))\n",
769 |     "cost"
770 |    ]
771 |   },
772 |   {
773 |    "cell_type": "code",
774 |    "execution_count": 73,
775 |    "metadata": {},
776 |    "outputs": [
777 |     {
778 |      "data": {
779 |       "image/png": "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\n",
780 |       "text/plain": [
781 |        "<Figure size 432x288 with 1 Axes>"
782 |       ]
783 |      },
784 |      "metadata": {
785 |       "needs_background": "light"
786 |      },
787 |      "output_type": "display_data"
788 |     }
789 |    ],
790 |    "source": [
791 |     "plt.figure()\n",
792 |     "plt.scatter(x=list(range(0, 10000)), y=J)\n",
793 |     "plt.show()"
794 |    ]
795 |   },
796 |   {
797 |    "cell_type": "code",
798 |    "execution_count": null,
799 |    "metadata": {},
800 |    "outputs": [],
801 |    "source": []
802 |   }
803 |  ],
804 |  "metadata": {
805 |   "kernelspec": {
806 |    "display_name": "Python 3",
807 |    "language": "python",
808 |    "name": "python3"
809 |   },
810 |   "language_info": {
811 |    "codemirror_mode": {
812 |     "name": "ipython",
813 |     "version": 3
814 |    },
815 |    "file_extension": ".py",
816 |    "mimetype": "text/x-python",
817 |    "name": "python",
818 |    "nbconvert_exporter": "python",
819 |    "pygments_lexer": "ipython3",
820 |    "version": "3.7.1"
821 |   }
822 |  },
823 |  "nbformat": 4,
824 |  "nbformat_minor": 2
825 | }
826 | 


--------------------------------------------------------------------------------
/linearRegressionWithOneVariable.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import pandas as pd"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 4,
 16 |    "metadata": {},
 17 |    "outputs": [
 18 |     {
 19 |      "data": {
 20 |       "text/html": [
 21 |        "<div>\n",
 22 |        "<style scoped>\n",
 23 |        "    .dataframe tbody tr th:only-of-type {\n",
 24 |        "        vertical-align: middle;\n",
 25 |        "    }\n",
 26 |        "\n",
 27 |        "    .dataframe tbody tr th {\n",
 28 |        "        vertical-align: top;\n",
 29 |        "    }\n",
 30 |        "\n",
 31 |        "    .dataframe thead th {\n",
 32 |        "        text-align: right;\n",
 33 |        "    }\n",
 34 |        "</style>\n",
 35 |        "<table border=\"1\" class=\"dataframe\">\n",
 36 |        "  <thead>\n",
 37 |        "    <tr style=\"text-align: right;\">\n",
 38 |        "      <th></th>\n",
 39 |        "      <th>0</th>\n",
 40 |        "      <th>1</th>\n",
 41 |        "    </tr>\n",
 42 |        "  </thead>\n",
 43 |        "  <tbody>\n",
 44 |        "    <tr>\n",
 45 |        "      <th>0</th>\n",
 46 |        "      <td>6.1101</td>\n",
 47 |        "      <td>17.5920</td>\n",
 48 |        "    </tr>\n",
 49 |        "    <tr>\n",
 50 |        "      <th>1</th>\n",
 51 |        "      <td>5.5277</td>\n",
 52 |        "      <td>9.1302</td>\n",
 53 |        "    </tr>\n",
 54 |        "    <tr>\n",
 55 |        "      <th>2</th>\n",
 56 |        "      <td>8.5186</td>\n",
 57 |        "      <td>13.6620</td>\n",
 58 |        "    </tr>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>3</th>\n",
 61 |        "      <td>7.0032</td>\n",
 62 |        "      <td>11.8540</td>\n",
 63 |        "    </tr>\n",
 64 |        "    <tr>\n",
 65 |        "      <th>4</th>\n",
 66 |        "      <td>5.8598</td>\n",
 67 |        "      <td>6.8233</td>\n",
 68 |        "    </tr>\n",
 69 |        "  </tbody>\n",
 70 |        "</table>\n",
 71 |        "</div>"
 72 |       ],
 73 |       "text/plain": [
 74 |        "        0        1\n",
 75 |        "0  6.1101  17.5920\n",
 76 |        "1  5.5277   9.1302\n",
 77 |        "2  8.5186  13.6620\n",
 78 |        "3  7.0032  11.8540\n",
 79 |        "4  5.8598   6.8233"
 80 |       ]
 81 |      },
 82 |      "execution_count": 4,
 83 |      "metadata": {},
 84 |      "output_type": "execute_result"
 85 |     }
 86 |    ],
 87 |    "source": [
 88 |     "df = pd.read_csv('ex1data1.txt', header = None)\n",
 89 |     "df.head()"
 90 |    ]
 91 |   },
 92 |   {
 93 |    "cell_type": "code",
 94 |    "execution_count": 5,
 95 |    "metadata": {},
 96 |    "outputs": [],
 97 |    "source": [
 98 |     "m = len(df)"
 99 |    ]
100 |   },
101 |   {
102 |    "cell_type": "code",
103 |    "execution_count": 6,
104 |    "metadata": {},
105 |    "outputs": [
106 |     {
107 |      "data": {
108 |       "text/plain": [
109 |        "97"
110 |       ]
111 |      },
112 |      "execution_count": 6,
113 |      "metadata": {},
114 |      "output_type": "execute_result"
115 |     }
116 |    ],
117 |    "source": [
118 |     "m"
119 |    ]
120 |   },
121 |   {
122 |    "cell_type": "code",
123 |    "execution_count": 13,
124 |    "metadata": {},
125 |    "outputs": [],
126 |    "source": [
127 |     "def hypothesis(theta, X):\n",
128 |     "    return theta[0] + theta[1]*X"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 111,
134 |    "metadata": {},
135 |    "outputs": [],
136 |    "source": [
137 |     "def cost_calc(theta, X, y):\n",
138 |     "    return (1/2*m) * np.sum((hypothesis(theta, X) - y)**2)"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 112,
144 |    "metadata": {},
145 |    "outputs": [],
146 |    "source": [
147 |     "m = len(df)\n",
148 |     "def gradient_descent(theta, X, y, epoch, alpha):\n",
149 |     "    cost = []\n",
150 |     "    i = 0\n",
151 |     "    while i < epoch:\n",
152 |     "        hx = hypothesis(theta, X)\n",
153 |     "        theta[0] -= alpha*(sum(hx-y)/m)\n",
154 |     "        theta[1] -= (alpha * np.sum((hx - y) * X))/m\n",
155 |     "        cost.append(cost_calc(theta, X, y))\n",
156 |     "        i += 1\n",
157 |     "    return theta, cost"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 113,
163 |    "metadata": {},
164 |    "outputs": [],
165 |    "source": [
166 |     "theta = [0,0]\n",
167 |     "def predict(theta, X, y, epoch, alpha):\n",
168 |     "    theta, cost = gradient_descent(theta, X, y, epoch, alpha)\n",
169 |     "    return hypothesis(theta, X), cost, theta"
170 |    ]
171 |   },
172 |   {
173 |    "cell_type": "code",
174 |    "execution_count": 129,
175 |    "metadata": {},
176 |    "outputs": [],
177 |    "source": [
178 |     "y_predict, cost, theta = predict(theta, df[0], df[1], 2000, 0.01)"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 130,
184 |    "metadata": {},
185 |    "outputs": [
186 |     {
187 |      "data": {
188 |       "text/plain": [
189 |        "[-3.8957755979447293, 1.1930331137194408]"
190 |       ]
191 |      },
192 |      "execution_count": 130,
193 |      "metadata": {},
194 |      "output_type": "execute_result"
195 |     }
196 |    ],
197 |    "source": [
198 |     "theta"
199 |    ]
200 |   },
201 |   {
202 |    "cell_type": "code",
203 |    "execution_count": 140,
204 |    "metadata": {},
205 |    "outputs": [
206 |     {
207 |      "data": {
208 |       "image/png": 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\n",
209 |       "text/plain": [
210 |        "<Figure size 432x288 with 1 Axes>"
211 |       ]
212 |      },
213 |      "metadata": {
214 |       "needs_background": "light"
215 |      },
216 |      "output_type": "display_data"
217 |     }
218 |    ],
219 |    "source": [
220 |     "%matplotlib inline\n",
221 |     "import matplotlib.pyplot as plt\n",
222 |     "plt.figure()\n",
223 |     "plt.scatter(df[0], df[1], label = 'Original y')\n",
224 |     "plt.scatter(df[0], y_predict, label = 'predicted y')\n",
225 |     "plt.legend(loc = \"upper left\")\n",
226 |     "plt.xlabel(\"input feature\")\n",
227 |     "plt.ylabel(\"Original and Predicted Output\")\n",
228 |     "plt.show()"
229 |    ]
230 |   },
231 |   {
232 |    "cell_type": "code",
233 |    "execution_count": 133,
234 |    "metadata": {},
235 |    "outputs": [
236 |     {
237 |      "data": {
238 |       "image/png": 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\n",
239 |       "text/plain": [
240 |        "<Figure size 432x288 with 1 Axes>"
241 |       ]
242 |      },
243 |      "metadata": {
244 |       "needs_background": "light"
245 |      },
246 |      "output_type": "display_data"
247 |     }
248 |    ],
249 |    "source": [
250 |     "%matplotlib inline\n",
251 |     "import matplotlib.pyplot as plt\n",
252 |     "plt.figure()\n",
253 |     "plt.scatter(df[0], df[1])\n",
254 |     "plt.show()"
255 |    ]
256 |   },
257 |   {
258 |    "cell_type": "code",
259 |    "execution_count": 132,
260 |    "metadata": {},
261 |    "outputs": [
262 |     {
263 |      "data": {
264 |       "image/png": 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\n",
265 |       "text/plain": [
266 |        "<Figure size 432x288 with 1 Axes>"
267 |       ]
268 |      },
269 |      "metadata": {
270 |       "needs_background": "light"
271 |      },
272 |      "output_type": "display_data"
273 |     }
274 |    ],
275 |    "source": [
276 |     "plt.figure()\n",
277 |     "plt.scatter(range(len(cost)), cost)\n",
278 |     "plt.show()"
279 |    ]
280 |   },
281 |   {
282 |    "cell_type": "code",
283 |    "execution_count": 135,
284 |    "metadata": {},
285 |    "outputs": [
286 |     {
287 |      "data": {
288 |       "text/html": [
289 |        "<div>\n",
290 |        "<style scoped>\n",
291 |        "    .dataframe tbody tr th:only-of-type {\n",
292 |        "        vertical-align: middle;\n",
293 |        "    }\n",
294 |        "\n",
295 |        "    .dataframe tbody tr th {\n",
296 |        "        vertical-align: top;\n",
297 |        "    }\n",
298 |        "\n",
299 |        "    .dataframe thead th {\n",
300 |        "        text-align: right;\n",
301 |        "    }\n",
302 |        "</style>\n",
303 |        "<table border=\"1\" class=\"dataframe\">\n",
304 |        "  <thead>\n",
305 |        "    <tr style=\"text-align: right;\">\n",
306 |        "      <th></th>\n",
307 |        "      <th>X</th>\n",
308 |        "      <th>Y</th>\n",
309 |        "    </tr>\n",
310 |        "  </thead>\n",
311 |        "  <tbody>\n",
312 |        "    <tr>\n",
313 |        "      <th>0</th>\n",
314 |        "      <td>7.6</td>\n",
315 |        "      <td>157</td>\n",
316 |        "    </tr>\n",
317 |        "    <tr>\n",
318 |        "      <th>1</th>\n",
319 |        "      <td>7.1</td>\n",
320 |        "      <td>174</td>\n",
321 |        "    </tr>\n",
322 |        "    <tr>\n",
323 |        "      <th>2</th>\n",
324 |        "      <td>8.2</td>\n",
325 |        "      <td>175</td>\n",
326 |        "    </tr>\n",
327 |        "    <tr>\n",
328 |        "      <th>3</th>\n",
329 |        "      <td>7.5</td>\n",
330 |        "      <td>188</td>\n",
331 |        "    </tr>\n",
332 |        "    <tr>\n",
333 |        "      <th>4</th>\n",
334 |        "      <td>7.4</td>\n",
335 |        "      <td>171</td>\n",
336 |        "    </tr>\n",
337 |        "    <tr>\n",
338 |        "      <th>5</th>\n",
339 |        "      <td>7.8</td>\n",
340 |        "      <td>143</td>\n",
341 |        "    </tr>\n",
342 |        "    <tr>\n",
343 |        "      <th>6</th>\n",
344 |        "      <td>7.3</td>\n",
345 |        "      <td>217</td>\n",
346 |        "    </tr>\n",
347 |        "    <tr>\n",
348 |        "      <th>7</th>\n",
349 |        "      <td>8.0</td>\n",
350 |        "      <td>190</td>\n",
351 |        "    </tr>\n",
352 |        "    <tr>\n",
353 |        "      <th>8</th>\n",
354 |        "      <td>7.1</td>\n",
355 |        "      <td>142</td>\n",
356 |        "    </tr>\n",
357 |        "    <tr>\n",
358 |        "      <th>9</th>\n",
359 |        "      <td>7.5</td>\n",
360 |        "      <td>190</td>\n",
361 |        "    </tr>\n",
362 |        "    <tr>\n",
363 |        "      <th>10</th>\n",
364 |        "      <td>8.1</td>\n",
365 |        "      <td>215</td>\n",
366 |        "    </tr>\n",
367 |        "    <tr>\n",
368 |        "      <th>11</th>\n",
369 |        "      <td>7.0</td>\n",
370 |        "      <td>199</td>\n",
371 |        "    </tr>\n",
372 |        "    <tr>\n",
373 |        "      <th>12</th>\n",
374 |        "      <td>7.3</td>\n",
375 |        "      <td>262</td>\n",
376 |        "    </tr>\n",
377 |        "    <tr>\n",
378 |        "      <th>13</th>\n",
379 |        "      <td>7.8</td>\n",
380 |        "      <td>105</td>\n",
381 |        "    </tr>\n",
382 |        "    <tr>\n",
383 |        "      <th>14</th>\n",
384 |        "      <td>7.3</td>\n",
385 |        "      <td>121</td>\n",
386 |        "    </tr>\n",
387 |        "    <tr>\n",
388 |        "      <th>15</th>\n",
389 |        "      <td>8.0</td>\n",
390 |        "      <td>81</td>\n",
391 |        "    </tr>\n",
392 |        "    <tr>\n",
393 |        "      <th>16</th>\n",
394 |        "      <td>8.5</td>\n",
395 |        "      <td>82</td>\n",
396 |        "    </tr>\n",
397 |        "    <tr>\n",
398 |        "      <th>17</th>\n",
399 |        "      <td>7.1</td>\n",
400 |        "      <td>210</td>\n",
401 |        "    </tr>\n",
402 |        "    <tr>\n",
403 |        "      <th>18</th>\n",
404 |        "      <td>8.2</td>\n",
405 |        "      <td>202</td>\n",
406 |        "    </tr>\n",
407 |        "    <tr>\n",
408 |        "      <th>19</th>\n",
409 |        "      <td>7.9</td>\n",
410 |        "      <td>155</td>\n",
411 |        "    </tr>\n",
412 |        "    <tr>\n",
413 |        "      <th>20</th>\n",
414 |        "      <td>7.6</td>\n",
415 |        "      <td>157</td>\n",
416 |        "    </tr>\n",
417 |        "    <tr>\n",
418 |        "      <th>21</th>\n",
419 |        "      <td>8.8</td>\n",
420 |        "      <td>147</td>\n",
421 |        "    </tr>\n",
422 |        "    <tr>\n",
423 |        "      <th>22</th>\n",
424 |        "      <td>7.2</td>\n",
425 |        "      <td>133</td>\n",
426 |        "    </tr>\n",
427 |        "    <tr>\n",
428 |        "      <th>23</th>\n",
429 |        "      <td>7.9</td>\n",
430 |        "      <td>53</td>\n",
431 |        "    </tr>\n",
432 |        "    <tr>\n",
433 |        "      <th>24</th>\n",
434 |        "      <td>8.1</td>\n",
435 |        "      <td>56</td>\n",
436 |        "    </tr>\n",
437 |        "    <tr>\n",
438 |        "      <th>25</th>\n",
439 |        "      <td>7.7</td>\n",
440 |        "      <td>113</td>\n",
441 |        "    </tr>\n",
442 |        "    <tr>\n",
443 |        "      <th>26</th>\n",
444 |        "      <td>8.4</td>\n",
445 |        "      <td>35</td>\n",
446 |        "    </tr>\n",
447 |        "    <tr>\n",
448 |        "      <th>27</th>\n",
449 |        "      <td>7.4</td>\n",
450 |        "      <td>125</td>\n",
451 |        "    </tr>\n",
452 |        "    <tr>\n",
453 |        "      <th>28</th>\n",
454 |        "      <td>7.3</td>\n",
455 |        "      <td>76</td>\n",
456 |        "    </tr>\n",
457 |        "    <tr>\n",
458 |        "      <th>29</th>\n",
459 |        "      <td>8.5</td>\n",
460 |        "      <td>48</td>\n",
461 |        "    </tr>\n",
462 |        "    <tr>\n",
463 |        "      <th>30</th>\n",
464 |        "      <td>7.8</td>\n",
465 |        "      <td>147</td>\n",
466 |        "    </tr>\n",
467 |        "    <tr>\n",
468 |        "      <th>31</th>\n",
469 |        "      <td>6.7</td>\n",
470 |        "      <td>117</td>\n",
471 |        "    </tr>\n",
472 |        "    <tr>\n",
473 |        "      <th>32</th>\n",
474 |        "      <td>7.1</td>\n",
475 |        "      <td>182</td>\n",
476 |        "    </tr>\n",
477 |        "    <tr>\n",
478 |        "      <th>33</th>\n",
479 |        "      <td>7.3</td>\n",
480 |        "      <td>87</td>\n",
481 |        "    </tr>\n",
482 |        "  </tbody>\n",
483 |        "</table>\n",
484 |        "</div>"
485 |       ],
486 |       "text/plain": [
487 |        "      X    Y\n",
488 |        "0   7.6  157\n",
489 |        "1   7.1  174\n",
490 |        "2   8.2  175\n",
491 |        "3   7.5  188\n",
492 |        "4   7.4  171\n",
493 |        "5   7.8  143\n",
494 |        "6   7.3  217\n",
495 |        "7   8.0  190\n",
496 |        "8   7.1  142\n",
497 |        "9   7.5  190\n",
498 |        "10  8.1  215\n",
499 |        "11  7.0  199\n",
500 |        "12  7.3  262\n",
501 |        "13  7.8  105\n",
502 |        "14  7.3  121\n",
503 |        "15  8.0   81\n",
504 |        "16  8.5   82\n",
505 |        "17  7.1  210\n",
506 |        "18  8.2  202\n",
507 |        "19  7.9  155\n",
508 |        "20  7.6  157\n",
509 |        "21  8.8  147\n",
510 |        "22  7.2  133\n",
511 |        "23  7.9   53\n",
512 |        "24  8.1   56\n",
513 |        "25  7.7  113\n",
514 |        "26  8.4   35\n",
515 |        "27  7.4  125\n",
516 |        "28  7.3   76\n",
517 |        "29  8.5   48\n",
518 |        "30  7.8  147\n",
519 |        "31  6.7  117\n",
520 |        "32  7.1  182\n",
521 |        "33  7.3   87"
522 |       ]
523 |      },
524 |      "execution_count": 135,
525 |      "metadata": {},
526 |      "output_type": "execute_result"
527 |     }
528 |    ],
529 |    "source": [
530 |     "data = pd.read_excel('pH_vs_bicarbonate.xlsx')\n",
531 |     "data"
532 |    ]
533 |   },
534 |   {
535 |    "cell_type": "code",
536 |    "execution_count": 137,
537 |    "metadata": {},
538 |    "outputs": [
539 |     {
540 |      "data": {
541 |       "image/png": "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\n",
542 |       "text/plain": [
543 |        "<Figure size 432x288 with 1 Axes>"
544 |       ]
545 |      },
546 |      "metadata": {
547 |       "needs_background": "light"
548 |      },
549 |      "output_type": "display_data"
550 |     }
551 |    ],
552 |    "source": [
553 |     "plt.figure()\n",
554 |     "plt.scatter(data['X'], data['Y'])\n",
555 |     "plt.show()"
556 |    ]
557 |   },
558 |   {
559 |    "cell_type": "code",
560 |    "execution_count": null,
561 |    "metadata": {},
562 |    "outputs": [],
563 |    "source": []
564 |   }
565 |  ],
566 |  "metadata": {
567 |   "kernelspec": {
568 |    "display_name": "Python 3",
569 |    "language": "python",
570 |    "name": "python3"
571 |   },
572 |   "language_info": {
573 |    "codemirror_mode": {
574 |     "name": "ipython",
575 |     "version": 3
576 |    },
577 |    "file_extension": ".py",
578 |    "mimetype": "text/x-python",
579 |    "name": "python",
580 |    "nbconvert_exporter": "python",
581 |    "pygments_lexer": "ipython3",
582 |    "version": "3.7.1"
583 |   }
584 |  },
585 |  "nbformat": 4,
586 |  "nbformat_minor": 2
587 | }
588 | 


--------------------------------------------------------------------------------
/linearRegressionWithOneVariable1.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import pandas as pd"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 4,
 16 |    "metadata": {},
 17 |    "outputs": [
 18 |     {
 19 |      "data": {
 20 |       "text/html": [
 21 |        "<div>\n",
 22 |        "<style scoped>\n",
 23 |        "    .dataframe tbody tr th:only-of-type {\n",
 24 |        "        vertical-align: middle;\n",
 25 |        "    }\n",
 26 |        "\n",
 27 |        "    .dataframe tbody tr th {\n",
 28 |        "        vertical-align: top;\n",
 29 |        "    }\n",
 30 |        "\n",
 31 |        "    .dataframe thead th {\n",
 32 |        "        text-align: right;\n",
 33 |        "    }\n",
 34 |        "</style>\n",
 35 |        "<table border=\"1\" class=\"dataframe\">\n",
 36 |        "  <thead>\n",
 37 |        "    <tr style=\"text-align: right;\">\n",
 38 |        "      <th></th>\n",
 39 |        "      <th>0</th>\n",
 40 |        "      <th>1</th>\n",
 41 |        "    </tr>\n",
 42 |        "  </thead>\n",
 43 |        "  <tbody>\n",
 44 |        "    <tr>\n",
 45 |        "      <th>0</th>\n",
 46 |        "      <td>6.1101</td>\n",
 47 |        "      <td>17.5920</td>\n",
 48 |        "    </tr>\n",
 49 |        "    <tr>\n",
 50 |        "      <th>1</th>\n",
 51 |        "      <td>5.5277</td>\n",
 52 |        "      <td>9.1302</td>\n",
 53 |        "    </tr>\n",
 54 |        "    <tr>\n",
 55 |        "      <th>2</th>\n",
 56 |        "      <td>8.5186</td>\n",
 57 |        "      <td>13.6620</td>\n",
 58 |        "    </tr>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>3</th>\n",
 61 |        "      <td>7.0032</td>\n",
 62 |        "      <td>11.8540</td>\n",
 63 |        "    </tr>\n",
 64 |        "    <tr>\n",
 65 |        "      <th>4</th>\n",
 66 |        "      <td>5.8598</td>\n",
 67 |        "      <td>6.8233</td>\n",
 68 |        "    </tr>\n",
 69 |        "  </tbody>\n",
 70 |        "</table>\n",
 71 |        "</div>"
 72 |       ],
 73 |       "text/plain": [
 74 |        "        0        1\n",
 75 |        "0  6.1101  17.5920\n",
 76 |        "1  5.5277   9.1302\n",
 77 |        "2  8.5186  13.6620\n",
 78 |        "3  7.0032  11.8540\n",
 79 |        "4  5.8598   6.8233"
 80 |       ]
 81 |      },
 82 |      "execution_count": 4,
 83 |      "metadata": {},
 84 |      "output_type": "execute_result"
 85 |     }
 86 |    ],
 87 |    "source": [
 88 |     "df = pd.read_csv('ex1data1.txt', header = None)\n",
 89 |     "df.head()"
 90 |    ]
 91 |   },
 92 |   {
 93 |    "cell_type": "code",
 94 |    "execution_count": 5,
 95 |    "metadata": {},
 96 |    "outputs": [],
 97 |    "source": [
 98 |     "m = len(df)"
 99 |    ]
100 |   },
101 |   {
102 |    "cell_type": "code",
103 |    "execution_count": 6,
104 |    "metadata": {},
105 |    "outputs": [
106 |     {
107 |      "data": {
108 |       "text/plain": [
109 |        "97"
110 |       ]
111 |      },
112 |      "execution_count": 6,
113 |      "metadata": {},
114 |      "output_type": "execute_result"
115 |     }
116 |    ],
117 |    "source": [
118 |     "m"
119 |    ]
120 |   },
121 |   {
122 |    "cell_type": "code",
123 |    "execution_count": 13,
124 |    "metadata": {},
125 |    "outputs": [],
126 |    "source": [
127 |     "def hypothesis(theta, X):\n",
128 |     "    return theta[0] + theta[1]*X"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 111,
134 |    "metadata": {},
135 |    "outputs": [],
136 |    "source": [
137 |     "def cost_calc(theta, X, y):\n",
138 |     "    return (1/2*m) * np.sum((hypothesis(theta, X) - y)**2)"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 112,
144 |    "metadata": {},
145 |    "outputs": [],
146 |    "source": [
147 |     "m = len(df)\n",
148 |     "def gradient_descent(theta, X, y, epoch, alpha):\n",
149 |     "    cost = []\n",
150 |     "    i = 0\n",
151 |     "    while i < epoch:\n",
152 |     "        hx = hypothesis(theta, X)\n",
153 |     "        theta[0] -= alpha*(sum(hx-y)/m)\n",
154 |     "        theta[1] -= (alpha * np.sum((hx - y) * X))/m\n",
155 |     "        cost.append(cost_calc(theta, X, y))\n",
156 |     "        i += 1\n",
157 |     "    return theta, cost"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 113,
163 |    "metadata": {},
164 |    "outputs": [],
165 |    "source": [
166 |     "theta = [0,0]\n",
167 |     "def predict(theta, X, y, epoch, alpha):\n",
168 |     "    theta, cost = gradient_descent(theta, X, y, epoch, alpha)\n",
169 |     "    return hypothesis(theta, X), cost, theta"
170 |    ]
171 |   },
172 |   {
173 |    "cell_type": "code",
174 |    "execution_count": 129,
175 |    "metadata": {},
176 |    "outputs": [],
177 |    "source": [
178 |     "y_predict, cost, theta = predict(theta, df[0], df[1], 2000, 0.01)"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 130,
184 |    "metadata": {},
185 |    "outputs": [
186 |     {
187 |      "data": {
188 |       "text/plain": [
189 |        "[-3.8957755979447293, 1.1930331137194408]"
190 |       ]
191 |      },
192 |      "execution_count": 130,
193 |      "metadata": {},
194 |      "output_type": "execute_result"
195 |     }
196 |    ],
197 |    "source": [
198 |     "theta"
199 |    ]
200 |   },
201 |   {
202 |    "cell_type": "code",
203 |    "execution_count": 140,
204 |    "metadata": {},
205 |    "outputs": [
206 |     {
207 |      "data": {
208 |       "image/png": 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\n",
209 |       "text/plain": [
210 |        "<Figure size 432x288 with 1 Axes>"
211 |       ]
212 |      },
213 |      "metadata": {
214 |       "needs_background": "light"
215 |      },
216 |      "output_type": "display_data"
217 |     }
218 |    ],
219 |    "source": [
220 |     "%matplotlib inline\n",
221 |     "import matplotlib.pyplot as plt\n",
222 |     "plt.figure()\n",
223 |     "plt.scatter(df[0], df[1], label = 'Original y')\n",
224 |     "plt.scatter(df[0], y_predict, label = 'predicted y')\n",
225 |     "plt.legend(loc = \"upper left\")\n",
226 |     "plt.xlabel(\"input feature\")\n",
227 |     "plt.ylabel(\"Original and Predicted Output\")\n",
228 |     "plt.show()"
229 |    ]
230 |   },
231 |   {
232 |    "cell_type": "code",
233 |    "execution_count": 133,
234 |    "metadata": {},
235 |    "outputs": [
236 |     {
237 |      "data": {
238 |       "image/png": 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AUhdb5O7+rKR3tPv4tNQSapRuVUHUt44rM7NaYmqaxKXwJC5VW+bnb/lp5ERaDJYB0EyuR3Ym0Wxlm7SqIKJax7WkGjVzYivqk3UnQ/ZpyQPlU4hE3iw5NasESWv62Wat47gl0hpFda9InVe90JIHyinXA4Kk+ME1UV0no8NDLVWDRI3CjKsJb6WkcGhwQB8+b7VGm3T31D6swsR1EzHsHSin3CfyZslpYqoSWiHSSpdK3AdFXOu4lT74G69Ypy+Mr9NDmy+KTOa1bxztDJZh2DtQTrlP5FFJqJZwZxpGfi6xExN9nLhWbLPW8cRURUeOLv4gCdP4DaFZsm536TaGvQPllPs+8qh67QGz0L7pWuFIZWZWf3PnDk0+d0hfGF8X+fxxrdhNG9aGDsK58OyR0IucQ4NLdGzBT5hVsdkI0ai+/3ZW64mKlWHvQH/LfSKPSk5JLjC6pO8+/LwknZDMW1lMIirhRl3kXL70dYlHb6a9tBoDhoByKsTiy2FVK7X67SRM0lf/7JzIxY4bJRn23myIfjcGIAEor7gBQbnvI48SNQthGNdr09ZGtaQHzOiPBlBIuU/kUVUlko5fEEyi1ucd1Se+4N50CtlGTMMKIC9yn8jjRjk2K+WrV2spp9WSbreyBADSlvuLnUlqo8MuiNYzSReePRK5b/39rUj7YiUAtCP3LfIkLejG1vEpgyf+WS7p7u0VTUxVNL5+VO9/16gs4n4AKJrcJ/KkfdH1S6UtW/q6Rc9TP8jngT3TiypOGMoOoKhyn8hrre1lpwzWbXX9w389EblCfVx3DEPZAfST3Cfyml/PLRy/PTu3oMNH5iJXqI9b6Z7SQQD9pBCJPG6q2PpukWbzn9RWur/w7BFKBwH0jUIk8iRdHpWZ2eM154ePzEXuNzs3rwf2TFM6CKBv5L78UIqeOKvegFniRR72z8xSOgigbxSiRZ5kOP68e+KLlbW+8GYLSgBAURSiRd640HGY2ujOJBNpHTl6TH8/sUt3b6+wLBqAwivE7If1wmYvrM1WKCVfCDlq7czR4SE9tPmi2BiYKhZAVuJmPyxEi7xe2Jzba944pOvu2ql5d5mkpScP6NWj8xoI5hofCJlzPOrjiwWOARRNIfrIG9WP4rzw7BE99Myh44naJb16dF5/cd5qPXPjpdq75bLQhSOisMAxgKIpZCKvd8cjvwzd/t2Hnz9+8XLALHSfRkkmz2JUKIC8KUzXSli/tKSmre1al0ezfer7ymuTZ429ZXlkN0lUKSSjQgH0SiFa5GGLS2z6j53627t2NH3c7Ny8rrtrZ8M8La8ZMGt58iwWlACQN4VI5GH90nMLroUEXd/z7nrl18c0OHBi98rQ4EBkS70yMxtZV86CEgDypqOuFTO7RNLXJA1I+pa7b0klqgad9j/PLbiGhwa19HUnJV7AuVk1SjdGhVLSCKBdbSdyMxuQ9HVJfyJpn6Sfm9m97v7faQVXk2SIfpwXZ+e043MXL9rerO68fkm5bqKkEUAnOulaOVfS0+7+rLsflfQ9SZenE9aJNm1Yq2R1J9HCLkbWd5NEyaIahZJGAJ3oJJGPSqqv/dsXbEvd+PrRyAE8STS7GBm3gHMW1SiUNALoRCeJPKyRvCjfmtlGM5s0s8np6em2DxaVaKNqxAfMWroY2ctqFBa6ANCJThL5Pkln1P3+Zkn7G3dy95vdfczdx0ZGWl+pviYq0V75u2eEbv/yB9+hX2y5TA9tvihRP3Mvq1EoaQTQiU6qVn4u6SwzO1NSRdKHJP15KlGFCJtjpVbZMfaW5alUfPRqjvJmfxsAxGk7kbv7MTO7RtI2VcsPb3X3J1KLrAX9sEhEP/wNAHqjozpyd79P0n0pxdIUJXoAEK4QIzslSvQAIEphEnlUKV6nA4UAoOgKk8ijSvFMYq1NAKVWmEQeNbrTpY66V1iAGUDRFSaRNxvd2e4IyLDpca/fuotkDqBQCpPIpejRne2OgOQCKoB+UKhEnvYISOY4AdAPCpXI0x5GzxwnAPpBYdbsrElzBOSmDWsXzUfOHCcAiqZwiTxNzHECoB+UOpFLzHECoPgK1UcOAFgs9y1yFiUGgOZynciZ8RAA4uW6a4UBOwAQL9eJnAE7ABAv14mcATsAEC/XiZxFiQEgXq4vdjJgBwDi5TqRSwzYAYA4ue5aAQDEI5EDQMGRyAGg4EjkAFBwJHIAKDhzj1rSuAsHM5uW9FybD18h6VcphtNtxNt9RYuZeLuraPFKyWN+i7uPRN2ZaSLvhJlNuvtYr+NIini7r2gxE293FS1eKb2Y6VoBgIIjkQNAwRUpkd/c6wBaRLzdV7SYibe7ihavlFLMhekjBwCEK1KLHAAQIneJ3Mz2mtkuM9thZpMh95uZ/YuZPW1mj5nZO3sRZxDL2iDO2r+XzOzahn0uMLMX6/b5bMYx3mpmL5jZ43XblpvZ/Wb2VPBzWcRjrwr2ecrMrupxzDeZ2Z7gPb/HzIYjHtv0/Mkw3hvMrFL3vl8a8dhLzOzJ4Hze3MN476yLda+Z7Yh4bC9e3zPM7AEz221mT5jZJ4PtuTyPm8TbvXPY3XP1T9JeSSua3H+ppB9JMknnSXqk1zEHcQ1I+l9V6z3rt18g6Qc9jOuPJL1T0uN12/5Z0ubg9mZJXwx53HJJzwY/lwW3l/Uw5oslnRTc/mJYzEnOnwzjvUHS3yU4Z56R9FZJJ0vaKeltvYi34f4vS/psjl7flZLeGdw+VdL/SHpbXs/jJvF27RzOXYs8gcslfcerHpY0bGYrex2UpHdLesbd2x3w1BXu/qCkQw2bL5d0W3D7NknjIQ/dIOl+dz/k7ocl3S/pkq4FWicsZnf/sbsfC359WNKbs4gliYjXOIlzJT3t7s+6+1FJ31P1vemqZvGamUn6oKQ7uh1HUu5+wN0fDW6/LGm3pFHl9DyOireb53AeE7lL+rGZbTezjSH3j0r6Zd3v+4JtvfYhRZ/8v2dmO83sR2b2O1kGFeF0dz8gVU86SaeF7JPX11mSrlb1W1mYuPMnS9cEX6Nvjfjan8fX+A8lHXT3pyLu7+nra2ZrJK2X9IgKcB43xFsv1XM4jwtLnO/u+83sNEn3m9meoAVRYyGP6WnpjZmdLOm9kq4PuftRVbtbXgn6SScknZVlfG3K3essSWb2GUnHJN0esUvc+ZOVb0j6vKqv2edV7a64umGfPL7GV6p5a7xnr6+ZvUHS3ZKudfeXql8e4h8Wsi2T17gx3rrtqZ/DuWuRu/v+4OcLku5R9etnvX2Szqj7/c2S9mcTXaQ/lfSoux9svMPdX3L3V4Lb90kaNLMVWQfY4GCtOyr4+ULIPrl7nYMLVe+R9GEPOhMbJTh/MuHuB9193t0XJH0zIo5cvcZmdpKkKyTdGbVPr15fMxtUNSne7u5bg825PY8j4u3aOZyrRG5mS83s1NptVS8OPN6w272SPmpV50l6sfb1qociWzFm9qag31Fmdq6qr/n/ZRhbmHsl1a7eXyXp+yH7bJN0sZktC7oFLg629YSZXSLpU5Le6+5HIvZJcv5kouG6zfsi4vi5pLPM7MzgW92HVH1veuWPJe1x931hd/bq9Q3+/9wiabe7f6Xurlyex1HxdvUc7ubV2zau9r5V1Sv3OyU9IekzwfZPSPpEcNskfV3Vq/27JI31OOZTVE3Mv1W3rT7ea4K/ZaeqFzh+P+P47pB0QNKcqq2Tj0l6o6SfSHoq+Lk82HdM0rfqHnu1pKeDf3/V45ifVrWvc0fw71+DfVdJuq/Z+dOjeP8tOD8fUzXhrGyMN/j9UlWrGp7pZbzB9m/Xztu6ffPw+v6Bqt0hj9W9/5fm9TxuEm/XzmFGdgJAweWqawUA0DoSOQAUHIkcAAqORA4ABUciB4CCI5EDQMGRyAGg4EjkAFBw/w8j6+eXSXXJ7AAAAABJRU5ErkJggg==\n",
239 |       "text/plain": [
240 |        "<Figure size 432x288 with 1 Axes>"
241 |       ]
242 |      },
243 |      "metadata": {
244 |       "needs_background": "light"
245 |      },
246 |      "output_type": "display_data"
247 |     }
248 |    ],
249 |    "source": [
250 |     "%matplotlib inline\n",
251 |     "import matplotlib.pyplot as plt\n",
252 |     "plt.figure()\n",
253 |     "plt.scatter(df[0], df[1])\n",
254 |     "plt.show()"
255 |    ]
256 |   },
257 |   {
258 |    "cell_type": "code",
259 |    "execution_count": null,
260 |    "metadata": {},
261 |    "outputs": [],
262 |    "source": []
263 |   }
264 |  ],
265 |  "metadata": {
266 |   "kernelspec": {
267 |    "display_name": "Python 3",
268 |    "language": "python",
269 |    "name": "python3"
270 |   },
271 |   "language_info": {
272 |    "codemirror_mode": {
273 |     "name": "ipython",
274 |     "version": 3
275 |    },
276 |    "file_extension": ".py",
277 |    "mimetype": "text/x-python",
278 |    "name": "python",
279 |    "nbconvert_exporter": "python",
280 |    "pygments_lexer": "ipython3",
281 |    "version": "3.7.1"
282 |   }
283 |  },
284 |  "nbformat": 4,
285 |  "nbformat_minor": 2
286 | }
287 | 


--------------------------------------------------------------------------------
/logisticRegressionWithOptimizationFunc.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 6,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 7,
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "data": {
 21 |       "text/html": [
 22 |        "<div>\n",
 23 |        "<style scoped>\n",
 24 |        "    .dataframe tbody tr th:only-of-type {\n",
 25 |        "        vertical-align: middle;\n",
 26 |        "    }\n",
 27 |        "\n",
 28 |        "    .dataframe tbody tr th {\n",
 29 |        "        vertical-align: top;\n",
 30 |        "    }\n",
 31 |        "\n",
 32 |        "    .dataframe thead th {\n",
 33 |        "        text-align: right;\n",
 34 |        "    }\n",
 35 |        "</style>\n",
 36 |        "<table border=\"1\" class=\"dataframe\">\n",
 37 |        "  <thead>\n",
 38 |        "    <tr style=\"text-align: right;\">\n",
 39 |        "      <th></th>\n",
 40 |        "      <th>0</th>\n",
 41 |        "      <th>1</th>\n",
 42 |        "      <th>2</th>\n",
 43 |        "    </tr>\n",
 44 |        "  </thead>\n",
 45 |        "  <tbody>\n",
 46 |        "    <tr>\n",
 47 |        "      <th>0</th>\n",
 48 |        "      <td>34.623660</td>\n",
 49 |        "      <td>78.024693</td>\n",
 50 |        "      <td>0</td>\n",
 51 |        "    </tr>\n",
 52 |        "    <tr>\n",
 53 |        "      <th>1</th>\n",
 54 |        "      <td>30.286711</td>\n",
 55 |        "      <td>43.894998</td>\n",
 56 |        "      <td>0</td>\n",
 57 |        "    </tr>\n",
 58 |        "    <tr>\n",
 59 |        "      <th>2</th>\n",
 60 |        "      <td>35.847409</td>\n",
 61 |        "      <td>72.902198</td>\n",
 62 |        "      <td>0</td>\n",
 63 |        "    </tr>\n",
 64 |        "    <tr>\n",
 65 |        "      <th>3</th>\n",
 66 |        "      <td>60.182599</td>\n",
 67 |        "      <td>86.308552</td>\n",
 68 |        "      <td>1</td>\n",
 69 |        "    </tr>\n",
 70 |        "    <tr>\n",
 71 |        "      <th>4</th>\n",
 72 |        "      <td>79.032736</td>\n",
 73 |        "      <td>75.344376</td>\n",
 74 |        "      <td>1</td>\n",
 75 |        "    </tr>\n",
 76 |        "  </tbody>\n",
 77 |        "</table>\n",
 78 |        "</div>"
 79 |       ],
 80 |       "text/plain": [
 81 |        "           0          1  2\n",
 82 |        "0  34.623660  78.024693  0\n",
 83 |        "1  30.286711  43.894998  0\n",
 84 |        "2  35.847409  72.902198  0\n",
 85 |        "3  60.182599  86.308552  1\n",
 86 |        "4  79.032736  75.344376  1"
 87 |       ]
 88 |      },
 89 |      "execution_count": 7,
 90 |      "metadata": {},
 91 |      "output_type": "execute_result"
 92 |     }
 93 |    ],
 94 |    "source": [
 95 |     "df = pd.read_csv('ex2data1.txt', header=None)\n",
 96 |     "df.head()"
 97 |    ]
 98 |   },
 99 |   {
100 |    "cell_type": "code",
101 |    "execution_count": 8,
102 |    "metadata": {},
103 |    "outputs": [
104 |     {
105 |      "data": {
106 |       "text/html": [
107 |        "<div>\n",
108 |        "<style scoped>\n",
109 |        "    .dataframe tbody tr th:only-of-type {\n",
110 |        "        vertical-align: middle;\n",
111 |        "    }\n",
112 |        "\n",
113 |        "    .dataframe tbody tr th {\n",
114 |        "        vertical-align: top;\n",
115 |        "    }\n",
116 |        "\n",
117 |        "    .dataframe thead th {\n",
118 |        "        text-align: right;\n",
119 |        "    }\n",
120 |        "</style>\n",
121 |        "<table border=\"1\" class=\"dataframe\">\n",
122 |        "  <thead>\n",
123 |        "    <tr style=\"text-align: right;\">\n",
124 |        "      <th></th>\n",
125 |        "      <th>0</th>\n",
126 |        "      <th>1</th>\n",
127 |        "    </tr>\n",
128 |        "  </thead>\n",
129 |        "  <tbody>\n",
130 |        "    <tr>\n",
131 |        "      <th>0</th>\n",
132 |        "      <td>34.623660</td>\n",
133 |        "      <td>78.024693</td>\n",
134 |        "    </tr>\n",
135 |        "    <tr>\n",
136 |        "      <th>1</th>\n",
137 |        "      <td>30.286711</td>\n",
138 |        "      <td>43.894998</td>\n",
139 |        "    </tr>\n",
140 |        "    <tr>\n",
141 |        "      <th>2</th>\n",
142 |        "      <td>35.847409</td>\n",
143 |        "      <td>72.902198</td>\n",
144 |        "    </tr>\n",
145 |        "  </tbody>\n",
146 |        "</table>\n",
147 |        "</div>"
148 |       ],
149 |       "text/plain": [
150 |        "           0          1\n",
151 |        "0  34.623660  78.024693\n",
152 |        "1  30.286711  43.894998\n",
153 |        "2  35.847409  72.902198"
154 |       ]
155 |      },
156 |      "execution_count": 8,
157 |      "metadata": {},
158 |      "output_type": "execute_result"
159 |     }
160 |    ],
161 |    "source": [
162 |     "X = df.iloc[:, :-1]\n",
163 |     "X.head(3)"
164 |    ]
165 |   },
166 |   {
167 |    "cell_type": "code",
168 |    "execution_count": 9,
169 |    "metadata": {},
170 |    "outputs": [],
171 |    "source": [
172 |     "y = df.iloc[:, -1]"
173 |    ]
174 |   },
175 |   {
176 |    "cell_type": "code",
177 |    "execution_count": 10,
178 |    "metadata": {},
179 |    "outputs": [
180 |     {
181 |      "data": {
182 |       "text/plain": [
183 |        "0    0\n",
184 |        "1    0\n",
185 |        "2    0\n",
186 |        "Name: 2, dtype: int64"
187 |       ]
188 |      },
189 |      "execution_count": 10,
190 |      "metadata": {},
191 |      "output_type": "execute_result"
192 |     }
193 |    ],
194 |    "source": [
195 |     "y[:3]"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": 11,
201 |    "metadata": {},
202 |    "outputs": [
203 |     {
204 |      "data": {
205 |       "text/plain": [
206 |        "array([[ 1.        , 34.62365962, 78.02469282],\n",
207 |        "       [ 1.        , 30.28671077, 43.89499752],\n",
208 |        "       [ 1.        , 35.84740877, 72.90219803],\n",
209 |        "       [ 1.        , 60.18259939, 86.3085521 ],\n",
210 |        "       [ 1.        , 79.03273605, 75.34437644],\n",
211 |        "       [ 1.        , 45.08327748, 56.31637178],\n",
212 |        "       [ 1.        , 61.10666454, 96.51142588],\n",
213 |        "       [ 1.        , 75.02474557, 46.55401354],\n",
214 |        "       [ 1.        , 76.0987867 , 87.42056972],\n",
215 |        "       [ 1.        , 84.43281996, 43.53339331]])"
216 |       ]
217 |      },
218 |      "execution_count": 11,
219 |      "metadata": {},
220 |      "output_type": "execute_result"
221 |     }
222 |    ],
223 |    "source": [
224 |     "X = np.c_[np.ones((X.shape[0], 1)), X]\n",
225 |     "X[:10]"
226 |    ]
227 |   },
228 |   {
229 |    "cell_type": "code",
230 |    "execution_count": 12,
231 |    "metadata": {},
232 |    "outputs": [
233 |     {
234 |      "data": {
235 |       "text/plain": [
236 |        "array([[0],\n",
237 |        "       [0],\n",
238 |        "       [0],\n",
239 |        "       [1],\n",
240 |        "       [1],\n",
241 |        "       [0],\n",
242 |        "       [1],\n",
243 |        "       [1],\n",
244 |        "       [1],\n",
245 |        "       [1]], dtype=int64)"
246 |       ]
247 |      },
248 |      "execution_count": 12,
249 |      "metadata": {},
250 |      "output_type": "execute_result"
251 |     }
252 |    ],
253 |    "source": [
254 |     "y = y[:, np.newaxis]\n",
255 |     "y[:10]"
256 |    ]
257 |   },
258 |   {
259 |    "cell_type": "code",
260 |    "execution_count": 13,
261 |    "metadata": {},
262 |    "outputs": [
263 |     {
264 |      "data": {
265 |       "text/plain": [
266 |        "array([[0.],\n",
267 |        "       [0.],\n",
268 |        "       [0.]])"
269 |       ]
270 |      },
271 |      "execution_count": 13,
272 |      "metadata": {},
273 |      "output_type": "execute_result"
274 |     }
275 |    ],
276 |    "source": [
277 |     "theta = np.zeros((X.shape[1], 1))\n",
278 |     "theta"
279 |    ]
280 |   },
281 |   {
282 |    "cell_type": "code",
283 |    "execution_count": 14,
284 |    "metadata": {},
285 |    "outputs": [
286 |     {
287 |      "data": {
288 |       "text/plain": [
289 |        "array([[0.],\n",
290 |        "       [0.],\n",
291 |        "       [0.],\n",
292 |        "       [0.],\n",
293 |        "       [0.]])"
294 |       ]
295 |      },
296 |      "execution_count": 14,
297 |      "metadata": {},
298 |      "output_type": "execute_result"
299 |     }
300 |    ],
301 |    "source": [
302 |     "z = np.dot(X, theta)\n",
303 |     "z[:5]"
304 |    ]
305 |   },
306 |   {
307 |    "cell_type": "code",
308 |    "execution_count": 15,
309 |    "metadata": {},
310 |    "outputs": [
311 |     {
312 |      "data": {
313 |       "text/plain": [
314 |        "array([[0.5],\n",
315 |        "       [0.5],\n",
316 |        "       [0.5],\n",
317 |        "       [0.5],\n",
318 |        "       [0.5]])"
319 |       ]
320 |      },
321 |      "execution_count": 15,
322 |      "metadata": {},
323 |      "output_type": "execute_result"
324 |     }
325 |    ],
326 |    "source": [
327 |     "h = 1/(1+np.exp(-z))\n",
328 |     "h[:5]"
329 |    ]
330 |   },
331 |   {
332 |    "cell_type": "code",
333 |    "execution_count": 16,
334 |    "metadata": {},
335 |    "outputs": [
336 |     {
337 |      "data": {
338 |       "text/plain": [
339 |        "100"
340 |       ]
341 |      },
342 |      "execution_count": 16,
343 |      "metadata": {},
344 |      "output_type": "execute_result"
345 |     }
346 |    ],
347 |    "source": [
348 |     "m = X.shape[0]\n",
349 |     "m"
350 |    ]
351 |   },
352 |   {
353 |    "cell_type": "code",
354 |    "execution_count": 17,
355 |    "metadata": {},
356 |    "outputs": [
357 |     {
358 |      "data": {
359 |       "text/plain": [
360 |        "0.6931471805599453"
361 |       ]
362 |      },
363 |      "execution_count": 17,
364 |      "metadata": {},
365 |      "output_type": "execute_result"
366 |     }
367 |    ],
368 |    "source": [
369 |     "cost = -(1/m)*np.sum(y*np.log(h) + (1-y)*np.log(1-h))\n",
370 |     "cost"
371 |    ]
372 |   },
373 |   {
374 |    "cell_type": "code",
375 |    "execution_count": 18,
376 |    "metadata": {},
377 |    "outputs": [
378 |     {
379 |      "data": {
380 |       "text/plain": [
381 |        "array([[ -0.1       ],\n",
382 |        "       [-12.00921659],\n",
383 |        "       [-11.26284221]])"
384 |       ]
385 |      },
386 |      "execution_count": 18,
387 |      "metadata": {},
388 |      "output_type": "execute_result"
389 |     }
390 |    ],
391 |    "source": [
392 |     "grad = (1/m) * np.dot(X.T, (h-y))\n",
393 |     "grad"
394 |    ]
395 |   },
396 |   {
397 |    "cell_type": "code",
398 |    "execution_count": 19,
399 |    "metadata": {},
400 |    "outputs": [],
401 |    "source": [
402 |     "def sigmoid(x, theta):\n",
403 |     "    z= np.dot(x, theta)\n",
404 |     "    return 1/(1+np.exp(-z))"
405 |    ]
406 |   },
407 |   {
408 |    "cell_type": "code",
409 |    "execution_count": 20,
410 |    "metadata": {},
411 |    "outputs": [],
412 |    "source": [
413 |     "def hypothesis(theta, x):\n",
414 |     "    return sigmoid(x, theta)"
415 |    ]
416 |   },
417 |   {
418 |    "cell_type": "code",
419 |    "execution_count": 21,
420 |    "metadata": {},
421 |    "outputs": [],
422 |    "source": [
423 |     "def cost_function(theta, x, y):\n",
424 |     "    m = X.shape[0]\n",
425 |     "    h = hypothesis(theta, x)\n",
426 |     "    return -(1/m)*np.sum(y*np.log(h) + (1-y)*np.log(1-h))"
427 |    ]
428 |   },
429 |   {
430 |    "cell_type": "code",
431 |    "execution_count": 22,
432 |    "metadata": {},
433 |    "outputs": [],
434 |    "source": [
435 |     "def gradient(theta, x, y):\n",
436 |     "    m = X.shape[0]\n",
437 |     "    h = hypothesis(theta, x)\n",
438 |     "    return (1/m) * np.dot(X.T, (h-y))    "
439 |    ]
440 |   },
441 |   {
442 |    "cell_type": "code",
443 |    "execution_count": 23,
444 |    "metadata": {},
445 |    "outputs": [],
446 |    "source": [
447 |     "from scipy.optimize import minimize,fmin_tnc\n",
448 |     "def fit(x, y, theta):\n",
449 |     "    opt_weights = fmin_tnc(func=cost_function, x0=theta, fprime=gradient, args=(x, y.flatten()))\n",
450 |     "    return opt_weights[0]\n",
451 |     "parameters = fit(X, y, theta)"
452 |    ]
453 |   },
454 |   {
455 |    "cell_type": "code",
456 |    "execution_count": 27,
457 |    "metadata": {},
458 |    "outputs": [
459 |     {
460 |      "data": {
461 |       "text/plain": [
462 |        "array([-25.16131854,   0.20623159,   0.20147149])"
463 |       ]
464 |      },
465 |      "execution_count": 27,
466 |      "metadata": {},
467 |      "output_type": "execute_result"
468 |     }
469 |    ],
470 |    "source": [
471 |     "parameters"
472 |    ]
473 |   },
474 |   {
475 |    "cell_type": "code",
476 |    "execution_count": 28,
477 |    "metadata": {},
478 |    "outputs": [],
479 |    "source": [
480 |     "h = hypothesis(parameters, X)"
481 |    ]
482 |   },
483 |   {
484 |    "cell_type": "code",
485 |    "execution_count": 36,
486 |    "metadata": {},
487 |    "outputs": [
488 |     {
489 |      "data": {
490 |       "text/plain": [
491 |        "array([9.10339294e-02, 4.22589668e-05, 4.39088910e-02, 9.90424697e-01,\n",
492 |        "       9.98198750e-01, 1.07975521e-02, 9.98978138e-01, 4.23227057e-01,\n",
493 |        "       9.99710020e-01, 7.35387955e-01, 9.09674394e-01, 2.85967738e-02,\n",
494 |        "       9.99270142e-01, 9.99853945e-01, 1.56037085e-01, 9.80959345e-01,\n",
495 |        "       4.27716900e-01, 1.48789237e-01, 9.99706543e-01, 5.74280760e-01,\n",
496 |        "       6.68350391e-02, 9.98627244e-01, 7.41803067e-03, 1.01155397e-04,\n",
497 |        "       9.91878247e-01, 8.55048631e-01, 6.00298722e-01, 8.65184829e-01,\n",
498 |        "       9.23893094e-02, 1.68289210e-02, 8.97130763e-01, 9.81037346e-01,\n",
499 |        "       1.54911024e-01, 3.91938615e-01, 7.41289966e-02, 3.31107865e-02,\n",
500 |        "       8.52267971e-01, 9.87564309e-01, 2.03987785e-01, 4.95799793e-02,\n",
501 |        "       9.70300369e-01, 6.11823902e-03, 9.99461484e-01, 5.02285122e-01,\n",
502 |        "       4.48870646e-03, 1.37023865e-01, 9.92991802e-01, 9.99996181e-01,\n",
503 |        "       9.99204915e-01, 9.99990761e-01, 9.98106782e-01, 9.99500487e-01,\n",
504 |        "       9.05015908e-01, 2.80614817e-03, 8.52272267e-03, 5.28909917e-02,\n",
505 |        "       9.99856630e-01, 6.93707647e-01, 9.85496664e-01, 9.95728611e-01,\n",
506 |        "       9.99531647e-01, 2.22376854e-04, 3.50540644e-03, 1.27066902e-04,\n",
507 |        "       7.16567336e-02, 4.08761466e-02, 9.44426172e-01, 1.00705520e-02,\n",
508 |        "       9.99952447e-01, 7.09300663e-01, 6.20395621e-05, 9.77396161e-01,\n",
509 |        "       9.99893358e-01, 8.84276399e-01, 9.05240586e-01, 9.99954904e-01,\n",
510 |        "       9.17694807e-01, 6.26743821e-01, 1.58401454e-02, 5.99469179e-01,\n",
511 |        "       9.99282492e-01, 9.73471586e-01, 8.94532155e-01, 2.03192640e-01,\n",
512 |        "       9.99941048e-01, 9.97982478e-01, 3.54560019e-01, 9.99820029e-01,\n",
513 |        "       9.99973211e-01, 1.06959272e-01, 9.99943953e-01, 9.99985225e-01,\n",
514 |        "       1.42428239e-03, 9.99321741e-01, 9.24570135e-01, 8.58639054e-01,\n",
515 |        "       7.50881910e-01, 9.99896606e-01, 3.39275423e-01, 9.99750932e-01])"
516 |       ]
517 |      },
518 |      "execution_count": 36,
519 |      "metadata": {},
520 |      "output_type": "execute_result"
521 |     }
522 |    ],
523 |    "source": [
524 |     "h"
525 |    ]
526 |   },
527 |   {
528 |    "cell_type": "code",
529 |    "execution_count": 42,
530 |    "metadata": {},
531 |    "outputs": [],
532 |    "source": [
533 |     "def predict(h):\n",
534 |     "    h1 = []\n",
535 |     "    for i in h:\n",
536 |     "        if i>=0.5:\n",
537 |     "            h1.append(1)\n",
538 |     "        else:\n",
539 |     "            h1.append(0)\n",
540 |     "    return h1\n",
541 |     "y_pred = predict(h)"
542 |    ]
543 |   },
544 |   {
545 |    "cell_type": "code",
546 |    "execution_count": 45,
547 |    "metadata": {},
548 |    "outputs": [
549 |     {
550 |      "data": {
551 |       "text/plain": [
552 |        "0.89"
553 |       ]
554 |      },
555 |      "execution_count": 45,
556 |      "metadata": {},
557 |      "output_type": "execute_result"
558 |     }
559 |    ],
560 |    "source": [
561 |     "accuracy = 0\n",
562 |     "for i in range(0, len(y_pred)):\n",
563 |     "    if y_pred[i] == y[i]:\n",
564 |     "        accuracy += 1\n",
565 |     "accuracy/len(y)"
566 |    ]
567 |   }
568 |  ],
569 |  "metadata": {
570 |   "kernelspec": {
571 |    "display_name": "Python 3",
572 |    "language": "python",
573 |    "name": "python3"
574 |   },
575 |   "language_info": {
576 |    "codemirror_mode": {
577 |     "name": "ipython",
578 |     "version": 3
579 |    },
580 |    "file_extension": ".py",
581 |    "mimetype": "text/x-python",
582 |    "name": "python",
583 |    "nbconvert_exporter": "python",
584 |    "pygments_lexer": "ipython3",
585 |    "version": "3.7.1"
586 |   }
587 |  },
588 |  "nbformat": 4,
589 |  "nbformat_minor": 2
590 | }
591 | 


--------------------------------------------------------------------------------
/polynomial regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 171,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "data": {
 10 |       "text/html": [
 11 |        "<div>\n",
 12 |        "<style scoped>\n",
 13 |        "    .dataframe tbody tr th:only-of-type {\n",
 14 |        "        vertical-align: middle;\n",
 15 |        "    }\n",
 16 |        "\n",
 17 |        "    .dataframe tbody tr th {\n",
 18 |        "        vertical-align: top;\n",
 19 |        "    }\n",
 20 |        "\n",
 21 |        "    .dataframe thead th {\n",
 22 |        "        text-align: right;\n",
 23 |        "    }\n",
 24 |        "</style>\n",
 25 |        "<table border=\"1\" class=\"dataframe\">\n",
 26 |        "  <thead>\n",
 27 |        "    <tr style=\"text-align: right;\">\n",
 28 |        "      <th></th>\n",
 29 |        "      <th>Position</th>\n",
 30 |        "      <th>Level</th>\n",
 31 |        "      <th>Salary</th>\n",
 32 |        "    </tr>\n",
 33 |        "  </thead>\n",
 34 |        "  <tbody>\n",
 35 |        "    <tr>\n",
 36 |        "      <th>0</th>\n",
 37 |        "      <td>Business Analyst</td>\n",
 38 |        "      <td>1</td>\n",
 39 |        "      <td>45000</td>\n",
 40 |        "    </tr>\n",
 41 |        "    <tr>\n",
 42 |        "      <th>1</th>\n",
 43 |        "      <td>Junior Consultant</td>\n",
 44 |        "      <td>2</td>\n",
 45 |        "      <td>50000</td>\n",
 46 |        "    </tr>\n",
 47 |        "    <tr>\n",
 48 |        "      <th>2</th>\n",
 49 |        "      <td>Senior Consultant</td>\n",
 50 |        "      <td>3</td>\n",
 51 |        "      <td>60000</td>\n",
 52 |        "    </tr>\n",
 53 |        "    <tr>\n",
 54 |        "      <th>3</th>\n",
 55 |        "      <td>Manager</td>\n",
 56 |        "      <td>4</td>\n",
 57 |        "      <td>80000</td>\n",
 58 |        "    </tr>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>4</th>\n",
 61 |        "      <td>Country Manager</td>\n",
 62 |        "      <td>5</td>\n",
 63 |        "      <td>110000</td>\n",
 64 |        "    </tr>\n",
 65 |        "  </tbody>\n",
 66 |        "</table>\n",
 67 |        "</div>"
 68 |       ],
 69 |       "text/plain": [
 70 |        "            Position  Level  Salary\n",
 71 |        "0   Business Analyst      1   45000\n",
 72 |        "1  Junior Consultant      2   50000\n",
 73 |        "2  Senior Consultant      3   60000\n",
 74 |        "3            Manager      4   80000\n",
 75 |        "4    Country Manager      5  110000"
 76 |       ]
 77 |      },
 78 |      "execution_count": 171,
 79 |      "metadata": {},
 80 |      "output_type": "execute_result"
 81 |     }
 82 |    ],
 83 |    "source": [
 84 |     "import pandas as pd\n",
 85 |     "df = pd.read_csv('position_salaries.csv')\n",
 86 |     "df.head()"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 172,
 92 |    "metadata": {},
 93 |    "outputs": [
 94 |     {
 95 |      "data": {
 96 |       "text/plain": [
 97 |        "10"
 98 |       ]
 99 |      },
100 |      "execution_count": 172,
101 |      "metadata": {},
102 |      "output_type": "execute_result"
103 |     }
104 |    ],
105 |    "source": [
106 |     "m=len(df)\n",
107 |     "m"
108 |    ]
109 |   },
110 |   {
111 |    "cell_type": "code",
112 |    "execution_count": 173,
113 |    "metadata": {},
114 |    "outputs": [
115 |     {
116 |      "data": {
117 |       "image/png": "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\n",
118 |       "text/plain": [
119 |        "<Figure size 432x288 with 1 Axes>"
120 |       ]
121 |      },
122 |      "metadata": {
123 |       "needs_background": "light"
124 |      },
125 |      "output_type": "display_data"
126 |     }
127 |    ],
128 |    "source": [
129 |     "%matplotlib inline\n",
130 |     "import matplotlib.pyplot as plt\n",
131 |     "plt.figure()\n",
132 |     "plt.scatter(df['Level'], df['Salary'])\n",
133 |     "plt.show()"
134 |    ]
135 |   },
136 |   {
137 |    "cell_type": "code",
138 |    "execution_count": 174,
139 |    "metadata": {},
140 |    "outputs": [
141 |     {
142 |      "data": {
143 |       "text/html": [
144 |        "<div>\n",
145 |        "<style scoped>\n",
146 |        "    .dataframe tbody tr th:only-of-type {\n",
147 |        "        vertical-align: middle;\n",
148 |        "    }\n",
149 |        "\n",
150 |        "    .dataframe tbody tr th {\n",
151 |        "        vertical-align: top;\n",
152 |        "    }\n",
153 |        "\n",
154 |        "    .dataframe thead th {\n",
155 |        "        text-align: right;\n",
156 |        "    }\n",
157 |        "</style>\n",
158 |        "<table border=\"1\" class=\"dataframe\">\n",
159 |        "  <thead>\n",
160 |        "    <tr style=\"text-align: right;\">\n",
161 |        "      <th></th>\n",
162 |        "      <th>00</th>\n",
163 |        "      <th>Position</th>\n",
164 |        "      <th>Level</th>\n",
165 |        "      <th>Salary</th>\n",
166 |        "    </tr>\n",
167 |        "  </thead>\n",
168 |        "  <tbody>\n",
169 |        "    <tr>\n",
170 |        "      <th>0</th>\n",
171 |        "      <td>1</td>\n",
172 |        "      <td>Business Analyst</td>\n",
173 |        "      <td>1</td>\n",
174 |        "      <td>45000</td>\n",
175 |        "    </tr>\n",
176 |        "    <tr>\n",
177 |        "      <th>1</th>\n",
178 |        "      <td>1</td>\n",
179 |        "      <td>Junior Consultant</td>\n",
180 |        "      <td>2</td>\n",
181 |        "      <td>50000</td>\n",
182 |        "    </tr>\n",
183 |        "    <tr>\n",
184 |        "      <th>2</th>\n",
185 |        "      <td>1</td>\n",
186 |        "      <td>Senior Consultant</td>\n",
187 |        "      <td>3</td>\n",
188 |        "      <td>60000</td>\n",
189 |        "    </tr>\n",
190 |        "    <tr>\n",
191 |        "      <th>3</th>\n",
192 |        "      <td>1</td>\n",
193 |        "      <td>Manager</td>\n",
194 |        "      <td>4</td>\n",
195 |        "      <td>80000</td>\n",
196 |        "    </tr>\n",
197 |        "    <tr>\n",
198 |        "      <th>4</th>\n",
199 |        "      <td>1</td>\n",
200 |        "      <td>Country Manager</td>\n",
201 |        "      <td>5</td>\n",
202 |        "      <td>110000</td>\n",
203 |        "    </tr>\n",
204 |        "  </tbody>\n",
205 |        "</table>\n",
206 |        "</div>"
207 |       ],
208 |       "text/plain": [
209 |        "   00           Position  Level  Salary\n",
210 |        "0   1   Business Analyst      1   45000\n",
211 |        "1   1  Junior Consultant      2   50000\n",
212 |        "2   1  Senior Consultant      3   60000\n",
213 |        "3   1            Manager      4   80000\n",
214 |        "4   1    Country Manager      5  110000"
215 |       ]
216 |      },
217 |      "execution_count": 174,
218 |      "metadata": {},
219 |      "output_type": "execute_result"
220 |     }
221 |    ],
222 |    "source": [
223 |     "df = pd.concat([pd.Series(1, index=df.index, name='00'), df], axis=1)\n",
224 |     "df.head()"
225 |    ]
226 |   },
227 |   {
228 |    "cell_type": "code",
229 |    "execution_count": 175,
230 |    "metadata": {},
231 |    "outputs": [],
232 |    "source": [
233 |     "df = df.drop(columns='Position')"
234 |    ]
235 |   },
236 |   {
237 |    "cell_type": "code",
238 |    "execution_count": 176,
239 |    "metadata": {},
240 |    "outputs": [
241 |     {
242 |      "data": {
243 |       "text/plain": [
244 |        "0     45000\n",
245 |        "1     50000\n",
246 |        "2     60000\n",
247 |        "3     80000\n",
248 |        "4    110000\n",
249 |        "Name: Salary, dtype: int64"
250 |       ]
251 |      },
252 |      "execution_count": 176,
253 |      "metadata": {},
254 |      "output_type": "execute_result"
255 |     }
256 |    ],
257 |    "source": [
258 |     "y = df['Salary']\n",
259 |     "y[:5]"
260 |    ]
261 |   },
262 |   {
263 |    "cell_type": "code",
264 |    "execution_count": 177,
265 |    "metadata": {},
266 |    "outputs": [],
267 |    "source": [
268 |     "X = df.drop(columns = 'Salary')"
269 |    ]
270 |   },
271 |   {
272 |    "cell_type": "code",
273 |    "execution_count": 178,
274 |    "metadata": {},
275 |    "outputs": [
276 |     {
277 |      "data": {
278 |       "text/html": [
279 |        "<div>\n",
280 |        "<style scoped>\n",
281 |        "    .dataframe tbody tr th:only-of-type {\n",
282 |        "        vertical-align: middle;\n",
283 |        "    }\n",
284 |        "\n",
285 |        "    .dataframe tbody tr th {\n",
286 |        "        vertical-align: top;\n",
287 |        "    }\n",
288 |        "\n",
289 |        "    .dataframe thead th {\n",
290 |        "        text-align: right;\n",
291 |        "    }\n",
292 |        "</style>\n",
293 |        "<table border=\"1\" class=\"dataframe\">\n",
294 |        "  <thead>\n",
295 |        "    <tr style=\"text-align: right;\">\n",
296 |        "      <th></th>\n",
297 |        "      <th>00</th>\n",
298 |        "      <th>Level</th>\n",
299 |        "    </tr>\n",
300 |        "  </thead>\n",
301 |        "  <tbody>\n",
302 |        "    <tr>\n",
303 |        "      <th>0</th>\n",
304 |        "      <td>1</td>\n",
305 |        "      <td>1</td>\n",
306 |        "    </tr>\n",
307 |        "    <tr>\n",
308 |        "      <th>1</th>\n",
309 |        "      <td>1</td>\n",
310 |        "      <td>2</td>\n",
311 |        "    </tr>\n",
312 |        "    <tr>\n",
313 |        "      <th>2</th>\n",
314 |        "      <td>1</td>\n",
315 |        "      <td>3</td>\n",
316 |        "    </tr>\n",
317 |        "    <tr>\n",
318 |        "      <th>3</th>\n",
319 |        "      <td>1</td>\n",
320 |        "      <td>4</td>\n",
321 |        "    </tr>\n",
322 |        "    <tr>\n",
323 |        "      <th>4</th>\n",
324 |        "      <td>1</td>\n",
325 |        "      <td>5</td>\n",
326 |        "    </tr>\n",
327 |        "  </tbody>\n",
328 |        "</table>\n",
329 |        "</div>"
330 |       ],
331 |       "text/plain": [
332 |        "   00  Level\n",
333 |        "0   1      1\n",
334 |        "1   1      2\n",
335 |        "2   1      3\n",
336 |        "3   1      4\n",
337 |        "4   1      5"
338 |       ]
339 |      },
340 |      "execution_count": 178,
341 |      "metadata": {},
342 |      "output_type": "execute_result"
343 |     }
344 |    ],
345 |    "source": [
346 |     "X.head()"
347 |    ]
348 |   },
349 |   {
350 |    "cell_type": "code",
351 |    "execution_count": 179,
352 |    "metadata": {},
353 |    "outputs": [],
354 |    "source": [
355 |     "X['Level1'] = X['Level']**2"
356 |    ]
357 |   },
358 |   {
359 |    "cell_type": "code",
360 |    "execution_count": 180,
361 |    "metadata": {},
362 |    "outputs": [],
363 |    "source": [
364 |     "X['Level2'] = X['Level']**3"
365 |    ]
366 |   },
367 |   {
368 |    "cell_type": "code",
369 |    "execution_count": 181,
370 |    "metadata": {},
371 |    "outputs": [
372 |     {
373 |      "data": {
374 |       "text/html": [
375 |        "<div>\n",
376 |        "<style scoped>\n",
377 |        "    .dataframe tbody tr th:only-of-type {\n",
378 |        "        vertical-align: middle;\n",
379 |        "    }\n",
380 |        "\n",
381 |        "    .dataframe tbody tr th {\n",
382 |        "        vertical-align: top;\n",
383 |        "    }\n",
384 |        "\n",
385 |        "    .dataframe thead th {\n",
386 |        "        text-align: right;\n",
387 |        "    }\n",
388 |        "</style>\n",
389 |        "<table border=\"1\" class=\"dataframe\">\n",
390 |        "  <thead>\n",
391 |        "    <tr style=\"text-align: right;\">\n",
392 |        "      <th></th>\n",
393 |        "      <th>00</th>\n",
394 |        "      <th>Level</th>\n",
395 |        "      <th>Level1</th>\n",
396 |        "      <th>Level2</th>\n",
397 |        "    </tr>\n",
398 |        "  </thead>\n",
399 |        "  <tbody>\n",
400 |        "    <tr>\n",
401 |        "      <th>0</th>\n",
402 |        "      <td>1</td>\n",
403 |        "      <td>1</td>\n",
404 |        "      <td>1</td>\n",
405 |        "      <td>1</td>\n",
406 |        "    </tr>\n",
407 |        "    <tr>\n",
408 |        "      <th>1</th>\n",
409 |        "      <td>1</td>\n",
410 |        "      <td>2</td>\n",
411 |        "      <td>4</td>\n",
412 |        "      <td>8</td>\n",
413 |        "    </tr>\n",
414 |        "    <tr>\n",
415 |        "      <th>2</th>\n",
416 |        "      <td>1</td>\n",
417 |        "      <td>3</td>\n",
418 |        "      <td>9</td>\n",
419 |        "      <td>27</td>\n",
420 |        "    </tr>\n",
421 |        "    <tr>\n",
422 |        "      <th>3</th>\n",
423 |        "      <td>1</td>\n",
424 |        "      <td>4</td>\n",
425 |        "      <td>16</td>\n",
426 |        "      <td>64</td>\n",
427 |        "    </tr>\n",
428 |        "    <tr>\n",
429 |        "      <th>4</th>\n",
430 |        "      <td>1</td>\n",
431 |        "      <td>5</td>\n",
432 |        "      <td>25</td>\n",
433 |        "      <td>125</td>\n",
434 |        "    </tr>\n",
435 |        "  </tbody>\n",
436 |        "</table>\n",
437 |        "</div>"
438 |       ],
439 |       "text/plain": [
440 |        "   00  Level  Level1  Level2\n",
441 |        "0   1      1       1       1\n",
442 |        "1   1      2       4       8\n",
443 |        "2   1      3       9      27\n",
444 |        "3   1      4      16      64\n",
445 |        "4   1      5      25     125"
446 |       ]
447 |      },
448 |      "execution_count": 181,
449 |      "metadata": {},
450 |      "output_type": "execute_result"
451 |     }
452 |    ],
453 |    "source": [
454 |     "X.head()"
455 |    ]
456 |   },
457 |   {
458 |    "cell_type": "code",
459 |    "execution_count": 182,
460 |    "metadata": {},
461 |    "outputs": [],
462 |    "source": [
463 |     "X = X/X.max()"
464 |    ]
465 |   },
466 |   {
467 |    "cell_type": "code",
468 |    "execution_count": 183,
469 |    "metadata": {},
470 |    "outputs": [
471 |     {
472 |      "data": {
473 |       "text/plain": [
474 |        "array([0, 0, 0, 0])"
475 |       ]
476 |      },
477 |      "execution_count": 183,
478 |      "metadata": {},
479 |      "output_type": "execute_result"
480 |     }
481 |    ],
482 |    "source": [
483 |     "import numpy as np\n",
484 |     "theta = np.array([0]*len(X.columns))\n",
485 |     "theta"
486 |    ]
487 |   },
488 |   {
489 |    "cell_type": "code",
490 |    "execution_count": null,
491 |    "metadata": {},
492 |    "outputs": [],
493 |    "source": [
494 |     "def hypothesis(X, theta):\n",
495 |     "    y1 = theta*X\n",
496 |     "    return np.sum(y1, axis=1)"
497 |    ]
498 |   },
499 |   {
500 |    "cell_type": "code",
501 |    "execution_count": 184,
502 |    "metadata": {},
503 |    "outputs": [],
504 |    "source": [
505 |     "def cost(X, y, theta):\n",
506 |     "    y1 = hypothesis(X, theta)\n",
507 |     "    return sum(np.sqrt((y1-y)**2))/(2*m) "
508 |    ]
509 |   },
510 |   {
511 |    "cell_type": "code",
512 |    "execution_count": 185,
513 |    "metadata": {},
514 |    "outputs": [],
515 |    "source": [
516 |     "def gradientDescent(X, y, theta, alpha, epoch):\n",
517 |     "    J=[]\n",
518 |     "    k=0\n",
519 |     "    while k < epoch:\n",
520 |     "        y1 = hypothesis(X, theta)\n",
521 |     "        for c in range(0, len(X.columns)):\n",
522 |     "            theta[c] = theta[c] - alpha*sum((y1-y)* X.iloc[:, c])/m\n",
523 |     "        j = cost(X, y, theta)\n",
524 |     "        J.append(j)\n",
525 |     "        k += 1\n",
526 |     "    return J, j, theta"
527 |    ]
528 |   },
529 |   {
530 |    "cell_type": "code",
531 |    "execution_count": 186,
532 |    "metadata": {},
533 |    "outputs": [],
534 |    "source": [
535 |     "theta = np.array([0.0]*len(X.columns))\n",
536 |     "J, j, theta = gradientDescent(X, y, theta, 0.05, 700)"
537 |    ]
538 |   },
539 |   {
540 |    "cell_type": "code",
541 |    "execution_count": 187,
542 |    "metadata": {},
543 |    "outputs": [
544 |     {
545 |      "data": {
546 |       "text/plain": [
547 |        "array([-38494.26294331,  66878.12378747, 287369.29171661, 460744.26580885])"
548 |       ]
549 |      },
550 |      "execution_count": 187,
551 |      "metadata": {},
552 |      "output_type": "execute_result"
553 |     }
554 |    ],
555 |    "source": [
556 |     "theta"
557 |    ]
558 |   },
559 |   {
560 |    "cell_type": "code",
561 |    "execution_count": 188,
562 |    "metadata": {},
563 |    "outputs": [],
564 |    "source": [
565 |     "y_hat = theta*X\n",
566 |     "y_hat = np.sum(y_hat, axis=1)"
567 |    ]
568 |   },
569 |   {
570 |    "cell_type": "code",
571 |    "execution_count": 190,
572 |    "metadata": {},
573 |    "outputs": [
574 |     {
575 |      "data": {
576 |       "image/png": 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\n",
577 |       "text/plain": [
578 |        "<Figure size 432x288 with 1 Axes>"
579 |       ]
580 |      },
581 |      "metadata": {
582 |       "needs_background": "light"
583 |      },
584 |      "output_type": "display_data"
585 |     }
586 |    ],
587 |    "source": [
588 |     "%matplotlib inline\n",
589 |     "import matplotlib.pyplot as plt\n",
590 |     "plt.figure()\n",
591 |     "plt.scatter(x=X['Level'],y= y)          \n",
592 |     "plt.scatter(x=X['Level'], y=y_hat)\n",
593 |     "plt.show()"
594 |    ]
595 |   },
596 |   {
597 |    "cell_type": "code",
598 |    "execution_count": null,
599 |    "metadata": {},
600 |    "outputs": [],
601 |    "source": []
602 |   }
603 |  ],
604 |  "metadata": {
605 |   "kernelspec": {
606 |    "display_name": "Python 3",
607 |    "language": "python",
608 |    "name": "python3"
609 |   },
610 |   "language_info": {
611 |    "codemirror_mode": {
612 |     "name": "ipython",
613 |     "version": 3
614 |    },
615 |    "file_extension": ".py",
616 |    "mimetype": "text/x-python",
617 |    "name": "python",
618 |    "nbconvert_exporter": "python",
619 |    "pygments_lexer": "ipython3",
620 |    "version": "3.7.1"
621 |   }
622 |  },
623 |  "nbformat": 4,
624 |  "nbformat_minor": 2
625 | }
626 | 


--------------------------------------------------------------------------------
/position_salaries.csv:
--------------------------------------------------------------------------------
 1 | 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
12 | 


--------------------------------------------------------------------------------