├── 01_Linear_Regression
    ├── Linear_Regression.ipynb
    └── headbrain.csv
├── 02_Logistic_Regression
    ├── Logistic_Regression.ipynb
    └── titanic.xls
├── 03_KNN_Classifier
    ├── KNN_Classifier.ipynb
    └── credit_data.csv
├── 04_Naive_Bayes_Classifier
    ├── Naive_Bayes_Classifier.ipynb
    └── credit_data.csv
├── 05_Support_Vector_Machine
    ├── Support_Vector_Machine_(Digit_Recognition).ipynb
    └── Support_Vector_Machine_(Iris_Dataset).ipynb
├── 06_Decision_Trees
    ├── Decision_Trees.ipynb
    └── iris_data.csv
├── 07_Random_Forest_Classifier
    ├── Random_Forest_Classifier.ipynb
    └── credit_data.csv
├── 08_Boosting
    ├── Boosting.ipynb
    └── wine.csv
├── 09_Clustering
    ├── DBSCAN.ipynb
    ├── Hierarchical_Clustering.ipynb
    ├── K_Means_Clustering.ipynb
    └── Principal_Component_Analysis.ipynb
└── README.md


/01_Linear_Regression/Linear_Regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Linear Regression.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": []
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     }
 14 |   },
 15 |   "cells": [
 16 |     {
 17 |       "cell_type": "markdown",
 18 |       "metadata": {
 19 |         "id": "jPpVjmofy4u6",
 20 |         "colab_type": "text"
 21 |       },
 22 |       "source": [
 23 |         "# **Linear Regression**\n",
 24 |         "\n",
 25 |         "Linear regression is a common Statistical Data Analysis technique. It is used to determine the extent to which there is a linear relationship between a dependent variable and one or more independent variables.\n",
 26 |         "\n",
 27 |         "We have implemented here using Python libraries and a ***headbrain.csv*** dataset (from Kaggle). \n",
 28 |         "\n",
 29 |         "By [Muhammad Huzaifa Shahbaz](https://www.linkedin.com/in/mhuzaifadev)"
 30 |       ]
 31 |     },
 32 |     {
 33 |       "cell_type": "markdown",
 34 |       "metadata": {
 35 |         "id": "QI18bF5Jutaa",
 36 |         "colab_type": "text"
 37 |       },
 38 |       "source": [
 39 |         "## **Importing libraries**\n",
 40 |         "\n",
 41 |         "We will import NumPy library as *np*, Pandas as *pd*, math, Matplotlib as *plt*, mean_squared_error from sklearn.metrics and Linear regression from sklearn.linear_model.   "
 42 |       ]
 43 |     },
 44 |     {
 45 |       "cell_type": "code",
 46 |       "metadata": {
 47 |         "id": "DU794seugbR3",
 48 |         "colab_type": "code",
 49 |         "colab": {}
 50 |       },
 51 |       "source": [
 52 |         "import numpy as np\n",
 53 |         "import pandas as pd\n",
 54 |         "import matplotlib.pyplot as plt\n",
 55 |         "from sklearn.linear_model import LinearRegression\n",
 56 |         "from sklearn.metrics import mean_squared_error\n",
 57 |         "import math "
 58 |       ],
 59 |       "execution_count": 0,
 60 |       "outputs": []
 61 |     },
 62 |     {
 63 |       "cell_type": "markdown",
 64 |       "metadata": {
 65 |         "id": "qdBizDuGv6U1",
 66 |         "colab_type": "text"
 67 |       },
 68 |       "source": [
 69 |         "## **DataFraming**\n",
 70 |         "\n",
 71 |         "Read .csv data into a Dataframe"
 72 |       ]
 73 |     },
 74 |     {
 75 |       "cell_type": "code",
 76 |       "metadata": {
 77 |         "id": "YNmbLZUewP0q",
 78 |         "colab_type": "code",
 79 |         "colab": {
 80 |           "base_uri": "https://localhost:8080/",
 81 |           "height": 439
 82 |         },
 83 |         "outputId": "e0c9f03e-b602-4573-b77d-298005bfd0d9"
 84 |       },
 85 |       "source": [
 86 |         "dataset = pd.read_csv(\"./headbrain.csv\")\n",
 87 |         "size = dataset['Head Size(cm^3)']\n",
 88 |         "weight = dataset['Brain Weight(grams)']\n",
 89 |         "\n",
 90 |         "print(size)\n",
 91 |         "print(weight)"
 92 |       ],
 93 |       "execution_count": 2,
 94 |       "outputs": [
 95 |         {
 96 |           "output_type": "stream",
 97 |           "text": [
 98 |             "0      4512\n",
 99 |             "1      3738\n",
100 |             "2      4261\n",
101 |             "3      3777\n",
102 |             "4      4177\n",
103 |             "       ... \n",
104 |             "232    3214\n",
105 |             "233    3394\n",
106 |             "234    3233\n",
107 |             "235    3352\n",
108 |             "236    3391\n",
109 |             "Name: Head Size(cm^3), Length: 237, dtype: int64\n",
110 |             "0      1530\n",
111 |             "1      1297\n",
112 |             "2      1335\n",
113 |             "3      1282\n",
114 |             "4      1590\n",
115 |             "       ... \n",
116 |             "232    1110\n",
117 |             "233    1215\n",
118 |             "234    1104\n",
119 |             "235    1170\n",
120 |             "236    1120\n",
121 |             "Name: Brain Weight(grams), Length: 237, dtype: int64\n"
122 |           ],
123 |           "name": "stdout"
124 |         }
125 |       ]
126 |     },
127 |     {
128 |       "cell_type": "markdown",
129 |       "metadata": {
130 |         "id": "-Lo-TWfLyHZd",
131 |         "colab_type": "text"
132 |       },
133 |       "source": [
134 |         "## **Converting dataframes into NumPy Arrays**\n",
135 |         "\n",
136 |         "Machine Learning handle arrays not dataframes"
137 |       ]
138 |     },
139 |     {
140 |       "cell_type": "code",
141 |       "metadata": {
142 |         "id": "h9-uO1pcyyUP",
143 |         "colab_type": "code",
144 |         "colab": {}
145 |       },
146 |       "source": [
147 |         " x = np.array(size).reshape(-1,1)\n",
148 |         " y = np.array(weight).reshape(-1,1)"
149 |       ],
150 |       "execution_count": 0,
151 |       "outputs": []
152 |     },
153 |     {
154 |       "cell_type": "markdown",
155 |       "metadata": {
156 |         "id": "XiPwFWyEy2oc",
157 |         "colab_type": "text"
158 |       },
159 |       "source": [
160 |         "## **Training the Model**\n",
161 |         "\n",
162 |         "We are using Linear regression model as imported from sklearn library and then it's being trained on x and y (any 2 major axis of datasets)"
163 |       ]
164 |     },
165 |     {
166 |       "cell_type": "code",
167 |       "metadata": {
168 |         "id": "-8bgiHGp0ELK",
169 |         "colab_type": "code",
170 |         "colab": {
171 |           "base_uri": "https://localhost:8080/",
172 |           "height": 34
173 |         },
174 |         "outputId": "f04ef507-4042-423e-e548-c30395f9884d"
175 |       },
176 |       "source": [
177 |         "model = LinearRegression()\n",
178 |         "model.fit(x,y)"
179 |       ],
180 |       "execution_count": 4,
181 |       "outputs": [
182 |         {
183 |           "output_type": "execute_result",
184 |           "data": {
185 |             "text/plain": [
186 |               "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
187 |             ]
188 |           },
189 |           "metadata": {
190 |             "tags": []
191 |           },
192 |           "execution_count": 4
193 |         }
194 |       ]
195 |     },
196 |     {
197 |       "cell_type": "markdown",
198 |       "metadata": {
199 |         "id": "j2Fy1UoQ0G2g",
200 |         "colab_type": "text"
201 |       },
202 |       "source": [
203 |         "## **Mean Sqaured Error & R - Squared Value**\n",
204 |         "\n",
205 |         "The **mean squared error** or mean squared deviation of an estimator measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss.\n",
206 |         "\n",
207 |         "The **coefficient of determination**, denoted R² or r² and pronounced \"R squared\", is the proportion of the variance in the dependent variable that is predictable from the independent variable."
208 |       ]
209 |     },
210 |     {
211 |       "cell_type": "code",
212 |       "metadata": {
213 |         "id": "K8UKYT-F08nX",
214 |         "colab_type": "code",
215 |         "colab": {
216 |           "base_uri": "https://localhost:8080/",
217 |           "height": 52
218 |         },
219 |         "outputId": "57af052f-9bbe-42da-f5d5-f6e5c7b27d35"
220 |       },
221 |       "source": [
222 |         "regression_model_mse = mean_squared_error(x,y)\n",
223 |         "print('Mean Squared Error:\\t',math.sqrt(regression_model_mse))\n",
224 |         "print(\"R squared value\\t\\t\",model.score(x,y))"
225 |       ],
226 |       "execution_count": 7,
227 |       "outputs": [
228 |         {
229 |           "output_type": "stream",
230 |           "text": [
231 |             "Mean Squared Error:\t 2367.495611943946\n",
232 |             "R squared value\t\t 0.639311719957\n"
233 |           ],
234 |           "name": "stdout"
235 |         }
236 |       ]
237 |     },
238 |     {
239 |       "cell_type": "markdown",
240 |       "metadata": {
241 |         "id": "bMnyRGHT1hmT",
242 |         "colab_type": "text"
243 |       },
244 |       "source": [
245 |         "**Getting b values after the model fit**\n",
246 |         "\n",
247 |         "Thus the b0 and b1"
248 |       ]
249 |     },
250 |     {
251 |       "cell_type": "code",
252 |       "metadata": {
253 |         "id": "qRV2OMY_12kP",
254 |         "colab_type": "code",
255 |         "colab": {
256 |           "base_uri": "https://localhost:8080/",
257 |           "height": 52
258 |         },
259 |         "outputId": "40482de6-966d-419c-8aa1-a32d20bc931a"
260 |       },
261 |       "source": [
262 |         "print(model.coef_[0])\n",
263 |         "print(model.intercept_[0])"
264 |       ],
265 |       "execution_count": 8,
266 |       "outputs": [
267 |         {
268 |           "output_type": "stream",
269 |           "text": [
270 |             "[0.26342934]\n",
271 |             "325.5734210494426\n"
272 |           ],
273 |           "name": "stdout"
274 |         }
275 |       ]
276 |     },
277 |     {
278 |       "cell_type": "markdown",
279 |       "metadata": {
280 |         "id": "8FU4kKET2Drz",
281 |         "colab_type": "text"
282 |       },
283 |       "source": [
284 |         "## **Visualization**\n",
285 |         "\n",
286 |         "Visualize the dataset with the fitted model using matplotlib."
287 |       ]
288 |     },
289 |     {
290 |       "cell_type": "code",
291 |       "metadata": {
292 |         "id": "NgDeBCym2S3x",
293 |         "colab_type": "code",
294 |         "colab": {
295 |           "base_uri": "https://localhost:8080/",
296 |           "height": 313
297 |         },
298 |         "outputId": "0936b932-374c-4f3a-f60f-89f9cd79557e"
299 |       },
300 |       "source": [
301 |         "plt.scatter(x,y, color = 'orange')\n",
302 |         "plt.plot(x,model.predict(x), color = 'black')\n",
303 |         "plt.title('Linear Regression')\n",
304 |         "plt.xlabel('Head Size')\n",
305 |         "plt.ylabel('Brain Weight')"
306 |       ],
307 |       "execution_count": 12,
308 |       "outputs": [
309 |         {
310 |           "output_type": "execute_result",
311 |           "data": {
312 |             "text/plain": [
313 |               "Text(0, 0.5, 'Brain Weight')"
314 |             ]
315 |           },
316 |           "metadata": {
317 |             "tags": []
318 |           },
319 |           "execution_count": 12
320 |         },
321 |         {
322 |           "output_type": "display_data",
323 |           "data": {
324 |             "image/png": 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\n",
325 |             "text/plain": [
326 |               "<Figure size 432x288 with 1 Axes>"
327 |             ]
328 |           },
329 |           "metadata": {
330 |             "tags": [],
331 |             "needs_background": "light"
332 |           }
333 |         }
334 |       ]
335 |     },
336 |     {
337 |       "cell_type": "markdown",
338 |       "metadata": {
339 |         "id": "HUgdZsIp6vKx",
340 |         "colab_type": "text"
341 |       },
342 |       "source": [
343 |         "## **Prediction**\n",
344 |         "\n",
345 |         "We will predict the Brain weight by giving an input of Brain Size"
346 |       ]
347 |     },
348 |     {
349 |       "cell_type": "code",
350 |       "metadata": {
351 |         "id": "TfgYfaHk67kX",
352 |         "colab_type": "code",
353 |         "colab": {
354 |           "base_uri": "https://localhost:8080/",
355 |           "height": 34
356 |         },
357 |         "outputId": "c6adf2de-9904-4337-c5ea-91a8b0534c23"
358 |       },
359 |       "source": [
360 |         "print('Prediction by the model:\\t', model.predict([[2000]]))"
361 |       ],
362 |       "execution_count": 14,
363 |       "outputs": [
364 |         {
365 |           "output_type": "stream",
366 |           "text": [
367 |             "Prediction by the model:\t [[852.43210003]]\n"
368 |           ],
369 |           "name": "stdout"
370 |         }
371 |       ]
372 |     }
373 |   ]
374 | }


--------------------------------------------------------------------------------
/01_Linear_Regression/headbrain.csv:
--------------------------------------------------------------------------------
  1 | Gender,Age Range,Head Size(cm^3),Brain Weight(grams)
  2 | 1,1,4512,1530
  3 | 1,1,3738,1297
  4 | 1,1,4261,1335
  5 | 1,1,3777,1282
  6 | 1,1,4177,1590
  7 | 1,1,3585,1300
  8 | 1,1,3785,1400
  9 | 1,1,3559,1255
 10 | 1,1,3613,1355
 11 | 1,1,3982,1375
 12 | 1,1,3443,1340
 13 | 1,1,3993,1380
 14 | 1,1,3640,1355
 15 | 1,1,4208,1522
 16 | 1,1,3832,1208
 17 | 1,1,3876,1405
 18 | 1,1,3497,1358
 19 | 1,1,3466,1292
 20 | 1,1,3095,1340
 21 | 1,1,4424,1400
 22 | 1,1,3878,1357
 23 | 1,1,4046,1287
 24 | 1,1,3804,1275
 25 | 1,1,3710,1270
 26 | 1,1,4747,1635
 27 | 1,1,4423,1505
 28 | 1,1,4036,1490
 29 | 1,1,4022,1485
 30 | 1,1,3454,1310
 31 | 1,1,4175,1420
 32 | 1,1,3787,1318
 33 | 1,1,3796,1432
 34 | 1,1,4103,1364
 35 | 1,1,4161,1405
 36 | 1,1,4158,1432
 37 | 1,1,3814,1207
 38 | 1,1,3527,1375
 39 | 1,1,3748,1350
 40 | 1,1,3334,1236
 41 | 1,1,3492,1250
 42 | 1,1,3962,1350
 43 | 1,1,3505,1320
 44 | 1,1,4315,1525
 45 | 1,1,3804,1570
 46 | 1,1,3863,1340
 47 | 1,1,4034,1422
 48 | 1,1,4308,1506
 49 | 1,1,3165,1215
 50 | 1,1,3641,1311
 51 | 1,1,3644,1300
 52 | 1,1,3891,1224
 53 | 1,1,3793,1350
 54 | 1,1,4270,1335
 55 | 1,1,4063,1390
 56 | 1,1,4012,1400
 57 | 1,1,3458,1225
 58 | 1,1,3890,1310
 59 | 1,2,4166,1560
 60 | 1,2,3935,1330
 61 | 1,2,3669,1222
 62 | 1,2,3866,1415
 63 | 1,2,3393,1175
 64 | 1,2,4442,1330
 65 | 1,2,4253,1485
 66 | 1,2,3727,1470
 67 | 1,2,3329,1135
 68 | 1,2,3415,1310
 69 | 1,2,3372,1154
 70 | 1,2,4430,1510
 71 | 1,2,4381,1415
 72 | 1,2,4008,1468
 73 | 1,2,3858,1390
 74 | 1,2,4121,1380
 75 | 1,2,4057,1432
 76 | 1,2,3824,1240
 77 | 1,2,3394,1195
 78 | 1,2,3558,1225
 79 | 1,2,3362,1188
 80 | 1,2,3930,1252
 81 | 1,2,3835,1315
 82 | 1,2,3830,1245
 83 | 1,2,3856,1430
 84 | 1,2,3249,1279
 85 | 1,2,3577,1245
 86 | 1,2,3933,1309
 87 | 1,2,3850,1412
 88 | 1,2,3309,1120
 89 | 1,2,3406,1220
 90 | 1,2,3506,1280
 91 | 1,2,3907,1440
 92 | 1,2,4160,1370
 93 | 1,2,3318,1192
 94 | 1,2,3662,1230
 95 | 1,2,3899,1346
 96 | 1,2,3700,1290
 97 | 1,2,3779,1165
 98 | 1,2,3473,1240
 99 | 1,2,3490,1132
100 | 1,2,3654,1242
101 | 1,2,3478,1270
102 | 1,2,3495,1218
103 | 1,2,3834,1430
104 | 1,2,3876,1588
105 | 1,2,3661,1320
106 | 1,2,3618,1290
107 | 1,2,3648,1260
108 | 1,2,4032,1425
109 | 1,2,3399,1226
110 | 1,2,3916,1360
111 | 1,2,4430,1620
112 | 1,2,3695,1310
113 | 1,2,3524,1250
114 | 1,2,3571,1295
115 | 1,2,3594,1290
116 | 1,2,3383,1290
117 | 1,2,3499,1275
118 | 1,2,3589,1250
119 | 1,2,3900,1270
120 | 1,2,4114,1362
121 | 1,2,3937,1300
122 | 1,2,3399,1173
123 | 1,2,4200,1256
124 | 1,2,4488,1440
125 | 1,2,3614,1180
126 | 1,2,4051,1306
127 | 1,2,3782,1350
128 | 1,2,3391,1125
129 | 1,2,3124,1165
130 | 1,2,4053,1312
131 | 1,2,3582,1300
132 | 1,2,3666,1270
133 | 1,2,3532,1335
134 | 1,2,4046,1450
135 | 1,2,3667,1310
136 | 2,1,2857,1027
137 | 2,1,3436,1235
138 | 2,1,3791,1260
139 | 2,1,3302,1165
140 | 2,1,3104,1080
141 | 2,1,3171,1127
142 | 2,1,3572,1270
143 | 2,1,3530,1252
144 | 2,1,3175,1200
145 | 2,1,3438,1290
146 | 2,1,3903,1334
147 | 2,1,3899,1380
148 | 2,1,3401,1140
149 | 2,1,3267,1243
150 | 2,1,3451,1340
151 | 2,1,3090,1168
152 | 2,1,3413,1322
153 | 2,1,3323,1249
154 | 2,1,3680,1321
155 | 2,1,3439,1192
156 | 2,1,3853,1373
157 | 2,1,3156,1170
158 | 2,1,3279,1265
159 | 2,1,3707,1235
160 | 2,1,4006,1302
161 | 2,1,3269,1241
162 | 2,1,3071,1078
163 | 2,1,3779,1520
164 | 2,1,3548,1460
165 | 2,1,3292,1075
166 | 2,1,3497,1280
167 | 2,1,3082,1180
168 | 2,1,3248,1250
169 | 2,1,3358,1190
170 | 2,1,3803,1374
171 | 2,1,3566,1306
172 | 2,1,3145,1202
173 | 2,1,3503,1240
174 | 2,1,3571,1316
175 | 2,1,3724,1280
176 | 2,1,3615,1350
177 | 2,1,3203,1180
178 | 2,1,3609,1210
179 | 2,1,3561,1127
180 | 2,1,3979,1324
181 | 2,1,3533,1210
182 | 2,1,3689,1290
183 | 2,1,3158,1100
184 | 2,1,4005,1280
185 | 2,1,3181,1175
186 | 2,1,3479,1160
187 | 2,1,3642,1205
188 | 2,1,3632,1163
189 | 2,2,3069,1022
190 | 2,2,3394,1243
191 | 2,2,3703,1350
192 | 2,2,3165,1237
193 | 2,2,3354,1204
194 | 2,2,3000,1090
195 | 2,2,3687,1355
196 | 2,2,3556,1250
197 | 2,2,2773,1076
198 | 2,2,3058,1120
199 | 2,2,3344,1220
200 | 2,2,3493,1240
201 | 2,2,3297,1220
202 | 2,2,3360,1095
203 | 2,2,3228,1235
204 | 2,2,3277,1105
205 | 2,2,3851,1405
206 | 2,2,3067,1150
207 | 2,2,3692,1305
208 | 2,2,3402,1220
209 | 2,2,3995,1296
210 | 2,2,3318,1175
211 | 2,2,2720,955
212 | 2,2,2937,1070
213 | 2,2,3580,1320
214 | 2,2,2939,1060
215 | 2,2,2989,1130
216 | 2,2,3586,1250
217 | 2,2,3156,1225
218 | 2,2,3246,1180
219 | 2,2,3170,1178
220 | 2,2,3268,1142
221 | 2,2,3389,1130
222 | 2,2,3381,1185
223 | 2,2,2864,1012
224 | 2,2,3740,1280
225 | 2,2,3479,1103
226 | 2,2,3647,1408
227 | 2,2,3716,1300
228 | 2,2,3284,1246
229 | 2,2,4204,1380
230 | 2,2,3735,1350
231 | 2,2,3218,1060
232 | 2,2,3685,1350
233 | 2,2,3704,1220
234 | 2,2,3214,1110
235 | 2,2,3394,1215
236 | 2,2,3233,1104
237 | 2,2,3352,1170
238 | 2,2,3391,1120
239 | 


--------------------------------------------------------------------------------
/02_Logistic_Regression/Logistic_Regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Logistic Regression.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": []
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     }
 14 |   },
 15 |   "cells": [
 16 |     {
 17 |       "cell_type": "markdown",
 18 |       "metadata": {
 19 |         "id": "EeWekSPEB-Y-",
 20 |         "colab_type": "text"
 21 |       },
 22 |       "source": [
 23 |         "## **Logistic Regression**\n",
 24 |         "\n",
 25 |         "Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression).\n",
 26 |         "\n",
 27 |         "For Example: spam detection for emails, predicting if a customer will default in a loan, etc.\n",
 28 |         "\n",
 29 |         "By [Muhammad Huzaifa Shahbaz](https://www.linkedin.com/in/mhuzaifadev)"
 30 |       ]
 31 |     },
 32 |     {
 33 |       "cell_type": "markdown",
 34 |       "metadata": {
 35 |         "id": "fRehRCOpBXAB",
 36 |         "colab_type": "text"
 37 |       },
 38 |       "source": [
 39 |         "## **Importing libraries**\n",
 40 |         "\n",
 41 |         "We will import Pandas as *pd*, train_test_split from sklearn.model_selection, confusion_matrix & accuracy_score from sklearn.metrics and Logistic Regression from sklearn.linear_model.   "
 42 |       ]
 43 |     },
 44 |     {
 45 |       "cell_type": "code",
 46 |       "metadata": {
 47 |         "id": "j1UhjdP0B6m7",
 48 |         "colab_type": "code",
 49 |         "colab": {}
 50 |       },
 51 |       "source": [
 52 |         "import pandas as pd\n",
 53 |         "from sklearn.linear_model import LogisticRegression\n",
 54 |         "from sklearn.model_selection import train_test_split\n",
 55 |         "from sklearn.metrics import confusion_matrix\n",
 56 |         "from sklearn.metrics import accuracy_score"
 57 |       ],
 58 |       "execution_count": 0,
 59 |       "outputs": []
 60 |     },
 61 |     {
 62 |       "cell_type": "markdown",
 63 |       "metadata": {
 64 |         "id": "PsAOn1BiC-_o",
 65 |         "colab_type": "text"
 66 |       },
 67 |       "source": [
 68 |         "## **DataFraming**\n",
 69 |         "\n",
 70 |         "Read .csv data into a Dataframe "
 71 |       ]
 72 |     },
 73 |     {
 74 |       "cell_type": "code",
 75 |       "metadata": {
 76 |         "id": "A9gmwPuuDTq7",
 77 |         "colab_type": "code",
 78 |         "colab": {
 79 |           "base_uri": "https://localhost:8080/",
 80 |           "height": 527
 81 |         },
 82 |         "outputId": "3c95a830-36ec-4651-dc88-43de30614140"
 83 |       },
 84 |       "source": [
 85 |         "creditData = pd.read_csv(\"./titanic.xls\")\n",
 86 |         "print(creditData.head())\n",
 87 |         "print(creditData.describe())\n",
 88 |         "print(creditData.corr())"
 89 |       ],
 90 |       "execution_count": 13,
 91 |       "outputs": [
 92 |         {
 93 |           "output_type": "stream",
 94 |           "text": [
 95 |             "   Survived  Pclass  ...  Parents/Children Aboard     Fare\n",
 96 |             "0         0       3  ...                        0   7.2500\n",
 97 |             "1         1       1  ...                        0  71.2833\n",
 98 |             "2         1       3  ...                        0   7.9250\n",
 99 |             "3         1       1  ...                        0  53.1000\n",
100 |             "4         0       3  ...                        0   8.0500\n",
101 |             "\n",
102 |             "[5 rows x 7 columns]\n",
103 |             "         Survived      Pclass  ...  Parents/Children Aboard       Fare\n",
104 |             "count  887.000000  887.000000  ...               887.000000  887.00000\n",
105 |             "mean     0.385569    2.305524  ...                 0.383315   32.30542\n",
106 |             "std      0.487004    0.836662  ...                 0.807466   49.78204\n",
107 |             "min      0.000000    1.000000  ...                 0.000000    0.00000\n",
108 |             "25%      0.000000    2.000000  ...                 0.000000    7.92500\n",
109 |             "50%      0.000000    3.000000  ...                 0.000000   14.45420\n",
110 |             "75%      1.000000    3.000000  ...                 0.000000   31.13750\n",
111 |             "max      1.000000    3.000000  ...                 6.000000  512.32920\n",
112 |             "\n",
113 |             "[8 rows x 7 columns]\n",
114 |             "                         Survived    Pclass  ...  Parents/Children Aboard      Fare\n",
115 |             "Survived                 1.000000 -0.336528  ...                 0.080097  0.256179\n",
116 |             "Pclass                  -0.336528  1.000000  ...                 0.020252 -0.548919\n",
117 |             "Sex                     -0.542152  0.129507  ...                -0.244337 -0.181137\n",
118 |             "Age                     -0.059665 -0.391492  ...                -0.193741  0.112329\n",
119 |             "Siblings/Spouses Aboard -0.037082  0.085026  ...                 0.414244  0.158839\n",
120 |             "Parents/Children Aboard  0.080097  0.020252  ...                 1.000000  0.215470\n",
121 |             "Fare                     0.256179 -0.548919  ...                 0.215470  1.000000\n",
122 |             "\n",
123 |             "[7 rows x 7 columns]\n"
124 |           ],
125 |           "name": "stdout"
126 |         }
127 |       ]
128 |     },
129 |     {
130 |       "cell_type": "markdown",
131 |       "metadata": {
132 |         "id": "N0c60VUJEfr-",
133 |         "colab_type": "text"
134 |       },
135 |       "source": [
136 |         "## **Features Extraction**\n",
137 |         "\n",
138 |         "Extracting features and splitting data into test and train."
139 |       ]
140 |     },
141 |     {
142 |       "cell_type": "code",
143 |       "metadata": {
144 |         "id": "2KYihcjTE067",
145 |         "colab_type": "code",
146 |         "colab": {}
147 |       },
148 |       "source": [
149 |         "features = creditData[[\"Sex\",\"Age\",\"Pclass\"]]\n",
150 |         "target = creditData[[\"Survived\"]]\n",
151 |         "\n",
152 |         "feature_train, feature_test, target_train, target_test = train_test_split(features,target)"
153 |       ],
154 |       "execution_count": 0,
155 |       "outputs": []
156 |     },
157 |     {
158 |       "cell_type": "markdown",
159 |       "metadata": {
160 |         "id": "nHxk6jzYLx0m",
161 |         "colab_type": "text"
162 |       },
163 |       "source": [
164 |         "## **Training the Model**\n",
165 |         "\n",
166 |         "We are using Logistic regression model as imported from sklearn library and then it's being trained on feature_train and target_train"
167 |       ]
168 |     },
169 |     {
170 |       "cell_type": "code",
171 |       "metadata": {
172 |         "id": "zcVLyHh3L5Rv",
173 |         "colab_type": "code",
174 |         "colab": {
175 |           "base_uri": "https://localhost:8080/",
176 |           "height": 72
177 |         },
178 |         "outputId": "3e52b837-ceab-4b49-e0dc-025588880736"
179 |       },
180 |       "source": [
181 |         "model = LogisticRegression()\n",
182 |         "model.fit = model.fit(feature_train, target_train)\n",
183 |         "predictions = model.fit.predict(feature_test)"
184 |       ],
185 |       "execution_count": 21,
186 |       "outputs": [
187 |         {
188 |           "output_type": "stream",
189 |           "text": [
190 |             "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
191 |             "  y = column_or_1d(y, warn=True)\n"
192 |           ],
193 |           "name": "stderr"
194 |         }
195 |       ]
196 |     },
197 |     {
198 |       "cell_type": "markdown",
199 |       "metadata": {
200 |         "id": "tFBsaZpeMdIa",
201 |         "colab_type": "text"
202 |       },
203 |       "source": [
204 |         "## **Printing an Error Matrix and Accuracy Score**"
205 |       ]
206 |     },
207 |     {
208 |       "cell_type": "code",
209 |       "metadata": {
210 |         "id": "urqzo3v7MzYp",
211 |         "colab_type": "code",
212 |         "colab": {
213 |           "base_uri": "https://localhost:8080/",
214 |           "height": 70
215 |         },
216 |         "outputId": "4c38340a-d31e-4d14-ec18-2d22ab6b47a4"
217 |       },
218 |       "source": [
219 |         "print(confusion_matrix(target_test,predictions))\n",
220 |         "print(accuracy_score(target_test,predictions))"
221 |       ],
222 |       "execution_count": 22,
223 |       "outputs": [
224 |         {
225 |           "output_type": "stream",
226 |           "text": [
227 |             "[[128  18]\n",
228 |             " [ 18  58]]\n",
229 |             "0.8378378378378378\n"
230 |           ],
231 |           "name": "stdout"
232 |         }
233 |       ]
234 |     }
235 |   ]
236 | }


--------------------------------------------------------------------------------
/02_Logistic_Regression/titanic.xls:
--------------------------------------------------------------------------------
  1 | Survived,Pclass,Sex,Age,Siblings/Spouses Aboard,Parents/Children Aboard,Fare
  2 | 0,3,1,22,1,0,7.25
  3 | 1,1,0,38,1,0,71.2833
  4 | 1,3,0,26,0,0,7.925
  5 | 1,1,0,35,1,0,53.1
  6 | 0,3,1,35,0,0,8.05
  7 | 0,3,1,27,0,0,8.4583
  8 | 0,1,1,54,0,0,51.8625
  9 | 0,3,1,2,3,1,21.075
 10 | 1,3,0,27,0,2,11.1333
 11 | 1,2,0,14,1,0,30.0708
 12 | 1,3,0,4,1,1,16.7
 13 | 1,1,0,58,0,0,26.55
 14 | 0,3,1,20,0,0,8.05
 15 | 0,3,1,39,1,5,31.275
 16 | 0,3,0,14,0,0,7.8542
 17 | 1,2,0,55,0,0,16
 18 | 0,3,1,2,4,1,29.125
 19 | 1,2,1,23,0,0,13
 20 | 0,3,0,31,1,0,18
 21 | 1,3,0,22,0,0,7.225
 22 | 0,2,1,35,0,0,26
 23 | 1,2,1,34,0,0,13
 24 | 1,3,0,15,0,0,8.0292
 25 | 1,1,1,28,0,0,35.5
 26 | 0,3,0,8,3,1,21.075
 27 | 1,3,0,38,1,5,31.3875
 28 | 0,3,1,26,0,0,7.225
 29 | 0,1,1,19,3,2,263
 30 | 1,3,0,24,0,0,7.8792
 31 | 0,3,1,23,0,0,7.8958
 32 | 0,1,1,40,0,0,27.7208
 33 | 1,1,0,48,1,0,146.5208
 34 | 1,3,0,18,0,0,7.75
 35 | 0,2,1,66,0,0,10.5
 36 | 0,1,1,28,1,0,82.1708
 37 | 0,1,1,42,1,0,52
 38 | 1,3,1,18,0,0,7.2292
 39 | 0,3,1,21,0,0,8.05
 40 | 0,3,0,18,2,0,18
 41 | 1,3,0,14,1,0,11.2417
 42 | 0,3,0,40,1,0,9.475
 43 | 0,2,0,27,1,0,21
 44 | 1,2,0,3,1,2,41.5792
 45 | 1,3,0,19,0,0,7.8792
 46 | 0,3,1,30,0,0,8.05
 47 | 0,3,1,20,1,0,15.5
 48 | 1,3,0,27,0,0,7.75
 49 | 0,3,1,16,2,0,21.6792
 50 | 0,3,0,18,1,0,17.8
 51 | 0,3,1,7,4,1,39.6875
 52 | 0,3,1,21,0,0,7.8
 53 | 1,1,0,49,1,0,76.7292
 54 | 1,2,0,29,1,0,26
 55 | 0,1,1,65,0,1,61.9792
 56 | 1,1,1,46,0,0,35.5
 57 | 1,2,0,21,0,0,10.5
 58 | 0,3,1,28.5,0,0,7.2292
 59 | 1,2,0,5,1,2,27.75
 60 | 0,3,1,11,5,2,46.9
 61 | 0,3,1,22,0,0,7.2292
 62 | 1,1,0,38,0,0,80
 63 | 0,1,1,45,1,0,83.475
 64 | 0,3,1,4,3,2,27.9
 65 | 0,1,1,64,0,0,27.7208
 66 | 1,3,1,7,1,1,15.2458
 67 | 1,2,0,29,0,0,10.5
 68 | 0,3,1,19,0,0,8.1583
 69 | 1,3,0,17,4,2,7.925
 70 | 0,3,1,26,2,0,8.6625
 71 | 0,2,1,32,0,0,10.5
 72 | 0,3,0,16,5,2,46.9
 73 | 0,2,1,21,0,0,73.5
 74 | 0,3,1,26,1,0,14.4542
 75 | 1,3,1,32,0,0,56.4958
 76 | 0,3,1,25,0,0,7.65
 77 | 0,3,1,23,0,0,7.8958
 78 | 0,3,1,28,0,0,8.05
 79 | 1,2,1,0.83,0,2,29
 80 | 1,3,0,30,0,0,12.475
 81 | 0,3,1,22,0,0,9
 82 | 1,3,1,29,0,0,9.5
 83 | 1,3,0,31,0,0,7.7875
 84 | 0,1,1,28,0,0,47.1
 85 | 1,2,0,17,0,0,10.5
 86 | 1,3,0,33,3,0,15.85
 87 | 0,3,1,16,1,3,34.375
 88 | 0,3,1,20,0,0,8.05
 89 | 1,1,0,23,3,2,263
 90 | 0,3,1,24,0,0,8.05
 91 | 0,3,1,29,0,0,8.05
 92 | 0,3,1,20,0,0,7.8542
 93 | 0,1,1,46,1,0,61.175
 94 | 0,3,1,26,1,2,20.575
 95 | 0,3,1,59,0,0,7.25
 96 | 0,3,1,22,0,0,8.05
 97 | 0,1,1,71,0,0,34.6542
 98 | 1,1,1,23,0,1,63.3583
 99 | 1,2,0,34,0,1,23
100 | 0,2,1,34,1,0,26
101 | 0,3,0,28,0,0,7.8958
102 | 0,3,1,29,0,0,7.8958
103 | 0,1,1,21,0,1,77.2875
104 | 0,3,1,33,0,0,8.6542
105 | 0,3,1,37,2,0,7.925
106 | 0,3,1,28,0,0,7.8958
107 | 1,3,0,21,0,0,7.65
108 | 1,3,1,29,0,0,7.775
109 | 0,3,1,38,0,0,7.8958
110 | 1,3,0,28,1,0,24.15
111 | 0,1,1,47,0,0,52
112 | 0,3,0,14.5,1,0,14.4542
113 | 0,3,1,22,0,0,8.05
114 | 0,3,0,20,1,0,9.825
115 | 0,3,0,17,0,0,14.4583
116 | 0,3,1,21,0,0,7.925
117 | 0,3,1,70.5,0,0,7.75
118 | 0,2,1,29,1,0,21
119 | 0,1,1,24,0,1,247.5208
120 | 0,3,0,2,4,2,31.275
121 | 0,2,1,21,2,0,73.5
122 | 0,3,1,19,0,0,8.05
123 | 0,2,1,32.5,1,0,30.0708
124 | 1,2,0,32.5,0,0,13
125 | 0,1,1,54,0,1,77.2875
126 | 1,3,1,12,1,0,11.2417
127 | 0,3,1,19,0,0,7.75
128 | 1,3,1,24,0,0,7.1417
129 | 1,3,0,2,1,1,22.3583
130 | 0,3,1,45,0,0,6.975
131 | 0,3,1,33,0,0,7.8958
132 | 0,3,1,20,0,0,7.05
133 | 0,3,0,47,1,0,14.5
134 | 1,2,0,29,1,0,26
135 | 0,2,1,25,0,0,13
136 | 0,2,1,23,0,0,15.0458
137 | 1,1,0,19,0,2,26.2833
138 | 0,1,1,37,1,0,53.1
139 | 0,3,1,16,0,0,9.2167
140 | 0,1,1,24,0,0,79.2
141 | 0,3,0,40,0,2,15.2458
142 | 1,3,0,22,0,0,7.75
143 | 1,3,0,24,1,0,15.85
144 | 0,3,1,19,0,0,6.75
145 | 0,2,1,18,0,0,11.5
146 | 0,2,1,19,1,1,36.75
147 | 1,3,1,27,0,0,7.7958
148 | 0,3,0,9,2,2,34.375
149 | 0,2,1,36.5,0,2,26
150 | 0,2,1,42,0,0,13
151 | 0,2,1,51,0,0,12.525
152 | 1,1,0,22,1,0,66.6
153 | 0,3,1,55.5,0,0,8.05
154 | 0,3,1,40.5,0,2,14.5
155 | 0,3,1,27,0,0,7.3125
156 | 0,1,1,51,0,1,61.3792
157 | 1,3,0,16,0,0,7.7333
158 | 0,3,1,30,0,0,8.05
159 | 0,3,1,37,0,0,8.6625
160 | 0,3,1,5,8,2,69.55
161 | 0,3,1,44,0,1,16.1
162 | 1,2,0,40,0,0,15.75
163 | 0,3,1,26,0,0,7.775
164 | 0,3,1,17,0,0,8.6625
165 | 0,3,1,1,4,1,39.6875
166 | 1,3,1,9,0,2,20.525
167 | 1,1,0,48,0,1,55
168 | 0,3,0,45,1,4,27.9
169 | 0,1,1,60,0,0,25.925
170 | 0,3,1,28,0,0,56.4958
171 | 0,1,1,61,0,0,33.5
172 | 0,3,1,4,4,1,29.125
173 | 1,3,0,1,1,1,11.1333
174 | 0,3,1,21,0,0,7.925
175 | 0,1,1,56,0,0,30.6958
176 | 0,3,1,18,1,1,7.8542
177 | 0,3,1,5,3,1,25.4667
178 | 0,1,0,50,0,0,28.7125
179 | 0,2,1,30,0,0,13
180 | 0,3,1,36,0,0,0
181 | 0,3,0,8,8,2,69.55
182 | 0,2,1,39,0,0,15.05
183 | 0,3,1,9,4,2,31.3875
184 | 1,2,1,1,2,1,39
185 | 1,3,0,4,0,2,22.025
186 | 0,1,1,39,0,0,50
187 | 1,3,0,26,1,0,15.5
188 | 1,1,1,45,0,0,26.55
189 | 0,3,1,40,1,1,15.5
190 | 0,3,1,36,0,0,7.8958
191 | 1,2,0,32,0,0,13
192 | 0,2,1,19,0,0,13
193 | 1,3,0,19,1,0,7.8542
194 | 1,2,1,3,1,1,26
195 | 1,1,0,44,0,0,27.7208
196 | 1,1,0,58,0,0,146.5208
197 | 0,3,1,28,0,0,7.75
198 | 0,3,1,42,0,1,8.4042
199 | 1,3,0,21,0,0,7.75
200 | 0,2,0,24,0,0,13
201 | 0,3,1,28,0,0,9.5
202 | 0,3,1,17,8,2,69.55
203 | 0,3,1,34,0,0,6.4958
204 | 0,3,1,45.5,0,0,7.225
205 | 1,3,1,18,0,0,8.05
206 | 0,3,0,2,0,1,10.4625
207 | 0,3,1,32,1,0,15.85
208 | 1,3,1,26,0,0,18.7875
209 | 1,3,0,16,0,0,7.75
210 | 1,1,1,40,0,0,31
211 | 0,3,1,24,0,0,7.05
212 | 1,2,0,35,0,0,21
213 | 0,3,1,22,0,0,7.25
214 | 0,2,1,30,0,0,13
215 | 0,3,1,22,1,0,7.75
216 | 1,1,0,31,1,0,113.275
217 | 1,3,0,27,0,0,7.925
218 | 0,2,1,42,1,0,27
219 | 1,1,0,32,0,0,76.2917
220 | 0,2,1,30,0,0,10.5
221 | 1,3,1,16,0,0,8.05
222 | 0,2,1,27,0,0,13
223 | 0,3,1,51,0,0,8.05
224 | 0,3,1,22,0,0,7.8958
225 | 1,1,1,38,1,0,90
226 | 0,3,1,22,0,0,9.35
227 | 1,2,1,19,0,0,10.5
228 | 0,3,1,20.5,0,0,7.25
229 | 0,2,1,18,0,0,13
230 | 0,3,0,12,3,1,25.4667
231 | 1,1,0,35,1,0,83.475
232 | 0,3,1,29,0,0,7.775
233 | 0,2,1,59,0,0,13.5
234 | 1,3,0,5,4,2,31.3875
235 | 0,2,1,24,0,0,10.5
236 | 0,3,0,21,0,0,7.55
237 | 0,2,1,44,1,0,26
238 | 1,2,0,8,0,2,26.25
239 | 0,2,1,19,0,0,10.5
240 | 0,2,1,33,0,0,12.275
241 | 0,3,0,19,1,0,14.4542
242 | 1,3,0,18,1,0,15.5
243 | 0,2,1,29,0,0,10.5
244 | 0,3,1,22,0,0,7.125
245 | 0,3,1,30,0,0,7.225
246 | 0,1,1,44,2,0,90
247 | 0,3,0,25,0,0,7.775
248 | 1,2,0,24,0,2,14.5
249 | 1,1,1,37,1,1,52.5542
250 | 0,2,1,54,1,0,26
251 | 0,3,1,18,0,0,7.25
252 | 0,3,0,29,1,1,10.4625
253 | 0,1,1,62,0,0,26.55
254 | 0,3,1,30,1,0,16.1
255 | 0,3,0,41,0,2,20.2125
256 | 1,3,0,29,0,2,15.2458
257 | 1,1,0,38,0,0,79.2
258 | 1,1,0,30,0,0,86.5
259 | 1,1,0,35,0,0,512.3292
260 | 1,2,0,50,0,1,26
261 | 1,3,1,3,4,2,31.3875
262 | 0,1,1,52,1,1,79.65
263 | 0,1,1,40,0,0,0
264 | 0,3,0,21,0,0,7.75
265 | 0,2,1,36,0,0,10.5
266 | 0,3,1,16,4,1,39.6875
267 | 1,3,1,25,1,0,7.775
268 | 1,1,0,58,0,1,153.4625
269 | 1,1,0,35,0,0,135.6333
270 | 0,1,1,28,0,0,31
271 | 1,3,1,25,0,0,0
272 | 1,2,0,41,0,1,19.5
273 | 0,1,1,37,0,1,29.7
274 | 1,3,0,33,0,0,7.75
275 | 1,1,0,63,1,0,77.9583
276 | 0,3,0,45,0,0,7.75
277 | 0,2,1,21,0,0,0
278 | 0,3,1,7,4,1,29.125
279 | 1,3,0,35,1,1,20.25
280 | 0,3,1,65,0,0,7.75
281 | 0,3,1,28,0,0,7.8542
282 | 0,3,1,16,0,0,9.5
283 | 1,3,1,19,0,0,8.05
284 | 0,1,1,57,0,0,26
285 | 0,3,1,33,0,0,8.6625
286 | 1,3,1,30,0,0,9.5
287 | 0,3,1,22,0,0,7.8958
288 | 1,2,1,42,0,0,13
289 | 1,3,0,22,0,0,7.75
290 | 1,1,0,26,0,0,78.85
291 | 1,1,0,19,1,0,91.0792
292 | 0,2,1,36,0,0,12.875
293 | 0,3,0,24,0,0,8.85
294 | 0,3,1,24,0,0,7.8958
295 | 0,1,1,30,0,0,27.7208
296 | 0,3,1,23.5,0,0,7.2292
297 | 0,1,0,2,1,2,151.55
298 | 1,1,1,47,0,0,30.5
299 | 1,1,0,50,0,1,247.5208
300 | 1,3,0,20,0,0,7.75
301 | 1,3,1,24,2,0,23.25
302 | 0,3,1,19,0,0,0
303 | 1,2,0,46,0,0,12.35
304 | 0,3,1,28,0,0,8.05
305 | 1,1,1,0.92,1,2,151.55
306 | 1,1,0,42,0,0,110.8833
307 | 1,1,0,17,1,0,108.9
308 | 0,2,1,30,1,0,24
309 | 1,1,0,30,0,0,56.9292
310 | 1,1,0,24,0,0,83.1583
311 | 1,1,0,18,2,2,262.375
312 | 0,2,0,26,1,1,26
313 | 0,3,1,28,0,0,7.8958
314 | 0,2,1,43,1,1,26.25
315 | 1,3,0,26,0,0,7.8542
316 | 1,2,0,24,1,0,26
317 | 0,2,1,54,0,0,14
318 | 1,1,0,31,0,2,164.8667
319 | 1,1,0,40,1,1,134.5
320 | 0,3,1,22,0,0,7.25
321 | 0,3,1,27,0,0,7.8958
322 | 1,2,0,30,0,0,12.35
323 | 1,2,0,22,1,1,29
324 | 0,3,1,20,8,2,69.55
325 | 1,1,0,36,0,0,135.6333
326 | 0,3,1,61,0,0,6.2375
327 | 1,2,0,36,0,0,13
328 | 1,3,0,31,1,1,20.525
329 | 1,1,0,16,0,1,57.9792
330 | 1,3,0,28,2,0,23.25
331 | 0,1,1,45.5,0,0,28.5
332 | 0,1,1,38,0,1,153.4625
333 | 0,3,1,16,2,0,18
334 | 1,1,0,42,1,0,133.65
335 | 0,3,1,30,0,0,7.8958
336 | 0,1,1,29,1,0,66.6
337 | 1,1,0,41,0,0,134.5
338 | 1,3,1,45,0,0,8.05
339 | 0,1,1,45,0,0,35.5
340 | 1,2,1,2,1,1,26
341 | 1,1,0,24,3,2,263
342 | 0,2,1,28,0,0,13
343 | 0,2,1,25,0,0,13
344 | 0,2,1,36,0,0,13
345 | 1,2,0,24,0,0,13
346 | 1,2,0,40,0,0,13
347 | 1,3,0,34,1,0,16.1
348 | 1,3,1,3,1,1,15.9
349 | 0,3,1,42,0,0,8.6625
350 | 0,3,1,23,0,0,9.225
351 | 0,1,1,43,0,0,35
352 | 0,3,1,15,1,1,7.2292
353 | 0,3,1,25,1,0,17.8
354 | 0,3,1,23,0,0,7.225
355 | 0,3,1,28,0,0,9.5
356 | 1,1,0,22,0,1,55
357 | 0,2,0,38,0,0,13
358 | 1,3,0,22,0,0,7.8792
359 | 1,3,0,23,0,0,7.8792
360 | 0,3,1,40,1,4,27.9
361 | 0,2,1,29,1,0,27.7208
362 | 0,3,0,45,0,1,14.4542
363 | 0,3,1,35,0,0,7.05
364 | 0,3,1,27,1,0,15.5
365 | 0,3,1,30,0,0,7.25
366 | 1,1,0,60,1,0,75.25
367 | 1,3,0,35,0,0,7.2292
368 | 1,3,0,22,0,0,7.75
369 | 1,1,0,24,0,0,69.3
370 | 1,1,1,25,1,0,55.4417
371 | 0,3,1,18,1,0,6.4958
372 | 0,3,1,19,0,0,8.05
373 | 0,1,1,22,0,0,135.6333
374 | 0,3,0,3,3,1,21.075
375 | 1,1,0,25,1,0,82.1708
376 | 1,3,0,22,0,0,7.25
377 | 0,1,1,27,0,2,211.5
378 | 0,3,1,20,0,0,4.0125
379 | 0,3,1,19,0,0,7.775
380 | 1,1,0,42,0,0,227.525
381 | 1,3,0,1,0,2,15.7417
382 | 0,3,1,32,0,0,7.925
383 | 1,1,0,35,1,0,52
384 | 0,3,1,27,0,0,7.8958
385 | 0,2,1,18,0,0,73.5
386 | 0,3,1,1,5,2,46.9
387 | 1,2,0,36,0,0,13
388 | 0,3,1,19,0,0,7.7292
389 | 1,2,0,17,0,0,12
390 | 1,1,1,36,1,2,120
391 | 1,3,1,21,0,0,7.7958
392 | 0,3,1,28,2,0,7.925
393 | 1,1,0,23,1,0,113.275
394 | 1,3,0,24,0,2,16.7
395 | 0,3,1,22,0,0,7.7958
396 | 0,3,0,31,0,0,7.8542
397 | 0,2,1,46,0,0,26
398 | 0,2,1,23,0,0,10.5
399 | 1,2,0,28,0,0,12.65
400 | 1,3,1,39,0,0,7.925
401 | 0,3,1,26,0,0,8.05
402 | 0,3,0,21,1,0,9.825
403 | 0,3,1,28,1,0,15.85
404 | 0,3,0,20,0,0,8.6625
405 | 0,2,1,34,1,0,21
406 | 0,3,1,51,0,0,7.75
407 | 1,2,1,3,1,1,18.75
408 | 0,3,1,21,0,0,7.775
409 | 0,3,0,3,3,1,25.4667
410 | 0,3,1,42,0,0,7.8958
411 | 0,3,1,27,0,0,6.8583
412 | 1,1,0,33,1,0,90
413 | 0,2,1,22,0,0,0
414 | 1,3,1,44,0,0,7.925
415 | 0,3,0,32,0,0,8.05
416 | 1,2,0,34,1,1,32.5
417 | 1,2,0,18,0,2,13
418 | 0,2,1,30,0,0,13
419 | 0,3,0,10,0,2,24.15
420 | 0,3,1,21,0,0,7.7333
421 | 0,3,1,29,0,0,7.875
422 | 0,3,0,28,1,1,14.4
423 | 0,3,1,18,1,1,20.2125
424 | 0,3,1,54,0,0,7.25
425 | 1,2,0,28,1,0,26
426 | 1,2,0,19,0,0,26
427 | 0,3,1,28,0,0,7.75
428 | 1,3,1,32,0,0,8.05
429 | 1,1,1,28,0,0,26.55
430 | 1,3,0,33,1,0,16.1
431 | 1,2,0,42,1,0,26
432 | 0,3,1,17,0,0,7.125
433 | 0,1,1,50,1,0,55.9
434 | 1,1,0,14,1,2,120
435 | 0,3,0,21,2,2,34.375
436 | 1,2,0,24,2,3,18.75
437 | 0,1,1,64,1,4,263
438 | 0,2,1,31,0,0,10.5
439 | 1,2,0,45,1,1,26.25
440 | 0,3,1,20,0,0,9.5
441 | 0,3,1,25,1,0,7.775
442 | 1,2,0,28,0,0,13
443 | 1,3,1,29,0,0,8.1125
444 | 1,1,1,4,0,2,81.8583
445 | 1,2,0,13,0,1,19.5
446 | 1,1,1,34,0,0,26.55
447 | 1,3,0,5,2,1,19.2583
448 | 1,1,1,52,0,0,30.5
449 | 0,2,1,36,1,2,27.75
450 | 0,3,1,28,1,0,19.9667
451 | 0,1,1,30,0,0,27.75
452 | 1,1,1,49,1,0,89.1042
453 | 0,3,1,24,0,0,8.05
454 | 1,3,1,29,0,0,7.8958
455 | 0,1,1,65,0,0,26.55
456 | 1,1,0,41,1,0,51.8625
457 | 1,2,0,50,0,0,10.5
458 | 0,3,1,17,0,0,7.75
459 | 1,1,1,48,0,0,26.55
460 | 0,3,1,34,0,0,8.05
461 | 0,1,1,47,0,0,38.5
462 | 0,2,1,48,0,0,13
463 | 0,3,1,34,0,0,8.05
464 | 0,3,1,38,0,0,7.05
465 | 0,2,1,21,0,0,0
466 | 0,1,1,56,0,0,26.55
467 | 0,3,1,22,0,0,7.725
468 | 1,3,0,0.75,2,1,19.2583
469 | 0,3,1,39,0,0,7.25
470 | 0,3,1,38,0,0,8.6625
471 | 1,2,0,33,1,2,27.75
472 | 1,2,0,23,0,0,13.7917
473 | 0,3,0,22,0,0,9.8375
474 | 0,1,1,40,0,0,52
475 | 0,2,1,34,1,0,21
476 | 0,3,1,29,1,0,7.0458
477 | 0,3,1,22,0,0,7.5208
478 | 1,3,0,2,0,1,12.2875
479 | 0,3,1,9,5,2,46.9
480 | 0,2,1,37,0,0,0
481 | 0,3,1,50,0,0,8.05
482 | 1,3,0,63,0,0,9.5875
483 | 1,1,1,25,1,0,91.0792
484 | 0,3,0,8,3,1,25.4667
485 | 1,1,0,35,1,0,90
486 | 0,1,1,58,0,0,29.7
487 | 0,3,1,30,0,0,8.05
488 | 1,3,1,9,1,1,15.9
489 | 0,3,1,19,1,0,19.9667
490 | 0,3,1,21,0,0,7.25
491 | 0,1,1,55,0,0,30.5
492 | 0,1,1,71,0,0,49.5042
493 | 0,3,1,21,0,0,8.05
494 | 0,3,1,26,0,0,14.4583
495 | 1,1,0,54,1,0,78.2667
496 | 0,3,1,55,0,0,15.1
497 | 0,1,0,25,1,2,151.55
498 | 0,3,1,24,0,0,7.7958
499 | 0,3,1,17,0,0,8.6625
500 | 0,3,0,21,0,0,7.75
501 | 0,3,0,21,0,0,7.6292
502 | 0,3,0,37,0,0,9.5875
503 | 1,1,0,16,0,0,86.5
504 | 0,1,1,18,1,0,108.9
505 | 1,2,0,33,0,2,26
506 | 1,1,1,37,0,0,26.55
507 | 0,3,1,28,0,0,22.525
508 | 1,3,1,26,0,0,56.4958
509 | 1,3,1,29,0,0,7.75
510 | 0,3,1,66,0,0,8.05
511 | 1,1,1,36,0,0,26.2875
512 | 1,1,0,54,1,0,59.4
513 | 0,3,1,24,0,0,7.4958
514 | 0,1,1,47,0,0,34.0208
515 | 1,2,0,34,0,0,10.5
516 | 0,3,1,30,0,0,24.15
517 | 1,2,0,36,1,0,26
518 | 0,3,1,32,0,0,7.8958
519 | 1,1,0,30,0,0,93.5
520 | 0,3,1,22,0,0,7.8958
521 | 0,3,1,35,0,0,7.225
522 | 1,1,0,44,0,1,57.9792
523 | 0,3,1,18,0,0,7.2292
524 | 0,3,1,40.5,0,0,7.75
525 | 1,2,0,50,0,0,10.5
526 | 0,1,1,49,0,0,221.7792
527 | 0,3,1,39,0,0,7.925
528 | 0,2,1,23,2,1,11.5
529 | 1,2,0,2,1,1,26
530 | 0,3,1,17,0,0,7.2292
531 | 0,3,1,17,1,1,7.2292
532 | 1,3,0,24,0,2,22.3583
533 | 0,3,0,30,0,0,8.6625
534 | 1,2,0,7,0,2,26.25
535 | 0,1,1,45,0,0,26.55
536 | 1,1,0,30,0,0,106.425
537 | 0,3,1,69,0,0,14.5
538 | 1,1,0,22,0,2,49.5
539 | 1,1,0,36,0,2,71
540 | 0,3,0,9,4,2,31.275
541 | 0,3,0,11,4,2,31.275
542 | 1,2,1,32,1,0,26
543 | 0,1,1,50,1,0,106.425
544 | 0,1,1,64,0,0,26
545 | 1,2,0,19,1,0,26
546 | 1,2,1,27,0,0,13.8625
547 | 0,3,1,33,1,1,20.525
548 | 1,2,1,8,1,1,36.75
549 | 1,1,1,17,0,2,110.8833
550 | 0,2,1,27,0,0,26
551 | 0,3,1,21,0,0,7.8292
552 | 1,3,1,22,0,0,7.225
553 | 1,3,0,22,0,0,7.775
554 | 0,1,1,62,0,0,26.55
555 | 1,1,0,48,1,0,39.6
556 | 0,1,1,45,0,0,227.525
557 | 1,1,0,39,1,1,79.65
558 | 1,3,0,36,1,0,17.4
559 | 0,3,1,30,0,0,7.75
560 | 0,3,1,40,0,0,7.8958
561 | 0,2,1,28,0,0,13.5
562 | 0,3,1,40,0,0,8.05
563 | 0,3,0,62,0,0,8.05
564 | 0,3,1,24,2,0,24.15
565 | 0,3,1,19,0,0,7.8958
566 | 0,3,0,29,0,4,21.075
567 | 0,3,1,28,0,0,7.2292
568 | 1,3,1,32,0,0,7.8542
569 | 1,2,1,62,0,0,10.5
570 | 1,1,0,53,2,0,51.4792
571 | 1,1,1,36,0,0,26.3875
572 | 1,3,0,22,0,0,7.75
573 | 0,3,1,16,0,0,8.05
574 | 0,3,1,19,0,0,14.5
575 | 1,2,0,34,0,0,13
576 | 1,1,0,39,1,0,55.9
577 | 0,3,0,18,1,0,14.4583
578 | 1,3,1,32,0,0,7.925
579 | 1,2,0,25,1,1,30
580 | 1,1,0,39,1,1,110.8833
581 | 0,2,1,54,0,0,26
582 | 0,1,1,36,0,0,40.125
583 | 0,3,1,16,0,0,8.7125
584 | 1,1,0,18,0,2,79.65
585 | 0,2,1,47,0,0,15
586 | 1,1,1,60,1,1,79.2
587 | 0,3,1,22,0,0,8.05
588 | 0,3,1,22,0,0,8.05
589 | 0,3,1,35,0,0,7.125
590 | 1,1,0,52,1,0,78.2667
591 | 0,3,1,47,0,0,7.25
592 | 0,3,0,40,0,2,7.75
593 | 0,2,1,37,1,0,26
594 | 0,3,1,36,1,1,24.15
595 | 1,2,0,31,0,0,33
596 | 0,3,1,49,0,0,0
597 | 0,3,1,18,0,0,7.225
598 | 1,1,1,49,1,0,56.9292
599 | 1,2,0,24,2,1,27
600 | 0,3,1,42,0,0,7.8958
601 | 0,1,1,37,0,0,42.4
602 | 0,3,1,44,0,0,8.05
603 | 1,1,1,35,0,0,26.55
604 | 0,3,1,36,1,0,15.55
605 | 0,3,1,30,0,0,7.8958
606 | 1,1,1,27,0,0,30.5
607 | 1,2,0,22,1,2,41.5792
608 | 1,1,0,40,0,0,153.4625
609 | 0,3,0,39,1,5,31.275
610 | 0,3,1,21,0,0,7.05
611 | 1,3,0,18,1,0,15.5
612 | 0,3,1,22,0,0,7.75
613 | 0,3,1,35,0,0,8.05
614 | 1,2,0,24,1,2,65
615 | 0,3,1,34,1,1,14.4
616 | 0,3,0,26,1,0,16.1
617 | 1,2,0,4,2,1,39
618 | 0,2,1,26,0,0,10.5
619 | 0,3,1,27,1,0,14.4542
620 | 1,1,1,42,1,0,52.5542
621 | 1,3,1,20,1,1,15.7417
622 | 0,3,1,21,0,0,7.8542
623 | 0,3,1,21,0,0,16.1
624 | 0,1,1,61,0,0,32.3208
625 | 0,2,1,57,0,0,12.35
626 | 1,1,0,21,0,0,77.9583
627 | 0,3,1,26,0,0,7.8958
628 | 0,3,1,18,0,0,7.7333
629 | 1,1,1,80,0,0,30
630 | 0,3,1,51,0,0,7.0542
631 | 1,1,1,32,0,0,30.5
632 | 0,1,1,30,0,0,0
633 | 0,3,0,9,3,2,27.9
634 | 1,2,0,28,0,0,13
635 | 0,3,1,32,0,0,7.925
636 | 0,2,1,31,1,1,26.25
637 | 0,3,0,41,0,5,39.6875
638 | 0,3,1,37,1,0,16.1
639 | 0,3,1,20,0,0,7.8542
640 | 1,1,0,24,0,0,69.3
641 | 0,3,0,2,3,2,27.9
642 | 1,3,1,32,0,0,56.4958
643 | 1,3,0,0.75,2,1,19.2583
644 | 1,1,1,48,1,0,76.7292
645 | 0,3,1,19,0,0,7.8958
646 | 1,1,1,56,0,0,35.5
647 | 0,3,1,21,0,0,7.55
648 | 1,3,0,23,0,0,7.55
649 | 0,3,1,23,0,0,7.8958
650 | 1,2,0,18,0,1,23
651 | 0,3,1,21,0,0,8.4333
652 | 1,3,0,16,0,0,7.8292
653 | 0,3,0,18,0,0,6.75
654 | 0,2,1,24,2,0,73.5
655 | 0,3,1,27,0,0,7.8958
656 | 0,3,0,32,1,1,15.5
657 | 0,2,1,23,0,0,13
658 | 0,1,1,58,0,2,113.275
659 | 1,1,1,50,2,0,133.65
660 | 0,3,1,40,0,0,7.225
661 | 0,1,1,47,0,0,25.5875
662 | 0,3,1,36,0,0,7.4958
663 | 1,3,1,20,1,0,7.925
664 | 0,2,1,32,2,0,73.5
665 | 0,2,1,25,0,0,13
666 | 0,3,1,49,0,0,7.775
667 | 0,3,1,43,0,0,8.05
668 | 1,1,0,48,1,0,52
669 | 1,2,0,40,1,1,39
670 | 0,1,1,31,1,0,52
671 | 0,2,1,70,0,0,10.5
672 | 1,2,1,31,0,0,13
673 | 0,2,1,19,0,0,0
674 | 0,3,1,18,0,0,7.775
675 | 0,3,1,24.5,0,0,8.05
676 | 1,3,0,18,0,0,9.8417
677 | 0,3,0,43,1,6,46.9
678 | 1,1,1,36,0,1,512.3292
679 | 0,3,0,28,0,0,8.1375
680 | 1,1,1,27,0,0,76.7292
681 | 0,3,1,20,0,0,9.225
682 | 0,3,1,14,5,2,46.9
683 | 0,2,1,60,1,1,39
684 | 0,2,1,25,1,2,41.5792
685 | 0,3,1,14,4,1,39.6875
686 | 0,3,1,19,0,0,10.1708
687 | 0,3,1,18,0,0,7.7958
688 | 1,1,0,15,0,1,211.3375
689 | 1,1,1,31,1,0,57
690 | 1,3,0,4,0,1,13.4167
691 | 1,3,1,37,0,0,56.4958
692 | 0,3,1,25,0,0,7.225
693 | 0,1,1,60,0,0,26.55
694 | 0,2,1,52,0,0,13.5
695 | 0,3,1,44,0,0,8.05
696 | 1,3,0,19,0,0,7.7333
697 | 0,1,1,49,1,1,110.8833
698 | 0,3,1,42,0,0,7.65
699 | 1,1,0,18,1,0,227.525
700 | 1,1,1,35,0,0,26.2875
701 | 0,3,0,18,0,1,14.4542
702 | 0,3,1,25,0,0,7.7417
703 | 0,3,1,26,1,0,7.8542
704 | 0,2,1,39,0,0,26
705 | 1,2,0,45,0,0,13.5
706 | 1,1,1,42,0,0,26.2875
707 | 1,1,0,22,0,0,151.55
708 | 1,3,1,4,1,1,15.2458
709 | 1,1,0,24,0,0,49.5042
710 | 0,1,1,41,0,0,26.55
711 | 1,1,1,48,1,0,52
712 | 0,3,1,29,0,0,9.4833
713 | 0,2,1,52,0,0,13
714 | 0,3,1,19,0,0,7.65
715 | 1,1,0,38,0,0,227.525
716 | 1,2,0,27,0,0,10.5
717 | 0,3,1,33,0,0,7.775
718 | 1,2,0,6,0,1,33
719 | 0,3,1,17,1,0,7.0542
720 | 0,2,1,34,0,0,13
721 | 0,2,1,50,0,0,13
722 | 1,1,1,27,1,0,53.1
723 | 0,3,1,20,0,0,8.6625
724 | 1,2,0,30,3,0,21
725 | 1,3,0,28,0,0,7.7375
726 | 0,2,1,25,1,0,26
727 | 0,3,0,25,1,0,7.925
728 | 1,1,0,29,0,0,211.3375
729 | 0,3,1,11,0,0,18.7875
730 | 0,2,1,41,0,0,0
731 | 0,2,1,23,0,0,13
732 | 0,2,1,23,0,0,13
733 | 0,3,1,28.5,0,0,16.1
734 | 0,3,0,48,1,3,34.375
735 | 1,1,1,35,0,0,512.3292
736 | 0,3,1,20,0,0,7.8958
737 | 0,3,1,32,0,0,7.8958
738 | 1,1,1,45,0,0,30
739 | 0,1,1,36,1,0,78.85
740 | 1,1,0,21,2,2,262.375
741 | 0,3,1,24,1,0,16.1
742 | 1,3,1,31,0,0,7.925
743 | 0,1,1,70,1,1,71
744 | 0,3,1,16,1,1,20.25
745 | 1,2,0,30,0,0,13
746 | 0,1,1,19,1,0,53.1
747 | 0,3,1,31,0,0,7.75
748 | 1,2,0,4,1,1,23
749 | 1,3,1,6,0,1,12.475
750 | 0,3,1,33,0,0,9.5
751 | 0,3,1,23,0,0,7.8958
752 | 1,2,0,48,1,2,65
753 | 1,2,1,0.67,1,1,14.5
754 | 0,3,1,28,0,0,7.7958
755 | 0,2,1,18,0,0,11.5
756 | 0,3,1,34,0,0,8.05
757 | 1,1,0,33,0,0,86.5
758 | 0,3,1,23,0,0,14.5
759 | 0,3,1,41,0,0,7.125
760 | 1,3,1,20,0,0,7.2292
761 | 1,1,0,36,1,2,120
762 | 0,3,1,16,0,0,7.775
763 | 1,1,0,51,1,0,77.9583
764 | 0,1,1,46,0,0,39.6
765 | 0,3,0,30.5,0,0,7.75
766 | 0,3,1,28,1,0,24.15
767 | 0,3,1,32,0,0,8.3625
768 | 0,3,1,24,0,0,9.5
769 | 0,3,1,48,0,0,7.8542
770 | 0,2,0,57,0,0,10.5
771 | 0,3,1,29,0,0,7.225
772 | 1,2,0,54,1,3,23
773 | 0,3,1,18,0,0,7.75
774 | 0,3,1,20,0,0,7.75
775 | 1,3,0,5,0,0,12.475
776 | 0,3,1,22,0,0,7.7375
777 | 1,1,0,43,0,1,211.3375
778 | 1,3,0,13,0,0,7.2292
779 | 1,1,0,17,1,0,57
780 | 0,1,1,29,0,0,30
781 | 0,3,1,35,1,2,23.45
782 | 0,3,1,25,0,0,7.05
783 | 0,3,1,25,0,0,7.25
784 | 1,3,0,18,0,0,7.4958
785 | 0,3,1,8,4,1,29.125
786 | 1,3,1,1,1,2,20.575
787 | 0,1,1,46,0,0,79.2
788 | 0,3,1,20,0,0,7.75
789 | 0,2,1,16,0,0,26
790 | 0,3,0,21,8,2,69.55
791 | 0,1,1,43,0,0,30.6958
792 | 0,3,1,25,0,0,7.8958
793 | 0,2,1,39,0,0,13
794 | 1,1,0,49,0,0,25.9292
795 | 1,3,0,31,0,0,8.6833
796 | 0,3,1,30,0,0,7.2292
797 | 0,3,0,30,1,1,24.15
798 | 0,2,1,34,0,0,13
799 | 1,2,0,31,1,1,26.25
800 | 1,1,1,11,1,2,120
801 | 1,3,1,0.42,0,1,8.5167
802 | 1,3,1,27,0,0,6.975
803 | 0,3,1,31,0,0,7.775
804 | 0,1,1,39,0,0,0
805 | 0,3,0,18,0,0,7.775
806 | 0,2,1,39,0,0,13
807 | 1,1,0,33,1,0,53.1
808 | 0,3,1,26,0,0,7.8875
809 | 0,3,1,39,0,0,24.15
810 | 0,2,1,35,0,0,10.5
811 | 0,3,0,6,4,2,31.275
812 | 0,3,1,30.5,0,0,8.05
813 | 0,1,1,39,0,0,0
814 | 0,3,0,23,0,0,7.925
815 | 0,2,1,31,1,1,37.0042
816 | 0,3,1,43,0,0,6.45
817 | 0,3,1,10,3,2,27.9
818 | 1,1,0,52,1,1,93.5
819 | 1,3,1,27,0,0,8.6625
820 | 0,1,1,38,0,0,0
821 | 1,3,0,27,0,1,12.475
822 | 0,3,1,2,4,1,39.6875
823 | 0,3,1,36,0,0,6.95
824 | 0,3,1,23,0,0,56.4958
825 | 1,2,1,1,0,2,37.0042
826 | 1,3,1,19,0,0,7.75
827 | 1,1,0,62,0,0,80
828 | 1,3,0,15,1,0,14.4542
829 | 1,2,1,0.83,1,1,18.75
830 | 0,3,1,30,0,0,7.2292
831 | 0,3,1,23,0,0,7.8542
832 | 0,3,1,18,0,0,8.3
833 | 1,1,0,39,1,1,83.1583
834 | 0,3,1,21,0,0,8.6625
835 | 0,3,1,20,0,0,8.05
836 | 1,3,1,32,0,0,56.4958
837 | 1,1,1,29,0,0,29.7
838 | 0,3,1,20,0,0,7.925
839 | 0,2,1,16,0,0,10.5
840 | 1,1,0,30,0,0,31
841 | 0,3,1,34.5,0,0,6.4375
842 | 0,3,1,17,0,0,8.6625
843 | 0,3,1,42,0,0,7.55
844 | 0,3,1,18,8,2,69.55
845 | 0,3,1,35,0,0,7.8958
846 | 0,2,1,28,0,1,33
847 | 1,1,0,40,1,0,89.1042
848 | 0,3,1,4,4,2,31.275
849 | 0,3,1,74,0,0,7.775
850 | 0,3,0,9,1,1,15.2458
851 | 1,1,0,16,0,1,39.4
852 | 0,2,0,44,1,0,26
853 | 1,3,0,18,0,1,9.35
854 | 1,1,0,45,1,1,164.8667
855 | 1,1,1,51,0,0,26.55
856 | 1,3,0,24,0,3,19.2583
857 | 0,3,1,30,0,0,7.2292
858 | 0,3,1,41,2,0,14.1083
859 | 0,2,1,21,1,0,11.5
860 | 1,1,0,48,0,0,25.9292
861 | 0,3,0,14,8,2,69.55
862 | 0,2,1,24,0,0,13
863 | 1,2,0,42,0,0,13
864 | 1,2,0,27,1,0,13.8583
865 | 0,1,1,31,0,0,50.4958
866 | 0,3,1,23,0,0,9.5
867 | 1,3,1,4,1,1,11.1333
868 | 0,3,1,26,0,0,7.8958
869 | 1,1,0,47,1,1,52.5542
870 | 0,1,1,33,0,0,5
871 | 0,3,1,47,0,0,9
872 | 1,2,0,28,1,0,24
873 | 1,3,0,15,0,0,7.225
874 | 0,3,1,20,0,0,9.8458
875 | 0,3,1,19,0,0,7.8958
876 | 0,3,1,23,0,0,7.8958
877 | 1,1,0,56,0,1,83.1583
878 | 1,2,0,25,0,1,26
879 | 0,3,1,33,0,0,7.8958
880 | 0,3,0,22,0,0,10.5167
881 | 0,2,1,28,0,0,10.5
882 | 0,3,1,25,0,0,7.05
883 | 0,3,0,39,0,5,29.125
884 | 0,2,1,27,0,0,13
885 | 1,1,0,19,0,0,30
886 | 0,3,0,7,1,2,23.45
887 | 1,1,1,26,0,0,30
888 | 0,3,1,32,0,0,7.75
889 | 


--------------------------------------------------------------------------------
/03_KNN_Classifier/KNN_Classifier.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "KNN Classifier.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": []
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     }
 14 |   },
 15 |   "cells": [
 16 |     {
 17 |       "cell_type": "markdown",
 18 |       "metadata": {
 19 |         "id": "7AqXLpH1eh0r",
 20 |         "colab_type": "text"
 21 |       },
 22 |       "source": [
 23 |         "# **k-nearest neighbors algorithm**\n",
 24 |         "\n",
 25 |         "In pattern recognition, the k-nearest neighbors algorithm is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.\n",
 26 |         "\n",
 27 |         "By [Muhammad Huzaifa Shahbaz](https://www.linkedin.com/in/mhuzaifadev)"
 28 |       ]
 29 |     },
 30 |     {
 31 |       "cell_type": "markdown",
 32 |       "metadata": {
 33 |         "id": "EkXtM692ev1u",
 34 |         "colab_type": "text"
 35 |       },
 36 |       "source": [
 37 |         "## **Importing libraries**\n",
 38 |         "\n",
 39 |         "We will import NumPy as np, Pandas as *pd*, train_test_split & cross_val_score from sklearn.model_selection, confusion_matrix & accuracy_score from sklearn.metrics, KNeighborsClassifier from sklearn.neighbors and preprocessing  from sklearn."
 40 |       ]
 41 |     },
 42 |     {
 43 |       "cell_type": "code",
 44 |       "metadata": {
 45 |         "id": "hewapbOjdYLB",
 46 |         "colab_type": "code",
 47 |         "colab": {}
 48 |       },
 49 |       "source": [
 50 |         "import pandas as pd\n",
 51 |         "import numpy as np\n",
 52 |         "from sklearn.neighbors import KNeighborsClassifier\n",
 53 |         "from sklearn.model_selection import train_test_split\n",
 54 |         "from sklearn.model_selection import cross_val_score\n",
 55 |         "from sklearn.metrics import confusion_matrix\n",
 56 |         "from sklearn.metrics import accuracy_score\n",
 57 |         "from sklearn import preprocessing"
 58 |       ],
 59 |       "execution_count": 0,
 60 |       "outputs": []
 61 |     },
 62 |     {
 63 |       "cell_type": "markdown",
 64 |       "metadata": {
 65 |         "id": "PsAOn1BiC-_o",
 66 |         "colab_type": "text"
 67 |       },
 68 |       "source": [
 69 |         "## **DataFraming**\n",
 70 |         "\n",
 71 |         "Read .csv data into a Dataframe "
 72 |       ]
 73 |     },
 74 |     {
 75 |       "cell_type": "code",
 76 |       "metadata": {
 77 |         "id": "A9gmwPuuDTq7",
 78 |         "colab_type": "code",
 79 |         "outputId": "a25c3145-3b50-4fa2-c8c0-d83e4168d343",
 80 |         "colab": {
 81 |           "base_uri": "https://localhost:8080/",
 82 |           "height": 439
 83 |         }
 84 |       },
 85 |       "source": [
 86 |         "data = pd.read_csv(\"credit_data.csv\")\n",
 87 |         "print(data.head())\n",
 88 |         "print(data.describe())\n",
 89 |         "print(data.corr())"
 90 |       ],
 91 |       "execution_count": 22,
 92 |       "outputs": [
 93 |         {
 94 |           "output_type": "stream",
 95 |           "text": [
 96 |             "   clientid        income        age         loan       LTI  default\n",
 97 |             "0         1  66155.925095  59.017015  8106.532131  0.122537        0\n",
 98 |             "1         2  34415.153966  48.117153  6564.745018  0.190752        0\n",
 99 |             "2         3  57317.170063  63.108049  8020.953296  0.139940        0\n",
100 |             "3         4  42709.534201  45.751972  6103.642260  0.142911        0\n",
101 |             "4         5  66952.688845  18.584336  8770.099235  0.130989        1\n",
102 |             "          clientid        income  ...          LTI      default\n",
103 |             "count  2000.000000   2000.000000  ...  2000.000000  2000.000000\n",
104 |             "mean   1000.500000  45331.600018  ...     0.098403     0.141500\n",
105 |             "std     577.494589  14326.327119  ...     0.057620     0.348624\n",
106 |             "min       1.000000  20014.489470  ...     0.000049     0.000000\n",
107 |             "25%     500.750000  32796.459717  ...     0.047903     0.000000\n",
108 |             "50%    1000.500000  45789.117313  ...     0.099437     0.000000\n",
109 |             "75%    1500.250000  57791.281668  ...     0.147585     0.000000\n",
110 |             "max    2000.000000  69995.685578  ...     0.199938     1.000000\n",
111 |             "\n",
112 |             "[8 rows x 6 columns]\n",
113 |             "          clientid    income       age      loan       LTI   default\n",
114 |             "clientid  1.000000  0.039280 -0.030341  0.018931  0.002538 -0.020145\n",
115 |             "income    0.039280  1.000000 -0.034984  0.441117 -0.019862  0.002284\n",
116 |             "age      -0.030341 -0.034984  1.000000  0.006561  0.021588 -0.444765\n",
117 |             "loan      0.018931  0.441117  0.006561  1.000000  0.847495  0.377160\n",
118 |             "LTI       0.002538 -0.019862  0.021588  0.847495  1.000000  0.433261\n",
119 |             "default  -0.020145  0.002284 -0.444765  0.377160  0.433261  1.000000\n"
120 |           ],
121 |           "name": "stdout"
122 |         }
123 |       ]
124 |     },
125 |     {
126 |       "cell_type": "markdown",
127 |       "metadata": {
128 |         "id": "N0c60VUJEfr-",
129 |         "colab_type": "text"
130 |       },
131 |       "source": [
132 |         "## **Features Extraction**\n",
133 |         "\n",
134 |         "Extracting features and splitting data into test and train."
135 |       ]
136 |     },
137 |     {
138 |       "cell_type": "code",
139 |       "metadata": {
140 |         "id": "X4X2kjPahc0V",
141 |         "colab_type": "code",
142 |         "colab": {
143 |           "base_uri": "https://localhost:8080/",
144 |           "height": 107
145 |         },
146 |         "outputId": "8578c424-511f-48fd-870a-20a87293bf4d"
147 |       },
148 |       "source": [
149 |         "data.features = data[[\"income\",\"age\",\"loan\"]]\n",
150 |         "data.target = data.default\n",
151 |         "\n",
152 |         "data.features = preprocessing.MinMaxScaler().fit_transform(data.features)\n",
153 |         "\n",
154 |         "feature_train, feature_test, target_train, target_test = train_test_split(data.features,data.target)"
155 |       ],
156 |       "execution_count": 23,
157 |       "outputs": [
158 |         {
159 |           "output_type": "stream",
160 |           "text": [
161 |             "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
162 |             "  \"\"\"Entry point for launching an IPython kernel.\n",
163 |             "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
164 |             "  \n"
165 |           ],
166 |           "name": "stderr"
167 |         }
168 |       ]
169 |     },
170 |     {
171 |       "cell_type": "markdown",
172 |       "metadata": {
173 |         "id": "nHxk6jzYLx0m",
174 |         "colab_type": "text"
175 |       },
176 |       "source": [
177 |         "## **Training the Model**\n",
178 |         "\n",
179 |         "We are using KNeighborsClassifier model as imported from sklearn.neighbors library and then it's being trained on feature_train and target_train"
180 |       ]
181 |     },
182 |     {
183 |       "cell_type": "code",
184 |       "metadata": {
185 |         "id": "zcVLyHh3L5Rv",
186 |         "colab_type": "code",
187 |         "colab": {}
188 |       },
189 |       "source": [
190 |         "model = KNeighborsClassifier(n_neighbors=32)\n",
191 |         "fitModel = model.fit(feature_train, target_train)\n",
192 |         "predictions = fitModel.predict(feature_test)"
193 |       ],
194 |       "execution_count": 0,
195 |       "outputs": []
196 |     },
197 |     {
198 |       "cell_type": "markdown",
199 |       "metadata": {
200 |         "colab_type": "text",
201 |         "id": "MldzyDb_kWJt"
202 |       },
203 |       "source": [
204 |         "## **Finding Optimal K Value**\n",
205 |         "\n",
206 |         "We are going to use cross-validation in order to find the optimal k value. This optimal value is not going to have as good accuracy and precision as we have seen for any neighbors close to ***n*** but it is going to be much more realistic because we use cross-validation. So we are going to use the cross-validation scores and then we are going to make a simple iteration. Basically we are going to consider k values from 1 up to 100.\n"
207 |       ]
208 |     },
209 |     {
210 |       "cell_type": "code",
211 |       "metadata": {
212 |         "id": "MMj7k6ETlAmD",
213 |         "colab_type": "code",
214 |         "colab": {
215 |           "base_uri": "https://localhost:8080/",
216 |           "height": 34
217 |         },
218 |         "outputId": "f37a8951-0e7a-4499-d3a0-ef1f62acd9bb"
219 |       },
220 |       "source": [
221 |         "cross_valid_scores = []\n",
222 |         "\n",
223 |         "for k in range(1, 100):\n",
224 |         "  knn = KNeighborsClassifier(n_neighbors = k)\n",
225 |         "  scores = cross_val_score(knn,data.features, data.target, cv = 10, scoring = 'accuracy')\n",
226 |         "  cross_valid_scores.append(scores.mean())\n",
227 |         "\n",
228 |         "print(\"Optimal k with cross-validation: \\t\",np.argmax(cross_valid_scores))"
229 |       ],
230 |       "execution_count": 26,
231 |       "outputs": [
232 |         {
233 |           "output_type": "stream",
234 |           "text": [
235 |             "Optimal k with cross-validation: \t 32\n"
236 |           ],
237 |           "name": "stdout"
238 |         }
239 |       ]
240 |     },
241 |     {
242 |       "cell_type": "markdown",
243 |       "metadata": {
244 |         "id": "tFBsaZpeMdIa",
245 |         "colab_type": "text"
246 |       },
247 |       "source": [
248 |         "## **Printing an Error Matrix and Accuracy Score**"
249 |       ]
250 |     },
251 |     {
252 |       "cell_type": "code",
253 |       "metadata": {
254 |         "id": "urqzo3v7MzYp",
255 |         "colab_type": "code",
256 |         "outputId": "fd356366-5a2d-4b70-bad5-1ebda9f26903",
257 |         "colab": {
258 |           "base_uri": "https://localhost:8080/",
259 |           "height": 70
260 |         }
261 |       },
262 |       "source": [
263 |         "print(confusion_matrix(target_test,predictions))\n",
264 |         "print(accuracy_score(target_test,predictions))"
265 |       ],
266 |       "execution_count": 28,
267 |       "outputs": [
268 |         {
269 |           "output_type": "stream",
270 |           "text": [
271 |             "[[437   2]\n",
272 |             " [ 10  51]]\n",
273 |             "0.976\n"
274 |           ],
275 |           "name": "stdout"
276 |         }
277 |       ]
278 |     }
279 |   ]
280 | }


--------------------------------------------------------------------------------
/04_Naive_Bayes_Classifier/Naive_Bayes_Classifier.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Naive_Bayes Classifier.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": []
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     }
 14 |   },
 15 |   "cells": [
 16 |     {
 17 |       "cell_type": "markdown",
 18 |       "metadata": {
 19 |         "id": "7AqXLpH1eh0r",
 20 |         "colab_type": "text"
 21 |       },
 22 |       "source": [
 23 |         "# **Naive Bayes Classifier**\n",
 24 |         "\n",
 25 |         "Naive Bayes classifiers are a collection of classification algorithms based on Bayes' Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.\n",
 26 |         "\n",
 27 |         "By [Muhammad Huzaifa Shahbaz](https://www.linkedin.com/in/mhuzaifadev)"
 28 |       ]
 29 |     },
 30 |     {
 31 |       "cell_type": "markdown",
 32 |       "metadata": {
 33 |         "id": "EkXtM692ev1u",
 34 |         "colab_type": "text"
 35 |       },
 36 |       "source": [
 37 |         "## **Importing libraries**\n",
 38 |         "\n",
 39 |         "We will import Pandas as *pd*, train_test_split from sklearn.model_selection, confusion_matrix & accuracy_score from sklearn.metrics and GaussianNB from sklearn.naive_bayes."
 40 |       ]
 41 |     },
 42 |     {
 43 |       "cell_type": "code",
 44 |       "metadata": {
 45 |         "id": "hewapbOjdYLB",
 46 |         "colab_type": "code",
 47 |         "colab": {}
 48 |       },
 49 |       "source": [
 50 |         "import pandas as pd\n",
 51 |         "from sklearn.naive_bayes import GaussianNB\n",
 52 |         "from sklearn.model_selection import train_test_split\n",
 53 |         "from sklearn.metrics import confusion_matrix\n",
 54 |         "from sklearn.metrics import accuracy_score"
 55 |       ],
 56 |       "execution_count": 0,
 57 |       "outputs": []
 58 |     },
 59 |     {
 60 |       "cell_type": "markdown",
 61 |       "metadata": {
 62 |         "id": "PsAOn1BiC-_o",
 63 |         "colab_type": "text"
 64 |       },
 65 |       "source": [
 66 |         "## **DataFraming**\n",
 67 |         "\n",
 68 |         "Read .csv data into a Dataframe "
 69 |       ]
 70 |     },
 71 |     {
 72 |       "cell_type": "code",
 73 |       "metadata": {
 74 |         "id": "A9gmwPuuDTq7",
 75 |         "colab_type": "code",
 76 |         "outputId": "8a4009cd-79d1-4487-e057-e5da813dffce",
 77 |         "colab": {
 78 |           "base_uri": "https://localhost:8080/",
 79 |           "height": 439
 80 |         }
 81 |       },
 82 |       "source": [
 83 |         "data = pd.read_csv(\"/content/sample_data/credit_data.csv\")\n",
 84 |         "print(data.head())\n",
 85 |         "print(data.describe())\n",
 86 |         "print(data.corr())"
 87 |       ],
 88 |       "execution_count": 3,
 89 |       "outputs": [
 90 |         {
 91 |           "output_type": "stream",
 92 |           "text": [
 93 |             "   clientid        income        age         loan       LTI  default\n",
 94 |             "0         1  66155.925095  59.017015  8106.532131  0.122537        0\n",
 95 |             "1         2  34415.153966  48.117153  6564.745018  0.190752        0\n",
 96 |             "2         3  57317.170063  63.108049  8020.953296  0.139940        0\n",
 97 |             "3         4  42709.534201  45.751972  6103.642260  0.142911        0\n",
 98 |             "4         5  66952.688845  18.584336  8770.099235  0.130989        1\n",
 99 |             "          clientid        income  ...          LTI      default\n",
100 |             "count  2000.000000   2000.000000  ...  2000.000000  2000.000000\n",
101 |             "mean   1000.500000  45331.600018  ...     0.098403     0.141500\n",
102 |             "std     577.494589  14326.327119  ...     0.057620     0.348624\n",
103 |             "min       1.000000  20014.489470  ...     0.000049     0.000000\n",
104 |             "25%     500.750000  32796.459717  ...     0.047903     0.000000\n",
105 |             "50%    1000.500000  45789.117313  ...     0.099437     0.000000\n",
106 |             "75%    1500.250000  57791.281668  ...     0.147585     0.000000\n",
107 |             "max    2000.000000  69995.685578  ...     0.199938     1.000000\n",
108 |             "\n",
109 |             "[8 rows x 6 columns]\n",
110 |             "          clientid    income       age      loan       LTI   default\n",
111 |             "clientid  1.000000  0.039280 -0.030341  0.018931  0.002538 -0.020145\n",
112 |             "income    0.039280  1.000000 -0.034984  0.441117 -0.019862  0.002284\n",
113 |             "age      -0.030341 -0.034984  1.000000  0.006561  0.021588 -0.444765\n",
114 |             "loan      0.018931  0.441117  0.006561  1.000000  0.847495  0.377160\n",
115 |             "LTI       0.002538 -0.019862  0.021588  0.847495  1.000000  0.433261\n",
116 |             "default  -0.020145  0.002284 -0.444765  0.377160  0.433261  1.000000\n"
117 |           ],
118 |           "name": "stdout"
119 |         }
120 |       ]
121 |     },
122 |     {
123 |       "cell_type": "markdown",
124 |       "metadata": {
125 |         "id": "N0c60VUJEfr-",
126 |         "colab_type": "text"
127 |       },
128 |       "source": [
129 |         "## **Features Extraction**\n",
130 |         "\n",
131 |         "Extracting features and splitting data into test and train."
132 |       ]
133 |     },
134 |     {
135 |       "cell_type": "code",
136 |       "metadata": {
137 |         "id": "X4X2kjPahc0V",
138 |         "colab_type": "code",
139 |         "colab": {}
140 |       },
141 |       "source": [
142 |         "data.features = data[[\"income\",\"age\",\"loan\"]]\n",
143 |         "data.target = data.default\n",
144 |         "\n",
145 |         "feature_train, feature_test, target_train, target_test = train_test_split(data.features,data.target)"
146 |       ],
147 |       "execution_count": 0,
148 |       "outputs": []
149 |     },
150 |     {
151 |       "cell_type": "markdown",
152 |       "metadata": {
153 |         "id": "nHxk6jzYLx0m",
154 |         "colab_type": "text"
155 |       },
156 |       "source": [
157 |         "## **Training the Model**\n",
158 |         "\n",
159 |         "We are using GaussianNB model as imported from sklearn.naive_bayes library and then it's being trained on feature_train and target_train"
160 |       ]
161 |     },
162 |     {
163 |       "cell_type": "code",
164 |       "metadata": {
165 |         "id": "zcVLyHh3L5Rv",
166 |         "colab_type": "code",
167 |         "colab": {}
168 |       },
169 |       "source": [
170 |         "model = GaussianNB()\n",
171 |         "fitModel = model.fit(feature_train, target_train)\n",
172 |         "predictions = fitModel.predict(feature_test)"
173 |       ],
174 |       "execution_count": 0,
175 |       "outputs": []
176 |     },
177 |     {
178 |       "cell_type": "markdown",
179 |       "metadata": {
180 |         "id": "tFBsaZpeMdIa",
181 |         "colab_type": "text"
182 |       },
183 |       "source": [
184 |         "## **Printing an Error Matrix and Accuracy Score**"
185 |       ]
186 |     },
187 |     {
188 |       "cell_type": "code",
189 |       "metadata": {
190 |         "id": "urqzo3v7MzYp",
191 |         "colab_type": "code",
192 |         "outputId": "33289bcf-32b1-4e83-b511-f134d47b2781",
193 |         "colab": {
194 |           "base_uri": "https://localhost:8080/",
195 |           "height": 70
196 |         }
197 |       },
198 |       "source": [
199 |         "print(confusion_matrix(target_test,predictions))\n",
200 |         "print(accuracy_score(target_test,predictions))"
201 |       ],
202 |       "execution_count": 7,
203 |       "outputs": [
204 |         {
205 |           "output_type": "stream",
206 |           "text": [
207 |             "[[415  12]\n",
208 |             " [ 29  44]]\n",
209 |             "0.918\n"
210 |           ],
211 |           "name": "stdout"
212 |         }
213 |       ]
214 |     }
215 |   ]
216 | }


--------------------------------------------------------------------------------
/05_Support_Vector_Machine/Support_Vector_Machine_(Digit_Recognition).ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Copy of Support_Vector_Machine (Digit_Recognition).ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": []
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     }
 14 |   },
 15 |   "cells": [
 16 |     {
 17 |       "cell_type": "markdown",
 18 |       "metadata": {
 19 |         "id": "HFenp3yV-gcO",
 20 |         "colab_type": "text"
 21 |       },
 22 |       "source": [
 23 |         "## **Support Vector Machine (SVM)**\n",
 24 |         "\n",
 25 |         "A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.\n",
 26 |         "\n",
 27 |         "By [Muhammad Huzaifa Shahbaz](https://www.linkedin.com/in/mhuzaifadev)"
 28 |       ]
 29 |     },
 30 |     {
 31 |       "cell_type": "markdown",
 32 |       "metadata": {
 33 |         "id": "EkXtM692ev1u",
 34 |         "colab_type": "text"
 35 |       },
 36 |       "source": [
 37 |         "## **Importing libraries**\n"
 38 |       ]
 39 |     },
 40 |     {
 41 |       "cell_type": "code",
 42 |       "metadata": {
 43 |         "id": "hewapbOjdYLB",
 44 |         "colab_type": "code",
 45 |         "colab": {}
 46 |       },
 47 |       "source": [
 48 |         "import numpy as np\n",
 49 |         "import pandas as pd\n",
 50 |         "import matplotlib.pyplot as plt\n",
 51 |         "import matplotlib.cm as cm\n",
 52 |         "from sklearn import svm, datasets, metrics\n",
 53 |         "from sklearn.model_selection import train_test_split\n",
 54 |         "from sklearn.metrics import accuracy_score"
 55 |       ],
 56 |       "execution_count": 0,
 57 |       "outputs": []
 58 |     },
 59 |     {
 60 |       "cell_type": "markdown",
 61 |       "metadata": {
 62 |         "id": "PsAOn1BiC-_o",
 63 |         "colab_type": "text"
 64 |       },
 65 |       "source": [
 66 |         "### **Digits Loading**\n",
 67 |         "\n",
 68 |         "Loading data from sklearn datasets"
 69 |       ]
 70 |     },
 71 |     {
 72 |       "cell_type": "code",
 73 |       "metadata": {
 74 |         "id": "6djJF7NEAx_I",
 75 |         "colab_type": "code",
 76 |         "colab": {}
 77 |       },
 78 |       "source": [
 79 |         "digits = datasets.load_digits()\n",
 80 |         "#print(digits)\n",
 81 |         "images_and_labels = list(zip(digits.images,digits.target))"
 82 |       ],
 83 |       "execution_count": 0,
 84 |       "outputs": []
 85 |     },
 86 |     {
 87 |       "cell_type": "markdown",
 88 |       "metadata": {
 89 |         "id": "N0c60VUJEfr-",
 90 |         "colab_type": "text"
 91 |       },
 92 |       "source": [
 93 |         "## **Flattening the Images**\n",
 94 |         "\n",
 95 |         "To apply a classifier on this data, we need to flatten the images to turn the data into (samples, features) matrix."
 96 |       ]
 97 |     },
 98 |     {
 99 |       "cell_type": "code",
100 |       "metadata": {
101 |         "id": "pwLzYvYWBQcQ",
102 |         "colab_type": "code",
103 |         "colab": {}
104 |       },
105 |       "source": [
106 |         "n_samples = len(digits.images)\n",
107 |         "data = digits.images.reshape((n_samples, -1))"
108 |       ],
109 |       "execution_count": 0,
110 |       "outputs": []
111 |     },
112 |     {
113 |       "cell_type": "markdown",
114 |       "metadata": {
115 |         "id": "nHxk6jzYLx0m",
116 |         "colab_type": "text"
117 |       },
118 |       "source": [
119 |         "## **Training the Model**\n",
120 |         "\n",
121 |         "We are using SVM model as imported from sklearn library and then it's being trained on 75% of the digits."
122 |       ]
123 |     },
124 |     {
125 |       "cell_type": "code",
126 |       "metadata": {
127 |         "id": "1eAhZM71COWr",
128 |         "colab_type": "code",
129 |         "colab": {}
130 |       },
131 |       "source": [
132 |         "trainTestSplit = int(n_samples*0.75)\n",
133 |         "model = svm.SVC()\n",
134 |         "model.fit(data[:trainTestSplit], digits.target[:trainTestSplit])\n",
135 |         "target = digits.target[trainTestSplit:]\n",
136 |         "predictions = model.predict(data[trainTestSplit:])"
137 |       ],
138 |       "execution_count": 0,
139 |       "outputs": []
140 |     },
141 |     {
142 |       "cell_type": "markdown",
143 |       "metadata": {
144 |         "id": "tFBsaZpeMdIa",
145 |         "colab_type": "text"
146 |       },
147 |       "source": [
148 |         "## **Printing an Error Matrix and Accuracy Score**"
149 |       ]
150 |     },
151 |     {
152 |       "cell_type": "code",
153 |       "metadata": {
154 |         "id": "urqzo3v7MzYp",
155 |         "colab_type": "code",
156 |         "outputId": "236c2b2d-6eb7-4994-ed81-2a1b917f87aa",
157 |         "colab": {
158 |           "base_uri": "https://localhost:8080/",
159 |           "height": 210
160 |         }
161 |       },
162 |       "source": [
163 |         "print(metrics.confusion_matrix(target,predictions))\n",
164 |         "print(accuracy_score(target,predictions))"
165 |       ],
166 |       "execution_count": 11,
167 |       "outputs": [
168 |         {
169 |           "output_type": "stream",
170 |           "text": [
171 |             "[[42  0  0  0  1  0  0  0  0  0]\n",
172 |             " [ 0 46  0  0  0  0  0  0  0  0]\n",
173 |             " [ 0  0 43  0  0  0  0  0  0  0]\n",
174 |             " [ 0  0  0 37  0  2  0  2  6  0]\n",
175 |             " [ 0  0  0  0 45  0  0  0  1  2]\n",
176 |             " [ 0  0  0  0  0 44  1  0  0  0]\n",
177 |             " [ 0  0  0  0  0  0 47  0  0  0]\n",
178 |             " [ 0  0  0  0  0  0  0 44  1  0]\n",
179 |             " [ 0  2  0  0  0  0  0  0 38  1]\n",
180 |             " [ 0  0  0  1  0  1  0  1  1 41]]\n",
181 |             "0.9488888888888889\n"
182 |           ],
183 |           "name": "stdout"
184 |         }
185 |       ]
186 |     },
187 |     {
188 |       "cell_type": "markdown",
189 |       "metadata": {
190 |         "id": "5E1tYLLJJRSB",
191 |         "colab_type": "text"
192 |       },
193 |       "source": [
194 |         "## **Testing**"
195 |       ]
196 |     },
197 |     {
198 |       "cell_type": "code",
199 |       "metadata": {
200 |         "id": "6VDPd6RDJqxf",
201 |         "colab_type": "code",
202 |         "colab": {
203 |           "base_uri": "https://localhost:8080/",
204 |           "height": 282
205 |         },
206 |         "outputId": "3f17080d-fda6-40fa-bd60-95375186d3cd"
207 |       },
208 |       "source": [
209 |         "plt.imshow(digits.images[-3], cmap = plt.cm.gray_r, interpolation = \"nearest\")\n",
210 |         "print(\"Prediction for test image:\", model.predict(data[-3].reshape(1,-1)))\n",
211 |         "\n",
212 |         "plt.show()"
213 |       ],
214 |       "execution_count": 18,
215 |       "outputs": [
216 |         {
217 |           "output_type": "stream",
218 |           "text": [
219 |             "Prediction for test image: [8]\n"
220 |           ],
221 |           "name": "stdout"
222 |         },
223 |         {
224 |           "output_type": "display_data",
225 |           "data": {
226 |             "image/png": "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\n",
227 |             "text/plain": [
228 |               "<Figure size 432x288 with 1 Axes>"
229 |             ]
230 |           },
231 |           "metadata": {
232 |             "tags": [],
233 |             "needs_background": "light"
234 |           }
235 |         }
236 |       ]
237 |     }
238 |   ]
239 | }


--------------------------------------------------------------------------------
/05_Support_Vector_Machine/Support_Vector_Machine_(Iris_Dataset).ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Support_Vector_Machine (Iris Dataset).ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": []
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     }
 14 |   },
 15 |   "cells": [
 16 |     {
 17 |       "cell_type": "markdown",
 18 |       "metadata": {
 19 |         "id": "HFenp3yV-gcO",
 20 |         "colab_type": "text"
 21 |       },
 22 |       "source": [
 23 |         "## **Support Vector Machine (SVM)**\n",
 24 |         "\n",
 25 |         "A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.\n",
 26 |         "\n",
 27 |         "By [Muhammad Huzaifa Shahbaz](https://www.linkedin.com/in/mhuzaifadev)"
 28 |       ]
 29 |     },
 30 |     {
 31 |       "cell_type": "markdown",
 32 |       "metadata": {
 33 |         "id": "EkXtM692ev1u",
 34 |         "colab_type": "text"
 35 |       },
 36 |       "source": [
 37 |         "## **Importing libraries**\n",
 38 |         "\n",
 39 |         "We will import Numpy as *np*, Pandas as *pd*, MatplotLib.pyplot as *plt*, *svm* & *datasets* from sklearn,  train_test_split from sklearn.model_selection and confusion_matrix & accuracy_score from sklearn.metrics."
 40 |       ]
 41 |     },
 42 |     {
 43 |       "cell_type": "code",
 44 |       "metadata": {
 45 |         "id": "hewapbOjdYLB",
 46 |         "colab_type": "code",
 47 |         "colab": {}
 48 |       },
 49 |       "source": [
 50 |         "import numpy as np\n",
 51 |         "import pandas as pd\n",
 52 |         "import matplotlib.pyplot as plt\n",
 53 |         "from sklearn import svm\n",
 54 |         "from sklearn import datasets\n",
 55 |         "from sklearn.model_selection import train_test_split\n",
 56 |         "from sklearn.metrics import confusion_matrix\n",
 57 |         "from sklearn.metrics import accuracy_score"
 58 |       ],
 59 |       "execution_count": 0,
 60 |       "outputs": []
 61 |     },
 62 |     {
 63 |       "cell_type": "markdown",
 64 |       "metadata": {
 65 |         "id": "PsAOn1BiC-_o",
 66 |         "colab_type": "text"
 67 |       },
 68 |       "source": [
 69 |         "## **Data Loading**\n",
 70 |         "\n",
 71 |         "Loading data from sklearn datasets"
 72 |       ]
 73 |     },
 74 |     {
 75 |       "cell_type": "code",
 76 |       "metadata": {
 77 |         "id": "6djJF7NEAx_I",
 78 |         "colab_type": "code",
 79 |         "colab": {
 80 |           "base_uri": "https://localhost:8080/",
 81 |           "height": 1000
 82 |         },
 83 |         "outputId": "276a6c92-6bd6-4b46-ba24-f169f707a7bd"
 84 |       },
 85 |       "source": [
 86 |         "dataset = datasets.load_iris()\n",
 87 |         "#print(dataset)"
 88 |       ],
 89 |       "execution_count": 3,
 90 |       "outputs": [
 91 |         {
 92 |           "output_type": "stream",
 93 |           "text": [
 94 |             "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
 95 |             "       [4.9, 3. , 1.4, 0.2],\n",
 96 |             "       [4.7, 3.2, 1.3, 0.2],\n",
 97 |             "       [4.6, 3.1, 1.5, 0.2],\n",
 98 |             "       [5. , 3.6, 1.4, 0.2],\n",
 99 |             "       [5.4, 3.9, 1.7, 0.4],\n",
100 |             "       [4.6, 3.4, 1.4, 0.3],\n",
101 |             "       [5. , 3.4, 1.5, 0.2],\n",
102 |             "       [4.4, 2.9, 1.4, 0.2],\n",
103 |             "       [4.9, 3.1, 1.5, 0.1],\n",
104 |             "       [5.4, 3.7, 1.5, 0.2],\n",
105 |             "       [4.8, 3.4, 1.6, 0.2],\n",
106 |             "       [4.8, 3. , 1.4, 0.1],\n",
107 |             "       [4.3, 3. , 1.1, 0.1],\n",
108 |             "       [5.8, 4. , 1.2, 0.2],\n",
109 |             "       [5.7, 4.4, 1.5, 0.4],\n",
110 |             "       [5.4, 3.9, 1.3, 0.4],\n",
111 |             "       [5.1, 3.5, 1.4, 0.3],\n",
112 |             "       [5.7, 3.8, 1.7, 0.3],\n",
113 |             "       [5.1, 3.8, 1.5, 0.3],\n",
114 |             "       [5.4, 3.4, 1.7, 0.2],\n",
115 |             "       [5.1, 3.7, 1.5, 0.4],\n",
116 |             "       [4.6, 3.6, 1. , 0.2],\n",
117 |             "       [5.1, 3.3, 1.7, 0.5],\n",
118 |             "       [4.8, 3.4, 1.9, 0.2],\n",
119 |             "       [5. , 3. , 1.6, 0.2],\n",
120 |             "       [5. , 3.4, 1.6, 0.4],\n",
121 |             "       [5.2, 3.5, 1.5, 0.2],\n",
122 |             "       [5.2, 3.4, 1.4, 0.2],\n",
123 |             "       [4.7, 3.2, 1.6, 0.2],\n",
124 |             "       [4.8, 3.1, 1.6, 0.2],\n",
125 |             "       [5.4, 3.4, 1.5, 0.4],\n",
126 |             "       [5.2, 4.1, 1.5, 0.1],\n",
127 |             "       [5.5, 4.2, 1.4, 0.2],\n",
128 |             "       [4.9, 3.1, 1.5, 0.2],\n",
129 |             "       [5. , 3.2, 1.2, 0.2],\n",
130 |             "       [5.5, 3.5, 1.3, 0.2],\n",
131 |             "       [4.9, 3.6, 1.4, 0.1],\n",
132 |             "       [4.4, 3. , 1.3, 0.2],\n",
133 |             "       [5.1, 3.4, 1.5, 0.2],\n",
134 |             "       [5. , 3.5, 1.3, 0.3],\n",
135 |             "       [4.5, 2.3, 1.3, 0.3],\n",
136 |             "       [4.4, 3.2, 1.3, 0.2],\n",
137 |             "       [5. , 3.5, 1.6, 0.6],\n",
138 |             "       [5.1, 3.8, 1.9, 0.4],\n",
139 |             "       [4.8, 3. , 1.4, 0.3],\n",
140 |             "       [5.1, 3.8, 1.6, 0.2],\n",
141 |             "       [4.6, 3.2, 1.4, 0.2],\n",
142 |             "       [5.3, 3.7, 1.5, 0.2],\n",
143 |             "       [5. , 3.3, 1.4, 0.2],\n",
144 |             "       [7. , 3.2, 4.7, 1.4],\n",
145 |             "       [6.4, 3.2, 4.5, 1.5],\n",
146 |             "       [6.9, 3.1, 4.9, 1.5],\n",
147 |             "       [5.5, 2.3, 4. , 1.3],\n",
148 |             "       [6.5, 2.8, 4.6, 1.5],\n",
149 |             "       [5.7, 2.8, 4.5, 1.3],\n",
150 |             "       [6.3, 3.3, 4.7, 1.6],\n",
151 |             "       [4.9, 2.4, 3.3, 1. ],\n",
152 |             "       [6.6, 2.9, 4.6, 1.3],\n",
153 |             "       [5.2, 2.7, 3.9, 1.4],\n",
154 |             "       [5. , 2. , 3.5, 1. ],\n",
155 |             "       [5.9, 3. , 4.2, 1.5],\n",
156 |             "       [6. , 2.2, 4. , 1. ],\n",
157 |             "       [6.1, 2.9, 4.7, 1.4],\n",
158 |             "       [5.6, 2.9, 3.6, 1.3],\n",
159 |             "       [6.7, 3.1, 4.4, 1.4],\n",
160 |             "       [5.6, 3. , 4.5, 1.5],\n",
161 |             "       [5.8, 2.7, 4.1, 1. ],\n",
162 |             "       [6.2, 2.2, 4.5, 1.5],\n",
163 |             "       [5.6, 2.5, 3.9, 1.1],\n",
164 |             "       [5.9, 3.2, 4.8, 1.8],\n",
165 |             "       [6.1, 2.8, 4. , 1.3],\n",
166 |             "       [6.3, 2.5, 4.9, 1.5],\n",
167 |             "       [6.1, 2.8, 4.7, 1.2],\n",
168 |             "       [6.4, 2.9, 4.3, 1.3],\n",
169 |             "       [6.6, 3. , 4.4, 1.4],\n",
170 |             "       [6.8, 2.8, 4.8, 1.4],\n",
171 |             "       [6.7, 3. , 5. , 1.7],\n",
172 |             "       [6. , 2.9, 4.5, 1.5],\n",
173 |             "       [5.7, 2.6, 3.5, 1. ],\n",
174 |             "       [5.5, 2.4, 3.8, 1.1],\n",
175 |             "       [5.5, 2.4, 3.7, 1. ],\n",
176 |             "       [5.8, 2.7, 3.9, 1.2],\n",
177 |             "       [6. , 2.7, 5.1, 1.6],\n",
178 |             "       [5.4, 3. , 4.5, 1.5],\n",
179 |             "       [6. , 3.4, 4.5, 1.6],\n",
180 |             "       [6.7, 3.1, 4.7, 1.5],\n",
181 |             "       [6.3, 2.3, 4.4, 1.3],\n",
182 |             "       [5.6, 3. , 4.1, 1.3],\n",
183 |             "       [5.5, 2.5, 4. , 1.3],\n",
184 |             "       [5.5, 2.6, 4.4, 1.2],\n",
185 |             "       [6.1, 3. , 4.6, 1.4],\n",
186 |             "       [5.8, 2.6, 4. , 1.2],\n",
187 |             "       [5. , 2.3, 3.3, 1. ],\n",
188 |             "       [5.6, 2.7, 4.2, 1.3],\n",
189 |             "       [5.7, 3. , 4.2, 1.2],\n",
190 |             "       [5.7, 2.9, 4.2, 1.3],\n",
191 |             "       [6.2, 2.9, 4.3, 1.3],\n",
192 |             "       [5.1, 2.5, 3. , 1.1],\n",
193 |             "       [5.7, 2.8, 4.1, 1.3],\n",
194 |             "       [6.3, 3.3, 6. , 2.5],\n",
195 |             "       [5.8, 2.7, 5.1, 1.9],\n",
196 |             "       [7.1, 3. , 5.9, 2.1],\n",
197 |             "       [6.3, 2.9, 5.6, 1.8],\n",
198 |             "       [6.5, 3. , 5.8, 2.2],\n",
199 |             "       [7.6, 3. , 6.6, 2.1],\n",
200 |             "       [4.9, 2.5, 4.5, 1.7],\n",
201 |             "       [7.3, 2.9, 6.3, 1.8],\n",
202 |             "       [6.7, 2.5, 5.8, 1.8],\n",
203 |             "       [7.2, 3.6, 6.1, 2.5],\n",
204 |             "       [6.5, 3.2, 5.1, 2. ],\n",
205 |             "       [6.4, 2.7, 5.3, 1.9],\n",
206 |             "       [6.8, 3. , 5.5, 2.1],\n",
207 |             "       [5.7, 2.5, 5. , 2. ],\n",
208 |             "       [5.8, 2.8, 5.1, 2.4],\n",
209 |             "       [6.4, 3.2, 5.3, 2.3],\n",
210 |             "       [6.5, 3. , 5.5, 1.8],\n",
211 |             "       [7.7, 3.8, 6.7, 2.2],\n",
212 |             "       [7.7, 2.6, 6.9, 2.3],\n",
213 |             "       [6. , 2.2, 5. , 1.5],\n",
214 |             "       [6.9, 3.2, 5.7, 2.3],\n",
215 |             "       [5.6, 2.8, 4.9, 2. ],\n",
216 |             "       [7.7, 2.8, 6.7, 2. ],\n",
217 |             "       [6.3, 2.7, 4.9, 1.8],\n",
218 |             "       [6.7, 3.3, 5.7, 2.1],\n",
219 |             "       [7.2, 3.2, 6. , 1.8],\n",
220 |             "       [6.2, 2.8, 4.8, 1.8],\n",
221 |             "       [6.1, 3. , 4.9, 1.8],\n",
222 |             "       [6.4, 2.8, 5.6, 2.1],\n",
223 |             "       [7.2, 3. , 5.8, 1.6],\n",
224 |             "       [7.4, 2.8, 6.1, 1.9],\n",
225 |             "       [7.9, 3.8, 6.4, 2. ],\n",
226 |             "       [6.4, 2.8, 5.6, 2.2],\n",
227 |             "       [6.3, 2.8, 5.1, 1.5],\n",
228 |             "       [6.1, 2.6, 5.6, 1.4],\n",
229 |             "       [7.7, 3. , 6.1, 2.3],\n",
230 |             "       [6.3, 3.4, 5.6, 2.4],\n",
231 |             "       [6.4, 3.1, 5.5, 1.8],\n",
232 |             "       [6. , 3. , 4.8, 1.8],\n",
233 |             "       [6.9, 3.1, 5.4, 2.1],\n",
234 |             "       [6.7, 3.1, 5.6, 2.4],\n",
235 |             "       [6.9, 3.1, 5.1, 2.3],\n",
236 |             "       [5.8, 2.7, 5.1, 1.9],\n",
237 |             "       [6.8, 3.2, 5.9, 2.3],\n",
238 |             "       [6.7, 3.3, 5.7, 2.5],\n",
239 |             "       [6.7, 3. , 5.2, 2.3],\n",
240 |             "       [6.3, 2.5, 5. , 1.9],\n",
241 |             "       [6.5, 3. , 5.2, 2. ],\n",
242 |             "       [6.2, 3.4, 5.4, 2.3],\n",
243 |             "       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
244 |             "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
245 |             "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
246 |             "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
247 |             "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
248 |             "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
249 |             "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': '/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/iris.csv'}\n"
250 |           ],
251 |           "name": "stdout"
252 |         }
253 |       ]
254 |     },
255 |     {
256 |       "cell_type": "markdown",
257 |       "metadata": {
258 |         "id": "N0c60VUJEfr-",
259 |         "colab_type": "text"
260 |       },
261 |       "source": [
262 |         "## **Features Extraction**\n",
263 |         "\n",
264 |         "Extracting features and splitting data into test and train."
265 |       ]
266 |     },
267 |     {
268 |       "cell_type": "code",
269 |       "metadata": {
270 |         "id": "pwLzYvYWBQcQ",
271 |         "colab_type": "code",
272 |         "colab": {}
273 |       },
274 |       "source": [
275 |         "features = dataset.data\n",
276 |         "target_variables = dataset.target\n",
277 |         "\n",
278 |         "feature_train, feature_test, target_train, target_test = train_test_split(features,target_variables)"
279 |       ],
280 |       "execution_count": 0,
281 |       "outputs": []
282 |     },
283 |     {
284 |       "cell_type": "markdown",
285 |       "metadata": {
286 |         "id": "nHxk6jzYLx0m",
287 |         "colab_type": "text"
288 |       },
289 |       "source": [
290 |         "## **Training the Model**\n",
291 |         "\n",
292 |         "We are using SVM model as imported from sklearn library and then it's being trained on features and target_variables"
293 |       ]
294 |     },
295 |     {
296 |       "cell_type": "code",
297 |       "metadata": {
298 |         "id": "1eAhZM71COWr",
299 |         "colab_type": "code",
300 |         "colab": {}
301 |       },
302 |       "source": [
303 |         "model = svm.SVC()\n",
304 |         "fitModel = model.fit(feature_train,target_train)\n",
305 |         "predictions = fitModel.predict(feature_test)"
306 |       ],
307 |       "execution_count": 0,
308 |       "outputs": []
309 |     },
310 |     {
311 |       "cell_type": "markdown",
312 |       "metadata": {
313 |         "id": "tFBsaZpeMdIa",
314 |         "colab_type": "text"
315 |       },
316 |       "source": [
317 |         "## **Printing an Error Matrix and Accuracy Score**"
318 |       ]
319 |     },
320 |     {
321 |       "cell_type": "code",
322 |       "metadata": {
323 |         "id": "urqzo3v7MzYp",
324 |         "colab_type": "code",
325 |         "outputId": "ec7ef53c-c183-4945-e162-cf23ac97c847",
326 |         "colab": {
327 |           "base_uri": "https://localhost:8080/",
328 |           "height": 87
329 |         }
330 |       },
331 |       "source": [
332 |         "print(confusion_matrix(target_test,predictions))\n",
333 |         "print(accuracy_score(target_test,predictions))"
334 |       ],
335 |       "execution_count": 26,
336 |       "outputs": [
337 |         {
338 |           "output_type": "stream",
339 |           "text": [
340 |             "[[11  0  0]\n",
341 |             " [ 0 16  1]\n",
342 |             " [ 0  0 10]]\n",
343 |             "0.9736842105263158\n"
344 |           ],
345 |           "name": "stdout"
346 |         }
347 |       ]
348 |     }
349 |   ]
350 | }


--------------------------------------------------------------------------------
/06_Decision_Trees/Decision_Trees.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Decision_Trees.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": [],
  9 |       "toc_visible": true
 10 |     },
 11 |     "kernelspec": {
 12 |       "name": "python3",
 13 |       "display_name": "Python 3"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "markdown",
 19 |       "metadata": {
 20 |         "id": "HFenp3yV-gcO",
 21 |         "colab_type": "text"
 22 |       },
 23 |       "source": [
 24 |         "## **Decision Trees**\n",
 25 |         "\n",
 26 |         "A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes).\n",
 27 |         "\n",
 28 |         "By [Muhammad Huzaifa Shahbaz](https://www.linkedin.com/in/mhuzaifadev)"
 29 |       ]
 30 |     },
 31 |     {
 32 |       "cell_type": "markdown",
 33 |       "metadata": {
 34 |         "id": "EkXtM692ev1u",
 35 |         "colab_type": "text"
 36 |       },
 37 |       "source": [
 38 |         "## **Importing libraries**\n"
 39 |       ]
 40 |     },
 41 |     {
 42 |       "cell_type": "code",
 43 |       "metadata": {
 44 |         "id": "hewapbOjdYLB",
 45 |         "colab_type": "code",
 46 |         "colab": {}
 47 |       },
 48 |       "source": [
 49 |         "import pandas as pd\n",
 50 |         "import numpy as np\n",
 51 |         "from sklearn.tree import DecisionTreeClassifier \n",
 52 |         "from sklearn.model_selection import train_test_split, GridSearchCV\n",
 53 |         "from sklearn.metrics import accuracy_score, confusion_matrix"
 54 |       ],
 55 |       "execution_count": 0,
 56 |       "outputs": []
 57 |     },
 58 |     {
 59 |       "cell_type": "markdown",
 60 |       "metadata": {
 61 |         "id": "7H35nRxvE4gp",
 62 |         "colab_type": "text"
 63 |       },
 64 |       "source": [
 65 |         "## **Dataset Loading**\n",
 66 |         "\n",
 67 |         "We will load data from the csv dataset"
 68 |       ]
 69 |     },
 70 |     {
 71 |       "cell_type": "code",
 72 |       "metadata": {
 73 |         "id": "eT6Vu1-0F3jF",
 74 |         "colab_type": "code",
 75 |         "colab": {}
 76 |       },
 77 |       "source": [
 78 |         "data = pd.read_csv(\".\iris_data.csv\")"
 79 |       ],
 80 |       "execution_count": 0,
 81 |       "outputs": []
 82 |     },
 83 |     {
 84 |       "cell_type": "markdown",
 85 |       "metadata": {
 86 |         "id": "N0c60VUJEfr-",
 87 |         "colab_type": "text"
 88 |       },
 89 |       "source": [
 90 |         "## **Features Extraction**\n",
 91 |         "\n",
 92 |         "To apply a classifier on this data, we need to extract features and target data and split it into test and train."
 93 |       ]
 94 |     },
 95 |     {
 96 |       "cell_type": "code",
 97 |       "metadata": {
 98 |         "id": "CLm4haVcGf-N",
 99 |         "colab_type": "code",
100 |         "colab": {}
101 |       },
102 |       "source": [
103 |         "features = data[[\"SepalLength\",\"SepalWidth\",\"PetalLength\",\"PetalWidth\"]]\n",
104 |         "targets = data.Class\n",
105 |         "\n",
106 |         "feature_train, feature_test, target_train, target_test = train_test_split(features,targets)"
107 |       ],
108 |       "execution_count": 0,
109 |       "outputs": []
110 |     },
111 |     {
112 |       "cell_type": "markdown",
113 |       "metadata": {
114 |         "id": "avcFhrNaO1TB",
115 |         "colab_type": "text"
116 |       },
117 |       "source": [
118 |         "## **Finding an Optimal Value**"
119 |       ]
120 |     },
121 |     {
122 |       "cell_type": "code",
123 |       "metadata": {
124 |         "id": "CFjn9HyMO1ml",
125 |         "colab_type": "code",
126 |         "colab": {
127 |           "base_uri": "https://localhost:8080/",
128 |           "height": 34
129 |         },
130 |         "outputId": "526ee695-bb90-4ec2-bdd7-e8f8a910f719"
131 |       },
132 |       "source": [
133 |         "param_grid = {'max_depth': np.arange(1,10)}\n",
134 |         "\n",
135 |         "tree = GridSearchCV(DecisionTreeClassifier(), param_grid)\n",
136 |         "fit_tree = tree.fit(feature_train, target_train)\n",
137 |         "tree_predictions = fit_tree.predict_proba(feature_test)[:,1]\n",
138 |         "print(fit_tree.best_params_)"
139 |       ],
140 |       "execution_count": 41,
141 |       "outputs": [
142 |         {
143 |           "output_type": "stream",
144 |           "text": [
145 |             "{'max_depth': 7}\n"
146 |           ],
147 |           "name": "stdout"
148 |         }
149 |       ]
150 |     },
151 |     {
152 |       "cell_type": "markdown",
153 |       "metadata": {
154 |         "id": "nHxk6jzYLx0m",
155 |         "colab_type": "text"
156 |       },
157 |       "source": [
158 |         "## **Training the Model**\n",
159 |         "\n",
160 |         "We will use Decision Trees Classifier for training the model."
161 |       ]
162 |     },
163 |     {
164 |       "cell_type": "code",
165 |       "metadata": {
166 |         "id": "1eAhZM71COWr",
167 |         "colab_type": "code",
168 |         "colab": {}
169 |       },
170 |       "source": [
171 |         "model = DecisionTreeClassifier(criterion='entropy',max_depth=7)\n",
172 |         "fit_model = model.fit(feature_train, target_train)\n",
173 |         "\n",
174 |         "predictions = fit_model.predict(feature_test)"
175 |       ],
176 |       "execution_count": 0,
177 |       "outputs": []
178 |     },
179 |     {
180 |       "cell_type": "markdown",
181 |       "metadata": {
182 |         "id": "tFBsaZpeMdIa",
183 |         "colab_type": "text"
184 |       },
185 |       "source": [
186 |         "## **Printing an Error Matrix and Accuracy Score**"
187 |       ]
188 |     },
189 |     {
190 |       "cell_type": "code",
191 |       "metadata": {
192 |         "id": "urqzo3v7MzYp",
193 |         "colab_type": "code",
194 |         "outputId": "274cf1ac-e310-4118-ddee-211be90d2118",
195 |         "colab": {
196 |           "base_uri": "https://localhost:8080/",
197 |           "height": 87
198 |         }
199 |       },
200 |       "source": [
201 |         "print(confusion_matrix(target_test,predictions))\n",
202 |         "print(accuracy_score(target_test,predictions))"
203 |       ],
204 |       "execution_count": 45,
205 |       "outputs": [
206 |         {
207 |           "output_type": "stream",
208 |           "text": [
209 |             "[[13  0  0]\n",
210 |             " [ 0 11  0]\n",
211 |             " [ 0  1 13]]\n",
212 |             "0.9736842105263158\n"
213 |           ],
214 |           "name": "stdout"
215 |         }
216 |       ]
217 |     }
218 |   ]
219 | }


--------------------------------------------------------------------------------
/06_Decision_Trees/iris_data.csv:
--------------------------------------------------------------------------------
  1 | "SepalLength","SepalWidth","PetalLength","PetalWidth","Class"
  2 | 5.1,3.5,1.4,0.2,Iris-setosa
  3 | 4.9,3.0,1.4,0.2,Iris-setosa
  4 | 4.7,3.2,1.3,0.2,Iris-setosa
  5 | 4.6,3.1,1.5,0.2,Iris-setosa
  6 | 5.0,3.6,1.4,0.2,Iris-setosa
  7 | 5.4,3.9,1.7,0.4,Iris-setosa
  8 | 4.6,3.4,1.4,0.3,Iris-setosa
  9 | 5.0,3.4,1.5,0.2,Iris-setosa
 10 | 4.4,2.9,1.4,0.2,Iris-setosa
 11 | 4.9,3.1,1.5,0.1,Iris-setosa
 12 | 5.4,3.7,1.5,0.2,Iris-setosa
 13 | 4.8,3.4,1.6,0.2,Iris-setosa
 14 | 4.8,3.0,1.4,0.1,Iris-setosa
 15 | 4.3,3.0,1.1,0.1,Iris-setosa
 16 | 5.8,4.0,1.2,0.2,Iris-setosa
 17 | 5.7,4.4,1.5,0.4,Iris-setosa
 18 | 5.4,3.9,1.3,0.4,Iris-setosa
 19 | 5.1,3.5,1.4,0.3,Iris-setosa
 20 | 5.7,3.8,1.7,0.3,Iris-setosa
 21 | 5.1,3.8,1.5,0.3,Iris-setosa
 22 | 5.4,3.4,1.7,0.2,Iris-setosa
 23 | 5.1,3.7,1.5,0.4,Iris-setosa
 24 | 4.6,3.6,1.0,0.2,Iris-setosa
 25 | 5.1,3.3,1.7,0.5,Iris-setosa
 26 | 4.8,3.4,1.9,0.2,Iris-setosa
 27 | 5.0,3.0,1.6,0.2,Iris-setosa
 28 | 5.0,3.4,1.6,0.4,Iris-setosa
 29 | 5.2,3.5,1.5,0.2,Iris-setosa
 30 | 5.2,3.4,1.4,0.2,Iris-setosa
 31 | 4.7,3.2,1.6,0.2,Iris-setosa
 32 | 4.8,3.1,1.6,0.2,Iris-setosa
 33 | 5.4,3.4,1.5,0.4,Iris-setosa
 34 | 5.2,4.1,1.5,0.1,Iris-setosa
 35 | 5.5,4.2,1.4,0.2,Iris-setosa
 36 | 4.9,3.1,1.5,0.1,Iris-setosa
 37 | 5.0,3.2,1.2,0.2,Iris-setosa
 38 | 5.5,3.5,1.3,0.2,Iris-setosa
 39 | 4.9,3.1,1.5,0.1,Iris-setosa
 40 | 4.4,3.0,1.3,0.2,Iris-setosa
 41 | 5.1,3.4,1.5,0.2,Iris-setosa
 42 | 7.0,3.2,4.7,1.4,Iris-versicolor
 43 | 6.4,3.2,4.5,1.5,Iris-versicolor
 44 | 6.9,3.1,4.9,1.5,Iris-versicolor
 45 | 5.5,2.3,4.0,1.3,Iris-versicolor
 46 | 6.5,2.8,4.6,1.5,Iris-versicolor
 47 | 5.7,2.8,4.5,1.3,Iris-versicolor
 48 | 6.3,3.3,4.7,1.6,Iris-versicolor
 49 | 4.9,2.4,3.3,1.0,Iris-versicolor
 50 | 6.6,2.9,4.6,1.3,Iris-versicolor
 51 | 5.2,2.7,3.9,1.4,Iris-versicolor
 52 | 5.0,2.0,3.5,1.0,Iris-versicolor
 53 | 5.9,3.0,4.2,1.5,Iris-versicolor
 54 | 6.0,2.2,4.0,1.0,Iris-versicolor
 55 | 6.1,2.9,4.7,1.4,Iris-versicolor
 56 | 5.6,2.9,3.6,1.3,Iris-versicolor
 57 | 6.7,3.1,4.4,1.4,Iris-versicolor
 58 | 5.6,3.0,4.5,1.5,Iris-versicolor
 59 | 5.8,2.7,4.1,1.0,Iris-versicolor
 60 | 6.2,2.2,4.5,1.5,Iris-versicolor
 61 | 5.6,2.5,3.9,1.1,Iris-versicolor
 62 | 5.9,3.2,4.8,1.8,Iris-versicolor
 63 | 6.1,2.8,4.0,1.3,Iris-versicolor
 64 | 6.3,2.5,4.9,1.5,Iris-versicolor
 65 | 6.1,2.8,4.7,1.2,Iris-versicolor
 66 | 6.4,2.9,4.3,1.3,Iris-versicolor
 67 | 6.6,3.0,4.4,1.4,Iris-versicolor
 68 | 6.8,2.8,4.8,1.4,Iris-versicolor
 69 | 6.7,3.0,5.0,1.7,Iris-versicolor
 70 | 6.0,2.9,4.5,1.5,Iris-versicolor
 71 | 5.7,2.6,3.5,1.0,Iris-versicolor
 72 | 5.5,2.4,3.8,1.1,Iris-versicolor
 73 | 5.5,2.4,3.7,1.0,Iris-versicolor
 74 | 5.8,2.7,3.9,1.2,Iris-versicolor
 75 | 6.0,2.7,5.1,1.6,Iris-versicolor
 76 | 5.4,3.0,4.5,1.5,Iris-versicolor
 77 | 6.0,3.4,4.5,1.6,Iris-versicolor
 78 | 6.7,3.1,4.7,1.5,Iris-versicolor
 79 | 6.3,2.3,4.4,1.3,Iris-versicolor
 80 | 5.6,3.0,4.1,1.3,Iris-versicolor
 81 | 5.5,2.5,4.0,1.3,Iris-versicolor
 82 | 6.3,3.3,6.0,2.5,Iris-virginica
 83 | 5.8,2.7,5.1,1.9,Iris-virginica
 84 | 7.1,3.0,5.9,2.1,Iris-virginica
 85 | 6.3,2.9,5.6,1.8,Iris-virginica
 86 | 6.5,3.0,5.8,2.2,Iris-virginica
 87 | 7.6,3.0,6.6,2.1,Iris-virginica
 88 | 4.9,2.5,4.5,1.7,Iris-virginica
 89 | 7.3,2.9,6.3,1.8,Iris-virginica
 90 | 6.7,2.5,5.8,1.8,Iris-virginica
 91 | 7.2,3.6,6.1,2.5,Iris-virginica
 92 | 6.5,3.2,5.1,2.0,Iris-virginica
 93 | 6.4,2.7,5.3,1.9,Iris-virginica
 94 | 6.8,3.0,5.5,2.1,Iris-virginica
 95 | 5.7,2.5,5.0,2.0,Iris-virginica
 96 | 5.8,2.8,5.1,2.4,Iris-virginica
 97 | 6.4,3.2,5.3,2.3,Iris-virginica
 98 | 6.5,3.0,5.5,1.8,Iris-virginica
 99 | 7.7,3.8,6.7,2.2,Iris-virginica
100 | 7.7,2.6,6.9,2.3,Iris-virginica
101 | 6.0,2.2,5.0,1.5,Iris-virginica
102 | 6.9,3.2,5.7,2.3,Iris-virginica
103 | 5.6,2.8,4.9,2.0,Iris-virginica
104 | 7.7,2.8,6.7,2.0,Iris-virginica
105 | 6.3,2.7,4.9,1.8,Iris-virginica
106 | 6.7,3.3,5.7,2.1,Iris-virginica
107 | 7.2,3.2,6.0,1.8,Iris-virginica
108 | 6.2,2.8,4.8,1.8,Iris-virginica
109 | 6.1,3.0,4.9,1.8,Iris-virginica
110 | 6.4,2.8,5.6,2.1,Iris-virginica
111 | 7.2,3.0,5.8,1.6,Iris-virginica
112 | 7.4,2.8,6.1,1.9,Iris-virginica
113 | 7.9,3.8,6.4,2.0,Iris-virginica
114 | 6.4,2.8,5.6,2.2,Iris-virginica
115 | 6.3,2.8,5.1,1.5,Iris-virginica
116 | 6.1,2.6,5.6,1.4,Iris-virginica
117 | 7.7,3.0,6.1,2.3,Iris-virginica
118 | 6.3,3.4,5.6,2.4,Iris-virginica
119 | 6.4,3.1,5.5,1.8,Iris-virginica
120 | 6.0,3.0,4.8,1.8,Iris-virginica
121 | 6.9,3.1,5.4,2.1,Iris-virginica
122 | 5.0,3.5,1.3,0.3,Iris-setosa
123 | 4.5,2.3,1.3,0.3,Iris-setosa
124 | 4.4,3.2,1.3,0.2,Iris-setosa
125 | 5.0,3.5,1.6,0.6,Iris-setosa
126 | 5.1,3.8,1.9,0.4,Iris-setosa
127 | 4.8,3.0,1.4,0.3,Iris-setosa
128 | 5.1,3.8,1.6,0.2,Iris-setosa
129 | 4.6,3.2,1.4,0.2,Iris-setosa
130 | 5.3,3.7,1.5,0.2,Iris-setosa
131 | 5.0,3.3,1.4,0.2,Iris-setosa
132 | 5.5,2.6,4.4,1.2,Iris-versicolor
133 | 6.1,3.0,4.6,1.4,Iris-versicolor
134 | 5.8,2.6,4.0,1.2,Iris-versicolor
135 | 5.0,2.3,3.3,1.0,Iris-versicolor
136 | 5.6,2.7,4.2,1.3,Iris-versicolor
137 | 5.7,3.0,4.2,1.2,Iris-versicolor
138 | 5.7,2.9,4.2,1.3,Iris-versicolor
139 | 6.2,2.9,4.3,1.3,Iris-versicolor
140 | 5.1,2.5,3.0,1.1,Iris-versicolor
141 | 5.7,2.8,4.1,1.3,Iris-versicolor
142 | 6.7,3.1,5.6,2.4,Iris-virginica
143 | 6.9,3.1,5.1,2.3,Iris-virginica
144 | 5.8,2.7,5.1,1.9,Iris-virginica
145 | 6.8,3.2,5.9,2.3,Iris-virginica
146 | 6.7,3.3,5.7,2.5,Iris-virginica
147 | 6.7,3.0,5.2,2.3,Iris-virginica
148 | 6.3,2.5,5.0,1.9,Iris-virginica
149 | 6.5,3.0,5.2,2.0,Iris-virginica
150 | 6.2,3.4,5.4,2.3,Iris-virginica
151 | 5.9,3.0,5.1,1.8,Iris-virginica


--------------------------------------------------------------------------------
/07_Random_Forest_Classifier/Random_Forest_Classifier.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Random Forest Classifier.ipynb",
  7 |       "provenance": [],
  8 |       "toc_visible": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     },
 14 |     "accelerator": "TPU"
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "markdown",
 19 |       "metadata": {
 20 |         "id": "HFenp3yV-gcO",
 21 |         "colab_type": "text"
 22 |       },
 23 |       "source": [
 24 |         "## **Random Forest Classifier**\n",
 25 |         "\n",
 26 |         "Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set.\n",
 27 |         "\n",
 28 |         "By [Muhammad Huzaifa Shahbaz](https://www.linkedin.com/in/mhuzaifadev)"
 29 |       ]
 30 |     },
 31 |     {
 32 |       "cell_type": "code",
 33 |       "metadata": {
 34 |         "id": "_Q3IKg22Prjs",
 35 |         "colab_type": "code",
 36 |         "colab": {}
 37 |       },
 38 |       "source": [
 39 |         "import pandas as pd\n",
 40 |         "from sklearn.ensemble import RandomForestClassifier\n",
 41 |         "from sklearn.metrics import accuracy_score, confusion_matrix\n",
 42 |         "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_predict, KFold\n",
 43 |         "from sklearn import datasets"
 44 |       ],
 45 |       "execution_count": 0,
 46 |       "outputs": []
 47 |     },
 48 |     {
 49 |       "cell_type": "markdown",
 50 |       "metadata": {
 51 |         "id": "7H35nRxvE4gp",
 52 |         "colab_type": "text"
 53 |       },
 54 |       "source": [
 55 |         "## **Dataset Loading**\n",
 56 |         "\n",
 57 |         "We will load data from the csv dataset"
 58 |       ]
 59 |     },
 60 |     {
 61 |       "cell_type": "code",
 62 |       "metadata": {
 63 |         "id": "eT6Vu1-0F3jF",
 64 |         "colab_type": "code",
 65 |         "colab": {}
 66 |       },
 67 |       "source": [
 68 |         "credit_data = pd.read_csv(\"./credit_data.csv\")"
 69 |       ],
 70 |       "execution_count": 0,
 71 |       "outputs": []
 72 |     },
 73 |     {
 74 |       "cell_type": "markdown",
 75 |       "metadata": {
 76 |         "id": "N0c60VUJEfr-",
 77 |         "colab_type": "text"
 78 |       },
 79 |       "source": [
 80 |         "## **Features Extraction**\n",
 81 |         "\n",
 82 |         "To apply a classifier on this data, we need to extract features and target data and split it into test and train."
 83 |       ]
 84 |     },
 85 |     {
 86 |       "cell_type": "code",
 87 |       "metadata": {
 88 |         "id": "CLm4haVcGf-N",
 89 |         "colab_type": "code",
 90 |         "colab": {}
 91 |       },
 92 |       "source": [
 93 |         "features = credit_data[[\"income\",\"age\",\"loan\"]]\n",
 94 |         "targets = credit_data.default\n",
 95 |         "\n",
 96 |         "feature_train, feature_test, target_train, target_test = train_test_split(features,targets,test_size=0.2)"
 97 |       ],
 98 |       "execution_count": 0,
 99 |       "outputs": []
100 |     },
101 |     {
102 |       "cell_type": "markdown",
103 |       "metadata": {
104 |         "id": "avcFhrNaO1TB",
105 |         "colab_type": "text"
106 |       },
107 |       "source": [
108 |         "## **Finding an Optimal Value**\n",
109 |         "\n",
110 |         "It may take upto 45min in execution on Colab or maybe hours in your local desktop, based on processing power."
111 |       ]
112 |     },
113 |     {
114 |       "cell_type": "code",
115 |       "metadata": {
116 |         "id": "CFjn9HyMO1ml",
117 |         "colab_type": "code",
118 |         "outputId": "52f211e1-ecd3-4126-b430-33d726f99110",
119 |         "colab": {
120 |           "base_uri": "https://localhost:8080/",
121 |           "height": 34
122 |         }
123 |       },
124 |       "source": [
125 |         "param_grid = {\n",
126 |         "    'max_depth': [1,5,10,15],\n",
127 |         "    'n_estimators' : [10,100,500,1000],\n",
128 |         "    'min_samples_leaf' : [1,2,3,4,5,10,15,20,30,40,50]\n",
129 |         "    }\n",
130 |         "\n",
131 |         "grid_search = GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1,max_features = 'sqrt'), param_grid=param_grid,cv=10)\n",
132 |         "grid_search.fit(feature_train, target_train)\n",
133 |         "\n",
134 |         "print(grid_search.best_params_)\n",
135 |         "\n",
136 |         "optimal_estimators = grid_search.best_params_.get(\"n_estimators\")\n",
137 |         "optimal_depth = grid_search.best_params_.get(\"max_depth\")\n",
138 |         "optimal_leaf = grid_search.best_params_.get(\"min_samples_leaf\")"
139 |       ],
140 |       "execution_count": 14,
141 |       "outputs": [
142 |         {
143 |           "output_type": "stream",
144 |           "text": [
145 |             "{'max_depth': 10, 'min_samples_leaf': 3, 'n_estimators': 100}\n"
146 |           ],
147 |           "name": "stdout"
148 |         }
149 |       ]
150 |     },
151 |     {
152 |       "cell_type": "markdown",
153 |       "metadata": {
154 |         "id": "nHxk6jzYLx0m",
155 |         "colab_type": "text"
156 |       },
157 |       "source": [
158 |         "## **Training the Model**\n",
159 |         "\n",
160 |         "We will use Random Forest Classifier for training the model."
161 |       ]
162 |     },
163 |     {
164 |       "cell_type": "code",
165 |       "metadata": {
166 |         "id": "1eAhZM71COWr",
167 |         "colab_type": "code",
168 |         "colab": {
169 |           "base_uri": "https://localhost:8080/",
170 |           "height": 71
171 |         },
172 |         "outputId": "b79a709a-19e6-4276-b62a-5ed95564495c"
173 |       },
174 |       "source": [
175 |         "best_model = RandomForestClassifier(n_estimators=optimal_estimators, max_depth = optimal_depth,min_samples_leaf = optimal_leaf)\n",
176 |         "k_fold = KFold(n_splits=10,random_state=123)\n",
177 |         "\n",
178 |         "predictions = cross_val_predict(best_model,feature_test,target_test,cv=k_fold)"
179 |       ],
180 |       "execution_count": 25,
181 |       "outputs": [
182 |         {
183 |           "output_type": "stream",
184 |           "text": [
185 |             "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
186 |             "  FutureWarning\n"
187 |           ],
188 |           "name": "stderr"
189 |         }
190 |       ]
191 |     },
192 |     {
193 |       "cell_type": "markdown",
194 |       "metadata": {
195 |         "id": "tFBsaZpeMdIa",
196 |         "colab_type": "text"
197 |       },
198 |       "source": [
199 |         "## **Printing an Error Matrix and Accuracy Score**"
200 |       ]
201 |     },
202 |     {
203 |       "cell_type": "code",
204 |       "metadata": {
205 |         "id": "urqzo3v7MzYp",
206 |         "colab_type": "code",
207 |         "outputId": "5b90dcf4-982d-45b2-b5ab-8b27ce130490",
208 |         "colab": {
209 |           "base_uri": "https://localhost:8080/",
210 |           "height": 68
211 |         }
212 |       },
213 |       "source": [
214 |         "print(confusion_matrix(target_test,predictions))\n",
215 |         "print(accuracy_score(target_test,predictions))"
216 |       ],
217 |       "execution_count": 27,
218 |       "outputs": [
219 |         {
220 |           "output_type": "stream",
221 |           "text": [
222 |             "[[336   6]\n",
223 |             " [ 13  45]]\n",
224 |             "0.9525\n"
225 |           ],
226 |           "name": "stdout"
227 |         }
228 |       ]
229 |     }
230 |   ]
231 | }


--------------------------------------------------------------------------------
/08_Boosting/Boosting.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Boosting.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": [],
  9 |       "toc_visible": true
 10 |     },
 11 |     "kernelspec": {
 12 |       "name": "python3",
 13 |       "display_name": "Python 3"
 14 |     },
 15 |     "accelerator": "TPU"
 16 |   },
 17 |   "cells": [
 18 |     {
 19 |       "cell_type": "markdown",
 20 |       "metadata": {
 21 |         "id": "HFenp3yV-gcO",
 22 |         "colab_type": "text"
 23 |       },
 24 |       "source": [
 25 |         "## **Boosting**\n",
 26 |         "\n",
 27 |         "In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones.\n",
 28 |         "\n",
 29 |         "By [Muhammad Huzaifa Shahbaz](https://www.linkedin.com/in/mhuzaifadev)"
 30 |       ]
 31 |     },
 32 |     {
 33 |       "cell_type": "markdown",
 34 |       "metadata": {
 35 |         "id": "llRj-1Bp_YIW",
 36 |         "colab_type": "text"
 37 |       },
 38 |       "source": [
 39 |         "## **Importing Libraries**"
 40 |       ]
 41 |     },
 42 |     {
 43 |       "cell_type": "code",
 44 |       "metadata": {
 45 |         "id": "_Q3IKg22Prjs",
 46 |         "colab_type": "code",
 47 |         "colab": {}
 48 |       },
 49 |       "source": [
 50 |         "import pandas as pd\n",
 51 |         "from sklearn.ensemble import AdaBoostClassifier\n",
 52 |         "from sklearn.metrics import accuracy_score, confusion_matrix\n",
 53 |         "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_predict\n",
 54 |         "from sklearn import datasets"
 55 |       ],
 56 |       "execution_count": 0,
 57 |       "outputs": []
 58 |     },
 59 |     {
 60 |       "cell_type": "markdown",
 61 |       "metadata": {
 62 |         "id": "7H35nRxvE4gp",
 63 |         "colab_type": "text"
 64 |       },
 65 |       "source": [
 66 |         "## **Dataset Loading**\n",
 67 |         "\n",
 68 |         "We will load data from the csv dataset"
 69 |       ]
 70 |     },
 71 |     {
 72 |       "cell_type": "code",
 73 |       "metadata": {
 74 |         "id": "eT6Vu1-0F3jF",
 75 |         "colab_type": "code",
 76 |         "colab": {}
 77 |       },
 78 |       "source": [
 79 |         "data = pd.read_csv(\"./wine.csv\",sep = \";\")"
 80 |       ],
 81 |       "execution_count": 0,
 82 |       "outputs": []
 83 |     },
 84 |     {
 85 |       "cell_type": "markdown",
 86 |       "metadata": {
 87 |         "id": "9ChKTO43moQK",
 88 |         "colab_type": "text"
 89 |       },
 90 |       "source": [
 91 |         "## **Label Filtering Function: isTasty(quality)**\n",
 92 |         "\n",
 93 |         "Converting regression into logit"
 94 |       ]
 95 |     },
 96 |     {
 97 |       "cell_type": "code",
 98 |       "metadata": {
 99 |         "id": "8qs53wmumpgj",
100 |         "colab_type": "code",
101 |         "colab": {}
102 |       },
103 |       "source": [
104 |         "def isTasty(quality):\n",
105 |         "  if quality >= 7:\n",
106 |         "    return 1\n",
107 |         "  else:\n",
108 |         "    return 0"
109 |       ],
110 |       "execution_count": 0,
111 |       "outputs": []
112 |     },
113 |     {
114 |       "cell_type": "markdown",
115 |       "metadata": {
116 |         "id": "N0c60VUJEfr-",
117 |         "colab_type": "text"
118 |       },
119 |       "source": [
120 |         "## **Features Extraction**\n",
121 |         "\n",
122 |         "To apply a classifier on this data, we need to extract features and target data and split it into test and train."
123 |       ]
124 |     },
125 |     {
126 |       "cell_type": "code",
127 |       "metadata": {
128 |         "id": "CLm4haVcGf-N",
129 |         "colab_type": "code",
130 |         "colab": {}
131 |       },
132 |       "source": [
133 |         "features = data[[\"fixed acidity\",\"volatile acidity\",\"citric acid\",\"residual sugar\",\"chlorides\",\"free sulfur dioxide\",\"total sulfur dioxide\",\"density\",\"pH\",\"sulphates\",\"alcohol\"]]\n",
134 |         "data['tasty'] = data['quality'].apply(isTasty)\n",
135 |         "targets = data['tasty']\n",
136 |         "\n",
137 |         "feature_train, feature_test, target_train, target_test = train_test_split(features,targets,test_size=0.2)"
138 |       ],
139 |       "execution_count": 0,
140 |       "outputs": []
141 |     },
142 |     {
143 |       "cell_type": "markdown",
144 |       "metadata": {
145 |         "id": "avcFhrNaO1TB",
146 |         "colab_type": "text"
147 |       },
148 |       "source": [
149 |         "## **Finding an Optimal Value**\n",
150 |         "\n",
151 |         "It may take upto 45min in execution on Colab or maybe hours in your local desktop, based on processing power."
152 |       ]
153 |     },
154 |     {
155 |       "cell_type": "code",
156 |       "metadata": {
157 |         "id": "CFjn9HyMO1ml",
158 |         "colab_type": "code",
159 |         "outputId": "7e1a906b-744f-447c-869b-348502d5c62c",
160 |         "colab": {
161 |           "base_uri": "https://localhost:8080/",
162 |           "height": 35
163 |         }
164 |       },
165 |       "source": [
166 |         "param_grid = {\n",
167 |         "    'n_estimators' : [50,100,200,300,500,1000],\n",
168 |         "    'learning_rate' : [0.01,0.05,0.1,0.3,1],\n",
169 |         "    }\n",
170 |         "\n",
171 |         "grid_search = GridSearchCV(estimator=AdaBoostClassifier(), param_grid=param_grid,cv=10)\n",
172 |         "grid_search.fit(feature_train, target_train)\n",
173 |         "\n",
174 |         "print(grid_search.best_params_)\n",
175 |         "\n",
176 |         "optimal_estimators = grid_search.best_params_.get(\"n_estimators\")\n",
177 |         "optimal_lrate = grid_search.best_params_.get(\"learning_rate\")"
178 |       ],
179 |       "execution_count": 0,
180 |       "outputs": [
181 |         {
182 |           "output_type": "stream",
183 |           "text": [
184 |             "{'learning_rate': 0.3, 'n_estimators': 300}\n"
185 |           ],
186 |           "name": "stdout"
187 |         }
188 |       ]
189 |     },
190 |     {
191 |       "cell_type": "markdown",
192 |       "metadata": {
193 |         "id": "nHxk6jzYLx0m",
194 |         "colab_type": "text"
195 |       },
196 |       "source": [
197 |         "## **Training the Model**\n",
198 |         "\n",
199 |         "We will use AdaBoostClassifier for training the model."
200 |       ]
201 |     },
202 |     {
203 |       "cell_type": "code",
204 |       "metadata": {
205 |         "id": "1eAhZM71COWr",
206 |         "colab_type": "code",
207 |         "colab": {}
208 |       },
209 |       "source": [
210 |         "best_model = AdaBoostClassifier(n_estimators=optimal_estimators,learning_rate=optimal_lrate)\n",
211 |         "best_model.fit(feature_train, target_train)\n",
212 |         "\n",
213 |         "predictions = best_model.predict(feature_test)"
214 |       ],
215 |       "execution_count": 0,
216 |       "outputs": []
217 |     },
218 |     {
219 |       "cell_type": "markdown",
220 |       "metadata": {
221 |         "id": "tFBsaZpeMdIa",
222 |         "colab_type": "text"
223 |       },
224 |       "source": [
225 |         "## **Printing an Error Matrix and Accuracy Score**"
226 |       ]
227 |     },
228 |     {
229 |       "cell_type": "code",
230 |       "metadata": {
231 |         "id": "urqzo3v7MzYp",
232 |         "colab_type": "code",
233 |         "outputId": "4be01f9b-3308-4b38-df9b-eeb432505f38",
234 |         "colab": {
235 |           "base_uri": "https://localhost:8080/",
236 |           "height": 72
237 |         }
238 |       },
239 |       "source": [
240 |         "print(confusion_matrix(target_test,predictions))\n",
241 |         "print(accuracy_score(target_test,predictions))"
242 |       ],
243 |       "execution_count": 0,
244 |       "outputs": [
245 |         {
246 |           "output_type": "stream",
247 |           "text": [
248 |             "[[717  45]\n",
249 |             " [135  83]]\n",
250 |             "0.8163265306122449\n"
251 |           ],
252 |           "name": "stdout"
253 |         }
254 |       ]
255 |     }
256 |   ]
257 | }


--------------------------------------------------------------------------------
/09_Clustering/Hierarchical_Clustering.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Hierarchical Clustering.ipynb",
  7 |       "provenance": []
  8 |     },
  9 |     "kernelspec": {
 10 |       "name": "python3",
 11 |       "display_name": "Python 3"
 12 |     }
 13 |   },
 14 |   "cells": [
 15 |     {
 16 |       "cell_type": "markdown",
 17 |       "metadata": {
 18 |         "id": "OTbnV4K1CV4i",
 19 |         "colab_type": "text"
 20 |       },
 21 |       "source": [
 22 |         "## **Hierarchical Clustering**\n",
 23 |         "\n",
 24 |         "Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other."
 25 |       ]
 26 |     },
 27 |     {
 28 |       "cell_type": "markdown",
 29 |       "metadata": {
 30 |         "id": "j3F3bpdAWNCO",
 31 |         "colab_type": "text"
 32 |       },
 33 |       "source": [
 34 |         "## **Importing Libraries**"
 35 |       ]
 36 |     },
 37 |     {
 38 |       "cell_type": "code",
 39 |       "metadata": {
 40 |         "id": "CPIMeNgoCjZL",
 41 |         "colab_type": "code",
 42 |         "colab": {
 43 |           "base_uri": "https://localhost:8080/",
 44 |           "height": 70
 45 |         },
 46 |         "outputId": "f4425e29-071b-42c2-a14e-54c7e4af4c89"
 47 |       },
 48 |       "source": [
 49 |         "import matplotlib.pyplot as plt\n",
 50 |         "from scipy.cluster.hierarchy import linkage, dendrogram\n",
 51 |         "from sklearn.datasets.samples_generator import make_blobs\n",
 52 |         "import numpy as np"
 53 |       ],
 54 |       "execution_count": 2,
 55 |       "outputs": [
 56 |         {
 57 |           "output_type": "stream",
 58 |           "text": [
 59 |             "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.datasets.samples_generator module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
 60 |             "  warnings.warn(message, FutureWarning)\n"
 61 |           ],
 62 |           "name": "stderr"
 63 |         }
 64 |       ]
 65 |     },
 66 |     {
 67 |       "cell_type": "markdown",
 68 |       "metadata": {
 69 |         "id": "4anySMcgqROX",
 70 |         "colab_type": "text"
 71 |       },
 72 |       "source": [
 73 |         "## **Dataset Loading**\n",
 74 |         "\n"
 75 |       ]
 76 |     },
 77 |     {
 78 |       "cell_type": "code",
 79 |       "metadata": {
 80 |         "id": "H2fQIxhZDJa2",
 81 |         "colab_type": "code",
 82 |         "colab": {
 83 |           "base_uri": "https://localhost:8080/",
 84 |           "height": 264
 85 |         },
 86 |         "outputId": "96955c3c-9304-4bb4-8971-d9445d6a4d6f"
 87 |       },
 88 |       "source": [
 89 |         "x = np.array([[1,1],[1.5,1],[3,8],[4,4],[3,3.5],[3.5,4]])\n",
 90 |         "plt.scatter(x[:,0],x[:,1],s=50)\n",
 91 |         "\n",
 92 |         "plt.show()"
 93 |       ],
 94 |       "execution_count": 5,
 95 |       "outputs": [
 96 |         {
 97 |           "output_type": "display_data",
 98 |           "data": {
 99 |             "image/png": 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\n",
100 |             "text/plain": [
101 |               "<Figure size 432x288 with 1 Axes>"
102 |             ]
103 |           },
104 |           "metadata": {
105 |             "tags": [],
106 |             "needs_background": "light"
107 |           }
108 |         }
109 |       ]
110 |     },
111 |     {
112 |       "cell_type": "markdown",
113 |       "metadata": {
114 |         "id": "3OY0KOqzO7dy",
115 |         "colab_type": "text"
116 |       },
117 |       "source": [
118 |         "## **Nearest Point Algorithm**"
119 |       ]
120 |     },
121 |     {
122 |       "cell_type": "code",
123 |       "metadata": {
124 |         "id": "S7fTugJUPG6y",
125 |         "colab_type": "code",
126 |         "colab": {
127 |           "base_uri": "https://localhost:8080/",
128 |           "height": 100
129 |         },
130 |         "outputId": "5b0f6c8a-4e7f-4257-b31c-f1cec265a84c"
131 |       },
132 |       "source": [
133 |         "linkage_matrix = linkage(x,\"complete\")\n",
134 |         "print(linkage_matrix)"
135 |       ],
136 |       "execution_count": 6,
137 |       "outputs": [
138 |         {
139 |           "output_type": "stream",
140 |           "text": [
141 |             "[[0.         1.         0.5        2.        ]\n",
142 |             " [3.         5.         0.5        2.        ]\n",
143 |             " [4.         7.         1.11803399 3.        ]\n",
144 |             " [6.         8.         4.24264069 5.        ]\n",
145 |             " [2.         9.         7.28010989 6.        ]]\n"
146 |           ],
147 |           "name": "stdout"
148 |         }
149 |       ]
150 |     },
151 |     {
152 |       "cell_type": "markdown",
153 |       "metadata": {
154 |         "id": "EztAqL_GPNEM",
155 |         "colab_type": "text"
156 |       },
157 |       "source": [
158 |         "## **Pruning Tree Like Structure with Tuncade Mode**"
159 |       ]
160 |     },
161 |     {
162 |       "cell_type": "code",
163 |       "metadata": {
164 |         "id": "48lpPyFzPNZo",
165 |         "colab_type": "code",
166 |         "colab": {
167 |           "base_uri": "https://localhost:8080/",
168 |           "height": 284
169 |         },
170 |         "outputId": "890ef2c7-c1e6-4f20-f4bf-b63a6a862c84"
171 |       },
172 |       "source": [
173 |         "dendrogram = dendrogram(linkage_matrix,truncate_mode='none')\n",
174 |         "plt.title(\"Hierarchical Clustering\")\n",
175 |         "plt.show()"
176 |       ],
177 |       "execution_count": 8,
178 |       "outputs": [
179 |         {
180 |           "output_type": "display_data",
181 |           "data": {
182 |             "image/png": 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\n",
183 |             "text/plain": [
184 |               "<Figure size 432x288 with 1 Axes>"
185 |             ]
186 |           },
187 |           "metadata": {
188 |             "tags": [],
189 |             "needs_background": "light"
190 |           }
191 |         }
192 |       ]
193 |     }
194 |   ]
195 | }


--------------------------------------------------------------------------------
/09_Clustering/K_Means_Clustering.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "K Means Clustering.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": []
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     },
 14 |     "accelerator": "TPU"
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "markdown",
 19 |       "metadata": {
 20 |         "id": "lL35lKpnTYH1",
 21 |         "colab_type": "text"
 22 |       },
 23 |       "source": [
 24 |         "## **K Means Clustering**\n",
 25 |         "\n",
 26 |         "k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster."
 27 |       ]
 28 |     },
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "metadata": {
 32 |         "id": "j3F3bpdAWNCO",
 33 |         "colab_type": "text"
 34 |       },
 35 |       "source": [
 36 |         "## **Importing Libraries**"
 37 |       ]
 38 |     },
 39 |     {
 40 |       "cell_type": "code",
 41 |       "metadata": {
 42 |         "id": "-Hr1KUL9piT0",
 43 |         "colab_type": "code",
 44 |         "colab": {
 45 |           "base_uri": "https://localhost:8080/",
 46 |           "height": 90
 47 |         },
 48 |         "outputId": "e807b520-d422-4548-e063-ba18d159bc26"
 49 |       },
 50 |       "source": [
 51 |         "import collections\n",
 52 |         "import nltk\n",
 53 |         "nltk.download('stopwords')\n",
 54 |         "nltk.download('punkt')\n",
 55 |         "from nltk import word_tokenize\n",
 56 |         "from nltk.corpus import stopwords\n",
 57 |         "from nltk.stem import PorterStemmer\n",
 58 |         "from sklearn.cluster import KMeans\n",
 59 |         "from sklearn.feature_extraction.text import TfidfVectorizer\n",
 60 |         "from pprint import pprint"
 61 |       ],
 62 |       "execution_count": 17,
 63 |       "outputs": [
 64 |         {
 65 |           "output_type": "stream",
 66 |           "text": [
 67 |             "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
 68 |             "[nltk_data]   Package stopwords is already up-to-date!\n",
 69 |             "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
 70 |             "[nltk_data]   Package punkt is already up-to-date!\n"
 71 |           ],
 72 |           "name": "stdout"
 73 |         }
 74 |       ]
 75 |     },
 76 |     {
 77 |       "cell_type": "markdown",
 78 |       "metadata": {
 79 |         "id": "4anySMcgqROX",
 80 |         "colab_type": "text"
 81 |       },
 82 |       "source": [
 83 |         "## **Defining a function tokenizer(text)**\n",
 84 |         "\n"
 85 |       ]
 86 |     },
 87 |     {
 88 |       "cell_type": "code",
 89 |       "metadata": {
 90 |         "id": "YFXxYQrUqoPA",
 91 |         "colab_type": "code",
 92 |         "colab": {}
 93 |       },
 94 |       "source": [
 95 |         "def tokenizer(text):\n",
 96 |         "  tokens = word_tokenize(text)\n",
 97 |         "  stemmer = PorterStemmer()\n",
 98 |         "  tokens = [stemmer.stem(t) for t in tokens if t not in stopwords.words('english')]\n",
 99 |         "  return tokens"
100 |       ],
101 |       "execution_count": 0,
102 |       "outputs": []
103 |     },
104 |     {
105 |       "cell_type": "markdown",
106 |       "metadata": {
107 |         "colab_type": "text",
108 |         "id": "NHTdg_vevRu1"
109 |       },
110 |       "source": [
111 |         "## **Defining a function cluster_sentences(sentences,k=(int))**\n",
112 |         "\n",
113 |         "### ***(KMeans Clustering)***\n",
114 |         "\n"
115 |       ]
116 |     },
117 |     {
118 |       "cell_type": "code",
119 |       "metadata": {
120 |         "id": "LuBTUARBvtpu",
121 |         "colab_type": "code",
122 |         "colab": {}
123 |       },
124 |       "source": [
125 |         "def cluster_sentences(sentences, k):\n",
126 |         "  #Create tf ifd again: stopwords--> we filter out common words (I,my, the,and...)\n",
127 |         "  tfidf_vectorizer = TfidfVectorizer(tokenizer=tokenizer, stop_words=stopwords.words('english'),lowercase=True)\n",
128 |         "  #builds a tf-idf matrix for the sentences \n",
129 |         "  tfidf_matrix = tfidf_vectorizer.fit_transform(sentences)\n",
130 |         "  kmeans = KMeans(n_clusters=k)\n",
131 |         "  kmeans.fit(tfidf_matrix)\n",
132 |         "  clusters = collections.defaultdict(list)\n",
133 |         "  for i, label in enumerate(kmeans.labels_):\n",
134 |         "    clusters[label].append(i)\n",
135 |         "  return dict(clusters)"
136 |       ],
137 |       "execution_count": 0,
138 |       "outputs": []
139 |     },
140 |     {
141 |       "cell_type": "markdown",
142 |       "metadata": {
143 |         "id": "cqOBCOx9xbU6",
144 |         "colab_type": "text"
145 |       },
146 |       "source": [
147 |         "## **Main Body**"
148 |       ]
149 |     },
150 |     {
151 |       "cell_type": "code",
152 |       "metadata": {
153 |         "id": "ZZ1rKmCxxcLy",
154 |         "colab_type": "code",
155 |         "colab": {
156 |           "base_uri": "https://localhost:8080/",
157 |           "height": 492
158 |         },
159 |         "outputId": "7794636e-593b-4bfa-d598-e8202fa0b304"
160 |       },
161 |       "source": [
162 |         "if __name__ == \"__main__\":\n",
163 |         "  sentences= [\"Graphics designers are most creative people\",\n",
164 |         "              \"Snooker is a billiards sport for normally two players.\",\n",
165 |         "              \"Snooker is played on a large (12 feet by 6 feet) table that is covered with a smooth green material.\",\n",
166 |         "              \"FOREX is the stock market for trading currencies\",\n",
167 |         "              \"Software Engineering is hotter and hotter topic in Silicon Valley\",\n",
168 |         "              \"Love is blind\",\n",
169 |         "              \"Snooker is popular in the United Kingdom and many other countries.\",\n",
170 |         "              \"The flying or operating of aircraft is known as aviation.\",\n",
171 |         "              \"Falling in love is like being on drugs.\",\n",
172 |         "              \"Warren Buffet is famous for making good investments.He knows stock markets\",\n",
173 |         "              \"The biggest of the many uses of aviation are in air travel and military aircraft.\",\n",
174 |         "              \"All giant majors in Silicon Valley is focusing AI for their business productivity\",\n",
175 |         "              \"Investing in stocks and trading with them are not that easy\",\n",
176 |         "              \"Being in love is the number one reason why people wed.\",\n",
177 |         "              \"Aviation refers to flying using an aircraft, like an aeroplane.\",\n",
178 |         "              \"Graphics Designing is high rated freelance subject\",\n",
179 |         "              \"Loving from a long distance actually strengthens a relationship.\"\n",
180 |         "              ]\n",
181 |         "  k = 6\n",
182 |         "  clusters = cluster_sentences(sentences,k)\n",
183 |         "  for cluster in range (k):\n",
184 |         "    print(\"CLUSTER \",cluster,\":\")\n",
185 |         "    for i, sentence in enumerate(clusters[cluster]):\n",
186 |         "      print(\"\\t\",(i+1),\": \",sentences[sentence])\n"
187 |       ],
188 |       "execution_count": 27,
189 |       "outputs": [
190 |         {
191 |           "output_type": "stream",
192 |           "text": [
193 |             "CLUSTER  0 :\n",
194 |             "\t 1 :  Software Engineering is hotter and hotter topic in Silicon Valley\n",
195 |             "\t 2 :  All giant majors in Silicon Valley is focusing AI for their business productivity\n",
196 |             "CLUSTER  1 :\n",
197 |             "\t 1 :  Snooker is a billiards sport for normally two players.\n",
198 |             "\t 2 :  Snooker is played on a large (12 feet by 6 feet) table that is covered with a smooth green material.\n",
199 |             "\t 3 :  Snooker is popular in the United Kingdom and many other countries.\n",
200 |             "CLUSTER  2 :\n",
201 |             "\t 1 :  Love is blind\n",
202 |             "\t 2 :  Falling in love is like being on drugs.\n",
203 |             "\t 3 :  Being in love is the number one reason why people wed.\n",
204 |             "\t 4 :  Loving from a long distance actually strengthens a relationship.\n",
205 |             "CLUSTER  3 :\n",
206 |             "\t 1 :  FOREX is the stock market for trading currencies\n",
207 |             "\t 2 :  Warren Buffet is famous for making good investments.He knows stock markets\n",
208 |             "\t 3 :  Investing in stocks and trading with them are not that easy\n",
209 |             "CLUSTER  4 :\n",
210 |             "\t 1 :  Graphics designers are most creative people\n",
211 |             "\t 2 :  Graphics Designing is high rated freelance subject\n",
212 |             "CLUSTER  5 :\n",
213 |             "\t 1 :  The flying or operating of aircraft is known as aviation.\n",
214 |             "\t 2 :  The biggest of the many uses of aviation are in air travel and military aircraft.\n",
215 |             "\t 3 :  Aviation refers to flying using an aircraft, like an aeroplane.\n"
216 |           ],
217 |           "name": "stdout"
218 |         },
219 |         {
220 |           "output_type": "stream",
221 |           "text": [
222 |             "/usr/local/lib/python3.6/dist-packages/sklearn/feature_extraction/text.py:385: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens [\"'d\", \"'ll\", \"'re\", \"'s\", \"'ve\", 'could', 'might', 'must', \"n't\", 'need', 'sha', 'wo', 'would'] not in stop_words.\n",
223 |             "  'stop_words.' % sorted(inconsistent))\n"
224 |           ],
225 |           "name": "stderr"
226 |         }
227 |       ]
228 |     }
229 |   ]
230 | }


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/README.md:
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1 | # Machine_Learning_Algorithms
2 | In this repository you will get all necessary algorithms, for supervised and unsupervised learning (Clustering). It's simple and will be a good code guide for Kick-start in ML.
3 | 
4 | https://www.linkedin.com/in/mhuzaifadev/
5 | 


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