├── README.md
├── TASK 1.ipynb
└── TASK-2.ipynb


/README.md:
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 1 | # Spark-Foundation
 2 | The spark Foundation intership 
 3 | Hello Everyone i am doing an internship at the sparks foundation and this repository has task given by them i hope you guys like it and if you like do follow me on linkdin.
 4 | ## Mandatory Task
 5 | Improve your linkdin account: www.linkedin.com/in/shruthi-jain-81b4571ab
 6 | ## PROJECTS(TASKS)
 7 | There were many task given in which few are begginer level and some intermediate or advanced level , I have not completed all of them but i want to mention
 8 | all the task according to which you can open my file.
 9 | * Task-1 : Prediction Using Supervised Learning , predicting the percentage of student based on the number of hours studied.
10 | * Task-2: Prediction Using Unsupervised Learning. (iris dataset)
11 | * Task-3: Exploratory Data Analysis - Retail (As a manager figure out which area in business needs more attention)
12 | * Task-4 : Perform ‘Exploratory Data Analysis’ on dataset ‘Global Terrorism’
13 | * Task-5:Perform ‘Exploratory Data Analysis’ on dataset ‘Indian Premier League’
14 | * Task-6:Prediction using Decision Tree Algorithm
15 | * Task-7: Stock Market Prediction using Numerical and Textual Analysis
16 | * Task-8: Timeline Analysis : Covid-19
17 | # THANK YOU!!!!
18 | 


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/TASK 1.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# <span style='background :yellow' > ***The Spark Foundation - Task-1*** </span>"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "##  ***Prediction using supervised learning ,Predict the percentage of an student based on the no. of study hours.***"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "markdown",
 19 |    "metadata": {},
 20 |    "source": [
 21 |     "### ***Import The Required Libraries***"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 67,
 27 |    "metadata": {},
 28 |    "outputs": [],
 29 |    "source": [
 30 |     "import numpy as np\n",
 31 |     "import pandas as pd\n",
 32 |     "import matplotlib.pyplot as plt\n",
 33 |     "%matplotlib inline\n",
 34 |     "from sklearn.model_selection import train_test_split\n",
 35 |     "from sklearn.linear_model import LinearRegression\n",
 36 |     "from sklearn.metrics import mean_absolute_error"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "markdown",
 41 |    "metadata": {},
 42 |    "source": [
 43 |     "### ***Reading The CSV file***"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "code",
 48 |    "execution_count": 2,
 49 |    "metadata": {},
 50 |    "outputs": [
 51 |     {
 52 |      "data": {
 53 |       "text/html": [
 54 |        "<div>\n",
 55 |        "<style scoped>\n",
 56 |        "    .dataframe tbody tr th:only-of-type {\n",
 57 |        "        vertical-align: middle;\n",
 58 |        "    }\n",
 59 |        "\n",
 60 |        "    .dataframe tbody tr th {\n",
 61 |        "        vertical-align: top;\n",
 62 |        "    }\n",
 63 |        "\n",
 64 |        "    .dataframe thead th {\n",
 65 |        "        text-align: right;\n",
 66 |        "    }\n",
 67 |        "</style>\n",
 68 |        "<table border=\"1\" class=\"dataframe\">\n",
 69 |        "  <thead>\n",
 70 |        "    <tr style=\"text-align: right;\">\n",
 71 |        "      <th></th>\n",
 72 |        "      <th>Hours</th>\n",
 73 |        "      <th>Scores</th>\n",
 74 |        "    </tr>\n",
 75 |        "  </thead>\n",
 76 |        "  <tbody>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>0</th>\n",
 79 |        "      <td>2.5</td>\n",
 80 |        "      <td>21</td>\n",
 81 |        "    </tr>\n",
 82 |        "    <tr>\n",
 83 |        "      <th>1</th>\n",
 84 |        "      <td>5.1</td>\n",
 85 |        "      <td>47</td>\n",
 86 |        "    </tr>\n",
 87 |        "    <tr>\n",
 88 |        "      <th>2</th>\n",
 89 |        "      <td>3.2</td>\n",
 90 |        "      <td>27</td>\n",
 91 |        "    </tr>\n",
 92 |        "    <tr>\n",
 93 |        "      <th>3</th>\n",
 94 |        "      <td>8.5</td>\n",
 95 |        "      <td>75</td>\n",
 96 |        "    </tr>\n",
 97 |        "    <tr>\n",
 98 |        "      <th>4</th>\n",
 99 |        "      <td>3.5</td>\n",
100 |        "      <td>30</td>\n",
101 |        "    </tr>\n",
102 |        "    <tr>\n",
103 |        "      <th>5</th>\n",
104 |        "      <td>1.5</td>\n",
105 |        "      <td>20</td>\n",
106 |        "    </tr>\n",
107 |        "    <tr>\n",
108 |        "      <th>6</th>\n",
109 |        "      <td>9.2</td>\n",
110 |        "      <td>88</td>\n",
111 |        "    </tr>\n",
112 |        "    <tr>\n",
113 |        "      <th>7</th>\n",
114 |        "      <td>5.5</td>\n",
115 |        "      <td>60</td>\n",
116 |        "    </tr>\n",
117 |        "    <tr>\n",
118 |        "      <th>8</th>\n",
119 |        "      <td>8.3</td>\n",
120 |        "      <td>81</td>\n",
121 |        "    </tr>\n",
122 |        "    <tr>\n",
123 |        "      <th>9</th>\n",
124 |        "      <td>2.7</td>\n",
125 |        "      <td>25</td>\n",
126 |        "    </tr>\n",
127 |        "    <tr>\n",
128 |        "      <th>10</th>\n",
129 |        "      <td>7.7</td>\n",
130 |        "      <td>85</td>\n",
131 |        "    </tr>\n",
132 |        "    <tr>\n",
133 |        "      <th>11</th>\n",
134 |        "      <td>5.9</td>\n",
135 |        "      <td>62</td>\n",
136 |        "    </tr>\n",
137 |        "    <tr>\n",
138 |        "      <th>12</th>\n",
139 |        "      <td>4.5</td>\n",
140 |        "      <td>41</td>\n",
141 |        "    </tr>\n",
142 |        "    <tr>\n",
143 |        "      <th>13</th>\n",
144 |        "      <td>3.3</td>\n",
145 |        "      <td>42</td>\n",
146 |        "    </tr>\n",
147 |        "    <tr>\n",
148 |        "      <th>14</th>\n",
149 |        "      <td>1.1</td>\n",
150 |        "      <td>17</td>\n",
151 |        "    </tr>\n",
152 |        "    <tr>\n",
153 |        "      <th>15</th>\n",
154 |        "      <td>8.9</td>\n",
155 |        "      <td>95</td>\n",
156 |        "    </tr>\n",
157 |        "    <tr>\n",
158 |        "      <th>16</th>\n",
159 |        "      <td>2.5</td>\n",
160 |        "      <td>30</td>\n",
161 |        "    </tr>\n",
162 |        "    <tr>\n",
163 |        "      <th>17</th>\n",
164 |        "      <td>1.9</td>\n",
165 |        "      <td>24</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>18</th>\n",
169 |        "      <td>6.1</td>\n",
170 |        "      <td>67</td>\n",
171 |        "    </tr>\n",
172 |        "    <tr>\n",
173 |        "      <th>19</th>\n",
174 |        "      <td>7.4</td>\n",
175 |        "      <td>69</td>\n",
176 |        "    </tr>\n",
177 |        "    <tr>\n",
178 |        "      <th>20</th>\n",
179 |        "      <td>2.7</td>\n",
180 |        "      <td>30</td>\n",
181 |        "    </tr>\n",
182 |        "    <tr>\n",
183 |        "      <th>21</th>\n",
184 |        "      <td>4.8</td>\n",
185 |        "      <td>54</td>\n",
186 |        "    </tr>\n",
187 |        "    <tr>\n",
188 |        "      <th>22</th>\n",
189 |        "      <td>3.8</td>\n",
190 |        "      <td>35</td>\n",
191 |        "    </tr>\n",
192 |        "    <tr>\n",
193 |        "      <th>23</th>\n",
194 |        "      <td>6.9</td>\n",
195 |        "      <td>76</td>\n",
196 |        "    </tr>\n",
197 |        "    <tr>\n",
198 |        "      <th>24</th>\n",
199 |        "      <td>7.8</td>\n",
200 |        "      <td>86</td>\n",
201 |        "    </tr>\n",
202 |        "  </tbody>\n",
203 |        "</table>\n",
204 |        "</div>"
205 |       ],
206 |       "text/plain": [
207 |        "    Hours  Scores\n",
208 |        "0     2.5      21\n",
209 |        "1     5.1      47\n",
210 |        "2     3.2      27\n",
211 |        "3     8.5      75\n",
212 |        "4     3.5      30\n",
213 |        "5     1.5      20\n",
214 |        "6     9.2      88\n",
215 |        "7     5.5      60\n",
216 |        "8     8.3      81\n",
217 |        "9     2.7      25\n",
218 |        "10    7.7      85\n",
219 |        "11    5.9      62\n",
220 |        "12    4.5      41\n",
221 |        "13    3.3      42\n",
222 |        "14    1.1      17\n",
223 |        "15    8.9      95\n",
224 |        "16    2.5      30\n",
225 |        "17    1.9      24\n",
226 |        "18    6.1      67\n",
227 |        "19    7.4      69\n",
228 |        "20    2.7      30\n",
229 |        "21    4.8      54\n",
230 |        "22    3.8      35\n",
231 |        "23    6.9      76\n",
232 |        "24    7.8      86"
233 |       ]
234 |      },
235 |      "execution_count": 2,
236 |      "metadata": {},
237 |      "output_type": "execute_result"
238 |     }
239 |    ],
240 |    "source": [
241 |     "url=\"https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv\"\n",
242 |     "df=pd.read_csv(url)\n",
243 |     "df"
244 |    ]
245 |   },
246 |   {
247 |    "cell_type": "markdown",
248 |    "metadata": {},
249 |    "source": [
250 |     "### ***Checking how many null values are there in dataset***"
251 |    ]
252 |   },
253 |   {
254 |    "cell_type": "code",
255 |    "execution_count": 3,
256 |    "metadata": {},
257 |    "outputs": [
258 |     {
259 |      "data": {
260 |       "text/plain": [
261 |        "Hours     0\n",
262 |        "Scores    0\n",
263 |        "dtype: int64"
264 |       ]
265 |      },
266 |      "execution_count": 3,
267 |      "metadata": {},
268 |      "output_type": "execute_result"
269 |     }
270 |    ],
271 |    "source": [
272 |     "df.isna().sum()"
273 |    ]
274 |   },
275 |   {
276 |    "cell_type": "markdown",
277 |    "metadata": {},
278 |    "source": [
279 |     "### ***Descriptive analysis of the dataset using describe function***"
280 |    ]
281 |   },
282 |   {
283 |    "cell_type": "code",
284 |    "execution_count": 4,
285 |    "metadata": {},
286 |    "outputs": [
287 |     {
288 |      "data": {
289 |       "text/html": [
290 |        "<div>\n",
291 |        "<style scoped>\n",
292 |        "    .dataframe tbody tr th:only-of-type {\n",
293 |        "        vertical-align: middle;\n",
294 |        "    }\n",
295 |        "\n",
296 |        "    .dataframe tbody tr th {\n",
297 |        "        vertical-align: top;\n",
298 |        "    }\n",
299 |        "\n",
300 |        "    .dataframe thead th {\n",
301 |        "        text-align: right;\n",
302 |        "    }\n",
303 |        "</style>\n",
304 |        "<table border=\"1\" class=\"dataframe\">\n",
305 |        "  <thead>\n",
306 |        "    <tr style=\"text-align: right;\">\n",
307 |        "      <th></th>\n",
308 |        "      <th>Hours</th>\n",
309 |        "      <th>Scores</th>\n",
310 |        "    </tr>\n",
311 |        "  </thead>\n",
312 |        "  <tbody>\n",
313 |        "    <tr>\n",
314 |        "      <th>count</th>\n",
315 |        "      <td>25.000000</td>\n",
316 |        "      <td>25.000000</td>\n",
317 |        "    </tr>\n",
318 |        "    <tr>\n",
319 |        "      <th>mean</th>\n",
320 |        "      <td>5.012000</td>\n",
321 |        "      <td>51.480000</td>\n",
322 |        "    </tr>\n",
323 |        "    <tr>\n",
324 |        "      <th>std</th>\n",
325 |        "      <td>2.525094</td>\n",
326 |        "      <td>25.286887</td>\n",
327 |        "    </tr>\n",
328 |        "    <tr>\n",
329 |        "      <th>min</th>\n",
330 |        "      <td>1.100000</td>\n",
331 |        "      <td>17.000000</td>\n",
332 |        "    </tr>\n",
333 |        "    <tr>\n",
334 |        "      <th>25%</th>\n",
335 |        "      <td>2.700000</td>\n",
336 |        "      <td>30.000000</td>\n",
337 |        "    </tr>\n",
338 |        "    <tr>\n",
339 |        "      <th>50%</th>\n",
340 |        "      <td>4.800000</td>\n",
341 |        "      <td>47.000000</td>\n",
342 |        "    </tr>\n",
343 |        "    <tr>\n",
344 |        "      <th>75%</th>\n",
345 |        "      <td>7.400000</td>\n",
346 |        "      <td>75.000000</td>\n",
347 |        "    </tr>\n",
348 |        "    <tr>\n",
349 |        "      <th>max</th>\n",
350 |        "      <td>9.200000</td>\n",
351 |        "      <td>95.000000</td>\n",
352 |        "    </tr>\n",
353 |        "  </tbody>\n",
354 |        "</table>\n",
355 |        "</div>"
356 |       ],
357 |       "text/plain": [
358 |        "           Hours     Scores\n",
359 |        "count  25.000000  25.000000\n",
360 |        "mean    5.012000  51.480000\n",
361 |        "std     2.525094  25.286887\n",
362 |        "min     1.100000  17.000000\n",
363 |        "25%     2.700000  30.000000\n",
364 |        "50%     4.800000  47.000000\n",
365 |        "75%     7.400000  75.000000\n",
366 |        "max     9.200000  95.000000"
367 |       ]
368 |      },
369 |      "execution_count": 4,
370 |      "metadata": {},
371 |      "output_type": "execute_result"
372 |     }
373 |    ],
374 |    "source": [
375 |     "df.describe()"
376 |    ]
377 |   },
378 |   {
379 |    "cell_type": "markdown",
380 |    "metadata": {},
381 |    "source": [
382 |     "### ***information about the dataset***"
383 |    ]
384 |   },
385 |   {
386 |    "cell_type": "code",
387 |    "execution_count": 5,
388 |    "metadata": {},
389 |    "outputs": [
390 |     {
391 |      "name": "stdout",
392 |      "output_type": "stream",
393 |      "text": [
394 |       "<class 'pandas.core.frame.DataFrame'>\n",
395 |       "RangeIndex: 25 entries, 0 to 24\n",
396 |       "Data columns (total 2 columns):\n",
397 |       " #   Column  Non-Null Count  Dtype  \n",
398 |       "---  ------  --------------  -----  \n",
399 |       " 0   Hours   25 non-null     float64\n",
400 |       " 1   Scores  25 non-null     int64  \n",
401 |       "dtypes: float64(1), int64(1)\n",
402 |       "memory usage: 528.0 bytes\n"
403 |      ]
404 |     }
405 |    ],
406 |    "source": [
407 |     "df.info()"
408 |    ]
409 |   },
410 |   {
411 |    "cell_type": "markdown",
412 |    "metadata": {},
413 |    "source": [
414 |     "### ***Plotting a scatter plot showing relationship between No of Hours vs scores***  "
415 |    ]
416 |   },
417 |   {
418 |    "cell_type": "code",
419 |    "execution_count": 6,
420 |    "metadata": {},
421 |    "outputs": [
422 |     {
423 |      "data": {
424 |       "image/png": 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\n",
425 |       "text/plain": [
426 |        "<Figure size 432x288 with 1 Axes>"
427 |       ]
428 |      },
429 |      "metadata": {
430 |       "needs_background": "light"
431 |      },
432 |      "output_type": "display_data"
433 |     }
434 |    ],
435 |    "source": [
436 |     "plt.xlabel('Hours')\n",
437 |     "plt.ylabel('Scores')\n",
438 |     "plt.title('Hours VS Scores')\n",
439 |     "plt.scatter(df.Hours,df.Scores,color='green')\n",
440 |     "plt.show()"
441 |    ]
442 |   },
443 |   {
444 |    "cell_type": "markdown",
445 |    "metadata": {},
446 |    "source": [
447 |     "### ***Two variables for the regression***"
448 |    ]
449 |   },
450 |   {
451 |    "cell_type": "code",
452 |    "execution_count": 10,
453 |    "metadata": {},
454 |    "outputs": [],
455 |    "source": [
456 |     "x=np.array(df.Hours)\n",
457 |     "y=np.array(df.Scores)"
458 |    ]
459 |   },
460 |   {
461 |    "cell_type": "markdown",
462 |    "metadata": {},
463 |    "source": [
464 |     "### ***Reshaping the numpy array for vertical output***"
465 |    ]
466 |   },
467 |   {
468 |    "cell_type": "code",
469 |    "execution_count": 14,
470 |    "metadata": {},
471 |    "outputs": [],
472 |    "source": [
473 |     "x=x.reshape(-1,1)\n",
474 |     "y=y.reshape(-1,1)"
475 |    ]
476 |   },
477 |   {
478 |    "cell_type": "markdown",
479 |    "metadata": {},
480 |    "source": [
481 |     "### ***Splitting the data into test data and train data***"
482 |    ]
483 |   },
484 |   {
485 |    "cell_type": "code",
486 |    "execution_count": 16,
487 |    "metadata": {},
488 |    "outputs": [],
489 |    "source": [
490 |     "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
491 |    ]
492 |   },
493 |   {
494 |    "cell_type": "markdown",
495 |    "metadata": {},
496 |    "source": [
497 |     "### ***Calling the linear function and reshaping all the data and fitting it to the model***"
498 |    ]
499 |   },
500 |   {
501 |    "cell_type": "code",
502 |    "execution_count": 39,
503 |    "metadata": {},
504 |    "outputs": [
505 |     {
506 |      "data": {
507 |       "text/plain": [
508 |        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
509 |       ]
510 |      },
511 |      "execution_count": 39,
512 |      "metadata": {},
513 |      "output_type": "execute_result"
514 |     }
515 |    ],
516 |    "source": [
517 |     "reg=LinearRegression()\n",
518 |     "X_test=X_test.reshape(-1,1)\n",
519 |     "y_test=y_test.reshape(-1,1)\n",
520 |     "X_train=X_train.reshape(-1,1)\n",
521 |     "y_train=y_train.reshape(-1,1)\n",
522 |     "reg.fit(X_train,y_train)"
523 |    ]
524 |   },
525 |   {
526 |    "cell_type": "markdown",
527 |    "metadata": {},
528 |    "source": [
529 |     "### ***Formula of simple linear regression***"
530 |    ]
531 |   },
532 |   {
533 |    "cell_type": "code",
534 |    "execution_count": 40,
535 |    "metadata": {},
536 |    "outputs": [],
537 |    "source": [
538 |     "line1=reg.coef_*X_train+reg.intercept_"
539 |    ]
540 |   },
541 |   {
542 |    "cell_type": "markdown",
543 |    "metadata": {},
544 |    "source": [
545 |     "### ***Plotting a scatter plot for training dataset***"
546 |    ]
547 |   },
548 |   {
549 |    "cell_type": "code",
550 |    "execution_count": 41,
551 |    "metadata": {},
552 |    "outputs": [
553 |     {
554 |      "data": {
555 |       "text/plain": [
556 |        "Text(0.5, 1.0, 'TRAINING DATA')"
557 |       ]
558 |      },
559 |      "execution_count": 41,
560 |      "metadata": {},
561 |      "output_type": "execute_result"
562 |     },
563 |     {
564 |      "data": {
565 |       "image/png": 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R+lqs41Q70MG3epmNxaxZsHp15bi7bmsB30pvNhqlpYHl4b12rcPbWsYduFm9vGugFYQ7cLNarVrlpYFWKO7AzWqRFdw77QQvv9z6WsxS7sDNhvOlL1XfNdDhbTlzgFvHGPMugeUk+OpXh44tWODpEisMT6FYRxjTLoHlJkyArVsrxx3cVjDuwK0jjGqXwHKlpYHl4b1qlcPbCskduHWEuncJLOelgdaG3IFbR6h5l8By1ZYG/vWvDm8rPAe4dYSadgksJ8ERRwwdmzAhCe4dd2xClWaN5QC3jjBn1hwWn7qYnsk9CNEzuYfFpy7OvoC5YEH1pYGbNze9VrNG8W6E1l2ygvtLX4Lzzmt9LWY18m6E1t123jmZ1y7neW5rY55Csc5WWhpYHt4rVzq8re25A7fO5aWB1uHcgVvnufPO7PB++WWHt3UUd+DWWbKCe9y47FvjzdqcO3DrDF/5SvWlgQ5v61DuwK39ZQX3F79YuZOgWYdxgFv7mjgRXnqpctzz3NYlPIVi7ae0NLA8vFescHhbV3GAW3uRkqPMykVU7msyjIYf/mCWAwe4tYe77mrY0sDS4Q/9m/oJ4tXDHxzi1m4c4FZ8Ehx2WOV4RHY3PoKGHP5gVgAOcCuuiy6qvjRwDHPdYz78wawgHOBWTBJ89rNDx+bPb8hFylEf/mBWMA5wK5YZM6p33eef35CXGNXhD2YF5AC3YtiyJQnuBx8cOn7nnQ1fGljX4Q9mBeYDHSx/3jXQbFjVDnRwB275WbPGuwaajYFvpbd8ZAX3lCmwcWPrazFrU+7ArbW++c3qFykd3mZ1cYBb60hw9tlDx84/39MlZqPkAO8iue3/8frXV++65/vuR7PR8hx4lyjt/1G6hby0/wfQvOVzW7fChAmV46tWZd8ab2Z1cQfeJVq+/4eUHd4RdYW3dw00q84B3iVatv/H2rXeNdCsRRzgXaIl+39IcMghQ8f22MO7Bpo1Sc0BLmmcpJWSlqaPZ0i6Q9J6ST+RtGPzyrSxaur+H4sWVb9I+dRTo35a7xpoNrx6OvAzgTWDHi8EvhkRM4GngY80sjBrrKbt/yHBZz4zdOwrX/GugWYtUNMqFEkHACcDfcDZkgS8DfjH9EuuABYA325CjdYgc2bNadyKkz32gGeeqRxv4Jruvtl9Q1bOgHcNNBus1g58EfA54JX08RTgmYjYmj5+GNg/6xslzZO0TNKygYGBMRVrBVDaNbA8vFeu9K6BZi02Ygcu6RTgyYhYLunE0nDGl2b+642IxcBiSHYjHGWdVgQ57BrY0J8azDpMLR34ccBpkh4EriKZOlkE7C6p9B/AAcCjTanQ8nfvvdnh/dJLvg3eLEcjBnhEfD4iDoiI6cD7gd9ExBzgZuA96ZfNBX7etCotPxK86U2V4xGw886tr8fMXjWWdeDnkFzQ/BPJnPiljSnJCmHhwqYcKGxmjVPXXigRcQtwS/r7B4CjGl+S5S4ruP/5n+GCC1pfi5lV5c2sbLs994Snn64cd8dtVki+ld6SXQOlyvC+4w6Ht1mBuQPvdj5Q2KxtuQPvVtWWBr74Yk3h7W1ezfLnDrwbjbHrzuVwCDOr4A68m1x6aUOWBnqbV7NicIB3Cwk++tGhY//0T6Oa6/Y2r2bF4CmUTveWt8Btt1WOj+Ei5bTJ0+jf1J85bmat4w68U23blnTd5eF9111jXmHS1MMhzKxm7sA7UZOXBpYuVM6/aT4bNm1g2uRp9M3u8wVMsxZTtHC9b29vbyxbtqxlr9d17r8fDjywcvzll0d1JqWZFYOk5RHRWz7uDrxTZHXdM2bAAw+0vhYzawnPgbe7yy6rvjTQ4W3W0Rzg7UyCj5SdJb1woW+DN+sSnkJpRyecAL/9beW4g9usq7gDL5hh9xgpLQ0sD+877xxzeHtvE7P24w68QIbdY+TNH8j+pgZ03d7bxKw9uQMvkKw9RvZ54sXs8G7ggcLe28SsPbkDL5DyvURiQcYX9fTAgw829XVHGjezYnAHXiClvUTet7pKeEc0PLwHv26t42ZWDA7wAumb3UcsgJ9cM3R85Vl/39QVJt7bxKw9OcCL4sILM+e6l9x1JUd886qmvvScWXNYfOpieib3IETP5B4Wn7rYFzDNCs57oeTtlVdg3LjK8YceggMOaH09ZlY41fZCcQeepxNOqAzvAw9Mpksc3mY2Aq9CycNzz8Fuu1WOb94MEya0vh4za0vuwFvt+OMrw/vcc5Ou2+FtZnVwB94q69bBG95QOe79S8xslNyBt4JUGd4rVmSGt/ckMbNaOcCb6frrK/fqPvjgJLiPOKLiy0t7kvRv6ieIV/ckcYibWRYHeBVj6oQjkuA+7bSh4xs3wtq1Vb/Ne5KYWT0c4BnG1AkvWAA7lL2tn/50EupTpgz7rd6TxMzq4YuYGYbrhKvenVhtaeCWLTC+trd52uRp9G/qzxw3MyvnDjxD3Z3wCSdUhveVVyZdd43hDd6TxMzq4w48Q82d8H33JRcly41yaWCpu59/03w2bNrAtMnT6Jvd5z1JzCyTAzxD3+y+ISfUQEYnnHUS/IoVmatL6jFn1hwHtpnVxFMoGYbdnW/p0srwPuigqksDzcyaxbsR1iqicnUJwMAATJ1a11MtuXuJp0nMrGbejXAsspYGfvKTSaiPIrx9s46ZNYLnwIfTgKWB5Ua1RNHMLMOIHbik10q6WdIaSfdIOjMd31PSjZLWpx/3aH65LXT55ZXh/cMf1r00sJxv1jGzRqklibYCn42IFZJeAyyXdCPwIeCmiPiapHOBc4Fzmldqi/zlL9nTIg26VuCbdcysUUbswCPisYhYkf7+OWANsD/wLuCK9MuuAE5vVpEt84UvVIb3o482dMtX36xjZo1S10VMSdOBI4A7gH0i4jFIQh7Yu8r3zJO0TNKygYGBsVXbLPfdlywN/Jd/2T528cVJcO+7b0NfygcIm1mj1LyMUNKuwK1AX0RcK+mZiNh90Oefjohh58ELt4wwItkxcOnS7WO77gpPPAETJ1b/PjOzFhrTMkJJE4CfAksi4tp0+AlJ+6af3xd4slHFtsSttyZLAweH99KlycoTh7eZtYFaVqEIuBRYExEXDfrUdcDc9PdzgZ83vrwm2LwZenrgxBO3jx17LGzbBiefnFtZZmb1qqUDPw44A3ibpFXpr3cCXwPeIWk98I70ccM19IixK66AnXaCDYOW7K1aBbfdln2XpZlZgY24jDAifg9k7NwEwOzGljNU6a7F0o0vpbsWgfou+mUtDfz4x+E732lUqWZmLVfotrMhR4xlLQ185BGHt5m1vUIH+JjuWly/vnJp4KJFycqT/fZrUIVmZvkp9F4oo7prMWtp4KRJydLASZOaUKWZWT4K3YHXfdfib3+bvTTw+ecd3mbWcQrdgdd8xNjmzTBz5tDVJcceC7/7nVeXmFnHav8DHX7wA5g7d+jYqlVw2GGNfR0zs5xUuxOz0B34sJ56CqZMGTo2bx5897v51GNm1mLtOb8wf35leD/yiMPbzLpKe3Xg69cnBwgPtmgRnHlmPvWYmeWoPQI8Ak4/Ha67bvvYxInw5JNeXWJmXas9plAOPXRoeF9/PbzwgsPbzLpaewT4McckH48+GrZuhVNOybceM7MCaI8Av+SSZBrlD3+AcePyrsbMrBDaI8DNzKyCA9zMrE05wM3M2pQD3MysTTnAzczalAPczKxNOcDNzNqUA9zMrE21dD9wSQNA5Rlp1U0FNjapnNEqYk1QzLqKWBMUs64i1gTFrKuINUFz6+qJiL3KB1sa4PWStCxrE/M8FbEmKGZdRawJillXEWuCYtZVxJogn7o8hWJm1qYc4GZmbaroAb447wIyFLEmKGZdRawJillXEWuCYtZVxJogh7oKPQduZmbVFb0DNzOzKhzgZmZtqpABLukySU9KWp13LSWSXivpZklrJN0jKfeTlCXtLOk/JN2Z1nRe3jWVSBonaaWkpXnXUiLpQUl3S1olaVne9ZRI2l3SNZLWpn+/jsm5noPT96j061lJZ+VZU4mkz6R/11dL+rGknQtQ05lpPfe0+n0q5By4pOOB54EfRMShedcDIGlfYN+IWCHpNcBy4PSIuDfHmgRMiojnJU0Afg+cGRF/zKumEklnA73AbhFRiDPwJD0I9EZEoW4CkXQF8LuIuETSjsDEiHgm77og+Y8YeAT4bxFRz014zahlf5K/42+MiJckXQ38MiIuz7GmQ4GrgKOAzcANwCciYn0rXr+QHXhE/BZ4Ku86BouIxyJiRfr754A1wP451xQR8Xz6cEL6K/f/kSUdAJwMXJJ3LUUnaTfgeOBSgIjYXJTwTs0G7s87vAcZD+wiaTwwEXg053oOAf4YES9GxFbgVuDdrXrxQgZ40UmaDhwB3JFvJa9OVawCngRujIjcawIWAZ8DXsm7kDIB/FrScknz8i4m9TpgAPh+OuV0iaRJeRc1yPuBH+ddBEBEPAJcCGwAHgM2RcSv862K1cDxkqZImgi8E3htq17cAV4nSbsCPwXOiohn864nIrZFxOHAAcBR6Y90uZF0CvBkRCzPs44qjouII4GTgE+mU3V5Gw8cCXw7Io4AXgDOzbekRDqdcxrwb3nXAiBpD+BdwAxgP2CSpA/kWVNErAEWAjeSTJ/cCWxt1es7wOuQzjP/FFgSEdfmXc9g6Y/dtwB/m3MpxwGnpfPNVwFvk3RlviUlIuLR9OOTwM9I5i3z9jDw8KCfnK4hCfQiOAlYERFP5F1I6u3AnyNiICK2ANcCx+ZcExFxaUQcGRHHk0z9tmT+GxzgNUsvGF4KrImIi/KuB0DSXpJ2T3+/C8lf8LV51hQRn4+IAyJiOsmP37+JiFy7JABJk9KLz6RTFH9D8uNvriLiceAhSQenQ7OB3C6Ml/kHCjJ9ktoAHC1pYvrvcTbJtahcSdo7/TgN+Dta+J6Nb9UL1UPSj4ETgamSHga+HBGX5lsVxwFnAHenc84AX4iIX+ZY077AFelKgR2AqyOiMMv2CmYf4GfJv3vGAz+KiBvyLelVnwaWpFMWDwAfzrke0vncdwAfz7uWkoi4Q9I1wAqSaYqVFOO2+p9KmgJsAT4ZEU+36oULuYzQzMxG5ikUM7M25QA3M2tTDnAzszblADcza1MOcDOzNuUANzNrUw5wM7M29f8BAD2LU2nef/oAAAAASUVORK5CYII=\n",
566 |       "text/plain": [
567 |        "<Figure size 432x288 with 1 Axes>"
568 |       ]
569 |      },
570 |      "metadata": {
571 |       "needs_background": "light"
572 |      },
573 |      "output_type": "display_data"
574 |     }
575 |    ],
576 |    "source": [
577 |     "plt.scatter(X_train,y_train,color='green')\n",
578 |     "plt.plot(X_train,line1,color='red',linewidth=2)\n",
579 |     "plt.title('TRAINING DATA')"
580 |    ]
581 |   },
582 |   {
583 |    "cell_type": "code",
584 |    "execution_count": 44,
585 |    "metadata": {},
586 |    "outputs": [],
587 |    "source": [
588 |     "line2=reg.coef_*X_test+reg.intercept_"
589 |    ]
590 |   },
591 |   {
592 |    "cell_type": "markdown",
593 |    "metadata": {},
594 |    "source": [
595 |     "### ***Plotting  a scatter plot for testing dataset***"
596 |    ]
597 |   },
598 |   {
599 |    "cell_type": "code",
600 |    "execution_count": 45,
601 |    "metadata": {},
602 |    "outputs": [
603 |     {
604 |      "data": {
605 |       "text/plain": [
606 |        "Text(0.5, 1.0, 'TESTING DATA')"
607 |       ]
608 |      },
609 |      "execution_count": 45,
610 |      "metadata": {},
611 |      "output_type": "execute_result"
612 |     },
613 |     {
614 |      "data": {
615 |       "image/png": 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gIlXRrrxdpAxKKVDPvSeXJNPcJTH6lyBSxTzx3ycKhPe4K8fhWa7wlgBN4CJVxLad20i9N7VAXYd8lX3Rj3ORKiBrSlaB8P7g2g90yFfZL03gIiHa8OMGGg0JnvW9VYNWLP3z0pA6kijRBC4SkuvHXV8gvL+48QuFtxSbJnCRSrZi0wpaDG0RqJ2efjrvXfteSB1JVCnARSrR+S+cz8SFEwO1Jb2XkH5AekgdSZQpwEUqwfy182n7WNtAreuxXXnhNy+E1JEkAgW4SAVr+2hb5q+bH6itvX0tTVKbhNSRJIpibcQ0s8VmNsfMZpvZ9HitsZlNMrMF8etGRT2PSHUy7dtpWLYFwrvvqX3xLFd4S7koyQR+pruvzXe7HzDZ3e83s37x23eUa3ciEVVzUE125u0M1DbfuZm6NeuG1JEkorJ8jLATMCr+9Sigc9nbEYm2id9MxLItEN7Dfj0Mz3KFt5S74k7gDrxtZg485e4jgGbuvhLA3Vea2YGFPdDMMoFMgPR0bWmXxJTneSQPTC5Q33HXDlKSCx6USqQ8FHcCP83dTwAuAHqa2RnFfQF3H+Hu7d29fVpaWqmaFKnKXvj8hQLhnfObHDzLFd5SoYo1gbv7ivj1ajN7HegAfGdmzePTd3NgdQX2KVLl7MzdSc2/1ixQ18GnpLIUOYGbWV0zq7/7a+A84AtgHNA9frfuwBsV1aRIVTP046EFwnviNRN18CmpVMWZwJsBr8f/UdYAXnT3f5vZf4ExZtYDWApcXnFtilQNW3Zsod599QK12jVqs23AtpA6kuqsyAB390XAzwuprwPOroimRKqivpP68uBHDwZq0/4wjZNanBRSR1LdaU9MkSKs3bqWtAeDG+CPTjuaL2/6MqSORGIU4CL7cfWrV/PSFy8FavN7zeeIJkeE1JHITxTgIoVYsmEJGcMzArUL2lzAhK4TwmlIpBAKcJG9nP6P0/lg6QeB2oo+K2hev3lIHYkUTmfkEYn7YvUXWLYFwjvzhEw8yxXeUiVpAhcBWg1rxfKNywO19Xesp2HthiF1JFI0TeBSrX2w9AMs2wLhnfWrLDzLFd5S5WkCl2rJ3UkaWHB+2TZgG7Vr1A6hI5GS0wQu1c64+eMKhPeTFz2JZ7nCWyJFE7hUG7l5udQYVPCf/K67d5GcVPBQsCJVnSZwqRZGzhxZILxfveJVPMsV3hJZmsAloW3ftZ3agwsui+iQr5IINIFLwrr3/XsLhPeU7lN0yFdJGJrAJeFs3L6RA+4/IFBLS01j9e0654gkFk3gklB6TehVILxn3zBb4S0JSRO4JIRVm1fR/G/B3d07tOjAp3/4NKSORCqeAlwir9PoToybPy5QW3TzIlo3ah1SRyKVQwEukbXw+4W0eaRNoPbbo3/LK5e/ElJHIpVLAS6RdPxTxzN71exAbfVtq0mrm7aPR4gkHm3ElEiZtXIWlm2B8O79i954liu8pdrRBC6R0WhIIzb8uCFQ29hvI/Vr1Q+pI5FwaQKXKm/yoslYtgXC+4FzHsCzXOEt1ZomcKmy9nXI1+13badmcs0QOhKpWjSBS5U05ssxBcL7uU7P4Vmu8BaJ0wQuVcquvF2kDEopUM+9J5ck07whkl+x/0eYWbKZzTKz8fHbrc3sUzNbYGYvm5nGIimTx6Y9ViC8x181Hs9yhbdIIUoygd8CzAMaxG8PAYa5+2gzexLoATxRzv1JNbBt5zZS700N1Awj955cHTVQZD+KNdaYWUvgIuCZ+G0DzgLGxu8yCuhcEQ1KYrvrP3cVCO+PrvuIvCwdr1ukKMWdwP8O9AV2f2arCbDB3XfFby8HWhT2QDPLBDIB0tPTS9+pJJT129bT+IHGgdphjQ7jm5u/CakjkegpcgI3s4uB1e4+I3+5kLt6YY939xHu3t7d26elaU85gWvfuLZAeM+9aa7CW6SEijOBnwZcamYXArWJrYH/HWhoZjXiU3hLYEXFtSmJYPnG5bQa1ipQO6v1WUz+3eSQOhKJtiIncHe/091bunsGcCXwH3fvCkwBfhu/W3fgjQrrUiLv3H+eWyC8l/15mcJbpAzK8tmsO4A+ZvYNsTXxkeXTkiSSeWvmYdnGO4ve2VPr/vPueJbTskHLEDsTib4S7cjj7lOBqfGvFwEdyr8lSRSHP3I433wfXNde13cdjes03scjRKQktHeElLtPln+CZVsgvPv/sj+e5QpvkXKkXeml3Lg7yQOT8b0+kLSl/xZSU1L38SgRKS1N4FIu3lrwFkkDkwLh/fD5D+NZrvAWqSCawKVM8jyP5IHJBeo7795JjST98xKpSJrApdSe/+z5AuE9+rLReJYrvEUqgf6XSYntyN1Brb/WKlDPu0fHLxGpTJrApUQe/PDBAuE9qdskPMsV3iKVTBO4FMvmHZupf1/w/JP1atZj052bQupIRDSBS5H6TOxTILxnZM5QeIuETBO47NOaLWs48KEDA7Xjmh3HZ3/8LKSORCQ/BbgUqsvYLoz5ckygtuBPC2jTuE1IHYnI3hTgErB4w2JaD28dqF1yxCWMu2pcSB2JyL4owGWPU0eeysfLPw7UVt66koPqHRRSRyKyP9qIKXz+3edYtgXC+6b2N+FZrvAWqcI0gVdzB//tYFZuXhmo/dDvBxrUahBSRyJSXJrAq6n3lryHZVsgvAedOQjPcoW3SERoAq9m3J2kgQV/bm8bsI3aNWqH0JGIlJYm8GrkX1/9q0B4P33J03iWK7xFIkgTeDWQm5dLjUEF/6p33b2L5KSCh4IVkWjQBJ7gRswYUSC8/9XlX3iWK7xFIk4TeIL6cdeP1Blcp0Bdh3wVSRyawBPQoHcHFQjv937/ng75KpJgNIEnkB9+/IGGQxoGas3rNWfFrStC6khEKpIm8ARx4/gbC4T353/8XOEtksCKnMDNrDbwHlArfv+x7p5lZq2B0UBjYCbQzd13VGSzUtDKTSs5eOjBgdqprU7lw+s+DKkjEaksxZnAtwNnufvPgXbA+WZ2MjAEGObuhwPrgR4V12YCycmBjAxISopd5+SU+qkueemSAuG9+JbFCm+RaqLIAPeYzfGbKfGLA2cBY+P1UUDnCukwkeTkQGYmLFkC7rHrzMwSh/iCdQuwbGP81+P31Lr8rAue5RzS8JDy7lpEqqhibcQ0s2RgBtAGeAxYCGxw913xuywHWlRIh4lkwADYujVY27o1Vu/atVhPcdwTxzFn9ZxAbc3ta2ia2rS8uhSRiCjWRkx3z3X3dkBLoANwVGF3K+yxZpZpZtPNbPqaNWtK32kiWLq0ZPV8ZqyYgWVbILz7nNwHz3KFt0g1VaKPEbr7BjObCpwMNDSzGvEpvCVQ6Mcd3H0EMAKgffv2hYZ8tZGeHls2Kay+H/XurceWnVsCtU13bqJezXrl2Z2IREyRE7iZpZlZw/jXdYBzgHnAFOC38bt1B96oqCYTxuDBkJoarKWmxuqFeGfRO1i2BcL7oXMfwrNc4S0ixZrAmwOj4uvgScAYdx9vZnOB0Wb2V2AWMLIC+0wMu9e5BwyILZukp8fCe6/1730d8nX7XdupmVyzMjoVkQgw98pb1Wjfvr1Pnz690l4vikZ/MZqrXr0qUPvn//yTa467JqSORCRsZjbD3dvvXdeu9FXErrxdpAxKKVDPvSeXJNMOsyJSkJKhCnj404cLhPdbXd/Cs1zhLSL7pHQI0dadW7Fs45Z/37KnlpKUgmc557c5v3KaKMc9Q0WkcinAQ9J/cn/q3ls3UPukxyfsuLsSDydTTnuGikg4tBGzku3I3UGtv9YK1I5ocgTze82v/GYyMgr/XPohh8DixZXdjYjsgzZiVgHTvp3GtW9cG6jN6zmPtk3bhtNQGfYMFZHwaQmlEmzduZXb3r6NU0aewtw1cwHI+lUWnuXhhTfsew/QIvYMFZGqQRN4BZu6eCp/GPcHFq5fSJIlcfupt5PdMZs6KQXPV1npBg+OrXnnP8DWfvYMFZGqRQFeQTZu30jfSX15asZTABx74LGMvHQkJ7U4KeTO8inmnqEiUjUpwCvAhAUTuGH8DSzfuJyUpBTuOuMu+v2yX9XcDb5rVwW2SEQpwMvR2q1r6f3v3uTMiX0Mr0OLDoy8dCTHHHhMyJ2JSCJSgJcDd+eVua/Qa0Iv1mxdQ50adRh05iB6n9yb5KTksNsTkQSlAC+jFZtW0HNCT/711b8A6JjRkacveZo2jduE3JmIJDoFeCm5O/+Y/Q/6TOzDD9t/oH7N+jx47oNcf+L1On6JiFQKBXgpLN6wmMw3M5m0aBIAFx5+IU9e9CStDmgVcmciUp0owEsgz/N4dNqj9J/cny07t9CkThOGnz+cq4+9GjMLuz0RqWYU4MX01dqv6DGuBx8t+wiAK352BY9c8AgH1j0w5M5EpLpSgBdhZ+5OHvroIbLfzWZ77naa12vO4xc9Tue2ncNuTUSqOQX4fsxaOYse43owa9UsAK5rdx0PnfcQjeo0CrkzEREFeKF+3PUjg94dxJAPh5DruWQ0zGDExSM497Bzw25NRGQPBfhePl72MdeNu46v1n6FYdzc4WYGnz2YejXrhd2aiEiAAjxuy44tDPjPAB7+9GEc58gmRzLy0pGcln5a2K2JiBRKAQ68s+gdrn/zehZvWEyyJXPHaXdw96/upnaN2mG3JiKyT9U6wDf8uIHb3r6NkbNGAtDuoHY8e+mzHN/8+JA7ExEpWrUN8HHzx3Hj/97Iik0rqJlck6xfZXH7qbeTkpwSdmsiIsVS5EE7zKyVmU0xs3lm9qWZ3RKvNzazSWa2IH5dcZ+ty8mJnYA3KSl2XYazpq/ZsoYrx15Jp9GdWLFpBae2OpXP/vgZ/U/vr/AWkUgpzlGXdgG3uvtRwMlATzM7GugHTHb3w4HJ8dvlLycndtqvJUvAPXadmVniEHd3XpzzIkc9dhQvf/kyqSmpDD9/OO/9/r1wz0spIlJK5u4le4DZG8Cj8UtHd19pZs2Bqe5+5P4e2759e58+fXrJOszIiIX23g45BBYvLtZTLN+4nBv/90bGfz0egHMOPYcRF4+gdaPWJetFRCQEZjbD3dvvXS/RGriZZQDHA58Czdx9JUA8xAs9KIiZZQKZAOmlOdv50qUlq+fj7jw982lun3Q7G7dv5IBaBzD010O5tt21OviUiEResQPczOoBrwK93X1jcQPQ3UcAIyA2gZe4w/T0wifwIn4YLPx+Ide/eT1TFk8BoNORnXj8osc5uP7BJW5BRKQqKtaZB8wshVh457j7a/Hyd/GlE+LXqyukw8GDITU1WEtNjdULkZuXy9CPh3LsE8cyZfEU0lLTGH3ZaF7v8rrCW0QSSnE+hWLASGCeuw/N961xQPf4192BN8q/PWJnTB8xIrbmbRa7HjGi0DOpf7n6S0579jRufftWtu3aRtdjuzK351y6HNNFSyYiknCK3IhpZr8E3gfmAHnxcn9i6+BjgHRgKXC5u3+/v+cq1UbMYtiRu4MhHwxh0HuD2Jm3kxb1W/DUxU9x0REXlftriYhUtlJvxHT3D4B9ja9nl7Wxspq+Yjo9xvXg8+8+B+CGE29gyDlDOKD2ASF3JiJSsSK7J+a2ndv4y9S/8NDHD5HneRza6FCeueQZzmx9ZtitiYhUikgG+PtL3qfHuB4s+H4BSZZEn5P7MOisQaSmpBb9YBGRBBGpAN+0fRP93unH49MfB+DotKN59tJn+UXLX4TcmYhI5YtMgE/8ZiKZ4zNZ+sNSaiTV4M5f3smA0wdQq0atsFsTEQlFJAK876S+PPjRgwCc2PxEnu30LMc1Oy7krkREwhWJAD+11anUrlGb7I7Z9DmlDzWSItG2iEiFikQSdm7bmYU3L9SelCIi+RRrV/qqQOEtIhIUmQAXEZEgBbiISEQpwEVEIkoBLiISUQpwEZGIUoCLiESUAlxEJKJKfFb6Mr2Y2RqgkBNcVilNgbVhN1GOEun9JNJ7gcR6P3ovFesQd0/bu1ipAR4FZja9sDNfRFUivZ9Eei+QWO9H7yUcWkIREYkoBbiISEQpwAsaEXYD5SyR3k8ivRdIrPej9xICrYGLiESUJnARkYhSgIuIRJQCPM7MWpnZFDObZ2ZfmtktYfdUWmZW28ymmdln8feSHXZPZQF5hPoAAAKZSURBVGVmyWY2y8zGh91LWZnZYjObY2azzWx62P2UlZk1NLOxZvZV/P/PKWH3VBpmdmT872T3ZaOZ9Q67r/3RGnicmTUHmrv7TDOrD8wAOrv73JBbKzEzM6Cuu282sxTgA+AWd/8k5NZKzcz6AO2BBu5+cdj9lIWZLQbau3tV21mkVMxsFPC+uz9jZjWBVHffEHZfZWFmycC3wC/cvcrufKgJPM7dV7r7zPjXm4B5QItwuyodj9kcv5kSv0T2J7WZtQQuAp4JuxcJMrMGwBnASAB33xH18I47G1hYlcMbFOCFMrMM4Hjg03A7Kb34ksNsYDUwyd0j+16AvwN9gbywGyknDrxtZjPMLDPsZsroUGAN8I/4EtczZlY37KbKwZXAS2E3URQF+F7MrB7wKtDb3TeG3U9puXuuu7cDWgIdzOyYsHsqDTO7GFjt7jPC7qUcnebuJwAXAD3N7IywGyqDGsAJwBPufjywBegXbktlE18GuhR4JexeiqIAzye+XvwqkOPur4XdT3mI/zo7FTg/5FZK6zTg0vi68WjgLDN7IdyWysbdV8SvVwOvAx3C7ahMlgPL8/2GN5ZYoEfZBcBMd/8u7EaKogCPi2/4GwnMc/ehYfdTFmaWZmYN41/XAc4Bvgq3q9Jx9zvdvaW7ZxD7tfY/7n5NyG2VmpnVjW8kJ77UcB7wRbhdlZ67rwKWmdmR8dLZQOQ2/O/lKiKwfAKxX38k5jSgGzAnvnYM0N/dJ4TYU2k1B0bFt6QnAWPcPfIfv0sQzYDXY/MCNYAX3f3f4bZUZn8CcuJLD4uAa0Pup9TMLBU4F7gh7F6KQx8jFBGJKC2hiIhElAJcRCSiFOAiIhGlABcRiSgFuIhIRCnARUQiSgEuIhJR/w8X2Aq1Ddq8RgAAAABJRU5ErkJggg==\n",
616 |       "text/plain": [
617 |        "<Figure size 432x288 with 1 Axes>"
618 |       ]
619 |      },
620 |      "metadata": {
621 |       "needs_background": "light"
622 |      },
623 |      "output_type": "display_data"
624 |     }
625 |    ],
626 |    "source": [
627 |     "plt.scatter(X_test,y_test,color='red')\n",
628 |     "plt.plot(X_test,line2,color='green',linewidth=2)\n",
629 |     "plt.title('TESTING DATA')"
630 |    ]
631 |   },
632 |   {
633 |    "cell_type": "markdown",
634 |    "metadata": {},
635 |    "source": [
636 |     "### ***predicting the test value***"
637 |    ]
638 |   },
639 |   {
640 |    "cell_type": "code",
641 |    "execution_count": 49,
642 |    "metadata": {},
643 |    "outputs": [
644 |     {
645 |      "data": {
646 |       "text/plain": [
647 |        "array([[16.88414476],\n",
648 |        "       [33.73226078],\n",
649 |        "       [75.357018  ],\n",
650 |        "       [26.79480124],\n",
651 |        "       [60.49103328]])"
652 |       ]
653 |      },
654 |      "execution_count": 49,
655 |      "metadata": {},
656 |      "output_type": "execute_result"
657 |     }
658 |    ],
659 |    "source": [
660 |     "y_predict=reg.predict(X_test)\n",
661 |     "y_predict"
662 |    ]
663 |   },
664 |   {
665 |    "cell_type": "markdown",
666 |    "metadata": {},
667 |    "source": [
668 |     "### ***Comparing the actuals with the prediction to see the difference***"
669 |    ]
670 |   },
671 |   {
672 |    "cell_type": "code",
673 |    "execution_count": 56,
674 |    "metadata": {},
675 |    "outputs": [
676 |     {
677 |      "name": "stdout",
678 |      "output_type": "stream",
679 |      "text": [
680 |       "Comparing the actuals with prediction: \n"
681 |      ]
682 |     },
683 |     {
684 |      "data": {
685 |       "text/html": [
686 |        "<div>\n",
687 |        "<style scoped>\n",
688 |        "    .dataframe tbody tr th:only-of-type {\n",
689 |        "        vertical-align: middle;\n",
690 |        "    }\n",
691 |        "\n",
692 |        "    .dataframe tbody tr th {\n",
693 |        "        vertical-align: top;\n",
694 |        "    }\n",
695 |        "\n",
696 |        "    .dataframe thead th {\n",
697 |        "        text-align: right;\n",
698 |        "    }\n",
699 |        "</style>\n",
700 |        "<table border=\"1\" class=\"dataframe\">\n",
701 |        "  <thead>\n",
702 |        "    <tr style=\"text-align: right;\">\n",
703 |        "      <th></th>\n",
704 |        "      <th>ACTUAL</th>\n",
705 |        "      <th>prediction</th>\n",
706 |        "    </tr>\n",
707 |        "  </thead>\n",
708 |        "  <tbody>\n",
709 |        "    <tr>\n",
710 |        "      <th>0</th>\n",
711 |        "      <td>20</td>\n",
712 |        "      <td>16.884145</td>\n",
713 |        "    </tr>\n",
714 |        "    <tr>\n",
715 |        "      <th>1</th>\n",
716 |        "      <td>27</td>\n",
717 |        "      <td>33.732261</td>\n",
718 |        "    </tr>\n",
719 |        "    <tr>\n",
720 |        "      <th>2</th>\n",
721 |        "      <td>69</td>\n",
722 |        "      <td>75.357018</td>\n",
723 |        "    </tr>\n",
724 |        "    <tr>\n",
725 |        "      <th>3</th>\n",
726 |        "      <td>30</td>\n",
727 |        "      <td>26.794801</td>\n",
728 |        "    </tr>\n",
729 |        "    <tr>\n",
730 |        "      <th>4</th>\n",
731 |        "      <td>62</td>\n",
732 |        "      <td>60.491033</td>\n",
733 |        "    </tr>\n",
734 |        "  </tbody>\n",
735 |        "</table>\n",
736 |        "</div>"
737 |       ],
738 |       "text/plain": [
739 |        "   ACTUAL  prediction\n",
740 |        "0      20   16.884145\n",
741 |        "1      27   33.732261\n",
742 |        "2      69   75.357018\n",
743 |        "3      30   26.794801\n",
744 |        "4      62   60.491033"
745 |       ]
746 |      },
747 |      "execution_count": 56,
748 |      "metadata": {},
749 |      "output_type": "execute_result"
750 |     }
751 |    ],
752 |    "source": [
753 |     "a=y_test.flatten()\n",
754 |     "b=y_predict.flatten()\n",
755 |     "compare={\"ACTUAL\":a,\"prediction\":b}\n",
756 |     "label={0,1,2,3,4}\n",
757 |     "print(\"Comparing the actuals with prediction: \")\n",
758 |     "df=pd.DataFrame(compare,index=label)\n",
759 |     "df\n"
760 |    ]
761 |   },
762 |   {
763 |    "cell_type": "markdown",
764 |    "metadata": {},
765 |    "source": [
766 |     "### ***Evaluating the model***\n",
767 |     "#### ***Evaluation is a very impotant step for knowing the accuracy of the model  by using MAE***"
768 |    ]
769 |   },
770 |   {
771 |    "cell_type": "code",
772 |    "execution_count": 69,
773 |    "metadata": {},
774 |    "outputs": [
775 |     {
776 |      "data": {
777 |       "text/plain": [
778 |        "4.183859899002975"
779 |       ]
780 |      },
781 |      "execution_count": 69,
782 |      "metadata": {},
783 |      "output_type": "execute_result"
784 |     }
785 |    ],
786 |    "source": [
787 |     "mae=mean_absolute_error(y_test,y_predict)\n",
788 |     "mae"
789 |    ]
790 |   },
791 |   {
792 |    "cell_type": "markdown",
793 |    "metadata": {},
794 |    "source": [
795 |     "## ***prediction***\n",
796 |     "### ***What will be predicted score if a student studies for 9.25 hrs/ day?***"
797 |    ]
798 |   },
799 |   {
800 |    "cell_type": "code",
801 |    "execution_count": 71,
802 |    "metadata": {},
803 |    "outputs": [
804 |     {
805 |      "data": {
806 |       "text/plain": [
807 |        "array([[93.69173249]])"
808 |       ]
809 |      },
810 |      "execution_count": 71,
811 |      "metadata": {},
812 |      "output_type": "execute_result"
813 |     }
814 |    ],
815 |    "source": [
816 |     "data_predict=reg.predict([[9.25]])\n",
817 |     "data_predict"
818 |    ]
819 |   }
820 |  ],
821 |  "metadata": {
822 |   "kernelspec": {
823 |    "display_name": "Python 3",
824 |    "language": "python",
825 |    "name": "python3"
826 |   },
827 |   "language_info": {
828 |    "codemirror_mode": {
829 |     "name": "ipython",
830 |     "version": 3
831 |    },
832 |    "file_extension": ".py",
833 |    "mimetype": "text/x-python",
834 |    "name": "python",
835 |    "nbconvert_exporter": "python",
836 |    "pygments_lexer": "ipython3",
837 |    "version": "3.7.6"
838 |   }
839 |  },
840 |  "nbformat": 4,
841 |  "nbformat_minor": 4
842 | }
843 | 


--------------------------------------------------------------------------------
/TASK-2.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# ***The Sparks Foundation Task-2***"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "## ***From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually.***\n"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "markdown",
 19 |    "metadata": {},
 20 |    "source": [
 21 |     "### ***Import all the required libraries***"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 14,
 27 |    "metadata": {},
 28 |    "outputs": [],
 29 |    "source": [
 30 |     "import pandas as pd\n",
 31 |     "import numpy as np\n",
 32 |     "from sklearn.cluster import KMeans\n",
 33 |     "from sklearn.preprocessing import MinMaxScaler\n",
 34 |     "import matplotlib.pyplot as plt\n",
 35 |     "%matplotlib inline"
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "markdown",
 40 |    "metadata": {},
 41 |    "source": [
 42 |     "### ***Read the CSV file***"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 9,
 48 |    "metadata": {},
 49 |    "outputs": [
 50 |     {
 51 |      "data": {
 52 |       "text/html": [
 53 |        "<div>\n",
 54 |        "<style scoped>\n",
 55 |        "    .dataframe tbody tr th:only-of-type {\n",
 56 |        "        vertical-align: middle;\n",
 57 |        "    }\n",
 58 |        "\n",
 59 |        "    .dataframe tbody tr th {\n",
 60 |        "        vertical-align: top;\n",
 61 |        "    }\n",
 62 |        "\n",
 63 |        "    .dataframe thead th {\n",
 64 |        "        text-align: right;\n",
 65 |        "    }\n",
 66 |        "</style>\n",
 67 |        "<table border=\"1\" class=\"dataframe\">\n",
 68 |        "  <thead>\n",
 69 |        "    <tr style=\"text-align: right;\">\n",
 70 |        "      <th></th>\n",
 71 |        "      <th>Id</th>\n",
 72 |        "      <th>SepalLengthCm</th>\n",
 73 |        "      <th>SepalWidthCm</th>\n",
 74 |        "      <th>PetalLengthCm</th>\n",
 75 |        "      <th>PetalWidthCm</th>\n",
 76 |        "      <th>Species</th>\n",
 77 |        "    </tr>\n",
 78 |        "  </thead>\n",
 79 |        "  <tbody>\n",
 80 |        "    <tr>\n",
 81 |        "      <th>0</th>\n",
 82 |        "      <td>1</td>\n",
 83 |        "      <td>5.1</td>\n",
 84 |        "      <td>3.5</td>\n",
 85 |        "      <td>1.4</td>\n",
 86 |        "      <td>0.2</td>\n",
 87 |        "      <td>Iris-setosa</td>\n",
 88 |        "    </tr>\n",
 89 |        "    <tr>\n",
 90 |        "      <th>1</th>\n",
 91 |        "      <td>2</td>\n",
 92 |        "      <td>4.9</td>\n",
 93 |        "      <td>3.0</td>\n",
 94 |        "      <td>1.4</td>\n",
 95 |        "      <td>0.2</td>\n",
 96 |        "      <td>Iris-setosa</td>\n",
 97 |        "    </tr>\n",
 98 |        "    <tr>\n",
 99 |        "      <th>2</th>\n",
100 |        "      <td>3</td>\n",
101 |        "      <td>4.7</td>\n",
102 |        "      <td>3.2</td>\n",
103 |        "      <td>1.3</td>\n",
104 |        "      <td>0.2</td>\n",
105 |        "      <td>Iris-setosa</td>\n",
106 |        "    </tr>\n",
107 |        "    <tr>\n",
108 |        "      <th>3</th>\n",
109 |        "      <td>4</td>\n",
110 |        "      <td>4.6</td>\n",
111 |        "      <td>3.1</td>\n",
112 |        "      <td>1.5</td>\n",
113 |        "      <td>0.2</td>\n",
114 |        "      <td>Iris-setosa</td>\n",
115 |        "    </tr>\n",
116 |        "    <tr>\n",
117 |        "      <th>4</th>\n",
118 |        "      <td>5</td>\n",
119 |        "      <td>5.0</td>\n",
120 |        "      <td>3.6</td>\n",
121 |        "      <td>1.4</td>\n",
122 |        "      <td>0.2</td>\n",
123 |        "      <td>Iris-setosa</td>\n",
124 |        "    </tr>\n",
125 |        "    <tr>\n",
126 |        "      <th>...</th>\n",
127 |        "      <td>...</td>\n",
128 |        "      <td>...</td>\n",
129 |        "      <td>...</td>\n",
130 |        "      <td>...</td>\n",
131 |        "      <td>...</td>\n",
132 |        "      <td>...</td>\n",
133 |        "    </tr>\n",
134 |        "    <tr>\n",
135 |        "      <th>145</th>\n",
136 |        "      <td>146</td>\n",
137 |        "      <td>6.7</td>\n",
138 |        "      <td>3.0</td>\n",
139 |        "      <td>5.2</td>\n",
140 |        "      <td>2.3</td>\n",
141 |        "      <td>Iris-virginica</td>\n",
142 |        "    </tr>\n",
143 |        "    <tr>\n",
144 |        "      <th>146</th>\n",
145 |        "      <td>147</td>\n",
146 |        "      <td>6.3</td>\n",
147 |        "      <td>2.5</td>\n",
148 |        "      <td>5.0</td>\n",
149 |        "      <td>1.9</td>\n",
150 |        "      <td>Iris-virginica</td>\n",
151 |        "    </tr>\n",
152 |        "    <tr>\n",
153 |        "      <th>147</th>\n",
154 |        "      <td>148</td>\n",
155 |        "      <td>6.5</td>\n",
156 |        "      <td>3.0</td>\n",
157 |        "      <td>5.2</td>\n",
158 |        "      <td>2.0</td>\n",
159 |        "      <td>Iris-virginica</td>\n",
160 |        "    </tr>\n",
161 |        "    <tr>\n",
162 |        "      <th>148</th>\n",
163 |        "      <td>149</td>\n",
164 |        "      <td>6.2</td>\n",
165 |        "      <td>3.4</td>\n",
166 |        "      <td>5.4</td>\n",
167 |        "      <td>2.3</td>\n",
168 |        "      <td>Iris-virginica</td>\n",
169 |        "    </tr>\n",
170 |        "    <tr>\n",
171 |        "      <th>149</th>\n",
172 |        "      <td>150</td>\n",
173 |        "      <td>5.9</td>\n",
174 |        "      <td>3.0</td>\n",
175 |        "      <td>5.1</td>\n",
176 |        "      <td>1.8</td>\n",
177 |        "      <td>Iris-virginica</td>\n",
178 |        "    </tr>\n",
179 |        "  </tbody>\n",
180 |        "</table>\n",
181 |        "<p>150 rows × 6 columns</p>\n",
182 |        "</div>"
183 |       ],
184 |       "text/plain": [
185 |        "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
186 |        "0      1            5.1           3.5            1.4           0.2   \n",
187 |        "1      2            4.9           3.0            1.4           0.2   \n",
188 |        "2      3            4.7           3.2            1.3           0.2   \n",
189 |        "3      4            4.6           3.1            1.5           0.2   \n",
190 |        "4      5            5.0           3.6            1.4           0.2   \n",
191 |        "..   ...            ...           ...            ...           ...   \n",
192 |        "145  146            6.7           3.0            5.2           2.3   \n",
193 |        "146  147            6.3           2.5            5.0           1.9   \n",
194 |        "147  148            6.5           3.0            5.2           2.0   \n",
195 |        "148  149            6.2           3.4            5.4           2.3   \n",
196 |        "149  150            5.9           3.0            5.1           1.8   \n",
197 |        "\n",
198 |        "            Species  \n",
199 |        "0       Iris-setosa  \n",
200 |        "1       Iris-setosa  \n",
201 |        "2       Iris-setosa  \n",
202 |        "3       Iris-setosa  \n",
203 |        "4       Iris-setosa  \n",
204 |        "..              ...  \n",
205 |        "145  Iris-virginica  \n",
206 |        "146  Iris-virginica  \n",
207 |        "147  Iris-virginica  \n",
208 |        "148  Iris-virginica  \n",
209 |        "149  Iris-virginica  \n",
210 |        "\n",
211 |        "[150 rows x 6 columns]"
212 |       ]
213 |      },
214 |      "execution_count": 9,
215 |      "metadata": {},
216 |      "output_type": "execute_result"
217 |     }
218 |    ],
219 |    "source": [
220 |     "df=pd.read_csv(\"iris.csv\")\n",
221 |     "df"
222 |    ]
223 |   },
224 |   {
225 |    "cell_type": "markdown",
226 |    "metadata": {},
227 |    "source": [
228 |     "### ***Dropping the unnecessary columns***"
229 |    ]
230 |   },
231 |   {
232 |    "cell_type": "code",
233 |    "execution_count": 16,
234 |    "metadata": {},
235 |    "outputs": [],
236 |    "source": [
237 |     "df.drop(['Id', 'Species'],axis='columns',inplace=True)    "
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "code",
242 |    "execution_count": 44,
243 |    "metadata": {},
244 |    "outputs": [
245 |     {
246 |      "data": {
247 |       "text/html": [
248 |        "<div>\n",
249 |        "<style scoped>\n",
250 |        "    .dataframe tbody tr th:only-of-type {\n",
251 |        "        vertical-align: middle;\n",
252 |        "    }\n",
253 |        "\n",
254 |        "    .dataframe tbody tr th {\n",
255 |        "        vertical-align: top;\n",
256 |        "    }\n",
257 |        "\n",
258 |        "    .dataframe thead th {\n",
259 |        "        text-align: right;\n",
260 |        "    }\n",
261 |        "</style>\n",
262 |        "<table border=\"1\" class=\"dataframe\">\n",
263 |        "  <thead>\n",
264 |        "    <tr style=\"text-align: right;\">\n",
265 |        "      <th></th>\n",
266 |        "      <th>SepalLengthCm</th>\n",
267 |        "      <th>SepalWidthCm</th>\n",
268 |        "      <th>PetalLengthCm</th>\n",
269 |        "      <th>PetalWidthCm</th>\n",
270 |        "      <th>cluster</th>\n",
271 |        "    </tr>\n",
272 |        "  </thead>\n",
273 |        "  <tbody>\n",
274 |        "    <tr>\n",
275 |        "      <th>0</th>\n",
276 |        "      <td>5.1</td>\n",
277 |        "      <td>3.5</td>\n",
278 |        "      <td>1.4</td>\n",
279 |        "      <td>0.2</td>\n",
280 |        "      <td>1</td>\n",
281 |        "    </tr>\n",
282 |        "    <tr>\n",
283 |        "      <th>1</th>\n",
284 |        "      <td>4.9</td>\n",
285 |        "      <td>3.0</td>\n",
286 |        "      <td>1.4</td>\n",
287 |        "      <td>0.2</td>\n",
288 |        "      <td>1</td>\n",
289 |        "    </tr>\n",
290 |        "    <tr>\n",
291 |        "      <th>2</th>\n",
292 |        "      <td>4.7</td>\n",
293 |        "      <td>3.2</td>\n",
294 |        "      <td>1.3</td>\n",
295 |        "      <td>0.2</td>\n",
296 |        "      <td>1</td>\n",
297 |        "    </tr>\n",
298 |        "    <tr>\n",
299 |        "      <th>3</th>\n",
300 |        "      <td>4.6</td>\n",
301 |        "      <td>3.1</td>\n",
302 |        "      <td>1.5</td>\n",
303 |        "      <td>0.2</td>\n",
304 |        "      <td>1</td>\n",
305 |        "    </tr>\n",
306 |        "    <tr>\n",
307 |        "      <th>4</th>\n",
308 |        "      <td>5.0</td>\n",
309 |        "      <td>3.6</td>\n",
310 |        "      <td>1.4</td>\n",
311 |        "      <td>0.2</td>\n",
312 |        "      <td>1</td>\n",
313 |        "    </tr>\n",
314 |        "    <tr>\n",
315 |        "      <th>...</th>\n",
316 |        "      <td>...</td>\n",
317 |        "      <td>...</td>\n",
318 |        "      <td>...</td>\n",
319 |        "      <td>...</td>\n",
320 |        "      <td>...</td>\n",
321 |        "    </tr>\n",
322 |        "    <tr>\n",
323 |        "      <th>145</th>\n",
324 |        "      <td>6.7</td>\n",
325 |        "      <td>3.0</td>\n",
326 |        "      <td>5.2</td>\n",
327 |        "      <td>2.3</td>\n",
328 |        "      <td>0</td>\n",
329 |        "    </tr>\n",
330 |        "    <tr>\n",
331 |        "      <th>146</th>\n",
332 |        "      <td>6.3</td>\n",
333 |        "      <td>2.5</td>\n",
334 |        "      <td>5.0</td>\n",
335 |        "      <td>1.9</td>\n",
336 |        "      <td>2</td>\n",
337 |        "    </tr>\n",
338 |        "    <tr>\n",
339 |        "      <th>147</th>\n",
340 |        "      <td>6.5</td>\n",
341 |        "      <td>3.0</td>\n",
342 |        "      <td>5.2</td>\n",
343 |        "      <td>2.0</td>\n",
344 |        "      <td>0</td>\n",
345 |        "    </tr>\n",
346 |        "    <tr>\n",
347 |        "      <th>148</th>\n",
348 |        "      <td>6.2</td>\n",
349 |        "      <td>3.4</td>\n",
350 |        "      <td>5.4</td>\n",
351 |        "      <td>2.3</td>\n",
352 |        "      <td>0</td>\n",
353 |        "    </tr>\n",
354 |        "    <tr>\n",
355 |        "      <th>149</th>\n",
356 |        "      <td>5.9</td>\n",
357 |        "      <td>3.0</td>\n",
358 |        "      <td>5.1</td>\n",
359 |        "      <td>1.8</td>\n",
360 |        "      <td>2</td>\n",
361 |        "    </tr>\n",
362 |        "  </tbody>\n",
363 |        "</table>\n",
364 |        "<p>150 rows × 5 columns</p>\n",
365 |        "</div>"
366 |       ],
367 |       "text/plain": [
368 |        "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  cluster\n",
369 |        "0              5.1           3.5            1.4           0.2        1\n",
370 |        "1              4.9           3.0            1.4           0.2        1\n",
371 |        "2              4.7           3.2            1.3           0.2        1\n",
372 |        "3              4.6           3.1            1.5           0.2        1\n",
373 |        "4              5.0           3.6            1.4           0.2        1\n",
374 |        "..             ...           ...            ...           ...      ...\n",
375 |        "145            6.7           3.0            5.2           2.3        0\n",
376 |        "146            6.3           2.5            5.0           1.9        2\n",
377 |        "147            6.5           3.0            5.2           2.0        0\n",
378 |        "148            6.2           3.4            5.4           2.3        0\n",
379 |        "149            5.9           3.0            5.1           1.8        2\n",
380 |        "\n",
381 |        "[150 rows x 5 columns]"
382 |       ]
383 |      },
384 |      "execution_count": 44,
385 |      "metadata": {},
386 |      "output_type": "execute_result"
387 |     }
388 |    ],
389 |    "source": [
390 |     "df"
391 |    ]
392 |   },
393 |   {
394 |    "cell_type": "markdown",
395 |    "metadata": {},
396 |    "source": [
397 |     "### ***K-Means***"
398 |    ]
399 |   },
400 |   {
401 |    "cell_type": "code",
402 |    "execution_count": 37,
403 |    "metadata": {},
404 |    "outputs": [],
405 |    "source": [
406 |     "x=df.iloc[:,[0,1,2,3]].values\n",
407 |     "sse = []\n",
408 |     "k_rng = range(1,10)\n",
409 |     "for k in k_rng:\n",
410 |     "    km = KMeans(n_clusters=k)\n",
411 |     "    km.fit(x)\n",
412 |     "    sse.append(km.inertia_)"
413 |    ]
414 |   },
415 |   {
416 |    "cell_type": "markdown",
417 |    "metadata": {},
418 |    "source": [
419 |     "### ***Plotting an ELBOW GRAPH to find the correct number of cluster***"
420 |    ]
421 |   },
422 |   {
423 |    "cell_type": "code",
424 |    "execution_count": 31,
425 |    "metadata": {},
426 |    "outputs": [
427 |     {
428 |      "data": {
429 |       "text/plain": [
430 |        "[<matplotlib.lines.Line2D at 0xe330d08>]"
431 |       ]
432 |      },
433 |      "execution_count": 31,
434 |      "metadata": {},
435 |      "output_type": "execute_result"
436 |     },
437 |     {
438 |      "data": {
439 |       "image/png": 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\n",
440 |       "text/plain": [
441 |        "<Figure size 432x288 with 1 Axes>"
442 |       ]
443 |      },
444 |      "metadata": {
445 |       "needs_background": "light"
446 |      },
447 |      "output_type": "display_data"
448 |     }
449 |    ],
450 |    "source": [
451 |     "plt.xlabel('K MEANS')\n",
452 |     "plt.ylabel('SUM OF SQUARED ERROR')\n",
453 |     "plt.title('THE ELBOW ANALYSIS')\n",
454 |     "plt.plot(k_rng,sse,color='purple')"
455 |    ]
456 |   },
457 |   {
458 |    "cell_type": "markdown",
459 |    "metadata": {},
460 |    "source": [
461 |     "#### ***This show that 3 is optimum number of cluster to form in iris dataset***"
462 |    ]
463 |   },
464 |   {
465 |    "cell_type": "code",
466 |    "execution_count": 38,
467 |    "metadata": {},
468 |    "outputs": [
469 |     {
470 |      "data": {
471 |       "text/plain": [
472 |        "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
473 |        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
474 |        "       1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
475 |        "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
476 |        "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0,\n",
477 |        "       0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0,\n",
478 |        "       0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2])"
479 |       ]
480 |      },
481 |      "execution_count": 38,
482 |      "metadata": {},
483 |      "output_type": "execute_result"
484 |     }
485 |    ],
486 |    "source": [
487 |     "km = KMeans(n_clusters=3)\n",
488 |     "y_predict = km.fit_predict(x)\n",
489 |     "y_predict"
490 |    ]
491 |   },
492 |   {
493 |    "cell_type": "markdown",
494 |    "metadata": {},
495 |    "source": [
496 |     "## ***Adding a cluster column to show which cluster does the particular feature belong to***"
497 |    ]
498 |   },
499 |   {
500 |    "cell_type": "code",
501 |    "execution_count": 43,
502 |    "metadata": {},
503 |    "outputs": [
504 |     {
505 |      "data": {
506 |       "text/html": [
507 |        "<div>\n",
508 |        "<style scoped>\n",
509 |        "    .dataframe tbody tr th:only-of-type {\n",
510 |        "        vertical-align: middle;\n",
511 |        "    }\n",
512 |        "\n",
513 |        "    .dataframe tbody tr th {\n",
514 |        "        vertical-align: top;\n",
515 |        "    }\n",
516 |        "\n",
517 |        "    .dataframe thead th {\n",
518 |        "        text-align: right;\n",
519 |        "    }\n",
520 |        "</style>\n",
521 |        "<table border=\"1\" class=\"dataframe\">\n",
522 |        "  <thead>\n",
523 |        "    <tr style=\"text-align: right;\">\n",
524 |        "      <th></th>\n",
525 |        "      <th>SepalLengthCm</th>\n",
526 |        "      <th>SepalWidthCm</th>\n",
527 |        "      <th>PetalLengthCm</th>\n",
528 |        "      <th>PetalWidthCm</th>\n",
529 |        "      <th>cluster</th>\n",
530 |        "    </tr>\n",
531 |        "  </thead>\n",
532 |        "  <tbody>\n",
533 |        "    <tr>\n",
534 |        "      <th>0</th>\n",
535 |        "      <td>5.1</td>\n",
536 |        "      <td>3.5</td>\n",
537 |        "      <td>1.4</td>\n",
538 |        "      <td>0.2</td>\n",
539 |        "      <td>1</td>\n",
540 |        "    </tr>\n",
541 |        "    <tr>\n",
542 |        "      <th>1</th>\n",
543 |        "      <td>4.9</td>\n",
544 |        "      <td>3.0</td>\n",
545 |        "      <td>1.4</td>\n",
546 |        "      <td>0.2</td>\n",
547 |        "      <td>1</td>\n",
548 |        "    </tr>\n",
549 |        "    <tr>\n",
550 |        "      <th>2</th>\n",
551 |        "      <td>4.7</td>\n",
552 |        "      <td>3.2</td>\n",
553 |        "      <td>1.3</td>\n",
554 |        "      <td>0.2</td>\n",
555 |        "      <td>1</td>\n",
556 |        "    </tr>\n",
557 |        "    <tr>\n",
558 |        "      <th>3</th>\n",
559 |        "      <td>4.6</td>\n",
560 |        "      <td>3.1</td>\n",
561 |        "      <td>1.5</td>\n",
562 |        "      <td>0.2</td>\n",
563 |        "      <td>1</td>\n",
564 |        "    </tr>\n",
565 |        "    <tr>\n",
566 |        "      <th>4</th>\n",
567 |        "      <td>5.0</td>\n",
568 |        "      <td>3.6</td>\n",
569 |        "      <td>1.4</td>\n",
570 |        "      <td>0.2</td>\n",
571 |        "      <td>1</td>\n",
572 |        "    </tr>\n",
573 |        "    <tr>\n",
574 |        "      <th>...</th>\n",
575 |        "      <td>...</td>\n",
576 |        "      <td>...</td>\n",
577 |        "      <td>...</td>\n",
578 |        "      <td>...</td>\n",
579 |        "      <td>...</td>\n",
580 |        "    </tr>\n",
581 |        "    <tr>\n",
582 |        "      <th>145</th>\n",
583 |        "      <td>6.7</td>\n",
584 |        "      <td>3.0</td>\n",
585 |        "      <td>5.2</td>\n",
586 |        "      <td>2.3</td>\n",
587 |        "      <td>0</td>\n",
588 |        "    </tr>\n",
589 |        "    <tr>\n",
590 |        "      <th>146</th>\n",
591 |        "      <td>6.3</td>\n",
592 |        "      <td>2.5</td>\n",
593 |        "      <td>5.0</td>\n",
594 |        "      <td>1.9</td>\n",
595 |        "      <td>2</td>\n",
596 |        "    </tr>\n",
597 |        "    <tr>\n",
598 |        "      <th>147</th>\n",
599 |        "      <td>6.5</td>\n",
600 |        "      <td>3.0</td>\n",
601 |        "      <td>5.2</td>\n",
602 |        "      <td>2.0</td>\n",
603 |        "      <td>0</td>\n",
604 |        "    </tr>\n",
605 |        "    <tr>\n",
606 |        "      <th>148</th>\n",
607 |        "      <td>6.2</td>\n",
608 |        "      <td>3.4</td>\n",
609 |        "      <td>5.4</td>\n",
610 |        "      <td>2.3</td>\n",
611 |        "      <td>0</td>\n",
612 |        "    </tr>\n",
613 |        "    <tr>\n",
614 |        "      <th>149</th>\n",
615 |        "      <td>5.9</td>\n",
616 |        "      <td>3.0</td>\n",
617 |        "      <td>5.1</td>\n",
618 |        "      <td>1.8</td>\n",
619 |        "      <td>2</td>\n",
620 |        "    </tr>\n",
621 |        "  </tbody>\n",
622 |        "</table>\n",
623 |        "<p>150 rows × 5 columns</p>\n",
624 |        "</div>"
625 |       ],
626 |       "text/plain": [
627 |        "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  cluster\n",
628 |        "0              5.1           3.5            1.4           0.2        1\n",
629 |        "1              4.9           3.0            1.4           0.2        1\n",
630 |        "2              4.7           3.2            1.3           0.2        1\n",
631 |        "3              4.6           3.1            1.5           0.2        1\n",
632 |        "4              5.0           3.6            1.4           0.2        1\n",
633 |        "..             ...           ...            ...           ...      ...\n",
634 |        "145            6.7           3.0            5.2           2.3        0\n",
635 |        "146            6.3           2.5            5.0           1.9        2\n",
636 |        "147            6.5           3.0            5.2           2.0        0\n",
637 |        "148            6.2           3.4            5.4           2.3        0\n",
638 |        "149            5.9           3.0            5.1           1.8        2\n",
639 |        "\n",
640 |        "[150 rows x 5 columns]"
641 |       ]
642 |      },
643 |      "execution_count": 43,
644 |      "metadata": {},
645 |      "output_type": "execute_result"
646 |     }
647 |    ],
648 |    "source": [
649 |     "df['cluster']=y_predict\n",
650 |     "df"
651 |    ]
652 |   },
653 |   {
654 |    "cell_type": "code",
655 |    "execution_count": 23,
656 |    "metadata": {},
657 |    "outputs": [
658 |     {
659 |      "data": {
660 |       "text/plain": [
661 |        "array([1, 0, 2])"
662 |       ]
663 |      },
664 |      "execution_count": 23,
665 |      "metadata": {},
666 |      "output_type": "execute_result"
667 |     }
668 |    ],
669 |    "source": [
670 |     "df.cluster.unique()"
671 |    ]
672 |   },
673 |   {
674 |    "cell_type": "code",
675 |    "execution_count": 32,
676 |    "metadata": {},
677 |    "outputs": [],
678 |    "source": [
679 |     "df1 = df[df.cluster==0]\n",
680 |     "df2 = df[df.cluster==1]\n",
681 |     "df3 = df[df.cluster==2]"
682 |    ]
683 |   },
684 |   {
685 |    "cell_type": "markdown",
686 |    "metadata": {},
687 |    "source": [
688 |     "## ***Plotting a scatter plot showing the cluster***"
689 |    ]
690 |   },
691 |   {
692 |    "cell_type": "code",
693 |    "execution_count": 55,
694 |    "metadata": {},
695 |    "outputs": [
696 |     {
697 |      "data": {
698 |       "text/plain": [
699 |        "<matplotlib.legend.Legend at 0xe19ae48>"
700 |       ]
701 |      },
702 |      "execution_count": 55,
703 |      "metadata": {},
704 |      "output_type": "execute_result"
705 |     },
706 |     {
707 |      "data": {
708 |       "image/png": 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\n",
709 |       "text/plain": [
710 |        "<Figure size 432x288 with 1 Axes>"
711 |       ]
712 |      },
713 |      "metadata": {
714 |       "needs_background": "light"
715 |      },
716 |      "output_type": "display_data"
717 |     }
718 |    ],
719 |    "source": [
720 |     "plt.title('K Means Clustering')\n",
721 |     "plt.scatter(x[y_predict==0,0],x[y_predict==0,1],c='red',label='Iris-setosa')\n",
722 |     "plt.scatter(x[y_predict==1,0],x[y_predict==1,1],c='green',label='Iris-virginica')\n",
723 |     "plt.scatter(x[y_predict==2,0],x[y_predict==2,1],c='yellow',label='Iris-versicolor')\n",
724 |     "plt.legend(loc='best')"
725 |    ]
726 |   },
727 |   {
728 |    "cell_type": "markdown",
729 |    "metadata": {},
730 |    "source": [
731 |     "# ***THANK YOU FOR WATCHING***"
732 |    ]
733 |   }
734 |  ],
735 |  "metadata": {
736 |   "kernelspec": {
737 |    "display_name": "Python 3",
738 |    "language": "python",
739 |    "name": "python3"
740 |   },
741 |   "language_info": {
742 |    "codemirror_mode": {
743 |     "name": "ipython",
744 |     "version": 3
745 |    },
746 |    "file_extension": ".py",
747 |    "mimetype": "text/x-python",
748 |    "name": "python",
749 |    "nbconvert_exporter": "python",
750 |    "pygments_lexer": "ipython3",
751 |    "version": "3.7.6"
752 |   }
753 |  },
754 |  "nbformat": 4,
755 |  "nbformat_minor": 4
756 | }
757 | 


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