├── GB KNN Model.ipynb
├── Linear_Regression.ipynb
└── Logistic Regression.ipynb


/Linear_Regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "cells": [
  3 |     {
  4 |       "cell_type": "markdown",
  5 |       "metadata": {
  6 |         "id": "GzfdMfk10NE6"
  7 |       },
  8 |       "source": [
  9 |         "## **Linear Regression with Python Scikit Learn**\n",
 10 |         "In this section we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving two variables.\n",
 11 |         "\n",
 12 |         "### **Simple Linear Regression**\n",
 13 |         "In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables."
 14 |       ]
 15 |     },
 16 |     {
 17 |       "cell_type": "code",
 18 |       "execution_count": 1,
 19 |       "metadata": {
 20 |         "id": "V9QN2ZxC38pB"
 21 |       },
 22 |       "outputs": [],
 23 |       "source": [
 24 |         "# Importing all libraries required in this notebook\n",
 25 |         "import pandas as pd\n",
 26 |         "import numpy as np  \n",
 27 |         "import matplotlib.pyplot as plt  "
 28 |       ]
 29 |     },
 30 |     {
 31 |       "cell_type": "code",
 32 |       "execution_count": 2,
 33 |       "metadata": {
 34 |         "colab": {
 35 |           "base_uri": "https://localhost:8080/",
 36 |           "height": 380
 37 |         },
 38 |         "id": "LtU4YMEhqm9m",
 39 |         "outputId": "cae4d898-e81c-4cb0-cae3-b668a921a074"
 40 |       },
 41 |       "outputs": [
 42 |         {
 43 |           "name": "stdout",
 44 |           "output_type": "stream",
 45 |           "text": [
 46 |             "Data imported successfully\n"
 47 |           ]
 48 |         },
 49 |         {
 50 |           "data": {
 51 |             "text/html": [
 52 |               "<div>\n",
 53 |               "<style scoped>\n",
 54 |               "    .dataframe tbody tr th:only-of-type {\n",
 55 |               "        vertical-align: middle;\n",
 56 |               "    }\n",
 57 |               "\n",
 58 |               "    .dataframe tbody tr th {\n",
 59 |               "        vertical-align: top;\n",
 60 |               "    }\n",
 61 |               "\n",
 62 |               "    .dataframe thead th {\n",
 63 |               "        text-align: right;\n",
 64 |               "    }\n",
 65 |               "</style>\n",
 66 |               "<table border=\"1\" class=\"dataframe\">\n",
 67 |               "  <thead>\n",
 68 |               "    <tr style=\"text-align: right;\">\n",
 69 |               "      <th></th>\n",
 70 |               "      <th>Hours</th>\n",
 71 |               "      <th>Scores</th>\n",
 72 |               "    </tr>\n",
 73 |               "  </thead>\n",
 74 |               "  <tbody>\n",
 75 |               "    <tr>\n",
 76 |               "      <th>0</th>\n",
 77 |               "      <td>2.5</td>\n",
 78 |               "      <td>21</td>\n",
 79 |               "    </tr>\n",
 80 |               "    <tr>\n",
 81 |               "      <th>1</th>\n",
 82 |               "      <td>5.1</td>\n",
 83 |               "      <td>47</td>\n",
 84 |               "    </tr>\n",
 85 |               "    <tr>\n",
 86 |               "      <th>2</th>\n",
 87 |               "      <td>3.2</td>\n",
 88 |               "      <td>27</td>\n",
 89 |               "    </tr>\n",
 90 |               "    <tr>\n",
 91 |               "      <th>3</th>\n",
 92 |               "      <td>8.5</td>\n",
 93 |               "      <td>75</td>\n",
 94 |               "    </tr>\n",
 95 |               "    <tr>\n",
 96 |               "      <th>4</th>\n",
 97 |               "      <td>3.5</td>\n",
 98 |               "      <td>30</td>\n",
 99 |               "    </tr>\n",
100 |               "    <tr>\n",
101 |               "      <th>5</th>\n",
102 |               "      <td>1.5</td>\n",
103 |               "      <td>20</td>\n",
104 |               "    </tr>\n",
105 |               "    <tr>\n",
106 |               "      <th>6</th>\n",
107 |               "      <td>9.2</td>\n",
108 |               "      <td>88</td>\n",
109 |               "    </tr>\n",
110 |               "    <tr>\n",
111 |               "      <th>7</th>\n",
112 |               "      <td>5.5</td>\n",
113 |               "      <td>60</td>\n",
114 |               "    </tr>\n",
115 |               "    <tr>\n",
116 |               "      <th>8</th>\n",
117 |               "      <td>8.3</td>\n",
118 |               "      <td>81</td>\n",
119 |               "    </tr>\n",
120 |               "    <tr>\n",
121 |               "      <th>9</th>\n",
122 |               "      <td>2.7</td>\n",
123 |               "      <td>25</td>\n",
124 |               "    </tr>\n",
125 |               "  </tbody>\n",
126 |               "</table>\n",
127 |               "</div>"
128 |             ],
129 |             "text/plain": [
130 |               "   Hours  Scores\n",
131 |               "0    2.5      21\n",
132 |               "1    5.1      47\n",
133 |               "2    3.2      27\n",
134 |               "3    8.5      75\n",
135 |               "4    3.5      30\n",
136 |               "5    1.5      20\n",
137 |               "6    9.2      88\n",
138 |               "7    5.5      60\n",
139 |               "8    8.3      81\n",
140 |               "9    2.7      25"
141 |             ]
142 |           },
143 |           "execution_count": 2,
144 |           "metadata": {},
145 |           "output_type": "execute_result"
146 |         }
147 |       ],
148 |       "source": [
149 |         "# Reading data from remote link\n",
150 |         "url = \"https://raw.githubusercontent.com/mhassandata/Regression_model/main/score.csv\"\n",
151 |         "s_data = pd.read_csv(url)\n",
152 |         "print(\"Data imported successfully\")\n",
153 |         "s_data.head(10)"
154 |       ]
155 |     },
156 |     {
157 |       "cell_type": "markdown",
158 |       "metadata": {
159 |         "id": "RHsPneuM4NgB"
160 |       },
161 |       "source": [
162 |         "Let's plot our data points on 2-D graph to eyeball our dataset and see if we can manually find any relationship between the data. We can create the plot with the following script:"
163 |       ]
164 |     },
165 |     {
166 |       "cell_type": "code",
167 |       "execution_count": 3,
168 |       "metadata": {
169 |         "colab": {
170 |           "base_uri": "https://localhost:8080/",
171 |           "height": 472
172 |         },
173 |         "id": "qxYBZkhAqpn9",
174 |         "outputId": "36a7e99a-3ffa-41bf-da42-ce089c3e5dbd"
175 |       },
176 |       "outputs": [
177 |         {
178 |           "data": {
179 |             "image/png": 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",
180 |             "text/plain": [
181 |               "<Figure size 640x480 with 1 Axes>"
182 |             ]
183 |           },
184 |           "metadata": {},
185 |           "output_type": "display_data"
186 |         }
187 |       ],
188 |       "source": [
189 |         "# Plotting the distribution of scores\n",
190 |         "s_data.plot(x='Hours', y='Scores', style='o')  \n",
191 |         "plt.title('Hours vs Percentage')  \n",
192 |         "plt.xlabel('Hours Studied')  \n",
193 |         "plt.ylabel('Percentage Score')  \n",
194 |         "plt.show()"
195 |       ]
196 |     },
197 |     {
198 |       "cell_type": "markdown",
199 |       "metadata": {
200 |         "id": "fiQaULio4Rzr"
201 |       },
202 |       "source": [
203 |         "**From the graph above, we can clearly see that there is a positive linear relation between the number of hours studied and percentage of score.**"
204 |       ]
205 |     },
206 |     {
207 |       "cell_type": "markdown",
208 |       "metadata": {
209 |         "id": "WWtEr64M4jdz"
210 |       },
211 |       "source": [
212 |         "### **Preparing the data**\n",
213 |         "\n",
214 |         "The next step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."
215 |       ]
216 |     },
217 |     {
218 |       "cell_type": "code",
219 |       "execution_count": 4,
220 |       "metadata": {
221 |         "id": "LiJ5210e4tNX"
222 |       },
223 |       "outputs": [],
224 |       "source": [
225 |         "X = s_data.iloc[:, :-1].values  \n",
226 |         "y = s_data.iloc[:, 1].values  "
227 |       ]
228 |     },
229 |     {
230 |       "cell_type": "code",
231 |       "execution_count": 5,
232 |       "metadata": {
233 |         "colab": {
234 |           "base_uri": "https://localhost:8080/"
235 |         },
236 |         "id": "0DrNCxfV_0sS",
237 |         "outputId": "31344c84-6445-4b80-f7a9-17ea56604245"
238 |       },
239 |       "outputs": [
240 |         {
241 |           "data": {
242 |             "text/plain": [
243 |               "array([[2.5],\n",
244 |               "       [5.1],\n",
245 |               "       [3.2],\n",
246 |               "       [8.5],\n",
247 |               "       [3.5],\n",
248 |               "       [1.5],\n",
249 |               "       [9.2],\n",
250 |               "       [5.5],\n",
251 |               "       [8.3],\n",
252 |               "       [2.7],\n",
253 |               "       [7.7],\n",
254 |               "       [5.9],\n",
255 |               "       [4.5],\n",
256 |               "       [3.3],\n",
257 |               "       [1.1],\n",
258 |               "       [8.9],\n",
259 |               "       [2.5],\n",
260 |               "       [1.9],\n",
261 |               "       [6.1],\n",
262 |               "       [7.4],\n",
263 |               "       [2.7],\n",
264 |               "       [4.8],\n",
265 |               "       [3.8],\n",
266 |               "       [6.9],\n",
267 |               "       [7.8]])"
268 |             ]
269 |           },
270 |           "execution_count": 5,
271 |           "metadata": {},
272 |           "output_type": "execute_result"
273 |         }
274 |       ],
275 |       "source": [
276 |         "X"
277 |       ]
278 |     },
279 |     {
280 |       "cell_type": "code",
281 |       "execution_count": 6,
282 |       "metadata": {
283 |         "colab": {
284 |           "base_uri": "https://localhost:8080/"
285 |         },
286 |         "id": "HKJA37KL_0sT",
287 |         "outputId": "6ecc3cb9-084d-42eb-de9f-1c57256cca6d"
288 |       },
289 |       "outputs": [
290 |         {
291 |           "data": {
292 |             "text/plain": [
293 |               "array([21, 47, 27, 75, 30, 20, 88, 60, 81, 25, 85, 62, 41, 42, 17, 95, 30,\n",
294 |               "       24, 67, 69, 30, 54, 35, 76, 86])"
295 |             ]
296 |           },
297 |           "execution_count": 6,
298 |           "metadata": {},
299 |           "output_type": "execute_result"
300 |         }
301 |       ],
302 |       "source": [
303 |         "y"
304 |       ]
305 |     },
306 |     {
307 |       "cell_type": "markdown",
308 |       "metadata": {
309 |         "id": "Riz-ZiZ34fO4"
310 |       },
311 |       "source": [
312 |         "Now that we have our attributes and labels, the next step is to split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split() method:"
313 |       ]
314 |     },
315 |     {
316 |       "cell_type": "code",
317 |       "execution_count": 7,
318 |       "metadata": {
319 |         "id": "udFYso1M4BNw"
320 |       },
321 |       "outputs": [],
322 |       "source": [
323 |         "from sklearn.model_selection import train_test_split\n",
324 |         "\n",
325 |         "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
326 |         "                            test_size=0.2, random_state=0) "
327 |       ]
328 |     },
329 |     {
330 |       "cell_type": "code",
331 |       "execution_count": 8,
332 |       "metadata": {
333 |         "colab": {
334 |           "base_uri": "https://localhost:8080/"
335 |         },
336 |         "id": "LDQkaigQ_0sT",
337 |         "outputId": "6162a217-b98c-4c7d-a51a-e417cd8b95cd"
338 |       },
339 |       "outputs": [
340 |         {
341 |           "data": {
342 |             "text/plain": [
343 |               "array([[3.8],\n",
344 |               "       [1.9],\n",
345 |               "       [7.8],\n",
346 |               "       [6.9],\n",
347 |               "       [1.1],\n",
348 |               "       [5.1],\n",
349 |               "       [7.7],\n",
350 |               "       [3.3],\n",
351 |               "       [8.3],\n",
352 |               "       [9.2],\n",
353 |               "       [6.1],\n",
354 |               "       [3.5],\n",
355 |               "       [2.7],\n",
356 |               "       [5.5],\n",
357 |               "       [2.7],\n",
358 |               "       [8.5],\n",
359 |               "       [2.5],\n",
360 |               "       [4.8],\n",
361 |               "       [8.9],\n",
362 |               "       [4.5]])"
363 |             ]
364 |           },
365 |           "execution_count": 8,
366 |           "metadata": {},
367 |           "output_type": "execute_result"
368 |         }
369 |       ],
370 |       "source": [
371 |         "X_train"
372 |       ]
373 |     },
374 |     {
375 |       "cell_type": "markdown",
376 |       "metadata": {
377 |         "id": "a6WXptFU5CkC"
378 |       },
379 |       "source": [
380 |         "### **Training the Algorithm**\n",
381 |         "Now that we have split our data into training and testing sets, we can finally train our algorithm on it. "
382 |       ]
383 |     },
384 |     {
385 |       "cell_type": "code",
386 |       "execution_count": 9,
387 |       "metadata": {
388 |         "colab": {
389 |           "base_uri": "https://localhost:8080/"
390 |         },
391 |         "id": "qddCuaS84fpK",
392 |         "outputId": "41a29249-38ab-4773-f7f6-e1ede34e7536"
393 |       },
394 |       "outputs": [],
395 |       "source": [
396 |         "from sklearn.linear_model import LinearRegression  \n",
397 |         "\n",
398 |         "regressor = LinearRegression()"
399 |       ]
400 |     },
401 |     {
402 |       "cell_type": "code",
403 |       "execution_count": 10,
404 |       "metadata": {},
405 |       "outputs": [
406 |         {
407 |           "name": "stdout",
408 |           "output_type": "stream",
409 |           "text": [
410 |             "Training complete.\n"
411 |           ]
412 |         }
413 |       ],
414 |       "source": [
415 |         "regressor.fit(X_train, y_train) \n",
416 |         "\n",
417 |         "print(\"Training complete.\")"
418 |       ]
419 |     },
420 |     {
421 |       "cell_type": "code",
422 |       "execution_count": 11,
423 |       "metadata": {},
424 |       "outputs": [
425 |         {
426 |           "data": {
427 |             "text/plain": [
428 |               "array([16.88414476, 33.73226078, 75.357018  , 26.79480124, 60.49103328])"
429 |             ]
430 |           },
431 |           "execution_count": 11,
432 |           "metadata": {},
433 |           "output_type": "execute_result"
434 |         }
435 |       ],
436 |       "source": [
437 |         "regressor.predict(X_test)"
438 |       ]
439 |     },
440 |     {
441 |       "cell_type": "code",
442 |       "execution_count": 12,
443 |       "metadata": {
444 |         "colab": {
445 |           "base_uri": "https://localhost:8080/",
446 |           "height": 430
447 |         },
448 |         "id": "LO3cIcQV_0sU",
449 |         "outputId": "9f399f85-9909-4797-b6e2-8b54b017aeda"
450 |       },
451 |       "outputs": [
452 |         {
453 |           "data": {
454 |             "text/plain": [
455 |               "<matplotlib.collections.PathCollection at 0x157e824f110>"
456 |             ]
457 |           },
458 |           "execution_count": 12,
459 |           "metadata": {},
460 |           "output_type": "execute_result"
461 |         },
462 |         {
463 |           "data": {
464 |             "image/png": 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",
465 |             "text/plain": [
466 |               "<Figure size 640x480 with 1 Axes>"
467 |             ]
468 |           },
469 |           "metadata": {},
470 |           "output_type": "display_data"
471 |         }
472 |       ],
473 |       "source": [
474 |         "plt.scatter(X,y)"
475 |       ]
476 |     },
477 |     {
478 |       "cell_type": "code",
479 |       "execution_count": 13,
480 |       "metadata": {},
481 |       "outputs": [
482 |         {
483 |           "name": "stdout",
484 |           "output_type": "stream",
485 |           "text": [
486 |             "The calculated parameters are theta_1: 9.910656480642233, and theta_2: 2.0181600414346974\n"
487 |           ]
488 |         }
489 |       ],
490 |       "source": [
491 |         "print(f\"The calculated parameters are theta_1: {regressor.coef_[0]}, and theta_2: {regressor.intercept_}\")"
492 |       ]
493 |     },
494 |     {
495 |       "cell_type": "code",
496 |       "execution_count": 14,
497 |       "metadata": {},
498 |       "outputs": [],
499 |       "source": [
500 |         "# Plotting the regression line\n",
501 |         "line = regressor.coef_*X_test+regressor.intercept_"
502 |       ]
503 |     },
504 |     {
505 |       "cell_type": "code",
506 |       "execution_count": 15,
507 |       "metadata": {},
508 |       "outputs": [
509 |         {
510 |           "data": {
511 |             "text/plain": [
512 |               "array([21, 47, 27, 75, 30, 20, 88, 60, 81, 25, 85, 62, 41, 42, 17, 95, 30,\n",
513 |               "       24, 67, 69, 30, 54, 35, 76, 86])"
514 |             ]
515 |           },
516 |           "execution_count": 15,
517 |           "metadata": {},
518 |           "output_type": "execute_result"
519 |         }
520 |       ],
521 |       "source": [
522 |         "y"
523 |       ]
524 |     },
525 |     {
526 |       "cell_type": "code",
527 |       "execution_count": 16,
528 |       "metadata": {
529 |         "colab": {
530 |           "base_uri": "https://localhost:8080/",
531 |           "height": 430
532 |         },
533 |         "id": "J61NX2_2-px7",
534 |         "outputId": "20d96bf4-8f2c-4a15-c004-b34a63f0f815"
535 |       },
536 |       "outputs": [
537 |         {
538 |           "data": {
539 |             "image/png": 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",
540 |             "text/plain": [
541 |               "<Figure size 640x480 with 1 Axes>"
542 |             ]
543 |           },
544 |           "metadata": {},
545 |           "output_type": "display_data"
546 |         },
547 |         {
548 |           "data": {
549 |             "image/png": 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",
550 |             "text/plain": [
551 |               "<Figure size 640x480 with 1 Axes>"
552 |             ]
553 |           },
554 |           "metadata": {},
555 |           "output_type": "display_data"
556 |         }
557 |       ],
558 |       "source": [
559 |         "preds = regressor.predict(X)\n",
560 |         "\n",
561 |         "plt.subplot(1, 2, 1)\n",
562 |         "plt.scatter(X, y)\n",
563 |         "plt.plot(X, preds)\n",
564 |         "plt.show()\n",
565 |         "\n",
566 |         "# Plotting for the test data\n",
567 |         "plt.subplot(1, 2, 2)\n",
568 |         "plt.scatter(X_test, y_test, color=\"red\")\n",
569 |         "plt.plot(X_test, line)\n",
570 |         "plt.show()"
571 |       ]
572 |     },
573 |     {
574 |       "cell_type": "markdown",
575 |       "metadata": {
576 |         "id": "JCQn-g4m5OK2"
577 |       },
578 |       "source": [
579 |         "### **Making Predictions**\n",
580 |         "Now that we have trained our algorithm, it's time to make some predictions."
581 |       ]
582 |     },
583 |     {
584 |       "cell_type": "code",
585 |       "execution_count": 15,
586 |       "metadata": {
587 |         "colab": {
588 |           "base_uri": "https://localhost:8080/"
589 |         },
590 |         "id": "Tt-Fmzu55EGM",
591 |         "outputId": "f03a010b-399e-49c2-ab18-1f41f4b13a89"
592 |       },
593 |       "outputs": [
594 |         {
595 |           "name": "stdout",
596 |           "output_type": "stream",
597 |           "text": [
598 |             "[[1.5]\n",
599 |             " [3.2]\n",
600 |             " [7.4]\n",
601 |             " [2.5]\n",
602 |             " [5.9]]\n"
603 |           ]
604 |         }
605 |       ],
606 |       "source": [
607 |         "print(X_test) # Testing data - In Hours\n",
608 |         "y_pred = regressor.predict(X_test) # Predicting the scores"
609 |       ]
610 |     },
611 |     {
612 |       "cell_type": "code",
613 |       "execution_count": 16,
614 |       "metadata": {
615 |         "colab": {
616 |           "base_uri": "https://localhost:8080/",
617 |           "height": 206
618 |         },
619 |         "id": "6bmZUMZh5QLb",
620 |         "outputId": "943e567f-fe9d-43ad-9c81-a841d1026ef1"
621 |       },
622 |       "outputs": [
623 |         {
624 |           "data": {
625 |             "text/html": [
626 |               "\n",
627 |               "  <div id=\"df-5a243475-491f-4283-84c1-d07c62c74953\">\n",
628 |               "    <div class=\"colab-df-container\">\n",
629 |               "      <div>\n",
630 |               "<style scoped>\n",
631 |               "    .dataframe tbody tr th:only-of-type {\n",
632 |               "        vertical-align: middle;\n",
633 |               "    }\n",
634 |               "\n",
635 |               "    .dataframe tbody tr th {\n",
636 |               "        vertical-align: top;\n",
637 |               "    }\n",
638 |               "\n",
639 |               "    .dataframe thead th {\n",
640 |               "        text-align: right;\n",
641 |               "    }\n",
642 |               "</style>\n",
643 |               "<table border=\"1\" class=\"dataframe\">\n",
644 |               "  <thead>\n",
645 |               "    <tr style=\"text-align: right;\">\n",
646 |               "      <th></th>\n",
647 |               "      <th>Actual</th>\n",
648 |               "      <th>Predicted</th>\n",
649 |               "    </tr>\n",
650 |               "  </thead>\n",
651 |               "  <tbody>\n",
652 |               "    <tr>\n",
653 |               "      <th>0</th>\n",
654 |               "      <td>20</td>\n",
655 |               "      <td>16.884145</td>\n",
656 |               "    </tr>\n",
657 |               "    <tr>\n",
658 |               "      <th>1</th>\n",
659 |               "      <td>27</td>\n",
660 |               "      <td>33.732261</td>\n",
661 |               "    </tr>\n",
662 |               "    <tr>\n",
663 |               "      <th>2</th>\n",
664 |               "      <td>69</td>\n",
665 |               "      <td>75.357018</td>\n",
666 |               "    </tr>\n",
667 |               "    <tr>\n",
668 |               "      <th>3</th>\n",
669 |               "      <td>30</td>\n",
670 |               "      <td>26.794801</td>\n",
671 |               "    </tr>\n",
672 |               "    <tr>\n",
673 |               "      <th>4</th>\n",
674 |               "      <td>62</td>\n",
675 |               "      <td>60.491033</td>\n",
676 |               "    </tr>\n",
677 |               "  </tbody>\n",
678 |               "</table>\n",
679 |               "</div>\n",
680 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a243475-491f-4283-84c1-d07c62c74953')\"\n",
681 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
682 |               "              style=\"display:none;\">\n",
683 |               "        \n",
684 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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688 |               "  </svg>\n",
689 |               "      </button>\n",
690 |               "      \n",
691 |               "  <style>\n",
692 |               "    .colab-df-container {\n",
693 |               "      display:flex;\n",
694 |               "      flex-wrap:wrap;\n",
695 |               "      gap: 12px;\n",
696 |               "    }\n",
697 |               "\n",
698 |               "    .colab-df-convert {\n",
699 |               "      background-color: #E8F0FE;\n",
700 |               "      border: none;\n",
701 |               "      border-radius: 50%;\n",
702 |               "      cursor: pointer;\n",
703 |               "      display: none;\n",
704 |               "      fill: #1967D2;\n",
705 |               "      height: 32px;\n",
706 |               "      padding: 0 0 0 0;\n",
707 |               "      width: 32px;\n",
708 |               "    }\n",
709 |               "\n",
710 |               "    .colab-df-convert:hover {\n",
711 |               "      background-color: #E2EBFA;\n",
712 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
713 |               "      fill: #174EA6;\n",
714 |               "    }\n",
715 |               "\n",
716 |               "    [theme=dark] .colab-df-convert {\n",
717 |               "      background-color: #3B4455;\n",
718 |               "      fill: #D2E3FC;\n",
719 |               "    }\n",
720 |               "\n",
721 |               "    [theme=dark] .colab-df-convert:hover {\n",
722 |               "      background-color: #434B5C;\n",
723 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
724 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
725 |               "      fill: #FFFFFF;\n",
726 |               "    }\n",
727 |               "  </style>\n",
728 |               "\n",
729 |               "      <script>\n",
730 |               "        const buttonEl =\n",
731 |               "          document.querySelector('#df-5a243475-491f-4283-84c1-d07c62c74953 button.colab-df-convert');\n",
732 |               "        buttonEl.style.display =\n",
733 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
734 |               "\n",
735 |               "        async function convertToInteractive(key) {\n",
736 |               "          const element = document.querySelector('#df-5a243475-491f-4283-84c1-d07c62c74953');\n",
737 |               "          const dataTable =\n",
738 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
739 |               "                                                     [key], {});\n",
740 |               "          if (!dataTable) return;\n",
741 |               "\n",
742 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
743 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
744 |               "            + ' to learn more about interactive tables.';\n",
745 |               "          element.innerHTML = '';\n",
746 |               "          dataTable['output_type'] = 'display_data';\n",
747 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
748 |               "          const docLink = document.createElement('div');\n",
749 |               "          docLink.innerHTML = docLinkHtml;\n",
750 |               "          element.appendChild(docLink);\n",
751 |               "        }\n",
752 |               "      </script>\n",
753 |               "    </div>\n",
754 |               "  </div>\n",
755 |               "  "
756 |             ],
757 |             "text/plain": [
758 |               "   Actual  Predicted\n",
759 |               "0      20  16.884145\n",
760 |               "1      27  33.732261\n",
761 |               "2      69  75.357018\n",
762 |               "3      30  26.794801\n",
763 |               "4      62  60.491033"
764 |             ]
765 |           },
766 |           "execution_count": 16,
767 |           "metadata": {},
768 |           "output_type": "execute_result"
769 |         }
770 |       ],
771 |       "source": [
772 |         "# Comparing Actual vs Predicted\n",
773 |         "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
774 |         "df "
775 |       ]
776 |     },
777 |     {
778 |       "cell_type": "code",
779 |       "execution_count": 21,
780 |       "metadata": {
781 |         "colab": {
782 |           "base_uri": "https://localhost:8080/"
783 |         },
784 |         "id": "KAFO8zbx-AH1",
785 |         "outputId": "3c003ec3-e681-47fb-a684-e71ab2cee7f5"
786 |       },
787 |       "outputs": [
788 |         {
789 |           "name": "stdout",
790 |           "output_type": "stream",
791 |           "text": [
792 |             "No of Hours = 9.5\n",
793 |             "Predicted Score = 96.16939660753593\n"
794 |           ]
795 |         }
796 |       ],
797 |       "source": [
798 |         "#You can also test with your own data\n",
799 |         "hours = 9.5\n",
800 |         "own_pred = regressor.predict([[9.5]])\n",
801 |         "print(\"No of Hours = {}\".format(hours))\n",
802 |         "print(\"Predicted Score = {}\".format(own_pred[0]))"
803 |       ]
804 |     },
805 |     {
806 |       "cell_type": "markdown",
807 |       "metadata": {
808 |         "id": "0AAsPVA_6KmK"
809 |       },
810 |       "source": [
811 |         "### **Evaluating the model**\n",
812 |         "\n",
813 |         "The final step is to evaluate the performance of algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. For simplicity here, we have chosen the mean square error. There are many such metrics."
814 |       ]
815 |     },
816 |     {
817 |       "cell_type": "code",
818 |       "execution_count": 22,
819 |       "metadata": {
820 |         "colab": {
821 |           "base_uri": "https://localhost:8080/"
822 |         },
823 |         "id": "r5UOrRH-5VCQ",
824 |         "outputId": "a4bc5295-e596-40c4-faee-f72ef065f366"
825 |       },
826 |       "outputs": [
827 |         {
828 |           "name": "stdout",
829 |           "output_type": "stream",
830 |           "text": [
831 |             "Mean Absolute Error: 4.183859899002982\n"
832 |           ]
833 |         }
834 |       ],
835 |       "source": [
836 |         "from sklearn import metrics  \n",
837 |         "print('Mean Absolute Error:', \n",
838 |         "      metrics.mean_absolute_error(y_test, y_pred)) "
839 |       ]
840 |     },
841 |     {
842 |       "cell_type": "code",
843 |       "execution_count": 18,
844 |       "metadata": {
845 |         "id": "1MzDbtLh_0sX"
846 |       },
847 |       "outputs": [],
848 |       "source": []
849 |     }
850 |   ],
851 |   "metadata": {
852 |     "colab": {
853 |       "provenance": []
854 |     },
855 |     "kernelspec": {
856 |       "display_name": "Python 3 (ipykernel)",
857 |       "language": "python",
858 |       "name": "python3"
859 |     },
860 |     "language_info": {
861 |       "codemirror_mode": {
862 |         "name": "ipython",
863 |         "version": 3
864 |       },
865 |       "file_extension": ".py",
866 |       "mimetype": "text/x-python",
867 |       "name": "python",
868 |       "nbconvert_exporter": "python",
869 |       "pygments_lexer": "ipython3",
870 |       "version": "3.12.4"
871 |     }
872 |   },
873 |   "nbformat": 4,
874 |   "nbformat_minor": 0
875 | }
876 | 


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