├── Decision_Tree_Classifier.ipynb
├── ML_NBC.ipynb
├── ML__KNN.ipynb
├── ML_linear_regression.ipynb
├── Multi_linear_regression.ipynb
├── Multiple_Diseases.ipynb
├── README.md
├── Web_Scraping.ipynb
├── k_mean_clustering.ipynb
├── ml_LR.ipynb
├── ml_SVMC.ipynb
├── ml_SVMR.ipynb
└── polynomial_regression.ipynb
/Multi_linear_regression.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "nbformat": 4,
3 | "nbformat_minor": 0,
4 | "metadata": {
5 | "colab": {
6 | "provenance": [],
7 | "authorship_tag": "ABX9TyPJzBLEQ+pg3m5i7DW/SZLW",
8 | "include_colab_link": true
9 | },
10 | "kernelspec": {
11 | "name": "python3",
12 | "display_name": "Python 3"
13 | },
14 | "language_info": {
15 | "name": "python"
16 | }
17 | },
18 | "cells": [
19 | {
20 | "cell_type": "markdown",
21 | "metadata": {
22 | "id": "view-in-github",
23 | "colab_type": "text"
24 | },
25 | "source": [
26 | " "
27 | ]
28 | },
29 | {
30 | "cell_type": "code",
31 | "execution_count": 1,
32 | "metadata": {
33 | "id": "XwDzceNI9-Rn"
34 | },
35 | "outputs": [],
36 | "source": [
37 | "import numpy as np\n",
38 | "import pandas as pd\n",
39 | "import matplotlib.pyplot as plt"
40 | ]
41 | },
42 | {
43 | "cell_type": "code",
44 | "source": [
45 | "data=pd.read_csv(\"https://raw.githubusercontent.com/nandu19k/multiple-linear-regression/master/50_Startups.csv\")\n",
46 | "data.head(10)\n",
47 | "#to check NULL valus in the dataset data.isna().sum()"
48 | ],
49 | "metadata": {
50 | "colab": {
51 | "base_uri": "https://localhost:8080/",
52 | "height": 363
53 | },
54 | "id": "M23sM5lv-SR-",
55 | "outputId": "1c848b99-7ec2-4f9e-b66f-158211a06b64"
56 | },
57 | "execution_count": 4,
58 | "outputs": [
59 | {
60 | "output_type": "execute_result",
61 | "data": {
62 | "text/plain": [
63 | " R&D Spend Administration Marketing Spend State Profit\n",
64 | "0 165349.20 136897.80 471784.10 New York 192261.83\n",
65 | "1 162597.70 151377.59 443898.53 California 191792.06\n",
66 | "2 153441.51 101145.55 407934.54 Florida 191050.39\n",
67 | "3 144372.41 118671.85 383199.62 New York 182901.99\n",
68 | "4 142107.34 91391.77 366168.42 Florida 166187.94\n",
69 | "5 131876.90 99814.71 362861.36 New York 156991.12\n",
70 | "6 134615.46 147198.87 127716.82 California 156122.51\n",
71 | "7 130298.13 145530.06 323876.68 Florida 155752.60\n",
72 | "8 120542.52 148718.95 311613.29 New York 152211.77\n",
73 | "9 123334.88 108679.17 304981.62 California 149759.96"
74 | ],
75 | "text/html": [
76 | "\n",
77 | "
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78 | "
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79 | "\n",
92 | "
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93 | " \n",
94 | " \n",
95 | " \n",
96 | " R&D Spend \n",
97 | " Administration \n",
98 | " Marketing Spend \n",
99 | " State \n",
100 | " Profit \n",
101 | " \n",
102 | " \n",
103 | " \n",
104 | " \n",
105 | " 0 \n",
106 | " 165349.20 \n",
107 | " 136897.80 \n",
108 | " 471784.10 \n",
109 | " New York \n",
110 | " 192261.83 \n",
111 | " \n",
112 | " \n",
113 | " 1 \n",
114 | " 162597.70 \n",
115 | " 151377.59 \n",
116 | " 443898.53 \n",
117 | " California \n",
118 | " 191792.06 \n",
119 | " \n",
120 | " \n",
121 | " 2 \n",
122 | " 153441.51 \n",
123 | " 101145.55 \n",
124 | " 407934.54 \n",
125 | " Florida \n",
126 | " 191050.39 \n",
127 | " \n",
128 | " \n",
129 | " 3 \n",
130 | " 144372.41 \n",
131 | " 118671.85 \n",
132 | " 383199.62 \n",
133 | " New York \n",
134 | " 182901.99 \n",
135 | " \n",
136 | " \n",
137 | " 4 \n",
138 | " 142107.34 \n",
139 | " 91391.77 \n",
140 | " 366168.42 \n",
141 | " Florida \n",
142 | " 166187.94 \n",
143 | " \n",
144 | " \n",
145 | " 5 \n",
146 | " 131876.90 \n",
147 | " 99814.71 \n",
148 | " 362861.36 \n",
149 | " New York \n",
150 | " 156991.12 \n",
151 | " \n",
152 | " \n",
153 | " 6 \n",
154 | " 134615.46 \n",
155 | " 147198.87 \n",
156 | " 127716.82 \n",
157 | " California \n",
158 | " 156122.51 \n",
159 | " \n",
160 | " \n",
161 | " 7 \n",
162 | " 130298.13 \n",
163 | " 145530.06 \n",
164 | " 323876.68 \n",
165 | " Florida \n",
166 | " 155752.60 \n",
167 | " \n",
168 | " \n",
169 | " 8 \n",
170 | " 120542.52 \n",
171 | " 148718.95 \n",
172 | " 311613.29 \n",
173 | " New York \n",
174 | " 152211.77 \n",
175 | " \n",
176 | " \n",
177 | " 9 \n",
178 | " 123334.88 \n",
179 | " 108679.17 \n",
180 | " 304981.62 \n",
181 | " California \n",
182 | " 149759.96 \n",
183 | " \n",
184 | " \n",
185 | "
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186 | "
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187 | "
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326 | "
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327 | ]
328 | },
329 | "metadata": {},
330 | "execution_count": 4
331 | }
332 | ]
333 | },
334 | {
335 | "cell_type": "code",
336 | "source": [
337 | "x=data.iloc[:,:-2].values\n",
338 | "y=data.iloc[:,-1].values\n"
339 | ],
340 | "metadata": {
341 | "colab": {
342 | "base_uri": "https://localhost:8080/"
343 | },
344 | "id": "txim7D9F_GnK",
345 | "outputId": "2a878a49-f61f-4105-b1bf-98255d12ef25"
346 | },
347 | "execution_count": 8,
348 | "outputs": [
349 | {
350 | "output_type": "execute_result",
351 | "data": {
352 | "text/plain": [
353 | "array([192261.83, 191792.06, 191050.39, 182901.99, 166187.94, 156991.12,\n",
354 | " 156122.51, 155752.6 , 152211.77, 149759.96, 146121.95, 144259.4 ,\n",
355 | " 141585.52, 134307.35, 132602.65, 129917.04, 126992.93, 125370.37,\n",
356 | " 124266.9 , 122776.86, 118474.03, 111313.02, 110352.25, 108733.99,\n",
357 | " 108552.04, 107404.34, 105733.54, 105008.31, 103282.38, 101004.64,\n",
358 | " 99937.59, 97483.56, 97427.84, 96778.92, 96712.8 , 96479.51,\n",
359 | " 90708.19, 89949.14, 81229.06, 81005.76, 78239.91, 77798.83,\n",
360 | " 71498.49, 69758.98, 65200.33, 64926.08, 49490.75, 42559.73,\n",
361 | " 35673.41, 14681.4 ])"
362 | ]
363 | },
364 | "metadata": {},
365 | "execution_count": 8
366 | }
367 | ]
368 | },
369 | {
370 | "cell_type": "code",
371 | "source": [
372 | "from sklearn.model_selection import train_test_split\n",
373 | "xtrain,xtest,ytrain,ytest= train_test_split(x,y,test_size=0.2)\n"
374 | ],
375 | "metadata": {
376 | "colab": {
377 | "base_uri": "https://localhost:8080/"
378 | },
379 | "id": "kY2l3FPZAH2A",
380 | "outputId": "a8be40b2-e9a6-4c82-f9a2-9aa125f83c50"
381 | },
382 | "execution_count": 11,
383 | "outputs": [
384 | {
385 | "output_type": "execute_result",
386 | "data": {
387 | "text/plain": [
388 | "array([ 89949.14, 108552.04, 156122.51, 42559.73, 118474.03, 126992.93,\n",
389 | " 152211.77, 144259.4 , 141585.52, 96712.8 ])"
390 | ]
391 | },
392 | "metadata": {},
393 | "execution_count": 11
394 | }
395 | ]
396 | },
397 | {
398 | "cell_type": "code",
399 | "source": [
400 | "from sklearn.linear_model import LinearRegression\n",
401 | "reg=LinearRegression()\n",
402 | "reg.fit(xtrain,ytrain)"
403 | ],
404 | "metadata": {
405 | "colab": {
406 | "base_uri": "https://localhost:8080/",
407 | "height": 74
408 | },
409 | "id": "VUetvP2GAzFa",
410 | "outputId": "294e0ab2-3341-41ea-968a-22591a6b7168"
411 | },
412 | "execution_count": 14,
413 | "outputs": [
414 | {
415 | "output_type": "execute_result",
416 | "data": {
417 | "text/plain": [
418 | "LinearRegression()"
419 | ],
420 | "text/html": [
421 | "LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. "
422 | ]
423 | },
424 | "metadata": {},
425 | "execution_count": 14
426 | }
427 | ]
428 | },
429 | {
430 | "cell_type": "code",
431 | "source": [
432 | "yp=reg.predict(xtest)\n",
433 | "yp"
434 | ],
435 | "metadata": {
436 | "colab": {
437 | "base_uri": "https://localhost:8080/"
438 | },
439 | "id": "eVO32QZjCDaW",
440 | "outputId": "e948581c-95fb-41c2-b39c-9622b55c20b4"
441 | },
442 | "execution_count": 26,
443 | "outputs": [
444 | {
445 | "output_type": "execute_result",
446 | "data": {
447 | "text/plain": [
448 | "array([ 89205.58494492, 113322.87999409, 158380.89564843, 46418.42229589,\n",
449 | " 115344.27092221, 115826.18080423, 150533.22978411, 135041.09838769,\n",
450 | " 128268.57919246, 87746.96838292])"
451 | ]
452 | },
453 | "metadata": {},
454 | "execution_count": 26
455 | }
456 | ]
457 | },
458 | {
459 | "cell_type": "code",
460 | "source": [
461 | "ytest"
462 | ],
463 | "metadata": {
464 | "colab": {
465 | "base_uri": "https://localhost:8080/"
466 | },
467 | "id": "AHbY1tc2CqGx",
468 | "outputId": "9e22a3d9-4539-4dd9-b330-1f4a98e01903"
469 | },
470 | "execution_count": 17,
471 | "outputs": [
472 | {
473 | "output_type": "execute_result",
474 | "data": {
475 | "text/plain": [
476 | "array([ 89949.14, 108552.04, 156122.51, 42559.73, 118474.03, 126992.93,\n",
477 | " 152211.77, 144259.4 , 141585.52, 96712.8 ])"
478 | ]
479 | },
480 | "metadata": {},
481 | "execution_count": 17
482 | }
483 | ]
484 | },
485 | {
486 | "cell_type": "code",
487 | "source": [
488 | "from sklearn.metrics import mean_squared_error\n",
489 | "mean_squared_error(ytest,yp)"
490 | ],
491 | "metadata": {
492 | "colab": {
493 | "base_uri": "https://localhost:8080/"
494 | },
495 | "id": "nMy0qLGCCtnz",
496 | "outputId": "3398dadb-9d1a-4fbd-85c0-ca8264dccfc2"
497 | },
498 | "execution_count": 18,
499 | "outputs": [
500 | {
501 | "output_type": "execute_result",
502 | "data": {
503 | "text/plain": [
504 | "52331691.07559253"
505 | ]
506 | },
507 | "metadata": {},
508 | "execution_count": 18
509 | }
510 | ]
511 | },
512 | {
513 | "cell_type": "code",
514 | "source": [
515 | "from sklearn.metrics import r2_score\n",
516 | "r2_score(ytest,yp)"
517 | ],
518 | "metadata": {
519 | "colab": {
520 | "base_uri": "https://localhost:8080/"
521 | },
522 | "id": "lxTQT50vDBoe",
523 | "outputId": "c99c3acf-1686-4d4a-c4ad-e8fd6fe5946e"
524 | },
525 | "execution_count": 19,
526 | "outputs": [
527 | {
528 | "output_type": "execute_result",
529 | "data": {
530 | "text/plain": [
531 | "0.9522979351404004"
532 | ]
533 | },
534 | "metadata": {},
535 | "execution_count": 19
536 | }
537 | ]
538 | },
539 | {
540 | "cell_type": "code",
541 | "source": [
542 | "reg.coef_"
543 | ],
544 | "metadata": {
545 | "colab": {
546 | "base_uri": "https://localhost:8080/"
547 | },
548 | "id": "FI1TO9gFDN1G",
549 | "outputId": "f049e002-31f2-4cb5-8ee7-b01801cfcfe8"
550 | },
551 | "execution_count": 20,
552 | "outputs": [
553 | {
554 | "output_type": "execute_result",
555 | "data": {
556 | "text/plain": [
557 | "array([ 0.81571857, -0.03436096, 0.02003369])"
558 | ]
559 | },
560 | "metadata": {},
561 | "execution_count": 20
562 | }
563 | ]
564 | },
565 | {
566 | "cell_type": "code",
567 | "source": [
568 | "xtest"
569 | ],
570 | "metadata": {
571 | "colab": {
572 | "base_uri": "https://localhost:8080/"
573 | },
574 | "id": "cYh8s0iAE9xZ",
575 | "outputId": "656c0b0b-768b-42a8-991a-a9ea7cdbede5"
576 | },
577 | "execution_count": 24,
578 | "outputs": [
579 | {
580 | "output_type": "execute_result",
581 | "data": {
582 | "text/plain": [
583 | "array([[ 44069.95, 51283.14, 197029.42],\n",
584 | " [ 77044.01, 99281.34, 140574.81],\n",
585 | " [134615.46, 147198.87, 127716.82],\n",
586 | " [ 0. , 135426.92, 0. ],\n",
587 | " [ 76253.86, 113867.3 , 298664.47],\n",
588 | " [ 78013.11, 121597.55, 264346.06],\n",
589 | " [120542.52, 148718.95, 311613.29],\n",
590 | " [100671.96, 91790.61, 249744.55],\n",
591 | " [ 93863.75, 127320.38, 249839.44],\n",
592 | " [ 46426.07, 157693.92, 210797.67]])"
593 | ]
594 | },
595 | "metadata": {},
596 | "execution_count": 24
597 | }
598 | ]
599 | },
600 | {
601 | "cell_type": "code",
602 | "source": [],
603 | "metadata": {
604 | "id": "qkZ1-IwEGocB"
605 | },
606 | "execution_count": null,
607 | "outputs": []
608 | }
609 | ]
610 | }
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Machine_Learn
2 | machine learning
3 |
--------------------------------------------------------------------------------
/Web_Scraping.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "nbformat": 4,
3 | "nbformat_minor": 0,
4 | "metadata": {
5 | "colab": {
6 | "provenance": [],
7 | "authorship_tag": "ABX9TyMRge7eQZaIfj15dGzYUY9J",
8 | "include_colab_link": true
9 | },
10 | "kernelspec": {
11 | "name": "python3",
12 | "display_name": "Python 3"
13 | },
14 | "language_info": {
15 | "name": "python"
16 | }
17 | },
18 | "cells": [
19 | {
20 | "cell_type": "markdown",
21 | "metadata": {
22 | "id": "view-in-github",
23 | "colab_type": "text"
24 | },
25 | "source": [
26 | " "
27 | ]
28 | },
29 | {
30 | "cell_type": "code",
31 | "source": [
32 | "#web scraping"
33 | ],
34 | "metadata": {
35 | "id": "6qcBeWnspS1K"
36 | },
37 | "execution_count": null,
38 | "outputs": []
39 | },
40 | {
41 | "cell_type": "code",
42 | "execution_count": 1,
43 | "metadata": {
44 | "id": "QtVKtGY6oXTk"
45 | },
46 | "outputs": [],
47 | "source": [
48 | "import requests"
49 | ]
50 | },
51 | {
52 | "cell_type": "code",
53 | "source": [
54 | "url=\"https://quotes.toscrape.com/\""
55 | ],
56 | "metadata": {
57 | "id": "X2WCnxLvo8Hy"
58 | },
59 | "execution_count": 2,
60 | "outputs": []
61 | },
62 | {
63 | "cell_type": "code",
64 | "source": [
65 | "response =requests.get(url)"
66 | ],
67 | "metadata": {
68 | "id": "HmDLss7TpD77"
69 | },
70 | "execution_count": 3,
71 | "outputs": []
72 | },
73 | {
74 | "cell_type": "code",
75 | "source": [
76 | "response.status_code"
77 | ],
78 | "metadata": {
79 | "colab": {
80 | "base_uri": "https://localhost:8080/"
81 | },
82 | "id": "f2nirmszpRBf",
83 | "outputId": "931ab2ac-3857-43ae-b88d-18946f64096c"
84 | },
85 | "execution_count": 4,
86 | "outputs": [
87 | {
88 | "output_type": "execute_result",
89 | "data": {
90 | "text/plain": [
91 | "200"
92 | ]
93 | },
94 | "metadata": {},
95 | "execution_count": 4
96 | }
97 | ]
98 | },
99 | {
100 | "cell_type": "code",
101 | "source": [
102 | "from bs4 import BeautifulSoup"
103 | ],
104 | "metadata": {
105 | "id": "vE-QhL1dperQ"
106 | },
107 | "execution_count": 5,
108 | "outputs": []
109 | },
110 | {
111 | "cell_type": "code",
112 | "source": [
113 | "soup= BeautifulSoup(response.content,\"lxml\")"
114 | ],
115 | "metadata": {
116 | "id": "3EUHDshOpqTO"
117 | },
118 | "execution_count": 6,
119 | "outputs": []
120 | },
121 | {
122 | "cell_type": "code",
123 | "source": [
124 | "#collets all the data\n",
125 | "quotes=soup.find_all('span', class_='text')\n",
126 | "quotes"
127 | ],
128 | "metadata": {
129 | "colab": {
130 | "base_uri": "https://localhost:8080/"
131 | },
132 | "id": "hu98ywaEqILv",
133 | "outputId": "9b6b42dc-2b59-465e-e363-24ac68cc2a32"
134 | },
135 | "execution_count": 11,
136 | "outputs": [
137 | {
138 | "output_type": "execute_result",
139 | "data": {
140 | "text/plain": [
141 | "[“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.” ,\n",
142 | " “It is our choices, Harry, that show what we truly are, far more than our abilities.” ,\n",
143 | " “There are only two ways to live your life. One is as though nothing is a miracle. The other is as though everything is a miracle.” ,\n",
144 | " “The person, be it gentleman or lady, who has not pleasure in a good novel, must be intolerably stupid.” ,\n",
145 | " “Imperfection is beauty, madness is genius and it's better to be absolutely ridiculous than absolutely boring.” ,\n",
146 | " “Try not to become a man of success. Rather become a man of value.” ,\n",
147 | " “It is better to be hated for what you are than to be loved for what you are not.” ,\n",
148 | " “I have not failed. I've just found 10,000 ways that won't work.” ,\n",
149 | " “A woman is like a tea bag; you never know how strong it is until it's in hot water.” ,\n",
150 | " “A day without sunshine is like, you know, night.” ]"
151 | ]
152 | },
153 | "metadata": {},
154 | "execution_count": 11
155 | }
156 | ]
157 | },
158 | {
159 | "cell_type": "code",
160 | "source": [
161 | "# to collect only quotes text\n",
162 | "quotes=[i.text for i in quotes]"
163 | ],
164 | "metadata": {
165 | "id": "q3po3Mn5qOsN"
166 | },
167 | "execution_count": 14,
168 | "outputs": []
169 | },
170 | {
171 | "cell_type": "code",
172 | "source": [
173 | "quotes"
174 | ],
175 | "metadata": {
176 | "colab": {
177 | "base_uri": "https://localhost:8080/"
178 | },
179 | "id": "i3iw8YZRqee7",
180 | "outputId": "42ae7996-c68c-4d6e-da74-89711b57dd1a"
181 | },
182 | "execution_count": 15,
183 | "outputs": [
184 | {
185 | "output_type": "execute_result",
186 | "data": {
187 | "text/plain": [
188 | "['“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.”',\n",
189 | " '“It is our choices, Harry, that show what we truly are, far more than our abilities.”',\n",
190 | " '“There are only two ways to live your life. One is as though nothing is a miracle. The other is as though everything is a miracle.”',\n",
191 | " '“The person, be it gentleman or lady, who has not pleasure in a good novel, must be intolerably stupid.”',\n",
192 | " \"“Imperfection is beauty, madness is genius and it's better to be absolutely ridiculous than absolutely boring.”\",\n",
193 | " '“Try not to become a man of success. Rather become a man of value.”',\n",
194 | " '“It is better to be hated for what you are than to be loved for what you are not.”',\n",
195 | " \"“I have not failed. I've just found 10,000 ways that won't work.”\",\n",
196 | " \"“A woman is like a tea bag; you never know how strong it is until it's in hot water.”\",\n",
197 | " '“A day without sunshine is like, you know, night.”']"
198 | ]
199 | },
200 | "metadata": {},
201 | "execution_count": 15
202 | }
203 | ]
204 | },
205 | {
206 | "cell_type": "code",
207 | "source": [
208 | "author=soup.find_all('small',class_='author')"
209 | ],
210 | "metadata": {
211 | "id": "Y3kFOLSErKbM"
212 | },
213 | "execution_count": 16,
214 | "outputs": []
215 | },
216 | {
217 | "cell_type": "code",
218 | "source": [
219 | "author=[i.text for i in author]\n",
220 | "author"
221 | ],
222 | "metadata": {
223 | "colab": {
224 | "base_uri": "https://localhost:8080/"
225 | },
226 | "id": "60I337q6rvUQ",
227 | "outputId": "ccaaf160-fa69-47ea-fce8-cebee3e26140"
228 | },
229 | "execution_count": 17,
230 | "outputs": [
231 | {
232 | "output_type": "execute_result",
233 | "data": {
234 | "text/plain": [
235 | "['Albert Einstein',\n",
236 | " 'J.K. Rowling',\n",
237 | " 'Albert Einstein',\n",
238 | " 'Jane Austen',\n",
239 | " 'Marilyn Monroe',\n",
240 | " 'Albert Einstein',\n",
241 | " 'André Gide',\n",
242 | " 'Thomas A. Edison',\n",
243 | " 'Eleanor Roosevelt',\n",
244 | " 'Steve Martin']"
245 | ]
246 | },
247 | "metadata": {},
248 | "execution_count": 17
249 | }
250 | ]
251 | },
252 | {
253 | "cell_type": "code",
254 | "source": [
255 | "tags=soup.find_all('div',class_='tags')"
256 | ],
257 | "metadata": {
258 | "id": "z11PB4xYr2X2"
259 | },
260 | "execution_count": 18,
261 | "outputs": []
262 | },
263 | {
264 | "cell_type": "code",
265 | "source": [
266 | "tags"
267 | ],
268 | "metadata": {
269 | "colab": {
270 | "base_uri": "https://localhost:8080/"
271 | },
272 | "id": "VcyvlQqEsHD5",
273 | "outputId": "a2945f42-a86c-472f-e285-7773d2b50676"
274 | },
275 | "execution_count": 19,
276 | "outputs": [
277 | {
278 | "output_type": "execute_result",
279 | "data": {
280 | "text/plain": [
281 | "[,\n",
289 | " ,\n",
295 | " ,\n",
304 | " ,\n",
312 | " ,\n",
318 | " ,\n",
325 | " ,\n",
331 | " ,\n",
339 | " ,\n",
344 | " ]"
351 | ]
352 | },
353 | "metadata": {},
354 | "execution_count": 19
355 | }
356 | ]
357 | },
358 | {
359 | "cell_type": "code",
360 | "source": [
361 | "tags=[i.text for i in tags]"
362 | ],
363 | "metadata": {
364 | "id": "yM3bhdhdsIeu"
365 | },
366 | "execution_count": 20,
367 | "outputs": []
368 | },
369 | {
370 | "cell_type": "code",
371 | "source": [
372 | "tags\n"
373 | ],
374 | "metadata": {
375 | "colab": {
376 | "base_uri": "https://localhost:8080/"
377 | },
378 | "id": "_DDRHpcUsOO5",
379 | "outputId": "9842ec8e-6bb3-4042-9016-f7201af95e6e"
380 | },
381 | "execution_count": 21,
382 | "outputs": [
383 | {
384 | "output_type": "execute_result",
385 | "data": {
386 | "text/plain": [
387 | "['\\n Tags:\\n \\nchange\\ndeep-thoughts\\nthinking\\nworld\\n',\n",
388 | " '\\n Tags:\\n \\nabilities\\nchoices\\n',\n",
389 | " '\\n Tags:\\n \\ninspirational\\nlife\\nlive\\nmiracle\\nmiracles\\n',\n",
390 | " '\\n Tags:\\n \\naliteracy\\nbooks\\nclassic\\nhumor\\n',\n",
391 | " '\\n Tags:\\n \\nbe-yourself\\ninspirational\\n',\n",
392 | " '\\n Tags:\\n \\nadulthood\\nsuccess\\nvalue\\n',\n",
393 | " '\\n Tags:\\n \\nlife\\nlove\\n',\n",
394 | " '\\n Tags:\\n \\nedison\\nfailure\\ninspirational\\nparaphrased\\n',\n",
395 | " '\\n Tags:\\n \\nmisattributed-eleanor-roosevelt\\n',\n",
396 | " '\\n Tags:\\n \\nhumor\\nobvious\\nsimile\\n']"
397 | ]
398 | },
399 | "metadata": {},
400 | "execution_count": 21
401 | }
402 | ]
403 | },
404 | {
405 | "cell_type": "code",
406 | "source": [
407 | "import pandas as pd"
408 | ],
409 | "metadata": {
410 | "id": "UVXnKPZFsQAX"
411 | },
412 | "execution_count": 22,
413 | "outputs": []
414 | },
415 | {
416 | "cell_type": "code",
417 | "source": [
418 | "dataset=pd.DataFrame()"
419 | ],
420 | "metadata": {
421 | "id": "u25t1Wkisu6_"
422 | },
423 | "execution_count": 23,
424 | "outputs": []
425 | },
426 | {
427 | "cell_type": "code",
428 | "source": [
429 | "dataset[\"quotes\"]=quotes\n",
430 | "dataset[\"tags\"]=tags\n",
431 | "dataset[\"author\"]=author\n",
432 | "dataset"
433 | ],
434 | "metadata": {
435 | "colab": {
436 | "base_uri": "https://localhost:8080/",
437 | "height": 363
438 | },
439 | "id": "XqqTH7Bfsy5A",
440 | "outputId": "9bfd358b-1f92-4980-e162-23bd6a32dbad"
441 | },
442 | "execution_count": 24,
443 | "outputs": [
444 | {
445 | "output_type": "execute_result",
446 | "data": {
447 | "text/plain": [
448 | " quotes \\\n",
449 | "0 “The world as we have created it is a process ... \n",
450 | "1 “It is our choices, Harry, that show what we t... \n",
451 | "2 “There are only two ways to live your life. On... \n",
452 | "3 “The person, be it gentleman or lady, who has ... \n",
453 | "4 “Imperfection is beauty, madness is genius and... \n",
454 | "5 “Try not to become a man of success. Rather be... \n",
455 | "6 “It is better to be hated for what you are tha... \n",
456 | "7 “I have not failed. I've just found 10,000 way... \n",
457 | "8 “A woman is like a tea bag; you never know how... \n",
458 | "9 “A day without sunshine is like, you know, nig... \n",
459 | "\n",
460 | " tags author \n",
461 | "0 \\n Tags:\\n \\nchange\\ndee... Albert Einstein \n",
462 | "1 \\n Tags:\\n \\nabilities\\n... J.K. Rowling \n",
463 | "2 \\n Tags:\\n \\ninspiration... Albert Einstein \n",
464 | "3 \\n Tags:\\n \\naliteracy\\n... Jane Austen \n",
465 | "4 \\n Tags:\\n \\nbe-yourself... Marilyn Monroe \n",
466 | "5 \\n Tags:\\n \\nadulthood\\n... Albert Einstein \n",
467 | "6 \\n Tags:\\n \\nlife\\nlove\\n André Gide \n",
468 | "7 \\n Tags:\\n \\nedison\\nfai... Thomas A. Edison \n",
469 | "8 \\n Tags:\\n \\nmisattribut... Eleanor Roosevelt \n",
470 | "9 \\n Tags:\\n \\nhumor\\nobvi... Steve Martin "
471 | ],
472 | "text/html": [
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772 | "metadata": {},
773 | "execution_count": 24
774 | }
775 | ]
776 | },
777 | {
778 | "cell_type": "code",
779 | "source": [
780 | "dataset.to_csv(\"quotes.csv\")"
781 | ],
782 | "metadata": {
783 | "id": "5Wybv9BytAwC"
784 | },
785 | "execution_count": 25,
786 | "outputs": []
787 | },
788 | {
789 | "cell_type": "code",
790 | "source": [],
791 | "metadata": {
792 | "id": "4FCy8TVOtObL"
793 | },
794 | "execution_count": null,
795 | "outputs": []
796 | }
797 | ]
798 | }
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8 | "include_colab_link": true
9 | },
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11 | "name": "python3",
12 | "display_name": "Python 3"
13 | },
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15 | "name": "python"
16 | }
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19 | {
20 | "cell_type": "markdown",
21 | "metadata": {
22 | "id": "view-in-github",
23 | "colab_type": "text"
24 | },
25 | "source": [
26 | " "
27 | ]
28 | },
29 | {
30 | "cell_type": "code",
31 | "execution_count": 47,
32 | "metadata": {
33 | "id": "rdQ1BItVdxe4"
34 | },
35 | "outputs": [],
36 | "source": [
37 | "#logistic Regression"
38 | ]
39 | },
40 | {
41 | "cell_type": "code",
42 | "source": [
43 | "import numpy as np\n",
44 | "import pandas as pd\n",
45 | "import matplotlib.pyplot as plt\n",
46 | "import seaborn as sns"
47 | ],
48 | "metadata": {
49 | "id": "KHcLEcT5d91h"
50 | },
51 | "execution_count": 48,
52 | "outputs": []
53 | },
54 | {
55 | "cell_type": "code",
56 | "source": [
57 | "df=pd.read_csv(\"/content/diabetes(LR binary).csv\")\n",
58 | "df.head()"
59 | ],
60 | "metadata": {
61 | "colab": {
62 | "base_uri": "https://localhost:8080/",
63 | "height": 206
64 | },
65 | "id": "vEcBTaOPjA2C",
66 | "outputId": "31b777cf-291d-405a-8a7e-61bc3bc61dde"
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68 | "execution_count": 49,
69 | "outputs": [
70 | {
71 | "output_type": "execute_result",
72 | "data": {
73 | "text/plain": [
74 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
75 | "0 6 148 72 35 0 33.6 \n",
76 | "1 1 85 66 29 0 26.6 \n",
77 | "2 8 183 64 0 0 23.3 \n",
78 | "3 1 89 66 23 94 28.1 \n",
79 | "4 0 137 40 35 168 43.1 \n",
80 | "\n",
81 | " DiabetesPedigreeFunction Age Outcome \n",
82 | "0 0.627 50 1 \n",
83 | "1 0.351 31 0 \n",
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182 | "
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183 | "
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184 | "
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391 | "
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392 | ]
393 | },
394 | "metadata": {},
395 | "execution_count": 49
396 | }
397 | ]
398 | },
399 | {
400 | "cell_type": "code",
401 | "source": [
402 | "df.isna().sum()"
403 | ],
404 | "metadata": {
405 | "colab": {
406 | "base_uri": "https://localhost:8080/"
407 | },
408 | "id": "caJURPdnjHMR",
409 | "outputId": "7a4f7307-1e15-4adf-84bc-56de4349b1b6"
410 | },
411 | "execution_count": 50,
412 | "outputs": [
413 | {
414 | "output_type": "execute_result",
415 | "data": {
416 | "text/plain": [
417 | "Pregnancies 0\n",
418 | "Glucose 0\n",
419 | "BloodPressure 0\n",
420 | "SkinThickness 0\n",
421 | "Insulin 0\n",
422 | "BMI 0\n",
423 | "DiabetesPedigreeFunction 0\n",
424 | "Age 0\n",
425 | "Outcome 0\n",
426 | "dtype: int64"
427 | ]
428 | },
429 | "metadata": {},
430 | "execution_count": 50
431 | }
432 | ]
433 | },
434 | {
435 | "cell_type": "code",
436 | "source": [
437 | "X=df.iloc[:,:-1].values\n",
438 | "Y=df.iloc[:,-1:].values"
439 | ],
440 | "metadata": {
441 | "id": "wEVWbmXoju-N"
442 | },
443 | "execution_count": 51,
444 | "outputs": []
445 | },
446 | {
447 | "cell_type": "code",
448 | "source": [
449 | "from sklearn.model_selection import train_test_split\n",
450 | "xtrain,xtest,ytrain,ytest = train_test_split(X,Y,test_size=0.20)"
451 | ],
452 | "metadata": {
453 | "id": "1B_VrAFhjPi8"
454 | },
455 | "execution_count": 52,
456 | "outputs": []
457 | },
458 | {
459 | "cell_type": "code",
460 | "source": [
461 | "#feature scaling\n",
462 | "from sklearn.preprocessing import StandardScaler\n",
463 | "sc=StandardScaler()\n",
464 | "xtrain=sc.fit_transform(xtrain)\n",
465 | "xtest=sc.transform(xtest)"
466 | ],
467 | "metadata": {
468 | "id": "lhYy2FSnjuAE"
469 | },
470 | "execution_count": 53,
471 | "outputs": []
472 | },
473 | {
474 | "cell_type": "code",
475 | "source": [
476 | "from sklearn.linear_model import LogisticRegression\n",
477 | "lr=LogisticRegression(random_state=0)\n",
478 | "lr.fit(xtrain,ytrain)"
479 | ],
480 | "metadata": {
481 | "colab": {
482 | "base_uri": "https://localhost:8080/",
483 | "height": 129
484 | },
485 | "id": "t3_dbMooknHp",
486 | "outputId": "a28058a0-ccfb-454e-8bfb-99c690b1f669"
487 | },
488 | "execution_count": 54,
489 | "outputs": [
490 | {
491 | "output_type": "stream",
492 | "name": "stderr",
493 | "text": [
494 | "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
495 | " y = column_or_1d(y, warn=True)\n"
496 | ]
497 | },
498 | {
499 | "output_type": "execute_result",
500 | "data": {
501 | "text/plain": [
502 | "LogisticRegression(random_state=0)"
503 | ],
504 | "text/html": [
505 | "LogisticRegression(random_state=0) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. "
506 | ]
507 | },
508 | "metadata": {},
509 | "execution_count": 54
510 | }
511 | ]
512 | },
513 | {
514 | "cell_type": "code",
515 | "source": [
516 | "ypred=lr.predict(xtest)"
517 | ],
518 | "metadata": {
519 | "id": "DzKnBF-olCIk"
520 | },
521 | "execution_count": 55,
522 | "outputs": []
523 | },
524 | {
525 | "cell_type": "code",
526 | "source": [
527 | "from sklearn.metrics import confusion_matrix\n",
528 | "cm=confusion_matrix(ytest,ypred)"
529 | ],
530 | "metadata": {
531 | "id": "iuIem02JlK8A"
532 | },
533 | "execution_count": 56,
534 | "outputs": []
535 | },
536 | {
537 | "cell_type": "code",
538 | "source": [
539 | "sns.heatmap(cm, annot=True)"
540 | ],
541 | "metadata": {
542 | "colab": {
543 | "base_uri": "https://localhost:8080/",
544 | "height": 447
545 | },
546 | "id": "RpzV9bK6lMUe",
547 | "outputId": "c9a20174-ee56-4723-ab50-1fd6e20586bb"
548 | },
549 | "execution_count": 57,
550 | "outputs": [
551 | {
552 | "output_type": "execute_result",
553 | "data": {
554 | "text/plain": [
555 | ""
556 | ]
557 | },
558 | "metadata": {},
559 | "execution_count": 57
560 | },
561 | {
562 | "output_type": "display_data",
563 | "data": {
564 | "text/plain": [
565 | ""
566 | ],
567 | "image/png": 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\n"
568 | },
569 | "metadata": {}
570 | }
571 | ]
572 | },
573 | {
574 | "cell_type": "code",
575 | "source": [
576 | "from sklearn.metrics import accuracy_score\n",
577 | "accuracy_score(ytest,ypred)"
578 | ],
579 | "metadata": {
580 | "colab": {
581 | "base_uri": "https://localhost:8080/"
582 | },
583 | "id": "Jeq3nokoljJ7",
584 | "outputId": "f6a93a0c-ce5e-49ff-e931-9f31ced8584c"
585 | },
586 | "execution_count": 58,
587 | "outputs": [
588 | {
589 | "output_type": "execute_result",
590 | "data": {
591 | "text/plain": [
592 | "0.8116883116883117"
593 | ]
594 | },
595 | "metadata": {},
596 | "execution_count": 58
597 | }
598 | ]
599 | },
600 | {
601 | "cell_type": "code",
602 | "source": [],
603 | "metadata": {
604 | "id": "y8iObAf1mih0"
605 | },
606 | "execution_count": 58,
607 | "outputs": []
608 | }
609 | ]
610 | }
--------------------------------------------------------------------------------
/ml_SVMC.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "nbformat": 4,
3 | "nbformat_minor": 0,
4 | "metadata": {
5 | "colab": {
6 | "provenance": [],
7 | "authorship_tag": "ABX9TyNXPB2Kbq2VHn170leQmQ30",
8 | "include_colab_link": true
9 | },
10 | "kernelspec": {
11 | "name": "python3",
12 | "display_name": "Python 3"
13 | },
14 | "language_info": {
15 | "name": "python"
16 | }
17 | },
18 | "cells": [
19 | {
20 | "cell_type": "markdown",
21 | "metadata": {
22 | "id": "view-in-github",
23 | "colab_type": "text"
24 | },
25 | "source": [
26 | " "
27 | ]
28 | },
29 | {
30 | "cell_type": "code",
31 | "execution_count": 5,
32 | "metadata": {
33 | "id": "I4NOjJDksAc_"
34 | },
35 | "outputs": [],
36 | "source": [
37 | "#support vector machine (classification)"
38 | ]
39 | },
40 | {
41 | "cell_type": "code",
42 | "source": [
43 | "import numpy as np\n",
44 | "import pandas as pd\n",
45 | "import matplotlib.pyplot as plt\n",
46 | "import seaborn as sns"
47 | ],
48 | "metadata": {
49 | "id": "byz5w0Yws8-y"
50 | },
51 | "execution_count": 6,
52 | "outputs": []
53 | },
54 | {
55 | "cell_type": "code",
56 | "source": [
57 | "from sklearn.datasets import load_iris\n",
58 | "dataset=load_iris()"
59 | ],
60 | "metadata": {
61 | "id": "UVRrXvQCsMNA"
62 | },
63 | "execution_count": 10,
64 | "outputs": []
65 | },
66 | {
67 | "cell_type": "code",
68 | "source": [
69 | "dataset.keys()"
70 | ],
71 | "metadata": {
72 | "colab": {
73 | "base_uri": "https://localhost:8080/"
74 | },
75 | "id": "arw49tFvtPJb",
76 | "outputId": "9b8023a2-1dcc-4373-b9a8-c5e391deb30b"
77 | },
78 | "execution_count": 11,
79 | "outputs": [
80 | {
81 | "output_type": "execute_result",
82 | "data": {
83 | "text/plain": [
84 | "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
85 | ]
86 | },
87 | "metadata": {},
88 | "execution_count": 11
89 | }
90 | ]
91 | },
92 | {
93 | "cell_type": "code",
94 | "source": [
95 | "df=pd.DataFrame(np.c_[dataset['data'],dataset['target']] , columns=(np.append(dataset['feature_names'],['target'])))"
96 | ],
97 | "metadata": {
98 | "id": "yRxn5hhCsk-L"
99 | },
100 | "execution_count": 18,
101 | "outputs": []
102 | },
103 | {
104 | "cell_type": "code",
105 | "source": [
106 | "df"
107 | ],
108 | "metadata": {
109 | "colab": {
110 | "base_uri": "https://localhost:8080/",
111 | "height": 423
112 | },
113 | "id": "qkmLZccZtfD4",
114 | "outputId": "390e28c3-ef7f-4212-dcf1-49c80ce65e8d"
115 | },
116 | "execution_count": 19,
117 | "outputs": [
118 | {
119 | "output_type": "execute_result",
120 | "data": {
121 | "text/plain": [
122 | " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n",
123 | "0 5.1 3.5 1.4 0.2 \n",
124 | "1 4.9 3.0 1.4 0.2 \n",
125 | "2 4.7 3.2 1.3 0.2 \n",
126 | "3 4.6 3.1 1.5 0.2 \n",
127 | "4 5.0 3.6 1.4 0.2 \n",
128 | ".. ... ... ... ... \n",
129 | "145 6.7 3.0 5.2 2.3 \n",
130 | "146 6.3 2.5 5.0 1.9 \n",
131 | "147 6.5 3.0 5.2 2.0 \n",
132 | "148 6.2 3.4 5.4 2.3 \n",
133 | "149 5.9 3.0 5.1 1.8 \n",
134 | "\n",
135 | " target \n",
136 | "0 0.0 \n",
137 | "1 0.0 \n",
138 | "2 0.0 \n",
139 | "3 0.0 \n",
140 | "4 0.0 \n",
141 | ".. ... \n",
142 | "145 2.0 \n",
143 | "146 2.0 \n",
144 | "147 2.0 \n",
145 | "148 2.0 \n",
146 | "149 2.0 \n",
147 | "\n",
148 | "[150 rows x 5 columns]"
149 | ],
150 | "text/html": [
151 | "\n",
152 | " \n",
153 | "
\n",
154 | "\n",
167 | "
\n",
168 | " \n",
169 | " \n",
170 | " \n",
171 | " sepal length (cm) \n",
172 | " sepal width (cm) \n",
173 | " petal length (cm) \n",
174 | " petal width (cm) \n",
175 | " target \n",
176 | " \n",
177 | " \n",
178 | " \n",
179 | " \n",
180 | " 0 \n",
181 | " 5.1 \n",
182 | " 3.5 \n",
183 | " 1.4 \n",
184 | " 0.2 \n",
185 | " 0.0 \n",
186 | " \n",
187 | " \n",
188 | " 1 \n",
189 | " 4.9 \n",
190 | " 3.0 \n",
191 | " 1.4 \n",
192 | " 0.2 \n",
193 | " 0.0 \n",
194 | " \n",
195 | " \n",
196 | " 2 \n",
197 | " 4.7 \n",
198 | " 3.2 \n",
199 | " 1.3 \n",
200 | " 0.2 \n",
201 | " 0.0 \n",
202 | " \n",
203 | " \n",
204 | " 3 \n",
205 | " 4.6 \n",
206 | " 3.1 \n",
207 | " 1.5 \n",
208 | " 0.2 \n",
209 | " 0.0 \n",
210 | " \n",
211 | " \n",
212 | " 4 \n",
213 | " 5.0 \n",
214 | " 3.6 \n",
215 | " 1.4 \n",
216 | " 0.2 \n",
217 | " 0.0 \n",
218 | " \n",
219 | " \n",
220 | " ... \n",
221 | " ... \n",
222 | " ... \n",
223 | " ... \n",
224 | " ... \n",
225 | " ... \n",
226 | " \n",
227 | " \n",
228 | " 145 \n",
229 | " 6.7 \n",
230 | " 3.0 \n",
231 | " 5.2 \n",
232 | " 2.3 \n",
233 | " 2.0 \n",
234 | " \n",
235 | " \n",
236 | " 146 \n",
237 | " 6.3 \n",
238 | " 2.5 \n",
239 | " 5.0 \n",
240 | " 1.9 \n",
241 | " 2.0 \n",
242 | " \n",
243 | " \n",
244 | " 147 \n",
245 | " 6.5 \n",
246 | " 3.0 \n",
247 | " 5.2 \n",
248 | " 2.0 \n",
249 | " 2.0 \n",
250 | " \n",
251 | " \n",
252 | " 148 \n",
253 | " 6.2 \n",
254 | " 3.4 \n",
255 | " 5.4 \n",
256 | " 2.3 \n",
257 | " 2.0 \n",
258 | " \n",
259 | " \n",
260 | " 149 \n",
261 | " 5.9 \n",
262 | " 3.0 \n",
263 | " 5.1 \n",
264 | " 1.8 \n",
265 | " 2.0 \n",
266 | " \n",
267 | " \n",
268 | "
\n",
269 | "
150 rows × 5 columns
\n",
270 | "
\n",
271 | "
\n",
478 | "
\n"
479 | ]
480 | },
481 | "metadata": {},
482 | "execution_count": 19
483 | }
484 | ]
485 | },
486 | {
487 | "cell_type": "code",
488 | "source": [
489 | "df.isna().sum()"
490 | ],
491 | "metadata": {
492 | "colab": {
493 | "base_uri": "https://localhost:8080/"
494 | },
495 | "id": "MT_EA4xDuny8",
496 | "outputId": "08c518c0-66e1-4afb-b386-4eadfe42690a"
497 | },
498 | "execution_count": 20,
499 | "outputs": [
500 | {
501 | "output_type": "execute_result",
502 | "data": {
503 | "text/plain": [
504 | "sepal length (cm) 0\n",
505 | "sepal width (cm) 0\n",
506 | "petal length (cm) 0\n",
507 | "petal width (cm) 0\n",
508 | "target 0\n",
509 | "dtype: int64"
510 | ]
511 | },
512 | "metadata": {},
513 | "execution_count": 20
514 | }
515 | ]
516 | },
517 | {
518 | "cell_type": "code",
519 | "source": [
520 | "X=df.iloc[:,:-1].values\n",
521 | "Y=df.iloc[:,-1].values"
522 | ],
523 | "metadata": {
524 | "id": "gjlYOS-Nusev"
525 | },
526 | "execution_count": 21,
527 | "outputs": []
528 | },
529 | {
530 | "cell_type": "code",
531 | "source": [
532 | "from sklearn.model_selection import train_test_split\n",
533 | "xtrain,xtest,ytrain,ytest=train_test_split(X,Y,test_size=0.22,random_state=0)"
534 | ],
535 | "metadata": {
536 | "id": "PomBPbUlu89n"
537 | },
538 | "execution_count": 29,
539 | "outputs": []
540 | },
541 | {
542 | "cell_type": "code",
543 | "source": [
544 | "from sklearn.preprocessing import StandardScaler\n",
545 | "sc=StandardScaler()\n",
546 | "xtrain=sc.fit_transform(xtrain)\n",
547 | "xtest=sc.transform(xtest)"
548 | ],
549 | "metadata": {
550 | "id": "DROQn2gfvRJK"
551 | },
552 | "execution_count": 23,
553 | "outputs": []
554 | },
555 | {
556 | "cell_type": "code",
557 | "source": [
558 | "from sklearn.svm import SVC\n",
559 | "svc=SVC(kernel='rbf')\n",
560 | "svc.fit(xtrain,ytrain)"
561 | ],
562 | "metadata": {
563 | "colab": {
564 | "base_uri": "https://localhost:8080/",
565 | "height": 51
566 | },
567 | "id": "bKtr_0Gdviyk",
568 | "outputId": "d0302b5e-6693-465b-d466-08a4532bfcd1"
569 | },
570 | "execution_count": 30,
571 | "outputs": [
572 | {
573 | "output_type": "execute_result",
574 | "data": {
575 | "text/plain": [
576 | "SVC()"
577 | ],
578 | "text/html": [
579 | "SVC() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. "
580 | ]
581 | },
582 | "metadata": {},
583 | "execution_count": 30
584 | }
585 | ]
586 | },
587 | {
588 | "cell_type": "code",
589 | "source": [
590 | "ypred=svc.predict(xtest)\n",
591 | "ypred"
592 | ],
593 | "metadata": {
594 | "colab": {
595 | "base_uri": "https://localhost:8080/"
596 | },
597 | "id": "p2UogC0dv501",
598 | "outputId": "804a9566-08f2-4da1-e705-4685de702d49"
599 | },
600 | "execution_count": 32,
601 | "outputs": [
602 | {
603 | "output_type": "execute_result",
604 | "data": {
605 | "text/plain": [
606 | "array([2., 1., 0., 2., 0., 2., 0., 1., 1., 1., 2., 1., 1., 1., 1., 0., 1.,\n",
607 | " 1., 0., 0., 2., 1., 0., 0., 2., 0., 0., 1., 1., 0., 2., 1., 0.])"
608 | ]
609 | },
610 | "metadata": {},
611 | "execution_count": 32
612 | }
613 | ]
614 | },
615 | {
616 | "cell_type": "code",
617 | "source": [
618 | "from sklearn.metrics import confusion_matrix\n",
619 | "cm=confusion_matrix(ytest,ypred)"
620 | ],
621 | "metadata": {
622 | "id": "AG-oRo8CwS0Z"
623 | },
624 | "execution_count": 34,
625 | "outputs": []
626 | },
627 | {
628 | "cell_type": "code",
629 | "source": [
630 | "sns.heatmap(cm,annot=True)"
631 | ],
632 | "metadata": {
633 | "colab": {
634 | "base_uri": "https://localhost:8080/",
635 | "height": 452
636 | },
637 | "id": "IhCw-ppVwjph",
638 | "outputId": "db2bd65c-a572-4763-f3da-de4a722756e2"
639 | },
640 | "execution_count": 35,
641 | "outputs": [
642 | {
643 | "output_type": "execute_result",
644 | "data": {
645 | "text/plain": [
646 | ""
647 | ]
648 | },
649 | "metadata": {},
650 | "execution_count": 35
651 | },
652 | {
653 | "output_type": "display_data",
654 | "data": {
655 | "text/plain": [
656 | ""
657 | ],
658 | "image/png": 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\n"
659 | },
660 | "metadata": {}
661 | }
662 | ]
663 | },
664 | {
665 | "cell_type": "code",
666 | "source": [
667 | "from sklearn.metrics import accuracy_score\n",
668 | "accuracy_score(ytest,ypred)"
669 | ],
670 | "metadata": {
671 | "colab": {
672 | "base_uri": "https://localhost:8080/"
673 | },
674 | "id": "Y-phoAF0wyr3",
675 | "outputId": "e7d7e785-a8f3-4736-9d24-4ded0c8bc39d"
676 | },
677 | "execution_count": 36,
678 | "outputs": [
679 | {
680 | "output_type": "execute_result",
681 | "data": {
682 | "text/plain": [
683 | "1.0"
684 | ]
685 | },
686 | "metadata": {},
687 | "execution_count": 36
688 | }
689 | ]
690 | },
691 | {
692 | "cell_type": "code",
693 | "source": [],
694 | "metadata": {
695 | "id": "Af1FkRbjxGTq"
696 | },
697 | "execution_count": null,
698 | "outputs": []
699 | }
700 | ]
701 | }
--------------------------------------------------------------------------------
/ml_SVMR.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "nbformat": 4,
3 | "nbformat_minor": 0,
4 | "metadata": {
5 | "colab": {
6 | "provenance": [],
7 | "authorship_tag": "ABX9TyMxG7ImSMxcRra3vv+ZQZr0",
8 | "include_colab_link": true
9 | },
10 | "kernelspec": {
11 | "name": "python3",
12 | "display_name": "Python 3"
13 | },
14 | "language_info": {
15 | "name": "python"
16 | }
17 | },
18 | "cells": [
19 | {
20 | "cell_type": "markdown",
21 | "metadata": {
22 | "id": "view-in-github",
23 | "colab_type": "text"
24 | },
25 | "source": [
26 | " "
27 | ]
28 | },
29 | {
30 | "cell_type": "code",
31 | "execution_count": 132,
32 | "metadata": {
33 | "id": "pFEwL7KPNxEp"
34 | },
35 | "outputs": [],
36 | "source": [
37 | "#support vector machine(Regression)\n",
38 | "import numpy as np\n",
39 | "import pandas as pd\n",
40 | "import matplotlib.pyplot as plt\n",
41 | "import seaborn as sns"
42 | ]
43 | },
44 | {
45 | "cell_type": "code",
46 | "source": [
47 | "df=pd.read_csv(\"https://raw.githubusercontent.com/nandu19k/Support-Vector-Regression/master/Data.csv\")"
48 | ],
49 | "metadata": {
50 | "id": "XPULFbtFOEu-"
51 | },
52 | "execution_count": 133,
53 | "outputs": []
54 | },
55 | {
56 | "cell_type": "code",
57 | "source": [
58 | "df.head()"
59 | ],
60 | "metadata": {
61 | "colab": {
62 | "base_uri": "https://localhost:8080/",
63 | "height": 206
64 | },
65 | "id": "v2QDQcpYPB2e",
66 | "outputId": "1cc2c8b0-65f0-4807-f453-ea382cccce93"
67 | },
68 | "execution_count": 134,
69 | "outputs": [
70 | {
71 | "output_type": "execute_result",
72 | "data": {
73 | "text/plain": [
74 | " AT V AP RH PE\n",
75 | "0 14.96 41.76 1024.07 73.17 463.26\n",
76 | "1 25.18 62.96 1020.04 59.08 444.37\n",
77 | "2 5.11 39.40 1012.16 92.14 488.56\n",
78 | "3 20.86 57.32 1010.24 76.64 446.48\n",
79 | "4 10.82 37.50 1009.23 96.62 473.90"
80 | ],
81 | "text/html": [
82 | "\n",
83 | " \n",
84 | "
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85 | "\n",
98 | "
\n",
99 | " \n",
100 | " \n",
101 | " \n",
102 | " AT \n",
103 | " V \n",
104 | " AP \n",
105 | " RH \n",
106 | " PE \n",
107 | " \n",
108 | " \n",
109 | " \n",
110 | " \n",
111 | " 0 \n",
112 | " 14.96 \n",
113 | " 41.76 \n",
114 | " 1024.07 \n",
115 | " 73.17 \n",
116 | " 463.26 \n",
117 | " \n",
118 | " \n",
119 | " 1 \n",
120 | " 25.18 \n",
121 | " 62.96 \n",
122 | " 1020.04 \n",
123 | " 59.08 \n",
124 | " 444.37 \n",
125 | " \n",
126 | " \n",
127 | " 2 \n",
128 | " 5.11 \n",
129 | " 39.40 \n",
130 | " 1012.16 \n",
131 | " 92.14 \n",
132 | " 488.56 \n",
133 | " \n",
134 | " \n",
135 | " 3 \n",
136 | " 20.86 \n",
137 | " 57.32 \n",
138 | " 1010.24 \n",
139 | " 76.64 \n",
140 | " 446.48 \n",
141 | " \n",
142 | " \n",
143 | " 4 \n",
144 | " 10.82 \n",
145 | " 37.50 \n",
146 | " 1009.23 \n",
147 | " 96.62 \n",
148 | " 473.90 \n",
149 | " \n",
150 | " \n",
151 | "
\n",
152 | "
\n",
153 | "
\n",
360 | "
\n"
361 | ]
362 | },
363 | "metadata": {},
364 | "execution_count": 134
365 | }
366 | ]
367 | },
368 | {
369 | "cell_type": "code",
370 | "source": [
371 | "df.isna().sum()"
372 | ],
373 | "metadata": {
374 | "colab": {
375 | "base_uri": "https://localhost:8080/"
376 | },
377 | "id": "Cgx-P4IfPPLh",
378 | "outputId": "f734027f-722b-4b1d-d499-8d6c82e8cfd4"
379 | },
380 | "execution_count": 135,
381 | "outputs": [
382 | {
383 | "output_type": "execute_result",
384 | "data": {
385 | "text/plain": [
386 | "AT 0\n",
387 | "V 0\n",
388 | "AP 0\n",
389 | "RH 0\n",
390 | "PE 0\n",
391 | "dtype: int64"
392 | ]
393 | },
394 | "metadata": {},
395 | "execution_count": 135
396 | }
397 | ]
398 | },
399 | {
400 | "cell_type": "code",
401 | "source": [
402 | "X=df.iloc[:,:-1].values\n",
403 | "Y=df.iloc[:,-1:].values\n",
404 | "Y"
405 | ],
406 | "metadata": {
407 | "colab": {
408 | "base_uri": "https://localhost:8080/"
409 | },
410 | "id": "MyVLiQnNPhYm",
411 | "outputId": "b497726d-ebb9-43f1-cfd8-eec5cb0932f7"
412 | },
413 | "execution_count": 136,
414 | "outputs": [
415 | {
416 | "output_type": "execute_result",
417 | "data": {
418 | "text/plain": [
419 | "array([[463.26],\n",
420 | " [444.37],\n",
421 | " [488.56],\n",
422 | " ...,\n",
423 | " [429.57],\n",
424 | " [435.74],\n",
425 | " [453.28]])"
426 | ]
427 | },
428 | "metadata": {},
429 | "execution_count": 136
430 | }
431 | ]
432 | },
433 | {
434 | "cell_type": "code",
435 | "source": [
436 | "#data spliting for training and testing\n",
437 | "from sklearn.model_selection import train_test_split\n",
438 | "xtrain,xtest,ytrain,ytest = train_test_split(X,Y,test_size=0.22,random_state=0)\n"
439 | ],
440 | "metadata": {
441 | "id": "kdwIW48OQTSB"
442 | },
443 | "execution_count": 137,
444 | "outputs": []
445 | },
446 | {
447 | "cell_type": "code",
448 | "source": [
449 | "#feature scaling\n",
450 | "from sklearn.preprocessing import StandardScaler\n",
451 | "sc=StandardScaler()\n",
452 | "xtrain=sc.fit_transform(xtrain)\n",
453 | "xtest=sc.transform(xtest)\n",
454 | "ytrain=sc.fit_transform(ytrain)\n",
455 | "# ytrain"
456 | ],
457 | "metadata": {
458 | "id": "E0ha0iDjQkbv"
459 | },
460 | "execution_count": 138,
461 | "outputs": []
462 | },
463 | {
464 | "cell_type": "code",
465 | "source": [
466 | "from sklearn.svm import SVR\n",
467 | "reg=SVR(kernel='rbf')\n",
468 | "reg.fit(xtrain,ytrain)"
469 | ],
470 | "metadata": {
471 | "colab": {
472 | "base_uri": "https://localhost:8080/",
473 | "height": 129
474 | },
475 | "id": "s2wX16KBRo-d",
476 | "outputId": "2b18cf44-cba4-4acb-b41c-4fabb37d67d3"
477 | },
478 | "execution_count": 139,
479 | "outputs": [
480 | {
481 | "output_type": "stream",
482 | "name": "stderr",
483 | "text": [
484 | "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
485 | " y = column_or_1d(y, warn=True)\n"
486 | ]
487 | },
488 | {
489 | "output_type": "execute_result",
490 | "data": {
491 | "text/plain": [
492 | "SVR()"
493 | ],
494 | "text/html": [
495 | "SVR() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. "
496 | ]
497 | },
498 | "metadata": {},
499 | "execution_count": 139
500 | }
501 | ]
502 | },
503 | {
504 | "cell_type": "code",
505 | "source": [
506 | "ypred=reg.predict(xtest)\n",
507 | "ypred"
508 | ],
509 | "metadata": {
510 | "colab": {
511 | "base_uri": "https://localhost:8080/"
512 | },
513 | "id": "AbxewDzbUPtI",
514 | "outputId": "c2e1bd10-2a54-45f8-cfee-d4229c07d236"
515 | },
516 | "execution_count": 140,
517 | "outputs": [
518 | {
519 | "output_type": "execute_result",
520 | "data": {
521 | "text/plain": [
522 | "array([-1.18863324, 0.21476506, 0.4079334 , ..., -1.21496278,\n",
523 | " 1.06710521, 1.0370534 ])"
524 | ]
525 | },
526 | "metadata": {},
527 | "execution_count": 140
528 | }
529 | ]
530 | },
531 | {
532 | "cell_type": "code",
533 | "source": [
534 | "# ypred=sc.inverse_transform(ypred)"
535 | ],
536 | "metadata": {
537 | "id": "172uDUDbV0qE"
538 | },
539 | "execution_count": 141,
540 | "outputs": []
541 | },
542 | {
543 | "cell_type": "code",
544 | "source": [
545 | "ypred = sc.inverse_transform(ypred)\n",
546 | "ypred"
547 | ],
548 | "metadata": {
549 | "colab": {
550 | "base_uri": "https://localhost:8080/",
551 | "height": 408
552 | },
553 | "id": "lqtvozAIX5OY",
554 | "outputId": "ae7e0350-76ec-4ee4-8c3c-86375a48e8bd"
555 | },
556 | "execution_count": 142,
557 | "outputs": [
558 | {
559 | "output_type": "error",
560 | "ename": "ValueError",
561 | "evalue": "ignored",
562 | "traceback": [
563 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
564 | "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
565 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mypred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mypred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mypred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
566 | "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36minverse_transform\u001b[0;34m(self, X, copy)\u001b[0m\n\u001b[1;32m 1032\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1033\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1034\u001b[0;31m X = check_array(\n\u001b[0m\u001b[1;32m 1035\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1036\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
567 | "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m 900\u001b[0m \u001b[0;31m# If input is 1D raise error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 901\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 902\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 903\u001b[0m \u001b[0;34m\"Expected 2D array, got 1D array instead:\\narray={}.\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 904\u001b[0m \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
568 | "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[-1.18863324 0.21476506 0.4079334 ... -1.21496278 1.06710521\n 1.0370534 ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
569 | ]
570 | }
571 | ]
572 | },
573 | {
574 | "cell_type": "code",
575 | "source": [
576 | "from sklearn.metrics import r2_score\n",
577 | "r2_score(ytest,ypred)"
578 | ],
579 | "metadata": {
580 | "id": "8uhz83biYdnw"
581 | },
582 | "execution_count": null,
583 | "outputs": []
584 | },
585 | {
586 | "cell_type": "code",
587 | "source": [],
588 | "metadata": {
589 | "id": "_ZtW2BHMY1kW"
590 | },
591 | "execution_count": null,
592 | "outputs": []
593 | }
594 | ]
595 | }
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