├── 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 | "\"Open" 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 | "
\n", 78 | "
\n", 79 | "\n", 92 | "\n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | "
R&D SpendAdministrationMarketing SpendStateProfit
0165349.20136897.80471784.10New York192261.83
1162597.70151377.59443898.53California191792.06
2153441.51101145.55407934.54Florida191050.39
3144372.41118671.85383199.62New York182901.99
4142107.3491391.77366168.42Florida166187.94
5131876.9099814.71362861.36New York156991.12
6134615.46147198.87127716.82California156122.51
7130298.13145530.06323876.68Florida155752.60
8120542.52148718.95311613.29New York152211.77
9123334.88108679.17304981.62California149759.96
\n", 186 | "
\n", 187 | "
\n", 188 | "\n", 189 | "
\n", 190 | " \n", 198 | "\n", 199 | " \n", 239 | "\n", 240 | " \n", 264 | "
\n", 265 | "\n", 266 | "\n", 267 | "
\n", 268 | " \n", 279 | "\n", 280 | "\n", 311 | "\n", 312 | " \n", 324 | "
\n", 325 | "
\n", 326 | "
\n" 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 | "\"Open" 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", 282 | " Tags:\n", 283 | " \n", 284 | " change\n", 285 | " deep-thoughts\n", 286 | " thinking\n", 287 | " world\n", 288 | "
,\n", 289 | "
\n", 290 | " Tags:\n", 291 | " \n", 292 | " abilities\n", 293 | " choices\n", 294 | "
,\n", 295 | "
\n", 296 | " Tags:\n", 297 | " \n", 298 | " inspirational\n", 299 | " life\n", 300 | " live\n", 301 | " miracle\n", 302 | " miracles\n", 303 | "
,\n", 304 | "
\n", 305 | " Tags:\n", 306 | " \n", 307 | " aliteracy\n", 308 | " books\n", 309 | " classic\n", 310 | " humor\n", 311 | "
,\n", 312 | "
\n", 313 | " Tags:\n", 314 | " \n", 315 | " be-yourself\n", 316 | " inspirational\n", 317 | "
,\n", 318 | "
\n", 319 | " Tags:\n", 320 | " \n", 321 | " adulthood\n", 322 | " success\n", 323 | " value\n", 324 | "
,\n", 325 | "
\n", 326 | " Tags:\n", 327 | " \n", 328 | " life\n", 329 | " love\n", 330 | "
,\n", 331 | "
\n", 332 | " Tags:\n", 333 | " \n", 334 | " edison\n", 335 | " failure\n", 336 | " inspirational\n", 337 | " paraphrased\n", 338 | "
,\n", 339 | "
\n", 340 | " Tags:\n", 341 | " \n", 342 | " misattributed-eleanor-roosevelt\n", 343 | "
,\n", 344 | "
\n", 345 | " Tags:\n", 346 | " \n", 347 | " humor\n", 348 | " obvious\n", 349 | " simile\n", 350 | "
]" 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": [ 473 | "\n", 474 | "
\n", 475 | "
\n", 476 | "\n", 489 | "\n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | "
quotestagsauthor
0“The world as we have created it is a process ...\\n Tags:\\n \\nchange\\ndee...Albert Einstein
1“It is our choices, Harry, that show what we t...\\n Tags:\\n \\nabilities\\n...J.K. Rowling
2“There are only two ways to live your life. On...\\n Tags:\\n \\ninspiration...Albert Einstein
3“The person, be it gentleman or lady, who has ...\\n Tags:\\n \\naliteracy\\n...Jane Austen
4“Imperfection is beauty, madness is genius and...\\n Tags:\\n \\nbe-yourself...Marilyn Monroe
5“Try not to become a man of success. Rather be...\\n Tags:\\n \\nadulthood\\n...Albert Einstein
6“It is better to be hated for what you are tha...\\n Tags:\\n \\nlife\\nlove\\nAndré Gide
7“I have not failed. I've just found 10,000 way...\\n Tags:\\n \\nedison\\nfai...Thomas A. Edison
8“A woman is like a tea bag; you never know how...\\n Tags:\\n \\nmisattribut...Eleanor Roosevelt
9“A day without sunshine is like, you know, nig...\\n Tags:\\n \\nhumor\\nobvi...Steve Martin
\n", 561 | "
\n", 562 | "
\n", 563 | "\n", 564 | "
\n", 565 | " \n", 573 | "\n", 574 | " \n", 614 | "\n", 615 | " \n", 639 | "
\n", 640 | "\n", 641 | "\n", 642 | "
\n", 643 | " \n", 654 | "\n", 655 | "\n", 744 | "\n", 745 | " \n", 767 | "
\n", 768 | "
\n", 769 | "
\n" 770 | ] 771 | }, 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 | } -------------------------------------------------------------------------------- /ml_LR.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyOSAdxZ7ft1oFso7pWVukh4", 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 | "\"Open" 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" 67 | }, 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", 84 | "2 0.672 32 1 \n", 85 | "3 0.167 21 0 \n", 86 | "4 2.288 33 1 " 87 | ], 88 | "text/html": [ 89 | "\n", 90 | "
\n", 91 | "
\n", 92 | "\n", 105 | "\n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | "
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
061487235033.60.627501
11856629026.60.351310
28183640023.30.672321
318966239428.10.167210
40137403516843.12.288331
\n", 183 | "
\n", 184 | "
\n", 185 | "\n", 186 | "
\n", 187 | " \n", 195 | "\n", 196 | " \n", 236 | "\n", 237 | " \n", 261 | "
\n", 262 | "\n", 263 | "\n", 264 | "
\n", 265 | " \n", 276 | "\n", 277 | "\n", 366 | "\n", 367 | " \n", 389 | "
\n", 390 | "
\n", 391 | "
\n" 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 | "\"Open" 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 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | "
sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target
05.13.51.40.20.0
14.93.01.40.20.0
24.73.21.30.20.0
34.63.11.50.20.0
45.03.61.40.20.0
..................
1456.73.05.22.32.0
1466.32.55.01.92.0
1476.53.05.22.02.0
1486.23.45.42.32.0
1495.93.05.11.82.0
\n", 269 | "

150 rows × 5 columns

\n", 270 | "
\n", 271 | "
\n", 272 | "\n", 273 | "
\n", 274 | " \n", 282 | "\n", 283 | " \n", 323 | "\n", 324 | " \n", 348 | "
\n", 349 | "\n", 350 | "\n", 351 | "
\n", 352 | " \n", 363 | "\n", 364 | "\n", 453 | "\n", 454 | " \n", 476 | "
\n", 477 | "
\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 | "\"Open" 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 | "
\n", 85 | "\n", 98 | "\n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | "
ATVAPRHPE
014.9641.761024.0773.17463.26
125.1862.961020.0459.08444.37
25.1139.401012.1692.14488.56
320.8657.321010.2476.64446.48
410.8237.501009.2396.62473.90
\n", 152 | "
\n", 153 | "
\n", 154 | "\n", 155 | "
\n", 156 | " \n", 164 | "\n", 165 | " \n", 205 | "\n", 206 | " \n", 230 | "
\n", 231 | "\n", 232 | "\n", 233 | "
\n", 234 | " \n", 245 | "\n", 246 | "\n", 335 | "\n", 336 | " \n", 358 | "
\n", 359 | "
\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 | } --------------------------------------------------------------------------------