├── README.md └── Shaurya_Sinha.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # SHAPEAI PYTHON AND MACHINE LEARNING BOOTCAMP 2 | Hi I made this project during the 7 Days Free Bootcamp, conducted by SHAPEAI 3 | . 4 | The instructor during the session was Mr. Shaurya Sinha (Data Analyst Intern at Jio). I got to 5 | learn a lot during these 7 days and it was an amazing experience learning with SHAPEAI. 6 |

Here's the link for you to watch the sessions as well
7 | 8 |
I got to have hands on experience on: 9 |
  • Python 10 |
  • Machine Learning 11 |
  • Tensorflow 12 |
    during these 7 days, and everything was explained from the very basics so that 13 | anyone with zero experience on programming can learn. 14 | I enjoyed these 7 days, you can as well. To register for next free 7 days bootcamp, visit: 15 | www.shapeai.tech 16 | or follow SHAPEAI on: 17 |
  • LinkedIn 19 |
  • Instagram 21 |
  • YouTu 24 | be 25 |
  • GitHub 27 | 28 | -------------------------------------------------------------------------------- /Shaurya_Sinha.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Shaurya_Sinha.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | }, 13 | "language_info": { 14 | "name": "python" 15 | } 16 | }, 17 | "cells": [ 18 | { 19 | "cell_type": "code", 20 | "metadata": { 21 | "id": "WIXb3W-v1AeN" 22 | }, 23 | "source": [ 24 | "import numpy as np\n", 25 | "import pandas as pd\n", 26 | "import sklearn" 27 | ], 28 | "execution_count": null, 29 | "outputs": [] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "metadata": { 34 | "id": "BGS-QWId1DL4" 35 | }, 36 | "source": [ 37 | "from sklearn.datasets import load_boston\n", 38 | "df = load_boston()" 39 | ], 40 | "execution_count": null, 41 | "outputs": [] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "metadata": { 46 | "colab": { 47 | "base_uri": "https://localhost:8080/" 48 | }, 49 | "id": "IESAMkc-1GS3", 50 | "outputId": "dc5e4ade-e963-4267-881d-8b88c77aac69" 51 | }, 52 | "source": [ 53 | "df.keys()" 54 | ], 55 | "execution_count": null, 56 | "outputs": [ 57 | { 58 | "output_type": "execute_result", 59 | "data": { 60 | "text/plain": [ 61 | "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])" 62 | ] 63 | }, 64 | "metadata": { 65 | "tags": [] 66 | }, 67 | "execution_count": 3 68 | } 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "metadata": { 74 | "colab": { 75 | "base_uri": "https://localhost:8080/", 76 | "height": 224 77 | }, 78 | "id": "a0cB_2-E1L0z", 79 | "outputId": "6315a86e-fdf8-44d8-9a71-1823c086a88a" 80 | }, 81 | "source": [ 82 | "boston = pd.DataFrame(df.data, columns=df.feature_names)\n", 83 | "boston.head()" 84 | ], 85 | "execution_count": null, 86 | "outputs": [ 87 | { 88 | "output_type": "execute_result", 89 | "data": { 90 | "text/html": [ 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 | " \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 | "
    CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
    00.0063218.02.310.00.5386.57565.24.09001.0296.015.3396.904.98
    10.027310.07.070.00.4696.42178.94.96712.0242.017.8396.909.14
    20.027290.07.070.00.4697.18561.14.96712.0242.017.8392.834.03
    30.032370.02.180.00.4586.99845.86.06223.0222.018.7394.632.94
    40.069050.02.180.00.4587.14754.26.06223.0222.018.7396.905.33
    \n", 207 | "
    " 208 | ], 209 | "text/plain": [ 210 | " CRIM ZN INDUS CHAS NOX ... RAD TAX PTRATIO B LSTAT\n", 211 | "0 0.00632 18.0 2.31 0.0 0.538 ... 1.0 296.0 15.3 396.90 4.98\n", 212 | "1 0.02731 0.0 7.07 0.0 0.469 ... 2.0 242.0 17.8 396.90 9.14\n", 213 | "2 0.02729 0.0 7.07 0.0 0.469 ... 2.0 242.0 17.8 392.83 4.03\n", 214 | "3 0.03237 0.0 2.18 0.0 0.458 ... 3.0 222.0 18.7 394.63 2.94\n", 215 | "4 0.06905 0.0 2.18 0.0 0.458 ... 3.0 222.0 18.7 396.90 5.33\n", 216 | "\n", 217 | "[5 rows x 13 columns]" 218 | ] 219 | }, 220 | "metadata": { 221 | "tags": [] 222 | }, 223 | "execution_count": 4 224 | } 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "metadata": { 230 | "colab": { 231 | "base_uri": "https://localhost:8080/", 232 | "height": 224 233 | }, 234 | "id": "7XAaiG291NEp", 235 | "outputId": "947850f1-7bc6-4760-c0dc-7f5443101b47" 236 | }, 237 | "source": [ 238 | "boston['MEDV'] = df.target\n", 239 | "boston.head()" 240 | ], 241 | "execution_count": null, 242 | "outputs": [ 243 | { 244 | "output_type": "execute_result", 245 | "data": { 246 | "text/html": [ 247 | "
    \n", 248 | "\n", 261 | "\n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | "
    CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
    00.0063218.02.310.00.5386.57565.24.09001.0296.015.3396.904.9824.0
    10.027310.07.070.00.4696.42178.94.96712.0242.017.8396.909.1421.6
    20.027290.07.070.00.4697.18561.14.96712.0242.017.8392.834.0334.7
    30.032370.02.180.00.4586.99845.86.06223.0222.018.7394.632.9433.4
    40.069050.02.180.00.4587.14754.26.06223.0222.018.7396.905.3336.2
    \n", 369 | "
    " 370 | ], 371 | "text/plain": [ 372 | " CRIM ZN INDUS CHAS NOX ... TAX PTRATIO B LSTAT MEDV\n", 373 | "0 0.00632 18.0 2.31 0.0 0.538 ... 296.0 15.3 396.90 4.98 24.0\n", 374 | "1 0.02731 0.0 7.07 0.0 0.469 ... 242.0 17.8 396.90 9.14 21.6\n", 375 | "2 0.02729 0.0 7.07 0.0 0.469 ... 242.0 17.8 392.83 4.03 34.7\n", 376 | "3 0.03237 0.0 2.18 0.0 0.458 ... 222.0 18.7 394.63 2.94 33.4\n", 377 | "4 0.06905 0.0 2.18 0.0 0.458 ... 222.0 18.7 396.90 5.33 36.2\n", 378 | "\n", 379 | "[5 rows x 14 columns]" 380 | ] 381 | }, 382 | "metadata": { 383 | "tags": [] 384 | }, 385 | "execution_count": 5 386 | } 387 | ] 388 | }, 389 | { 390 | "cell_type": "code", 391 | "metadata": { 392 | "colab": { 393 | "base_uri": "https://localhost:8080/", 394 | "height": 439 395 | }, 396 | "id": "4Pl58dP11Pxd", 397 | "outputId": "6ee7b6f7-c3aa-458b-8c2e-3064497da3d9" 398 | }, 399 | "source": [ 400 | "boston.isnull()" 401 | ], 402 | "execution_count": null, 403 | "outputs": [ 404 | { 405 | "output_type": "execute_result", 406 | "data": { 407 | "text/html": [ 408 | "
    \n", 409 | "\n", 422 | "\n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \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 | " \n", 561 | " \n", 562 | " \n", 563 | " \n", 564 | " \n", 565 | " \n", 566 | " \n", 567 | " \n", 568 | " \n", 569 | " \n", 570 | " \n", 571 | " \n", 572 | " \n", 573 | " \n", 574 | " \n", 575 | " \n", 576 | " \n", 577 | " \n", 578 | " \n", 579 | " \n", 580 | " \n", 581 | " \n", 582 | " \n", 583 | " \n", 584 | " \n", 585 | " \n", 586 | " \n", 587 | " \n", 588 | " \n", 589 | " \n", 590 | " \n", 591 | " \n", 592 | " \n", 593 | " \n", 594 | " \n", 595 | " \n", 596 | " \n", 597 | " \n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | " \n", 631 | "
    CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
    0FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
    1FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
    2FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
    3FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
    4FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
    .............................................
    501FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
    502FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
    503FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
    504FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
    505FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
    \n", 632 | "

    506 rows × 14 columns

    \n", 633 | "
    " 634 | ], 635 | "text/plain": [ 636 | " CRIM ZN INDUS CHAS NOX ... TAX PTRATIO B LSTAT MEDV\n", 637 | "0 False False False False False ... False False False False False\n", 638 | "1 False False False False False ... False False False False False\n", 639 | "2 False False False False False ... False False False False False\n", 640 | "3 False False False False False ... False False False False False\n", 641 | "4 False False False False False ... False False False False False\n", 642 | ".. ... ... ... ... ... ... ... ... ... ... ...\n", 643 | "501 False False False False False ... False False False False False\n", 644 | "502 False False False False False ... False False False False False\n", 645 | "503 False False False False False ... False False False False False\n", 646 | "504 False False False False False ... False False False False False\n", 647 | "505 False False False False False ... False False False False False\n", 648 | "\n", 649 | "[506 rows x 14 columns]" 650 | ] 651 | }, 652 | "metadata": { 653 | "tags": [] 654 | }, 655 | "execution_count": 6 656 | } 657 | ] 658 | }, 659 | { 660 | "cell_type": "code", 661 | "metadata": { 662 | "colab": { 663 | "base_uri": "https://localhost:8080/" 664 | }, 665 | "id": "eN8kAfnj1SFf", 666 | "outputId": "de8fc440-e213-4718-ea4c-dba3a6ce7423" 667 | }, 668 | "source": [ 669 | "boston.isnull().sum()" 670 | ], 671 | "execution_count": null, 672 | "outputs": [ 673 | { 674 | "output_type": "execute_result", 675 | "data": { 676 | "text/plain": [ 677 | "CRIM 0\n", 678 | "ZN 0\n", 679 | "INDUS 0\n", 680 | "CHAS 0\n", 681 | "NOX 0\n", 682 | "RM 0\n", 683 | "AGE 0\n", 684 | "DIS 0\n", 685 | "RAD 0\n", 686 | "TAX 0\n", 687 | "PTRATIO 0\n", 688 | "B 0\n", 689 | "LSTAT 0\n", 690 | "MEDV 0\n", 691 | "dtype: int64" 692 | ] 693 | }, 694 | "metadata": { 695 | "tags": [] 696 | }, 697 | "execution_count": 7 698 | } 699 | ] 700 | }, 701 | { 702 | "cell_type": "code", 703 | "metadata": { 704 | "colab": { 705 | "base_uri": "https://localhost:8080/" 706 | }, 707 | "id": "Zepb99391UBB", 708 | "outputId": "7fba68e7-c1ba-4cc5-f361-ce9f2d97dbfe" 709 | }, 710 | "source": [ 711 | "from sklearn.model_selection import train_test_split\n", 712 | "\n", 713 | "X = boston.drop('MEDV', axis=1)\n", 714 | "Y = boston['MEDV']\n", 715 | "\n", 716 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.15, random_state=5)\n", 717 | "\n", 718 | "print(X_train.shape)\n", 719 | "print(X_test.shape)\n", 720 | "print(Y_train.shape)\n", 721 | "print(Y_test.shape)" 722 | ], 723 | "execution_count": null, 724 | "outputs": [ 725 | { 726 | "output_type": "stream", 727 | "text": [ 728 | "(430, 13)\n", 729 | "(76, 13)\n", 730 | "(430,)\n", 731 | "(76,)\n" 732 | ], 733 | "name": "stdout" 734 | } 735 | ] 736 | }, 737 | { 738 | "cell_type": "code", 739 | "metadata": { 740 | "id": "LruUPstO1WAL" 741 | }, 742 | "source": [ 743 | "from sklearn.linear_model import LinearRegression\n", 744 | "from sklearn.metrics import mean_squared_error" 745 | ], 746 | "execution_count": null, 747 | "outputs": [] 748 | }, 749 | { 750 | "cell_type": "code", 751 | "metadata": { 752 | "id": "ftS3-sXQ1X3a", 753 | "colab": { 754 | "base_uri": "https://localhost:8080/" 755 | }, 756 | "outputId": "5f6c0992-2614-47fc-c88f-e8a5d7ee9055" 757 | }, 758 | "source": [ 759 | "## FITTING MODEL ON THE TRAINING DATASET\n", 760 | "\n", 761 | "lin_model = LinearRegression()\n", 762 | "\n", 763 | "lin_model.fit(X_train, Y_train)" 764 | ], 765 | "execution_count": null, 766 | "outputs": [ 767 | { 768 | "output_type": "execute_result", 769 | "data": { 770 | "text/plain": [ 771 | "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)" 772 | ] 773 | }, 774 | "metadata": { 775 | "tags": [] 776 | }, 777 | "execution_count": 10 778 | } 779 | ] 780 | }, 781 | { 782 | "cell_type": "code", 783 | "metadata": { 784 | "id": "cqZdPPFZ1ZuX", 785 | "colab": { 786 | "base_uri": "https://localhost:8080/" 787 | }, 788 | "outputId": "d6a926dc-1389-436e-88ec-1f9e0ee797cd" 789 | }, 790 | "source": [ 791 | "y_train_predict = lin_model.predict(X_train)\n", 792 | "rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict)))\n", 793 | "\n", 794 | "print(\"The model performance for training set\")\n", 795 | "print('RMSE is {}'.format(rmse))\n", 796 | "print(\"\\n\")\n", 797 | "\n", 798 | "# on testing set\n", 799 | "y_test_predict = lin_model.predict(X_test)\n", 800 | "rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict)))\n", 801 | "\n", 802 | "print(\"The model performance for testing set\")\n", 803 | "print('RMSE is {}'.format(rmse))\n" 804 | ], 805 | "execution_count": null, 806 | "outputs": [ 807 | { 808 | "output_type": "stream", 809 | "text": [ 810 | "The model performance for training set\n", 811 | "RMSE is 4.710901797319796\n", 812 | "\n", 813 | "\n", 814 | "The model performance for testing set\n", 815 | "RMSE is 4.687543527902972\n" 816 | ], 817 | "name": "stdout" 818 | } 819 | ] 820 | }, 821 | { 822 | "cell_type": "code", 823 | "metadata": { 824 | "id": "g2LsTrpi1brz" 825 | }, 826 | "source": [ 827 | "" 828 | ], 829 | "execution_count": null, 830 | "outputs": [] 831 | } 832 | ] 833 | } --------------------------------------------------------------------------------