├── 0_introduction └── lessons │ └── 0. Introduction.pdf ├── 1_python ├── client_data.csv ├── intro.py └── lessons │ └── 1. Python Crash Course.pdf ├── 2_data_preprocessing ├── client_data.csv ├── data_preprocessing.py └── lessons │ └── 3. Data Preprocessing.pdf ├── 3_supervised_ml ├── classification │ ├── Classification Course.ipynb │ ├── Social_Network_Ads.csv │ └── Wine.csv ├── lessons │ ├── 4. Supervised Learning - Classification.pdf │ └── 4. Supervised Learning - Regression.pdf └── regression │ ├── 50_Startups.csv │ ├── Regression Practical.ipynb │ ├── Salary_Data.csv │ ├── data_2d.csv │ ├── data_poly.csv │ ├── diamonds.csv │ ├── height_weight.csv │ └── height_weight_small.csv ├── 4_model_evaluation └── lessons │ └── 6. Evaluation Metrics.pdf ├── 5_unsupervised_ml └── Mall_Customers.csv ├── 6_deep_learning └── Churn_Modelling.csv ├── MLBC Syllabus AR.pdf ├── MLBC Syllabus EN.pdf ├── README.md └── course_image.png /0_introduction/lessons/0. Introduction.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Younes-Charfaoui/Machine-Learning-Beginner-Course/b39d9dea1d7f79c1b14590ea338a33d47e9ca49e/0_introduction/lessons/0. Introduction.pdf -------------------------------------------------------------------------------- /1_python/client_data.csv: -------------------------------------------------------------------------------- 1 | Country,Age,Salary,Purchased 2 | Algeria,44,72000,No 3 | Tunis,27,48000,Yes 4 | Morocco,30,54000,No 5 | Tunis,38,61000,No 6 | Morocco,40,,Yes 7 | Algeria,35,58000,Yes 8 | Tunis,,52000,No 9 | Algeria,48,79000,Yes 10 | Morocco,50,83000,No 11 | Algeria,37,67000,Yes 12 | Tunis,22,50000,No 13 | Algeria,33,53000,No 14 | Morocco,44,47000,Yes 15 | -------------------------------------------------------------------------------- /1_python/intro.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | -------------------------------------------------------------------------------- /1_python/lessons/1. Python Crash Course.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Younes-Charfaoui/Machine-Learning-Beginner-Course/b39d9dea1d7f79c1b14590ea338a33d47e9ca49e/1_python/lessons/1. Python Crash Course.pdf -------------------------------------------------------------------------------- /2_data_preprocessing/client_data.csv: -------------------------------------------------------------------------------- 1 | Country,Age,Salary,Purchased 2 | Algeria,44,72000,No 3 | Tunis,27,48000,Yes 4 | Morocco,30,54000,No 5 | Tunis,38,61000,No 6 | Morocco,40,,Yes 7 | Algeria,35,58000,Yes 8 | Tunis,,52000,No 9 | Algeria,48,79000,Yes 10 | Morocco,50,83000,No 11 | Algeria,37,67000,Yes 12 | Tunis,22,50000,No 13 | Algeria,33,53000,No 14 | Morocco,44,47000,Yes 15 | -------------------------------------------------------------------------------- /2_data_preprocessing/data_preprocessing.py: -------------------------------------------------------------------------------- 1 | # Importing the libraries. 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import numpy as np 5 | 6 | # Importing the dataset. 7 | data = pd.read_csv('client_data.csv') 8 | X = data.iloc[:,:3].values 9 | y = data.iloc[:,-1].values 10 | 11 | # Handle Missing data. 12 | from sklearn.preprocessing import Imputer 13 | im = Imputer() 14 | X[:, 1:3] = im.fit_transform(X[:,1:3]) 15 | # Encode the data. 16 | from sklearn.preprocessing import LabelEncoder , OneHotEncoder 17 | label_x = LabelEncoder() 18 | y = label_x.fit_transform(y) 19 | 20 | label_countries = LabelEncoder() 21 | X[:,0] = label_countries.fit_transform(X[:,0]) 22 | 23 | onehotencoder = OneHotEncoder(categorical_features = [0]) 24 | X = onehotencoder.fit_transform(X).toarray() 25 | 26 | # Feature Scaling 27 | from sklearn.preprocessing import StandardScaler 28 | sc = StandardScaler() 29 | X = sc.fit_transform(X) 30 | 31 | # Train/Test Splitting. 32 | from sklearn.model_selection import train_test_split 33 | x_train , x_test , y_train , y_test = train_test_split(X , y,test_size = 0.3 , random_state = 2424) 34 | -------------------------------------------------------------------------------- /2_data_preprocessing/lessons/3. Data Preprocessing.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Younes-Charfaoui/Machine-Learning-Beginner-Course/b39d9dea1d7f79c1b14590ea338a33d47e9ca49e/2_data_preprocessing/lessons/3. Data Preprocessing.pdf -------------------------------------------------------------------------------- /3_supervised_ml/classification/Classification Course.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "import matplotlib.pyplot\n", 12 | "import seaborn as sns\n" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "data = pd.read_csv('Wine.csv')" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 32, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "data": { 31 | "text/plain": [ 32 | "(178, 14)" 33 | ] 34 | }, 35 | "execution_count": 32, 36 | "metadata": {}, 37 | "output_type": "execute_result" 38 | } 39 | ], 40 | "source": [ 41 | "data.shape" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 3, 47 | "metadata": {}, 48 | "outputs": [ 49 | { 50 | "data": { 51 | "text/html": [ 52 | "
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AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280ProlineCustomer_Segment
014.231.712.4315.61272.803.060.282.295.641.043.9210651
113.201.782.1411.21002.652.760.261.284.381.053.4010501
213.162.362.6718.61012.803.240.302.815.681.033.1711851
314.371.952.5016.81133.853.490.242.187.800.863.4514801
413.242.592.8721.01182.802.690.391.824.321.042.937351
\n", 174 | "
" 175 | ], 176 | "text/plain": [ 177 | " Alcohol Malic_Acid Ash Ash_Alcanity Magnesium Total_Phenols \\\n", 178 | "0 14.23 1.71 2.43 15.6 127 2.80 \n", 179 | "1 13.20 1.78 2.14 11.2 100 2.65 \n", 180 | "2 13.16 2.36 2.67 18.6 101 2.80 \n", 181 | "3 14.37 1.95 2.50 16.8 113 3.85 \n", 182 | "4 13.24 2.59 2.87 21.0 118 2.80 \n", 183 | "\n", 184 | " Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue \\\n", 185 | "0 3.06 0.28 2.29 5.64 1.04 \n", 186 | "1 2.76 0.26 1.28 4.38 1.05 \n", 187 | "2 3.24 0.30 2.81 5.68 1.03 \n", 188 | "3 3.49 0.24 2.18 7.80 0.86 \n", 189 | "4 2.69 0.39 1.82 4.32 1.04 \n", 190 | "\n", 191 | " OD280 Proline Customer_Segment \n", 192 | "0 3.92 1065 1 \n", 193 | "1 3.40 1050 1 \n", 194 | "2 3.17 1185 1 \n", 195 | "3 3.45 1480 1 \n", 196 | "4 2.93 735 1 " 197 | ] 198 | }, 199 | "execution_count": 3, 200 | "metadata": {}, 201 | "output_type": "execute_result" 202 | } 203 | ], 204 | "source": [ 205 | "data.head()" 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": 4, 211 | "metadata": {}, 212 | "outputs": [ 213 | { 214 | "data": { 215 | "text/html": [ 216 | "
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AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280ProlineCustomer_Segment
count178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000
mean13.0006182.3363482.36651719.49494499.7415732.2951122.0292700.3618541.5908995.0580900.9574492.611685746.8932581.938202
std0.8118271.1171460.2743443.33956414.2824840.6258510.9988590.1244530.5723592.3182860.2285720.709990314.9074740.775035
min11.0300000.7400001.36000010.60000070.0000000.9800000.3400000.1300000.4100001.2800000.4800001.270000278.0000001.000000
25%12.3625001.6025002.21000017.20000088.0000001.7425001.2050000.2700001.2500003.2200000.7825001.937500500.5000001.000000
50%13.0500001.8650002.36000019.50000098.0000002.3550002.1350000.3400001.5550004.6900000.9650002.780000673.5000002.000000
75%13.6775003.0825002.55750021.500000107.0000002.8000002.8750000.4375001.9500006.2000001.1200003.170000985.0000003.000000
max14.8300005.8000003.23000030.000000162.0000003.8800005.0800000.6600003.58000013.0000001.7100004.0000001680.0000003.000000
\n", 389 | "
" 390 | ], 391 | "text/plain": [ 392 | " Alcohol Malic_Acid Ash Ash_Alcanity Magnesium \\\n", 393 | "count 178.000000 178.000000 178.000000 178.000000 178.000000 \n", 394 | "mean 13.000618 2.336348 2.366517 19.494944 99.741573 \n", 395 | "std 0.811827 1.117146 0.274344 3.339564 14.282484 \n", 396 | "min 11.030000 0.740000 1.360000 10.600000 70.000000 \n", 397 | "25% 12.362500 1.602500 2.210000 17.200000 88.000000 \n", 398 | "50% 13.050000 1.865000 2.360000 19.500000 98.000000 \n", 399 | "75% 13.677500 3.082500 2.557500 21.500000 107.000000 \n", 400 | "max 14.830000 5.800000 3.230000 30.000000 162.000000 \n", 401 | "\n", 402 | " Total_Phenols Flavanoids Nonflavanoid_Phenols Proanthocyanins \\\n", 403 | "count 178.000000 178.000000 178.000000 178.000000 \n", 404 | "mean 2.295112 2.029270 0.361854 1.590899 \n", 405 | "std 0.625851 0.998859 0.124453 0.572359 \n", 406 | "min 0.980000 0.340000 0.130000 0.410000 \n", 407 | "25% 1.742500 1.205000 0.270000 1.250000 \n", 408 | "50% 2.355000 2.135000 0.340000 1.555000 \n", 409 | "75% 2.800000 2.875000 0.437500 1.950000 \n", 410 | "max 3.880000 5.080000 0.660000 3.580000 \n", 411 | "\n", 412 | " Color_Intensity Hue OD280 Proline Customer_Segment \n", 413 | "count 178.000000 178.000000 178.000000 178.000000 178.000000 \n", 414 | "mean 5.058090 0.957449 2.611685 746.893258 1.938202 \n", 415 | "std 2.318286 0.228572 0.709990 314.907474 0.775035 \n", 416 | "min 1.280000 0.480000 1.270000 278.000000 1.000000 \n", 417 | "25% 3.220000 0.782500 1.937500 500.500000 1.000000 \n", 418 | "50% 4.690000 0.965000 2.780000 673.500000 2.000000 \n", 419 | "75% 6.200000 1.120000 3.170000 985.000000 3.000000 \n", 420 | "max 13.000000 1.710000 4.000000 1680.000000 3.000000 " 421 | ] 422 | }, 423 | "execution_count": 4, 424 | "metadata": {}, 425 | "output_type": "execute_result" 426 | } 427 | ], 428 | "source": [ 429 | "data.describe()" 430 | ] 431 | }, 432 | { 433 | "cell_type": "code", 434 | "execution_count": 5, 435 | "metadata": {}, 436 | "outputs": [ 437 | { 438 | "data": { 439 | "text/plain": [ 440 | "array([1, 2, 3], dtype=int64)" 441 | ] 442 | }, 443 | "execution_count": 5, 444 | "metadata": {}, 445 | "output_type": "execute_result" 446 | } 447 | ], 448 | "source": [ 449 | "data.Customer_Segment.unique()" 450 | ] 451 | }, 452 | { 453 | "cell_type": "code", 454 | "execution_count": 6, 455 | "metadata": {}, 456 | "outputs": [ 457 | { 458 | "data": { 459 | "text/plain": [ 460 | "" 461 | ] 462 | }, 463 | "execution_count": 6, 464 | "metadata": {}, 465 | "output_type": "execute_result" 466 | }, 467 | { 468 | "data": { 469 | "image/png": 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\n", 470 | "text/plain": [ 471 | "
" 472 | ] 473 | }, 474 | "metadata": { 475 | "needs_background": "light" 476 | }, 477 | "output_type": "display_data" 478 | } 479 | ], 480 | "source": [ 481 | "sns.countplot(data.Customer_Segment)" 482 | ] 483 | }, 484 | { 485 | "cell_type": "code", 486 | "execution_count": 7, 487 | "metadata": {}, 488 | "outputs": [], 489 | "source": [ 490 | "from sklearn.model_selection import train_test_split" 491 | ] 492 | }, 493 | { 494 | "cell_type": "code", 495 | "execution_count": 8, 496 | "metadata": {}, 497 | "outputs": [], 498 | "source": [ 499 | "X = data.drop(['Customer_Segment'] , axis = 1)\n", 500 | "y = data.Customer_Segment" 501 | ] 502 | }, 503 | { 504 | "cell_type": "code", 505 | "execution_count": 10, 506 | "metadata": {}, 507 | "outputs": [], 508 | "source": [ 509 | "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)" 510 | ] 511 | }, 512 | { 513 | "cell_type": "code", 514 | "execution_count": 11, 515 | "metadata": {}, 516 | "outputs": [], 517 | "source": [ 518 | "from sklearn.tree import DecisionTreeClassifier" 519 | ] 520 | }, 521 | { 522 | "cell_type": "code", 523 | "execution_count": 28, 524 | "metadata": {}, 525 | "outputs": [], 526 | "source": [ 527 | "clf = DecisionTreeClassifier(max_depth = 7)" 528 | ] 529 | }, 530 | { 531 | "cell_type": "code", 532 | "execution_count": 29, 533 | "metadata": {}, 534 | "outputs": [ 535 | { 536 | "data": { 537 | "text/plain": [ 538 | "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=8,\n", 539 | " max_features=None, max_leaf_nodes=None,\n", 540 | " min_impurity_decrease=0.0, min_impurity_split=None,\n", 541 | " min_samples_leaf=1, min_samples_split=2,\n", 542 | " min_weight_fraction_leaf=0.0, presort=False,\n", 543 | " random_state=None, splitter='best')" 544 | ] 545 | }, 546 | "execution_count": 29, 547 | "metadata": {}, 548 | "output_type": "execute_result" 549 | } 550 | ], 551 | "source": [ 552 | "clf.fit(x_train, y_train)" 553 | ] 554 | }, 555 | { 556 | "cell_type": "code", 557 | "execution_count": 30, 558 | "metadata": {}, 559 | "outputs": [], 560 | "source": [ 561 | "y_pred = clf.predict(x_test)" 562 | ] 563 | }, 564 | { 565 | "cell_type": "code", 566 | "execution_count": 31, 567 | "metadata": {}, 568 | "outputs": [ 569 | { 570 | "data": { 571 | "text/plain": [ 572 | "0.8888888888888888" 573 | ] 574 | }, 575 | "execution_count": 31, 576 | "metadata": {}, 577 | "output_type": "execute_result" 578 | } 579 | ], 580 | "source": [ 581 | "(y_pred == y_test).mean()" 582 | ] 583 | }, 584 | { 585 | "cell_type": "code", 586 | "execution_count": 26, 587 | "metadata": {}, 588 | "outputs": [], 589 | "source": [ 590 | "y_pred = clf.predict(x_train)" 591 | ] 592 | }, 593 | { 594 | "cell_type": "code", 595 | "execution_count": 27, 596 | "metadata": {}, 597 | "outputs": [ 598 | { 599 | "data": { 600 | "text/plain": [ 601 | "1.0" 602 | ] 603 | }, 604 | "execution_count": 27, 605 | "metadata": {}, 606 | "output_type": "execute_result" 607 | } 608 | ], 609 | "source": [ 610 | "(y_pred == y_train).mean()" 611 | ] 612 | }, 613 | { 614 | "cell_type": "code", 615 | "execution_count": 33, 616 | "metadata": {}, 617 | "outputs": [], 618 | "source": [ 619 | "from sklearn.linear_model import LogisticRegression" 620 | ] 621 | }, 622 | { 623 | "cell_type": "code", 624 | "execution_count": 34, 625 | "metadata": {}, 626 | "outputs": [ 627 | { 628 | "name": "stderr", 629 | "output_type": "stream", 630 | "text": [ 631 | "C:\\Anaconda\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", 632 | " FutureWarning)\n", 633 | "C:\\Anaconda\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n", 634 | " \"this warning.\", FutureWarning)\n" 635 | ] 636 | } 637 | ], 638 | "source": [ 639 | "clf = LogisticRegression()\n", 640 | "clf.fit(x_train, y_train)\n", 641 | "y_pred = clf.predict(x_test)" 642 | ] 643 | }, 644 | { 645 | "cell_type": "code", 646 | "execution_count": 35, 647 | "metadata": {}, 648 | "outputs": [ 649 | { 650 | "data": { 651 | "text/plain": [ 652 | "0.9722222222222222" 653 | ] 654 | }, 655 | "execution_count": 35, 656 | "metadata": {}, 657 | "output_type": "execute_result" 658 | } 659 | ], 660 | "source": [ 661 | "(y_pred == y_test).mean()" 662 | ] 663 | }, 664 | { 665 | "cell_type": "code", 666 | "execution_count": 36, 667 | "metadata": {}, 668 | "outputs": [], 669 | "source": [ 670 | "from sklearn.svm import SVC" 671 | ] 672 | }, 673 | { 674 | "cell_type": "code", 675 | "execution_count": 43, 676 | "metadata": {}, 677 | "outputs": [ 678 | { 679 | "data": { 680 | "text/plain": [ 681 | "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", 682 | " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", 683 | " kernel='linear', max_iter=-1, probability=False, random_state=None,\n", 684 | " shrinking=True, tol=0.001, verbose=False)" 685 | ] 686 | }, 687 | "execution_count": 43, 688 | "metadata": {}, 689 | "output_type": "execute_result" 690 | } 691 | ], 692 | "source": [ 693 | "clf = SVC(kernel='linear')\n", 694 | "clf.fit(x_train, y_train)" 695 | ] 696 | }, 697 | { 698 | "cell_type": "code", 699 | "execution_count": 44, 700 | "metadata": {}, 701 | "outputs": [ 702 | { 703 | "data": { 704 | "text/plain": [ 705 | "0.9444444444444444" 706 | ] 707 | }, 708 | "execution_count": 44, 709 | "metadata": {}, 710 | "output_type": "execute_result" 711 | } 712 | ], 713 | "source": [ 714 | "y_pred = clf.predict(x_test)\n", 715 | "(y_pred == y_test).mean()" 716 | ] 717 | }, 718 | { 719 | "cell_type": "code", 720 | "execution_count": 45, 721 | "metadata": {}, 722 | "outputs": [], 723 | "source": [ 724 | "from sklearn.naive_bayes import GaussianNB" 725 | ] 726 | }, 727 | { 728 | "cell_type": "code", 729 | "execution_count": 46, 730 | "metadata": {}, 731 | "outputs": [ 732 | { 733 | "data": { 734 | "text/plain": [ 735 | "GaussianNB(priors=None, var_smoothing=1e-09)" 736 | ] 737 | }, 738 | "execution_count": 46, 739 | "metadata": {}, 740 | "output_type": "execute_result" 741 | } 742 | ], 743 | "source": [ 744 | "clf = GaussianNB()\n", 745 | "clf.fit(x_train, y_train)" 746 | ] 747 | }, 748 | { 749 | "cell_type": "code", 750 | "execution_count": 47, 751 | "metadata": {}, 752 | "outputs": [ 753 | { 754 | "data": { 755 | "text/plain": [ 756 | "0.9722222222222222" 757 | ] 758 | }, 759 | "execution_count": 47, 760 | "metadata": {}, 761 | "output_type": "execute_result" 762 | } 763 | ], 764 | "source": [ 765 | "y_pred = clf.predict(x_test)\n", 766 | "(y_pred == y_test).mean()" 767 | ] 768 | }, 769 | { 770 | "cell_type": "code", 771 | "execution_count": 48, 772 | "metadata": {}, 773 | "outputs": [], 774 | "source": [ 775 | "from sklearn.neighbors import KNeighborsClassifier" 776 | ] 777 | }, 778 | { 779 | "cell_type": "code", 780 | "execution_count": 51, 781 | "metadata": {}, 782 | "outputs": [], 783 | "source": [ 784 | "from sklearn.preprocessing import StandardScaler\n", 785 | "std = StandardScaler()\n", 786 | "x_train = std.fit_transform(x_train)\n", 787 | "x_test = std.fit_transform(x_test)" 788 | ] 789 | }, 790 | { 791 | "cell_type": "code", 792 | "execution_count": 52, 793 | "metadata": {}, 794 | "outputs": [ 795 | { 796 | "data": { 797 | "text/plain": [ 798 | "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", 799 | " metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n", 800 | " weights='uniform')" 801 | ] 802 | }, 803 | "execution_count": 52, 804 | "metadata": {}, 805 | "output_type": "execute_result" 806 | } 807 | ], 808 | "source": [ 809 | "clf = KNeighborsClassifier()\n", 810 | "clf.fit(x_train, y_train)" 811 | ] 812 | }, 813 | { 814 | "cell_type": "code", 815 | "execution_count": null, 816 | "metadata": {}, 817 | "outputs": [], 818 | "source": [] 819 | }, 820 | { 821 | "cell_type": "code", 822 | "execution_count": 53, 823 | "metadata": {}, 824 | "outputs": [ 825 | { 826 | "data": { 827 | "text/plain": [ 828 | "0.9722222222222222" 829 | ] 830 | }, 831 | "execution_count": 53, 832 | "metadata": {}, 833 | "output_type": "execute_result" 834 | } 835 | ], 836 | "source": [ 837 | "y_pred = clf.predict(x_test)\n", 838 | "(y_pred == y_test).mean()" 839 | ] 840 | }, 841 | { 842 | "cell_type": "code", 843 | "execution_count": 54, 844 | "metadata": {}, 845 | "outputs": [ 846 | { 847 | "data": { 848 | "text/html": [ 849 | "
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\n", 1861 | "

142 rows × 13 columns

\n", 1862 | "
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2010 | ], 2011 | "metadata": { 2012 | "kernelspec": { 2013 | "display_name": "Python 3", 2014 | "language": "python", 2015 | "name": "python3" 2016 | }, 2017 | "language_info": { 2018 | "codemirror_mode": { 2019 | "name": "ipython", 2020 | "version": 3 2021 | }, 2022 | "file_extension": ".py", 2023 | "mimetype": "text/x-python", 2024 | "name": "python", 2025 | "nbconvert_exporter": "python", 2026 | "pygments_lexer": "ipython3", 2027 | "version": "3.7.3" 2028 | } 2029 | }, 2030 | "nbformat": 4, 2031 | "nbformat_minor": 2 2032 | } 2033 | -------------------------------------------------------------------------------- /3_supervised_ml/classification/Social_Network_Ads.csv: -------------------------------------------------------------------------------- 1 | User ID,Gender,Age,EstimatedSalary,Purchased 2 | 15624510,Male,19,19000,0 3 | 15810944,Male,35,20000,0 4 | 15668575,Female,26,43000,0 5 | 15603246,Female,27,57000,0 6 | 15804002,Male,19,76000,0 7 | 15728773,Male,27,58000,0 8 | 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320 | 15742204,Male,45,32000,1 321 | 15623502,Male,36,60000,0 322 | 15774872,Female,52,138000,1 323 | 15611191,Female,53,82000,1 324 | 15674331,Male,41,52000,0 325 | 15619465,Female,48,30000,1 326 | 15575247,Female,48,131000,1 327 | 15695679,Female,41,60000,0 328 | 15713463,Male,41,72000,0 329 | 15785170,Female,42,75000,0 330 | 15796351,Male,36,118000,1 331 | 15639576,Female,47,107000,1 332 | 15693264,Male,38,51000,0 333 | 15589715,Female,48,119000,1 334 | 15769902,Male,42,65000,0 335 | 15587177,Male,40,65000,0 336 | 15814553,Male,57,60000,1 337 | 15601550,Female,36,54000,0 338 | 15664907,Male,58,144000,1 339 | 15612465,Male,35,79000,0 340 | 15810800,Female,38,55000,0 341 | 15665760,Male,39,122000,1 342 | 15588080,Female,53,104000,1 343 | 15776844,Male,35,75000,0 344 | 15717560,Female,38,65000,0 345 | 15629739,Female,47,51000,1 346 | 15729908,Male,47,105000,1 347 | 15716781,Female,41,63000,0 348 | 15646936,Male,53,72000,1 349 | 15768151,Female,54,108000,1 350 | 15579212,Male,39,77000,0 351 | 15721835,Male,38,61000,0 352 | 15800515,Female,38,113000,1 353 | 15591279,Male,37,75000,0 354 | 15587419,Female,42,90000,1 355 | 15750335,Female,37,57000,0 356 | 15699619,Male,36,99000,1 357 | 15606472,Male,60,34000,1 358 | 15778368,Male,54,70000,1 359 | 15671387,Female,41,72000,0 360 | 15573926,Male,40,71000,1 361 | 15709183,Male,42,54000,0 362 | 15577514,Male,43,129000,1 363 | 15778830,Female,53,34000,1 364 | 15768072,Female,47,50000,1 365 | 15768293,Female,42,79000,0 366 | 15654456,Male,42,104000,1 367 | 15807525,Female,59,29000,1 368 | 15574372,Female,58,47000,1 369 | 15671249,Male,46,88000,1 370 | 15779744,Male,38,71000,0 371 | 15624755,Female,54,26000,1 372 | 15611430,Female,60,46000,1 373 | 15774744,Male,60,83000,1 374 | 15629885,Female,39,73000,0 375 | 15708791,Male,59,130000,1 376 | 15793890,Female,37,80000,0 377 | 15646091,Female,46,32000,1 378 | 15596984,Female,46,74000,0 379 | 15800215,Female,42,53000,0 380 | 15577806,Male,41,87000,1 381 | 15749381,Female,58,23000,1 382 | 15683758,Male,42,64000,0 383 | 15670615,Male,48,33000,1 384 | 15715622,Female,44,139000,1 385 | 15707634,Male,49,28000,1 386 | 15806901,Female,57,33000,1 387 | 15775335,Male,56,60000,1 388 | 15724150,Female,49,39000,1 389 | 15627220,Male,39,71000,0 390 | 15672330,Male,47,34000,1 391 | 15668521,Female,48,35000,1 392 | 15807837,Male,48,33000,1 393 | 15592570,Male,47,23000,1 394 | 15748589,Female,45,45000,1 395 | 15635893,Male,60,42000,1 396 | 15757632,Female,39,59000,0 397 | 15691863,Female,46,41000,1 398 | 15706071,Male,51,23000,1 399 | 15654296,Female,50,20000,1 400 | 15755018,Male,36,33000,0 401 | 15594041,Female,49,36000,1 -------------------------------------------------------------------------------- /3_supervised_ml/classification/Wine.csv: -------------------------------------------------------------------------------- 1 | Alcohol,Malic_Acid,Ash,Ash_Alcanity,Magnesium,Total_Phenols,Flavanoids,Nonflavanoid_Phenols,Proanthocyanins,Color_Intensity,Hue,OD280,Proline,Customer_Segment 2 | 14.23,1.71,2.43,15.6,127,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065,1 3 | 13.2,1.78,2.14,11.2,100,2.65,2.76,0.26,1.28,4.38,1.05,3.4,1050,1 4 | 13.16,2.36,2.67,18.6,101,2.8,3.24,0.3,2.81,5.68,1.03,3.17,1185,1 5 | 14.37,1.95,2.5,16.8,113,3.85,3.49,0.24,2.18,7.8,0.86,3.45,1480,1 6 | 13.24,2.59,2.87,21,118,2.8,2.69,0.39,1.82,4.32,1.04,2.93,735,1 7 | 14.2,1.76,2.45,15.2,112,3.27,3.39,0.34,1.97,6.75,1.05,2.85,1450,1 8 | 14.39,1.87,2.45,14.6,96,2.5,2.52,0.3,1.98,5.25,1.02,3.58,1290,1 9 | 14.06,2.15,2.61,17.6,121,2.6,2.51,0.31,1.25,5.05,1.06,3.58,1295,1 10 | 14.83,1.64,2.17,14,97,2.8,2.98,0.29,1.98,5.2,1.08,2.85,1045,1 11 | 13.86,1.35,2.27,16,98,2.98,3.15,0.22,1.85,7.22,1.01,3.55,1045,1 12 | 14.1,2.16,2.3,18,105,2.95,3.32,0.22,2.38,5.75,1.25,3.17,1510,1 13 | 14.12,1.48,2.32,16.8,95,2.2,2.43,0.26,1.57,5,1.17,2.82,1280,1 14 | 13.75,1.73,2.41,16,89,2.6,2.76,0.29,1.81,5.6,1.15,2.9,1320,1 15 | 14.75,1.73,2.39,11.4,91,3.1,3.69,0.43,2.81,5.4,1.25,2.73,1150,1 16 | 14.38,1.87,2.38,12,102,3.3,3.64,0.29,2.96,7.5,1.2,3,1547,1 17 | 13.63,1.81,2.7,17.2,112,2.85,2.91,0.3,1.46,7.3,1.28,2.88,1310,1 18 | 14.3,1.92,2.72,20,120,2.8,3.14,0.33,1.97,6.2,1.07,2.65,1280,1 19 | 13.83,1.57,2.62,20,115,2.95,3.4,0.4,1.72,6.6,1.13,2.57,1130,1 20 | 14.19,1.59,2.48,16.5,108,3.3,3.93,0.32,1.86,8.7,1.23,2.82,1680,1 21 | 13.64,3.1,2.56,15.2,116,2.7,3.03,0.17,1.66,5.1,0.96,3.36,845,1 22 | 14.06,1.63,2.28,16,126,3,3.17,0.24,2.1,5.65,1.09,3.71,780,1 23 | 12.93,3.8,2.65,18.6,102,2.41,2.41,0.25,1.98,4.5,1.03,3.52,770,1 24 | 13.71,1.86,2.36,16.6,101,2.61,2.88,0.27,1.69,3.8,1.11,4,1035,1 25 | 12.85,1.6,2.52,17.8,95,2.48,2.37,0.26,1.46,3.93,1.09,3.63,1015,1 26 | 13.5,1.81,2.61,20,96,2.53,2.61,0.28,1.66,3.52,1.12,3.82,845,1 27 | 13.05,2.05,3.22,25,124,2.63,2.68,0.47,1.92,3.58,1.13,3.2,830,1 28 | 13.39,1.77,2.62,16.1,93,2.85,2.94,0.34,1.45,4.8,0.92,3.22,1195,1 29 | 13.3,1.72,2.14,17,94,2.4,2.19,0.27,1.35,3.95,1.02,2.77,1285,1 30 | 13.87,1.9,2.8,19.4,107,2.95,2.97,0.37,1.76,4.5,1.25,3.4,915,1 31 | 14.02,1.68,2.21,16,96,2.65,2.33,0.26,1.98,4.7,1.04,3.59,1035,1 32 | 13.73,1.5,2.7,22.5,101,3,3.25,0.29,2.38,5.7,1.19,2.71,1285,1 33 | 13.58,1.66,2.36,19.1,106,2.86,3.19,0.22,1.95,6.9,1.09,2.88,1515,1 34 | 13.68,1.83,2.36,17.2,104,2.42,2.69,0.42,1.97,3.84,1.23,2.87,990,1 35 | 13.76,1.53,2.7,19.5,132,2.95,2.74,0.5,1.35,5.4,1.25,3,1235,1 36 | 13.51,1.8,2.65,19,110,2.35,2.53,0.29,1.54,4.2,1.1,2.87,1095,1 37 | 13.48,1.81,2.41,20.5,100,2.7,2.98,0.26,1.86,5.1,1.04,3.47,920,1 38 | 13.28,1.64,2.84,15.5,110,2.6,2.68,0.34,1.36,4.6,1.09,2.78,880,1 39 | 13.05,1.65,2.55,18,98,2.45,2.43,0.29,1.44,4.25,1.12,2.51,1105,1 40 | 13.07,1.5,2.1,15.5,98,2.4,2.64,0.28,1.37,3.7,1.18,2.69,1020,1 41 | 14.22,3.99,2.51,13.2,128,3,3.04,0.2,2.08,5.1,0.89,3.53,760,1 42 | 13.56,1.71,2.31,16.2,117,3.15,3.29,0.34,2.34,6.13,0.95,3.38,795,1 43 | 13.41,3.84,2.12,18.8,90,2.45,2.68,0.27,1.48,4.28,0.91,3,1035,1 44 | 13.88,1.89,2.59,15,101,3.25,3.56,0.17,1.7,5.43,0.88,3.56,1095,1 45 | 13.24,3.98,2.29,17.5,103,2.64,2.63,0.32,1.66,4.36,0.82,3,680,1 46 | 13.05,1.77,2.1,17,107,3,3,0.28,2.03,5.04,0.88,3.35,885,1 47 | 14.21,4.04,2.44,18.9,111,2.85,2.65,0.3,1.25,5.24,0.87,3.33,1080,1 48 | 14.38,3.59,2.28,16,102,3.25,3.17,0.27,2.19,4.9,1.04,3.44,1065,1 49 | 13.9,1.68,2.12,16,101,3.1,3.39,0.21,2.14,6.1,0.91,3.33,985,1 50 | 14.1,2.02,2.4,18.8,103,2.75,2.92,0.32,2.38,6.2,1.07,2.75,1060,1 51 | 13.94,1.73,2.27,17.4,108,2.88,3.54,0.32,2.08,8.9,1.12,3.1,1260,1 52 | 13.05,1.73,2.04,12.4,92,2.72,3.27,0.17,2.91,7.2,1.12,2.91,1150,1 53 | 13.83,1.65,2.6,17.2,94,2.45,2.99,0.22,2.29,5.6,1.24,3.37,1265,1 54 | 13.82,1.75,2.42,14,111,3.88,3.74,0.32,1.87,7.05,1.01,3.26,1190,1 55 | 13.77,1.9,2.68,17.1,115,3,2.79,0.39,1.68,6.3,1.13,2.93,1375,1 56 | 13.74,1.67,2.25,16.4,118,2.6,2.9,0.21,1.62,5.85,0.92,3.2,1060,1 57 | 13.56,1.73,2.46,20.5,116,2.96,2.78,0.2,2.45,6.25,0.98,3.03,1120,1 58 | 14.22,1.7,2.3,16.3,118,3.2,3,0.26,2.03,6.38,0.94,3.31,970,1 59 | 13.29,1.97,2.68,16.8,102,3,3.23,0.31,1.66,6,1.07,2.84,1270,1 60 | 13.72,1.43,2.5,16.7,108,3.4,3.67,0.19,2.04,6.8,0.89,2.87,1285,1 61 | 12.37,0.94,1.36,10.6,88,1.98,0.57,0.28,0.42,1.95,1.05,1.82,520,2 62 | 12.33,1.1,2.28,16,101,2.05,1.09,0.63,0.41,3.27,1.25,1.67,680,2 63 | 12.64,1.36,2.02,16.8,100,2.02,1.41,0.53,0.62,5.75,0.98,1.59,450,2 64 | 13.67,1.25,1.92,18,94,2.1,1.79,0.32,0.73,3.8,1.23,2.46,630,2 65 | 12.37,1.13,2.16,19,87,3.5,3.1,0.19,1.87,4.45,1.22,2.87,420,2 66 | 12.17,1.45,2.53,19,104,1.89,1.75,0.45,1.03,2.95,1.45,2.23,355,2 67 | 12.37,1.21,2.56,18.1,98,2.42,2.65,0.37,2.08,4.6,1.19,2.3,678,2 68 | 13.11,1.01,1.7,15,78,2.98,3.18,0.26,2.28,5.3,1.12,3.18,502,2 69 | 12.37,1.17,1.92,19.6,78,2.11,2,0.27,1.04,4.68,1.12,3.48,510,2 70 | 13.34,0.94,2.36,17,110,2.53,1.3,0.55,0.42,3.17,1.02,1.93,750,2 71 | 12.21,1.19,1.75,16.8,151,1.85,1.28,0.14,2.5,2.85,1.28,3.07,718,2 72 | 12.29,1.61,2.21,20.4,103,1.1,1.02,0.37,1.46,3.05,0.906,1.82,870,2 73 | 13.86,1.51,2.67,25,86,2.95,2.86,0.21,1.87,3.38,1.36,3.16,410,2 74 | 13.49,1.66,2.24,24,87,1.88,1.84,0.27,1.03,3.74,0.98,2.78,472,2 75 | 12.99,1.67,2.6,30,139,3.3,2.89,0.21,1.96,3.35,1.31,3.5,985,2 76 | 11.96,1.09,2.3,21,101,3.38,2.14,0.13,1.65,3.21,0.99,3.13,886,2 77 | 11.66,1.88,1.92,16,97,1.61,1.57,0.34,1.15,3.8,1.23,2.14,428,2 78 | 13.03,0.9,1.71,16,86,1.95,2.03,0.24,1.46,4.6,1.19,2.48,392,2 79 | 11.84,2.89,2.23,18,112,1.72,1.32,0.43,0.95,2.65,0.96,2.52,500,2 80 | 12.33,0.99,1.95,14.8,136,1.9,1.85,0.35,2.76,3.4,1.06,2.31,750,2 81 | 12.7,3.87,2.4,23,101,2.83,2.55,0.43,1.95,2.57,1.19,3.13,463,2 82 | 12,0.92,2,19,86,2.42,2.26,0.3,1.43,2.5,1.38,3.12,278,2 83 | 12.72,1.81,2.2,18.8,86,2.2,2.53,0.26,1.77,3.9,1.16,3.14,714,2 84 | 12.08,1.13,2.51,24,78,2,1.58,0.4,1.4,2.2,1.31,2.72,630,2 85 | 13.05,3.86,2.32,22.5,85,1.65,1.59,0.61,1.62,4.8,0.84,2.01,515,2 86 | 11.84,0.89,2.58,18,94,2.2,2.21,0.22,2.35,3.05,0.79,3.08,520,2 87 | 12.67,0.98,2.24,18,99,2.2,1.94,0.3,1.46,2.62,1.23,3.16,450,2 88 | 12.16,1.61,2.31,22.8,90,1.78,1.69,0.43,1.56,2.45,1.33,2.26,495,2 89 | 11.65,1.67,2.62,26,88,1.92,1.61,0.4,1.34,2.6,1.36,3.21,562,2 90 | 11.64,2.06,2.46,21.6,84,1.95,1.69,0.48,1.35,2.8,1,2.75,680,2 91 | 12.08,1.33,2.3,23.6,70,2.2,1.59,0.42,1.38,1.74,1.07,3.21,625,2 92 | 12.08,1.83,2.32,18.5,81,1.6,1.5,0.52,1.64,2.4,1.08,2.27,480,2 93 | 12,1.51,2.42,22,86,1.45,1.25,0.5,1.63,3.6,1.05,2.65,450,2 94 | 12.69,1.53,2.26,20.7,80,1.38,1.46,0.58,1.62,3.05,0.96,2.06,495,2 95 | 12.29,2.83,2.22,18,88,2.45,2.25,0.25,1.99,2.15,1.15,3.3,290,2 96 | 11.62,1.99,2.28,18,98,3.02,2.26,0.17,1.35,3.25,1.16,2.96,345,2 97 | 12.47,1.52,2.2,19,162,2.5,2.27,0.32,3.28,2.6,1.16,2.63,937,2 98 | 11.81,2.12,2.74,21.5,134,1.6,0.99,0.14,1.56,2.5,0.95,2.26,625,2 99 | 12.29,1.41,1.98,16,85,2.55,2.5,0.29,1.77,2.9,1.23,2.74,428,2 100 | 12.37,1.07,2.1,18.5,88,3.52,3.75,0.24,1.95,4.5,1.04,2.77,660,2 101 | 12.29,3.17,2.21,18,88,2.85,2.99,0.45,2.81,2.3,1.42,2.83,406,2 102 | 12.08,2.08,1.7,17.5,97,2.23,2.17,0.26,1.4,3.3,1.27,2.96,710,2 103 | 12.6,1.34,1.9,18.5,88,1.45,1.36,0.29,1.35,2.45,1.04,2.77,562,2 104 | 12.34,2.45,2.46,21,98,2.56,2.11,0.34,1.31,2.8,0.8,3.38,438,2 105 | 11.82,1.72,1.88,19.5,86,2.5,1.64,0.37,1.42,2.06,0.94,2.44,415,2 106 | 12.51,1.73,1.98,20.5,85,2.2,1.92,0.32,1.48,2.94,1.04,3.57,672,2 107 | 12.42,2.55,2.27,22,90,1.68,1.84,0.66,1.42,2.7,0.86,3.3,315,2 108 | 12.25,1.73,2.12,19,80,1.65,2.03,0.37,1.63,3.4,1,3.17,510,2 109 | 12.72,1.75,2.28,22.5,84,1.38,1.76,0.48,1.63,3.3,0.88,2.42,488,2 110 | 12.22,1.29,1.94,19,92,2.36,2.04,0.39,2.08,2.7,0.86,3.02,312,2 111 | 11.61,1.35,2.7,20,94,2.74,2.92,0.29,2.49,2.65,0.96,3.26,680,2 112 | 11.46,3.74,1.82,19.5,107,3.18,2.58,0.24,3.58,2.9,0.75,2.81,562,2 113 | 12.52,2.43,2.17,21,88,2.55,2.27,0.26,1.22,2,0.9,2.78,325,2 114 | 11.76,2.68,2.92,20,103,1.75,2.03,0.6,1.05,3.8,1.23,2.5,607,2 115 | 11.41,0.74,2.5,21,88,2.48,2.01,0.42,1.44,3.08,1.1,2.31,434,2 116 | 12.08,1.39,2.5,22.5,84,2.56,2.29,0.43,1.04,2.9,0.93,3.19,385,2 117 | 11.03,1.51,2.2,21.5,85,2.46,2.17,0.52,2.01,1.9,1.71,2.87,407,2 118 | 11.82,1.47,1.99,20.8,86,1.98,1.6,0.3,1.53,1.95,0.95,3.33,495,2 119 | 12.42,1.61,2.19,22.5,108,2,2.09,0.34,1.61,2.06,1.06,2.96,345,2 120 | 12.77,3.43,1.98,16,80,1.63,1.25,0.43,0.83,3.4,0.7,2.12,372,2 121 | 12,3.43,2,19,87,2,1.64,0.37,1.87,1.28,0.93,3.05,564,2 122 | 11.45,2.4,2.42,20,96,2.9,2.79,0.32,1.83,3.25,0.8,3.39,625,2 123 | 11.56,2.05,3.23,28.5,119,3.18,5.08,0.47,1.87,6,0.93,3.69,465,2 124 | 12.42,4.43,2.73,26.5,102,2.2,2.13,0.43,1.71,2.08,0.92,3.12,365,2 125 | 13.05,5.8,2.13,21.5,86,2.62,2.65,0.3,2.01,2.6,0.73,3.1,380,2 126 | 11.87,4.31,2.39,21,82,2.86,3.03,0.21,2.91,2.8,0.75,3.64,380,2 127 | 12.07,2.16,2.17,21,85,2.6,2.65,0.37,1.35,2.76,0.86,3.28,378,2 128 | 12.43,1.53,2.29,21.5,86,2.74,3.15,0.39,1.77,3.94,0.69,2.84,352,2 129 | 11.79,2.13,2.78,28.5,92,2.13,2.24,0.58,1.76,3,0.97,2.44,466,2 130 | 12.37,1.63,2.3,24.5,88,2.22,2.45,0.4,1.9,2.12,0.89,2.78,342,2 131 | 12.04,4.3,2.38,22,80,2.1,1.75,0.42,1.35,2.6,0.79,2.57,580,2 132 | 12.86,1.35,2.32,18,122,1.51,1.25,0.21,0.94,4.1,0.76,1.29,630,3 133 | 12.88,2.99,2.4,20,104,1.3,1.22,0.24,0.83,5.4,0.74,1.42,530,3 134 | 12.81,2.31,2.4,24,98,1.15,1.09,0.27,0.83,5.7,0.66,1.36,560,3 135 | 12.7,3.55,2.36,21.5,106,1.7,1.2,0.17,0.84,5,0.78,1.29,600,3 136 | 12.51,1.24,2.25,17.5,85,2,0.58,0.6,1.25,5.45,0.75,1.51,650,3 137 | 12.6,2.46,2.2,18.5,94,1.62,0.66,0.63,0.94,7.1,0.73,1.58,695,3 138 | 12.25,4.72,2.54,21,89,1.38,0.47,0.53,0.8,3.85,0.75,1.27,720,3 139 | 12.53,5.51,2.64,25,96,1.79,0.6,0.63,1.1,5,0.82,1.69,515,3 140 | 13.49,3.59,2.19,19.5,88,1.62,0.48,0.58,0.88,5.7,0.81,1.82,580,3 141 | 12.84,2.96,2.61,24,101,2.32,0.6,0.53,0.81,4.92,0.89,2.15,590,3 142 | 12.93,2.81,2.7,21,96,1.54,0.5,0.53,0.75,4.6,0.77,2.31,600,3 143 | 13.36,2.56,2.35,20,89,1.4,0.5,0.37,0.64,5.6,0.7,2.47,780,3 144 | 13.52,3.17,2.72,23.5,97,1.55,0.52,0.5,0.55,4.35,0.89,2.06,520,3 145 | 13.62,4.95,2.35,20,92,2,0.8,0.47,1.02,4.4,0.91,2.05,550,3 146 | 12.25,3.88,2.2,18.5,112,1.38,0.78,0.29,1.14,8.21,0.65,2,855,3 147 | 13.16,3.57,2.15,21,102,1.5,0.55,0.43,1.3,4,0.6,1.68,830,3 148 | 13.88,5.04,2.23,20,80,0.98,0.34,0.4,0.68,4.9,0.58,1.33,415,3 149 | 12.87,4.61,2.48,21.5,86,1.7,0.65,0.47,0.86,7.65,0.54,1.86,625,3 150 | 13.32,3.24,2.38,21.5,92,1.93,0.76,0.45,1.25,8.42,0.55,1.62,650,3 151 | 13.08,3.9,2.36,21.5,113,1.41,1.39,0.34,1.14,9.4,0.57,1.33,550,3 152 | 13.5,3.12,2.62,24,123,1.4,1.57,0.22,1.25,8.6,0.59,1.3,500,3 153 | 12.79,2.67,2.48,22,112,1.48,1.36,0.24,1.26,10.8,0.48,1.47,480,3 154 | 13.11,1.9,2.75,25.5,116,2.2,1.28,0.26,1.56,7.1,0.61,1.33,425,3 155 | 13.23,3.3,2.28,18.5,98,1.8,0.83,0.61,1.87,10.52,0.56,1.51,675,3 156 | 12.58,1.29,2.1,20,103,1.48,0.58,0.53,1.4,7.6,0.58,1.55,640,3 157 | 13.17,5.19,2.32,22,93,1.74,0.63,0.61,1.55,7.9,0.6,1.48,725,3 158 | 13.84,4.12,2.38,19.5,89,1.8,0.83,0.48,1.56,9.01,0.57,1.64,480,3 159 | 12.45,3.03,2.64,27,97,1.9,0.58,0.63,1.14,7.5,0.67,1.73,880,3 160 | 14.34,1.68,2.7,25,98,2.8,1.31,0.53,2.7,13,0.57,1.96,660,3 161 | 13.48,1.67,2.64,22.5,89,2.6,1.1,0.52,2.29,11.75,0.57,1.78,620,3 162 | 12.36,3.83,2.38,21,88,2.3,0.92,0.5,1.04,7.65,0.56,1.58,520,3 163 | 13.69,3.26,2.54,20,107,1.83,0.56,0.5,0.8,5.88,0.96,1.82,680,3 164 | 12.85,3.27,2.58,22,106,1.65,0.6,0.6,0.96,5.58,0.87,2.11,570,3 165 | 12.96,3.45,2.35,18.5,106,1.39,0.7,0.4,0.94,5.28,0.68,1.75,675,3 166 | 13.78,2.76,2.3,22,90,1.35,0.68,0.41,1.03,9.58,0.7,1.68,615,3 167 | 13.73,4.36,2.26,22.5,88,1.28,0.47,0.52,1.15,6.62,0.78,1.75,520,3 168 | 13.45,3.7,2.6,23,111,1.7,0.92,0.43,1.46,10.68,0.85,1.56,695,3 169 | 12.82,3.37,2.3,19.5,88,1.48,0.66,0.4,0.97,10.26,0.72,1.75,685,3 170 | 13.58,2.58,2.69,24.5,105,1.55,0.84,0.39,1.54,8.66,0.74,1.8,750,3 171 | 13.4,4.6,2.86,25,112,1.98,0.96,0.27,1.11,8.5,0.67,1.92,630,3 172 | 12.2,3.03,2.32,19,96,1.25,0.49,0.4,0.73,5.5,0.66,1.83,510,3 173 | 12.77,2.39,2.28,19.5,86,1.39,0.51,0.48,0.64,9.899999,0.57,1.63,470,3 174 | 14.16,2.51,2.48,20,91,1.68,0.7,0.44,1.24,9.7,0.62,1.71,660,3 175 | 13.71,5.65,2.45,20.5,95,1.68,0.61,0.52,1.06,7.7,0.64,1.74,740,3 176 | 13.4,3.91,2.48,23,102,1.8,0.75,0.43,1.41,7.3,0.7,1.56,750,3 177 | 13.27,4.28,2.26,20,120,1.59,0.69,0.43,1.35,10.2,0.59,1.56,835,3 178 | 13.17,2.59,2.37,20,120,1.65,0.68,0.53,1.46,9.3,0.6,1.62,840,3 179 | 14.13,4.1,2.74,24.5,96,2.05,0.76,0.56,1.35,9.2,0.61,1.6,560,3 -------------------------------------------------------------------------------- /3_supervised_ml/lessons/4. Supervised Learning - Classification.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Younes-Charfaoui/Machine-Learning-Beginner-Course/b39d9dea1d7f79c1b14590ea338a33d47e9ca49e/3_supervised_ml/lessons/4. Supervised Learning - Classification.pdf -------------------------------------------------------------------------------- /3_supervised_ml/lessons/4. Supervised Learning - Regression.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Younes-Charfaoui/Machine-Learning-Beginner-Course/b39d9dea1d7f79c1b14590ea338a33d47e9ca49e/3_supervised_ml/lessons/4. Supervised Learning - Regression.pdf -------------------------------------------------------------------------------- /3_supervised_ml/regression/50_Startups.csv: -------------------------------------------------------------------------------- 1 | R&D Spend,Administration,Marketing Spend,State,Profit 2 | 165349.2,136897.8,471784.1,New York,192261.83 3 | 162597.7,151377.59,443898.53,California,191792.06 4 | 153441.51,101145.55,407934.54,Florida,191050.39 5 | 144372.41,118671.85,383199.62,New York,182901.99 6 | 142107.34,91391.77,366168.42,Florida,166187.94 7 | 131876.9,99814.71,362861.36,New York,156991.12 8 | 134615.46,147198.87,127716.82,California,156122.51 9 | 130298.13,145530.06,323876.68,Florida,155752.6 10 | 120542.52,148718.95,311613.29,New York,152211.77 11 | 123334.88,108679.17,304981.62,California,149759.96 12 | 101913.08,110594.11,229160.95,Florida,146121.95 13 | 100671.96,91790.61,249744.55,California,144259.4 14 | 93863.75,127320.38,249839.44,Florida,141585.52 15 | 91992.39,135495.07,252664.93,California,134307.35 16 | 119943.24,156547.42,256512.92,Florida,132602.65 17 | 114523.61,122616.84,261776.23,New York,129917.04 18 | 78013.11,121597.55,264346.06,California,126992.93 19 | 94657.16,145077.58,282574.31,New York,125370.37 20 | 91749.16,114175.79,294919.57,Florida,124266.9 21 | 86419.7,153514.11,0,New York,122776.86 22 | 76253.86,113867.3,298664.47,California,118474.03 23 | 78389.47,153773.43,299737.29,New York,111313.02 24 | 73994.56,122782.75,303319.26,Florida,110352.25 25 | 67532.53,105751.03,304768.73,Florida,108733.99 26 | 77044.01,99281.34,140574.81,New York,108552.04 27 | 64664.71,139553.16,137962.62,California,107404.34 28 | 75328.87,144135.98,134050.07,Florida,105733.54 29 | 72107.6,127864.55,353183.81,New York,105008.31 30 | 66051.52,182645.56,118148.2,Florida,103282.38 31 | 65605.48,153032.06,107138.38,New York,101004.64 32 | 61994.48,115641.28,91131.24,Florida,99937.59 33 | 61136.38,152701.92,88218.23,New York,97483.56 34 | 63408.86,129219.61,46085.25,California,97427.84 35 | 55493.95,103057.49,214634.81,Florida,96778.92 36 | 46426.07,157693.92,210797.67,California,96712.8 37 | 46014.02,85047.44,205517.64,New York,96479.51 38 | 28663.76,127056.21,201126.82,Florida,90708.19 39 | 44069.95,51283.14,197029.42,California,89949.14 40 | 20229.59,65947.93,185265.1,New York,81229.06 41 | 38558.51,82982.09,174999.3,California,81005.76 42 | 28754.33,118546.05,172795.67,California,78239.91 43 | 27892.92,84710.77,164470.71,Florida,77798.83 44 | 23640.93,96189.63,148001.11,California,71498.49 45 | 15505.73,127382.3,35534.17,New York,69758.98 46 | 22177.74,154806.14,28334.72,California,65200.33 47 | 1000.23,124153.04,1903.93,New York,64926.08 48 | 1315.46,115816.21,297114.46,Florida,49490.75 49 | 0,135426.92,0,California,42559.73 50 | 542.05,51743.15,0,New York,35673.41 51 | 0,116983.8,45173.06,California,14681.4 -------------------------------------------------------------------------------- /3_supervised_ml/regression/Regression Practical.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import pandas as pd\n", 11 | "import matplotlib.pyplot as plt" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "data = pd.read_csv('housing.csv')" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 3, 26 | "metadata": {}, 27 | "outputs": [ 28 | { 29 | "data": { 30 | "text/html": [ 31 | "
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RMLSTATPTRATIOMEDV
06.5754.9815.3504000.0
16.4219.1417.8453600.0
27.1854.0317.8728700.0
36.9982.9418.7701400.0
47.1475.3318.7760200.0
\n", 93 | "
" 94 | ], 95 | "text/plain": [ 96 | " RM LSTAT PTRATIO MEDV\n", 97 | "0 6.575 4.98 15.3 504000.0\n", 98 | "1 6.421 9.14 17.8 453600.0\n", 99 | "2 7.185 4.03 17.8 728700.0\n", 100 | "3 6.998 2.94 18.7 701400.0\n", 101 | "4 7.147 5.33 18.7 760200.0" 102 | ] 103 | }, 104 | "execution_count": 3, 105 | "metadata": {}, 106 | "output_type": "execute_result" 107 | } 108 | ], 109 | "source": [ 110 | "data.head()" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": 5, 116 | "metadata": {}, 117 | "outputs": [ 118 | { 119 | "data": { 120 | "text/plain": [ 121 | "RM 0\n", 122 | "LSTAT 0\n", 123 | "PTRATIO 0\n", 124 | "MEDV 0\n", 125 | "dtype: int64" 126 | ] 127 | }, 128 | "execution_count": 5, 129 | "metadata": {}, 130 | "output_type": "execute_result" 131 | } 132 | ], 133 | "source": [ 134 | "data.isnull().sum()" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 6, 140 | "metadata": {}, 141 | "outputs": [ 142 | { 143 | "name": "stdout", 144 | "output_type": "stream", 145 | "text": [ 146 | "\n", 147 | "RangeIndex: 489 entries, 0 to 488\n", 148 | "Data columns (total 4 columns):\n", 149 | "RM 489 non-null float64\n", 150 | "LSTAT 489 non-null float64\n", 151 | "PTRATIO 489 non-null float64\n", 152 | "MEDV 489 non-null float64\n", 153 | "dtypes: float64(4)\n", 154 | "memory usage: 15.4 KB\n" 155 | ] 156 | } 157 | ], 158 | "source": [ 159 | "data.info()" 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": 73, 165 | "metadata": {}, 166 | "outputs": [], 167 | "source": [ 168 | "X = data.iloc[:,:-1]\n", 169 | "y = data.iloc[:,-1]" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 74, 175 | "metadata": {}, 176 | "outputs": [], 177 | "source": [ 178 | "from sklearn.preprocessing import StandardScaler" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": 75, 184 | "metadata": {}, 185 | "outputs": [], 186 | "source": [ 187 | "scaler = StandardScaler()\n", 188 | "X = scaler.fit_transform(X)" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 17, 194 | "metadata": {}, 195 | "outputs": [], 196 | "source": [ 197 | "from sklearn.model_selection import train_test_split" 198 | ] 199 | }, 200 | { 201 | "cell_type": "code", 202 | "execution_count": 84, 203 | "metadata": {}, 204 | "outputs": [], 205 | "source": [ 206 | "x_train, x_test, y_train, y_test = train_test_split(X_poly, y, test_size = 0.2, shuffle = True)" 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 85, 212 | "metadata": {}, 213 | "outputs": [], 214 | "source": [ 215 | "from sklearn.linear_model import LinearRegression" 216 | ] 217 | }, 218 | { 219 | "cell_type": "code", 220 | "execution_count": 86, 221 | "metadata": {}, 222 | "outputs": [], 223 | "source": [ 224 | "regressor = LinearRegression()" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": 87, 230 | "metadata": {}, 231 | "outputs": [ 232 | { 233 | "data": { 234 | "text/plain": [ 235 | "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" 236 | ] 237 | }, 238 | "execution_count": 87, 239 | "metadata": {}, 240 | "output_type": "execute_result" 241 | } 242 | ], 243 | "source": [ 244 | "regressor.fit(x_train,y_train)" 245 | ] 246 | }, 247 | { 248 | "cell_type": "code", 249 | "execution_count": 88, 250 | "metadata": {}, 251 | "outputs": [ 252 | { 253 | "data": { 254 | "text/plain": [ 255 | "0.8123763373367298" 256 | ] 257 | }, 258 | "execution_count": 88, 259 | "metadata": {}, 260 | "output_type": "execute_result" 261 | } 262 | ], 263 | "source": [ 264 | "regressor.score(x_test, y_test)" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": 89, 270 | "metadata": {}, 271 | "outputs": [], 272 | "source": [ 273 | "from sklearn.neighbors import KNeighborsRegressor\n", 274 | "reg = KNeighborsRegressor(n_neighbors = 10)" 275 | ] 276 | }, 277 | { 278 | "cell_type": "code", 279 | "execution_count": 90, 280 | "metadata": {}, 281 | "outputs": [ 282 | { 283 | "data": { 284 | "text/plain": [ 285 | "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n", 286 | " metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n", 287 | " weights='uniform')" 288 | ] 289 | }, 290 | "execution_count": 90, 291 | "metadata": {}, 292 | "output_type": "execute_result" 293 | } 294 | ], 295 | "source": [ 296 | "reg.fit(x_train, y_train)" 297 | ] 298 | }, 299 | { 300 | "cell_type": "code", 301 | "execution_count": 91, 302 | "metadata": {}, 303 | "outputs": [], 304 | "source": [ 305 | "y_pred = reg.predict(x_test)" 306 | ] 307 | }, 308 | { 309 | "cell_type": "code", 310 | "execution_count": 92, 311 | "metadata": {}, 312 | "outputs": [], 313 | "source": [ 314 | "from sklearn.metrics import r2_score" 315 | ] 316 | }, 317 | { 318 | "cell_type": "code", 319 | "execution_count": 93, 320 | "metadata": {}, 321 | "outputs": [ 322 | { 323 | "data": { 324 | "text/plain": [ 325 | "0.7528827030083509" 326 | ] 327 | }, 328 | "execution_count": 93, 329 | "metadata": {}, 330 | "output_type": "execute_result" 331 | } 332 | ], 333 | "source": [ 334 | "r2_score(y_test, y_pred)" 335 | ] 336 | }, 337 | { 338 | "cell_type": "code", 339 | "execution_count": 61, 340 | "metadata": {}, 341 | "outputs": [], 342 | "source": [ 343 | "from sklearn.tree import DecisionTreeRegressor\n", 344 | "re = DecisionTreeRegressor(max_depth = 10)" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": 62, 350 | "metadata": {}, 351 | "outputs": [ 352 | { 353 | "data": { 354 | "text/plain": [ 355 | "DecisionTreeRegressor(criterion='mse', max_depth=10, max_features=None,\n", 356 | " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", 357 | " min_impurity_split=None, min_samples_leaf=1,\n", 358 | " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", 359 | " presort=False, random_state=None, splitter='best')" 360 | ] 361 | }, 362 | "execution_count": 62, 363 | "metadata": {}, 364 | "output_type": "execute_result" 365 | } 366 | ], 367 | "source": [ 368 | "re.fit(x_train, y_train)" 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": 63, 374 | "metadata": {}, 375 | "outputs": [], 376 | "source": [ 377 | "y_pred = re.predict(x_test)" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "execution_count": 64, 383 | "metadata": {}, 384 | "outputs": [ 385 | { 386 | "data": { 387 | "text/plain": [ 388 | "0.7613669853377097" 389 | ] 390 | }, 391 | "execution_count": 64, 392 | "metadata": {}, 393 | "output_type": "execute_result" 394 | } 395 | ], 396 | "source": [ 397 | "r2_score(y_test, y_pred)" 398 | ] 399 | }, 400 | { 401 | "cell_type": "code", 402 | "execution_count": 60, 403 | "metadata": {}, 404 | "outputs": [], 405 | "source": [ 406 | "??DecisionTreeRegressor" 407 | ] 408 | }, 409 | { 410 | "cell_type": "code", 411 | "execution_count": 65, 412 | "metadata": {}, 413 | "outputs": [], 414 | "source": [ 415 | "from sklearn.svm import SVR" 416 | ] 417 | }, 418 | { 419 | "cell_type": "code", 420 | "execution_count": 66, 421 | "metadata": {}, 422 | "outputs": [], 423 | "source": [ 424 | "rrrr = SVR()" 425 | ] 426 | }, 427 | { 428 | "cell_type": "code", 429 | "execution_count": 67, 430 | "metadata": {}, 431 | "outputs": [ 432 | { 433 | "data": { 434 | "text/plain": [ 435 | "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n", 436 | " kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)" 437 | ] 438 | }, 439 | "execution_count": 67, 440 | "metadata": {}, 441 | "output_type": "execute_result" 442 | } 443 | ], 444 | "source": [ 445 | "rrrr.fit(x_train, y_train)" 446 | ] 447 | }, 448 | { 449 | "cell_type": "code", 450 | "execution_count": 68, 451 | "metadata": {}, 452 | "outputs": [], 453 | "source": [ 454 | "y = rrrr.predict(x_test)" 455 | ] 456 | }, 457 | { 458 | "cell_type": "code", 459 | "execution_count": 69, 460 | "metadata": {}, 461 | "outputs": [ 462 | { 463 | "data": { 464 | "text/plain": [ 465 | "-15057497.241008699" 466 | ] 467 | }, 468 | "execution_count": 69, 469 | "metadata": {}, 470 | "output_type": "execute_result" 471 | } 472 | ], 473 | "source": [ 474 | "r2_score(y, y_test)" 475 | ] 476 | }, 477 | { 478 | "cell_type": "code", 479 | "execution_count": 83, 480 | "metadata": {}, 481 | "outputs": [], 482 | "source": [ 483 | "from sklearn.preprocessing import PolynomialFeatures\n", 484 | "poly_reg = PolynomialFeatures(degree = 4)\n", 485 | "X_poly = poly_reg.fit_transform(X)\n" 486 | ] 487 | }, 488 | { 489 | "cell_type": "code", 490 | "execution_count": 77, 491 | "metadata": {}, 492 | "outputs": [ 493 | { 494 | "data": { 495 | "text/plain": [ 496 | "array([[ 1. , 0.52055395, -1.1250769 , ..., -1.93044717,\n", 497 | " -2.61679213, -3.54715794],\n", 498 | " [ 1. , 0.28104837, -0.53706982, ..., -0.09799818,\n", 499 | " -0.06199315, -0.03921656],\n", 500 | " [ 1. , 1.46924486, -1.25935736, ..., -0.53883334,\n", 501 | " -0.14536571, -0.03921656],\n", 502 | " ...,\n", 503 | " [ 1. , 1.14420158, -1.03178731, ..., 1.25352939,\n", 504 | " -1.43053502, 1.63253487],\n", 505 | " [ 1. , 0.86114953, -0.91305511, ..., 0.98163072,\n", 506 | " -1.26591721, 1.63253487],\n", 507 | " [ 1. , -0.32704695, -0.71516812, ..., 0.60224117,\n", 508 | " -0.99155419, 1.63253487]])" 509 | ] 510 | }, 511 | "execution_count": 77, 512 | "metadata": {}, 513 | "output_type": "execute_result" 514 | } 515 | ], 516 | "source": [ 517 | "X_poly" 518 | ] 519 | }, 520 | { 521 | "cell_type": "code", 522 | "execution_count": null, 523 | "metadata": {}, 524 | "outputs": [], 525 | "source": [] 526 | } 527 | ], 528 | "metadata": { 529 | "kernelspec": { 530 | "display_name": "Python 3", 531 | "language": "python", 532 | "name": "python3" 533 | }, 534 | "language_info": { 535 | "codemirror_mode": { 536 | "name": "ipython", 537 | "version": 3 538 | }, 539 | "file_extension": ".py", 540 | "mimetype": "text/x-python", 541 | "name": "python", 542 | "nbconvert_exporter": "python", 543 | "pygments_lexer": "ipython3", 544 | "version": "3.7.1" 545 | } 546 | }, 547 | "nbformat": 4, 548 | "nbformat_minor": 2 549 | } 550 | -------------------------------------------------------------------------------- /3_supervised_ml/regression/Salary_Data.csv: -------------------------------------------------------------------------------- 1 | YearsExperience,Salary 2 | 1.1,39343.00 3 | 1.3,46205.00 4 | 1.5,37731.00 5 | 2.0,43525.00 6 | 2.2,39891.00 7 | 2.9,56642.00 8 | 3.0,60150.00 9 | 3.2,54445.00 10 | 3.2,64445.00 11 | 3.7,57189.00 12 | 3.9,63218.00 13 | 4.0,55794.00 14 | 4.0,56957.00 15 | 4.1,57081.00 16 | 4.5,61111.00 17 | 4.9,67938.00 18 | 5.1,66029.00 19 | 5.3,83088.00 20 | 5.9,81363.00 21 | 6.0,93940.00 22 | 6.8,91738.00 23 | 7.1,98273.00 24 | 7.9,101302.00 25 | 8.2,113812.00 26 | 8.7,109431.00 27 | 9.0,105582.00 28 | 9.5,116969.00 29 | 9.6,112635.00 30 | 10.3,122391.00 31 | 10.5,121872.00 32 | -------------------------------------------------------------------------------- /3_supervised_ml/regression/data_2d.csv: -------------------------------------------------------------------------------- 1 | 0,1,2 2 | 97.14469719,69.59328198,404.6344715 3 | 81.77590078,5.737648097,181.4851077 4 | 55.85434242,70.32590168,321.773638 5 | 49.36654999,75.11404016,322.4654856 6 | 3.192702465,29.25629886,94.6188109 7 | 49.20078406,86.14443851,356.3480927 8 | 21.8828039,46.8415052,181.6537692 9 | 79.50986272,87.39735554,423.5577432 10 | 88.1538875,65.20564193,369.2292454 11 | 60.74385434,99.9576339,427.6058037 12 | 67.41558195,50.36830961,292.4718216 13 | 48.31811577,99.12895314,395.5298114 14 | 28.82997197,87.18494885,319.0313485 15 | 43.85374266,64.47363908,287.4281441 16 | 25.31369409,83.54529426,292.7689088 17 | 10.80772668,45.69556859,159.6633077 18 | 98.36574588,82.69739353,438.7989639 19 | 29.14690997,66.36510676,250.986309 20 | 65.1003019,33.3538835,231.7115079 21 | 24.64411349,39.54005274,163.3981608 22 | 37.55980488,1.345727842,83.48015514 23 | 88.16450624,95.15366257,466.2658058 24 | 13.83462084,25.4940482,100.8864303 25 | 64.41084375,77.25983813,365.6410481 26 | 68.9259918,97.4536008,426.1400155 27 | 39.48844224,50.85612819,235.5323895 28 | 52.46317768,59.77650969,283.2916403 29 | 48.48478698,66.97035422,298.5814404 30 | 8.062087814,98.24260014,309.2341088 31 | 32.73188771,18.85353553,129.610139 32 | 11.6523788,66.26451174,224.1505421 33 | 13.73035353,70.47250913,235.3056656 34 | 8.185551765,41.85198942,153.4841895 35 | 53.60987615,94.56012164,394.9394443 36 | 95.36860989,47.29550696,336.1267389 37 | 87.33360921,93.80393433,449.363352 38 | 66.35761109,81.84755128,387.0148165 39 | 19.75471753,65.52330092,240.3894416 40 | 21.13344045,47.43718199,177.1482806 41 | 22.37386481,25.95562754,119.611258 42 | 93.99040405,0.12789052,196.7161667 43 | 86.7201981,18.41376679,236.2608084 44 | 98.99837299,60.23126569,384.3813447 45 | 3.593965644,96.25221732,293.237183 46 | 15.10236337,92.55690357,304.8908828 47 | 97.83414077,2.023908104,201.2935984 48 | 19.93821969,46.77827346,170.610093 49 | 30.37351114,58.77752516,242.3734842 50 | 73.29288315,67.66962776,353.0829913 51 | 52.23090088,81.90244825,348.7256886 52 | 86.42957611,66.5402276,365.959971 53 | 93.40080214,18.07524594,235.4723816 54 | 13.21346006,91.48885878,300.6068783 55 | 4.593462704,46.33593152,145.8187453 56 | 15.66929158,35.543744,138.8803347 57 | 52.95935977,68.72020961,317.1637077 58 | 56.81752123,47.57273192,254.9036313 59 | 51.13354308,78.04216746,334.5843335 60 | 7.862164715,17.72908178,69.35558881 61 | 54.6986037,92.74458414,386.8599372 62 | 86.39906301,41.88869459,294.8717136 63 | 11.94750602,42.96138674,156.7542195 64 | 70.35840106,83.70623451,391.8061353 65 | 29.02236633,84.32778308,319.3104629 66 | 42.75947991,97.49332608,376.2915895 67 | 96.21565644,25.83428258,280.6170439 68 | 53.22772766,27.90550857,194.4304651 69 | 30.36098967,0.939644215,69.64886318 70 | 83.27756539,73.17934857,384.597185 71 | 30.18769248,7.146538599,89.5390084 72 | 11.78841846,51.69776084,181.5506828 73 | 18.29242401,61.97797605,224.7733829 74 | 96.71266769,9.029101513,219.5670937 75 | 31.01273869,78.28338246,298.4902165 76 | 11.39726075,61.72869324,199.9440447 77 | 17.39255579,4.241140863,43.91569235 78 | 72.18269373,34.53907217,256.0683782 79 | 73.98002079,3.716493438,159.3725806 80 | 94.49305835,88.41719702,447.1327036 81 | 84.56282073,20.24116219,233.0788296 82 | 51.74247397,11.00974796,131.0701796 83 | 53.7485904,60.0251023,298.8143327 84 | 85.05083476,95.73699695,451.803523 85 | 46.77725045,90.20220624,368.366436 86 | 49.75843417,52.83449436,254.706774 87 | 24.1192565,42.10281078,168.3084328 88 | 27.20157645,29.97874929,146.34226 89 | 7.009596168,55.87605839,176.810149 90 | 97.64694967,8.147625127,219.1602804 91 | 1.382982509,84.94408692,252.905653 92 | 22.32353035,27.51507504,127.5704788 93 | 45.04540623,93.52040222,375.8223403 94 | 40.16399147,0.161699235,80.38901933 95 | 53.18273979,8.170316162,142.7181831 96 | 46.45677916,82.00017091,336.8761544 97 | 77.13030069,95.18875945,438.4605861 98 | 68.60060757,72.57118072,355.9002869 99 | 41.69388712,69.24112597,284.8346367 100 | 4.142669398,52.25472638,168.0344009 101 | 17.93020121,94.52059195,320.2595296 102 | -------------------------------------------------------------------------------- /3_supervised_ml/regression/data_poly.csv: -------------------------------------------------------------------------------- 1 | 76.7007086033,663.797275569 2 | 95.2735441552,1014.3622816 3 | 73.0957232493,618.938826916 4 | 46.9516354572,288.012877367 5 | 33.3137480056,144.977555864 6 | 58.8001284334,412.327812022 7 | 86.4775958313,844.415014024 8 | 26.1438291437,89.351909563 9 | 97.6793058523,1053.20525097 10 | 43.7453159626,240.908778383 11 | 27.3960599569,115.936712225 12 | 97.7709380144,1034.55054041 13 | 0.477748646463,3.44508498982 14 | 45.8230852085,254.058885722 15 | 54.8058554165,353.273447055 16 | 90.1526491199,926.945048811 17 | 7.25055401942,30.7577367238 18 | 45.32559552,241.735203861 19 | 78.0299288822,692.797188617 20 | 56.9992954919,382.995647161 21 | 94.0058295504,978.718285869 22 | 26.9031773559,98.2290062278 23 | 23.0527128171,84.9785703587 24 | 72.1620607036,584.521550996 25 | 99.03856132,1082.25149734 26 | 3.8567282135,5.37807424483 27 | 29.1370660814,122.013630198 28 | 86.1406581482,841.297543579 29 | 30.6637414672,131.606512241 30 | 45.7038082016,267.357505947 31 | 20.8365297577,52.1846807158 32 | 57.1870229314,388.795855138 33 | 35.6458427285,155.657694842 34 | 19.3869531878,87.1260781016 35 | 21.9669393722,83.7232155716 36 | 68.2754384479,530.711261039 37 | 0.0499320826943,14.4928408734 38 | 88.0160403117,843.24850174 39 | 52.302680128,340.811945309 40 | 30.8083679391,128.066959237 41 | 36.7450495784,172.783502591 42 | 32.497674786,147.228458255 43 | 69.2984401147,538.180640028 44 | 8.50775977301,18.1584427007 45 | 70.340432818,559.410466793 46 | 57.5800635973,384.884606047 47 | 88.4806455787,875.860995014 48 | 18.4605500858,58.9483005575 49 | 55.8041981373,376.843030927 50 | 16.4395710474,24.9459291843 51 | 11.5900497701,32.6626606312 52 | 46.8705089511,262.885902286 53 | 84.33492545,798.336107587 54 | 47.0879172063,278.115426472 55 | 25.3595168427,104.171319469 56 | 57.5279893076,411.579592637 57 | 64.1988259571,471.645130755 58 | 4.21211019461,-4.60107066464 59 | 54.0943345308,363.848079875 60 | 74.1390699605,634.232884203 61 | 73.6494046203,614.61698055 62 | 36.128161214,165.31378043 63 | 52.0563603735,326.302161959 64 | 47.1946296354,282.326194483 65 | 38.4175210129,199.427772234 66 | 54.6207713889,349.23680114 67 | 64.7683580294,489.500982377 68 | 84.4005467356,805.013871913 69 | 21.4404800201,67.0955374565 70 | 73.2615461079,611.057504623 71 | 20.1698478817,69.446653099 72 | 31.4337548498,138.906777891 73 | 12.5179786376,48.9516001981 74 | 3.61761195371,5.32627355217 75 | 81.2515736807,731.3845794 76 | 56.8424497509,378.289439943 77 | 82.2665238066,748.343138324 78 | 37.4722110019,175.748739041 79 | 84.6946340015,800.047623475 80 | 39.7685020685,203.056324686 81 | 2.05616850915,6.23518914756 82 | 97.1247091279,1042.6919584 83 | 31.1291991206,130.113663552 84 | 84.6126409599,790.864163252 85 | 85.9561287143,827.455642687 86 | 80.9652012247,738.33866824 87 | 88.8979965529,893.81365673 88 | 58.3641047494,402.882690162 89 | 50.3024480011,314.117036852 90 | 12.7419734701,12.9681174938 91 | 77.320666256,687.654958048 92 | 98.9646405094,1089.94766197 93 | 56.3266253207,372.340305154 94 | 79.8360680928,722.043849621 95 | 84.2993047538,798.10497093 96 | 45.3458596295,251.755172506 97 | 3.13987798949,4.26261828882 98 | 70.1062835334,550.923455067 99 | 80.3106828908,728.06984768 100 | 72.0680436903,581.130210853 101 | -------------------------------------------------------------------------------- /4_model_evaluation/lessons/6. Evaluation Metrics.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Younes-Charfaoui/Machine-Learning-Beginner-Course/b39d9dea1d7f79c1b14590ea338a33d47e9ca49e/4_model_evaluation/lessons/6. Evaluation Metrics.pdf -------------------------------------------------------------------------------- /5_unsupervised_ml/Mall_Customers.csv: -------------------------------------------------------------------------------- 1 | CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100) 2 | 0001,Male,19,15,39 3 | 0002,Male,21,15,81 4 | 0003,Female,20,16,6 5 | 0004,Female,23,16,77 6 | 0005,Female,31,17,40 7 | 0006,Female,22,17,76 8 | 0007,Female,35,18,6 9 | 0008,Female,23,18,94 10 | 0009,Male,64,19,3 11 | 0010,Female,30,19,72 12 | 0011,Male,67,19,14 13 | 0012,Female,35,19,99 14 | 0013,Female,58,20,15 15 | 0014,Female,24,20,77 16 | 0015,Male,37,20,13 17 | 0016,Male,22,20,79 18 | 0017,Female,35,21,35 19 | 0018,Male,20,21,66 20 | 0019,Male,52,23,29 21 | 0020,Female,35,23,98 22 | 0021,Male,35,24,35 23 | 0022,Male,25,24,73 24 | 0023,Female,46,25,5 25 | 0024,Male,31,25,73 26 | 0025,Female,54,28,14 27 | 0026,Male,29,28,82 28 | 0027,Female,45,28,32 29 | 0028,Male,35,28,61 30 | 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0183,Male,46,98,15 185 | 0184,Female,29,98,88 186 | 0185,Female,41,99,39 187 | 0186,Male,30,99,97 188 | 0187,Female,54,101,24 189 | 0188,Male,28,101,68 190 | 0189,Female,41,103,17 191 | 0190,Female,36,103,85 192 | 0191,Female,34,103,23 193 | 0192,Female,32,103,69 194 | 0193,Male,33,113,8 195 | 0194,Female,38,113,91 196 | 0195,Female,47,120,16 197 | 0196,Female,35,120,79 198 | 0197,Female,45,126,28 199 | 0198,Male,32,126,74 200 | 0199,Male,32,137,18 201 | 0200,Male,30,137,83 -------------------------------------------------------------------------------- /MLBC Syllabus AR.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Younes-Charfaoui/Machine-Learning-Beginner-Course/b39d9dea1d7f79c1b14590ea338a33d47e9ca49e/MLBC Syllabus AR.pdf -------------------------------------------------------------------------------- /MLBC Syllabus EN.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Younes-Charfaoui/Machine-Learning-Beginner-Course/b39d9dea1d7f79c1b14590ea338a33d47e9ca49e/MLBC Syllabus EN.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Beginner 💻📚👨‍🏫 2 | The code of the projects will be developed, datasets and the slides of the course will be found in this repository, all pull requests, issues or any contributions are welcomed to enhance the program content. 3 | 4 | # Introduction 🎉💯🎓 5 | Machine Learning Beginner course (MLBC) is a course devoted to beginners in the domain of machine learning, this course will reveal the idea behind machine learning and its philosophy, the types, techniques, and tools to know as newcomers. We’ll kick off our Python and machine learning journey with the basic, yet important concepts of machine learning. We will start with what machine learning is about, why we need it. We will then discuss typical machine learning tasks and explore several essential techniques of working with data and working with models. It is a great starting point of the subject and we will learn it in a fun way. At the end of this course a final challenge will have a place to practice what we learned. 6 | 7 | # Registration 📚✅📌 8 | Please note that the places in this course are limited so hurry up and subscribe by providing your information and follow this [link](https://docs.google.com/forms/d/e/1FAIpQLSdWNNNypECt4ueQ6s8FucsZT3uNCgiMEET0O7QKjNFe0uOZwQ/viewform) to register into the course. 9 | 10 | # Place & Time 🕧⏳📅 11 | Every Wednesday on `12:30` in the Classroom Number 11 in the `B` Building Block 12 | 13 | # Outline 👨‍🎓📋📏 14 | -Introduction. 15 | -Python Refresher. 16 | -Data Preprocessing. 17 | -Supervised Machine Learning. 18 | -Model Evaluations. 19 | -Unsupervised Machine Learning. 20 | -Deep Learning. 21 | -------------------------------------------------------------------------------- /course_image.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Younes-Charfaoui/Machine-Learning-Beginner-Course/b39d9dea1d7f79c1b14590ea338a33d47e9ca49e/course_image.png --------------------------------------------------------------------------------