├── Decision Trees ├── Decision Trees with python │ ├── data │ │ └── kyphosis.csv │ └── project-1.ipynb └── Note │ └── New Text Document.txt ├── K Means Clustering ├── K Means Clustering Project │ ├── K Means Project.ipynb │ ├── data │ │ └── College.csv │ └── output.png └── K Means Clustering with python │ ├── K_Means_Clustering_with_python.ipynb │ └── output.png ├── K Nearest Neighbors (KNN) ├── K Nearest Neighbors (KNN) Project │ ├── Data │ │ └── KNN_Project_Data │ └── K Nearest Neighbors (KNN) Project.ipynb └── KNN with Python │ ├── Data │ └── Classified Data │ ├── KNN_with_Python.ipynb │ ├── KNN_with_Python2.ipynb │ └── output.png ├── Linear Regression ├── Linear Regression Project On Ecommerce Customer │ ├── Linear_Regression_Project_On Ecommerce_Customer.ipynb │ └── data │ │ └── Ecommerce Customers.csv └── Linear Regression with Python │ ├── Linear_Regression.ipynb │ └── data │ └── USA_Housing.csv ├── Logistic Regression ├── Advertising Project With Logistic Regression │ ├── Advertising Project With Logistic Regression.ipynb │ ├── Data │ │ └── Advertising.csv │ ├── output.png │ └── output2.png └── Logistic Regression with Python │ ├── Logistic_Regression_For_titanic_File.ipynb │ └── data │ └── titanic_train.csv ├── Natural Language Processing (NLP) ├── NLP Project │ ├── NLP Project.ipynb │ ├── data │ │ └── yelp.csv │ └── output.png └── NLP with Python │ ├── NLP with Python.ipynb │ └── data │ ├── SMSSpamCollection │ └── readme ├── Principal Component Analysis(PCA) ├── Note │ └── New Text Document.txt └── Principal Component Analysis(PCA) Project with python │ ├── PCA Project.ipynb │ └── output.png ├── README.md ├── Random Forest ├── Note │ └── New Text Document.txt └── Random Forest With Python │ ├── data │ └── kyphosis.csv │ └── project-1.ipynb └── Support Vector Machines(SVM) ├── SVM Project ├── SVM Project.ipynb └── output.png └── Support Vector Machines with Python └── Support Vector Machines with Python.ipynb /Decision Trees/Decision Trees with python/data/kyphosis.csv: -------------------------------------------------------------------------------- 1 | "Kyphosis","Age","Number","Start" 2 | "absent",71,3,5 3 | "absent",158,3,14 4 | "present",128,4,5 5 | "absent",2,5,1 6 | "absent",1,4,15 7 | "absent",1,2,16 8 | "absent",61,2,17 9 | "absent",37,3,16 10 | "absent",113,2,16 11 | "present",59,6,12 12 | "present",82,5,14 13 | "absent",148,3,16 14 | "absent",18,5,2 15 | "absent",1,4,12 16 | "absent",168,3,18 17 | "absent",1,3,16 18 | "absent",78,6,15 19 | "absent",175,5,13 20 | "absent",80,5,16 21 | "absent",27,4,9 22 | "absent",22,2,16 23 | "present",105,6,5 24 | "present",96,3,12 25 | "absent",131,2,3 26 | "present",15,7,2 27 | "absent",9,5,13 28 | "absent",8,3,6 29 | "absent",100,3,14 30 | "absent",4,3,16 31 | "absent",151,2,16 32 | "absent",31,3,16 33 | "absent",125,2,11 34 | "absent",130,5,13 35 | "absent",112,3,16 36 | "absent",140,5,11 37 | "absent",93,3,16 38 | "absent",1,3,9 39 | "present",52,5,6 40 | "absent",20,6,9 41 | "present",91,5,12 42 | "present",73,5,1 43 | "absent",35,3,13 44 | "absent",143,9,3 45 | "absent",61,4,1 46 | "absent",97,3,16 47 | "present",139,3,10 48 | "absent",136,4,15 49 | "absent",131,5,13 50 | "present",121,3,3 51 | "absent",177,2,14 52 | "absent",68,5,10 53 | "absent",9,2,17 54 | "present",139,10,6 55 | "absent",2,2,17 56 | "absent",140,4,15 57 | "absent",72,5,15 58 | "absent",2,3,13 59 | "present",120,5,8 60 | "absent",51,7,9 61 | "absent",102,3,13 62 | "present",130,4,1 63 | "present",114,7,8 64 | "absent",81,4,1 65 | "absent",118,3,16 66 | "absent",118,4,16 67 | "absent",17,4,10 68 | "absent",195,2,17 69 | "absent",159,4,13 70 | "absent",18,4,11 71 | "absent",15,5,16 72 | "absent",158,5,14 73 | "absent",127,4,12 74 | "absent",87,4,16 75 | "absent",206,4,10 76 | "absent",11,3,15 77 | "absent",178,4,15 78 | "present",157,3,13 79 | "absent",26,7,13 80 | "absent",120,2,13 81 | "present",42,7,6 82 | "absent",36,4,13 83 | -------------------------------------------------------------------------------- /Decision Trees/Note/New Text Document.txt: -------------------------------------------------------------------------------- 1 | hey there 2 | -------------------------------------------------------------------------------- /K Means Clustering/K Means Clustering Project/output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-ML-/05ad673aadcf1181de762d5af3288e7298043733/K Means Clustering/K Means Clustering Project/output.png -------------------------------------------------------------------------------- /K Means Clustering/K Means Clustering with python/output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-ML-/05ad673aadcf1181de762d5af3288e7298043733/K Means Clustering/K Means Clustering with python/output.png 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Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S 866 | 865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S 867 | 866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S 868 | 867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C 869 | 868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S 870 | 869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S 871 | 870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S 872 | 871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S 873 | 872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S 874 | 873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S 875 | 874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S 876 | 875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C 877 | 876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C 878 | 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S 879 | 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S 880 | 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S 881 | 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C 882 | 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S 883 | 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S 884 | 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S 885 | 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S 886 | 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S 887 | 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q 888 | 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S 889 | 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S 890 | 889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S 891 | 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C 892 | 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q 893 | -------------------------------------------------------------------------------- /Natural Language Processing (NLP)/NLP Project/output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-ML-/05ad673aadcf1181de762d5af3288e7298043733/Natural Language Processing (NLP)/NLP Project/output.png -------------------------------------------------------------------------------- /Natural Language Processing (NLP)/NLP with Python/NLP with Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 74, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "import seaborn as sns\n", 13 | "import plotly.graph_objs as go\n", 14 | "import cufflinks as cf\n", 15 | "import cv2\n", 16 | "import nltk\n", 17 | "import string\n", 18 | "\n", 19 | "\n", 20 | "from chart_studio import plotly as py \n", 21 | "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot\n", 22 | "from pandas_datareader import data,wb\n", 23 | "from nltk.corpus import stopwords\n", 24 | "\n", 25 | "%matplotlib inline" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 75, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "# nltk.download_shell()" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": {}, 40 | "source": [ 41 | "Loading Data" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 76, 47 | "metadata": {}, 48 | "outputs": [], 49 | "source": [ 50 | "messages = [line.rstrip() for line in open(r\".\\data\\SMSSpamCollection\")]" 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "metadata": {}, 56 | "source": [ 57 | "Data Processing" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 77, 63 | "metadata": {}, 64 | "outputs": [ 65 | { 66 | "name": "stdout", 67 | "output_type": "stream", 68 | "text": [ 69 | "5574\n" 70 | ] 71 | } 72 | ], 73 | "source": [ 74 | "print(len(messages))" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 78, 80 | "metadata": {}, 81 | "outputs": [ 82 | { 83 | "data": { 84 | "text/plain": [ 85 | "'ham\\tWhat you thinked about me. First time you saw me in class.'" 86 | ] 87 | }, 88 | "execution_count": 78, 89 | "metadata": {}, 90 | "output_type": "execute_result" 91 | } 92 | ], 93 | "source": [ 94 | "messages[50]" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 79, 100 | "metadata": {}, 101 | "outputs": [ 102 | { 103 | "name": "stdout", 104 | "output_type": "stream", 105 | "text": [ 106 | "0 ham\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n", 107 | "\n", 108 | "\n", 109 | "1 ham\tOk lar... Joking wif u oni...\n", 110 | "\n", 111 | "\n", 112 | "2 spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\n", 113 | "\n", 114 | "\n", 115 | "3 ham\tU dun say so early hor... U c already then say...\n", 116 | "\n", 117 | "\n", 118 | "4 ham\tNah I don't think he goes to usf, he lives around here though\n", 119 | "\n", 120 | "\n", 121 | "5 spam\tFreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50 to rcv\n", 122 | "\n", 123 | "\n", 124 | "6 ham\tEven my brother is not like to speak with me. They treat me like aids patent.\n", 125 | "\n", 126 | "\n", 127 | "7 ham\tAs per your request 'Melle Melle (Oru Minnaminunginte Nurungu Vettam)' has been set as your callertune for all Callers. Press *9 to copy your friends Callertune\n", 128 | "\n", 129 | "\n", 130 | "8 spam\tWINNER!! As a valued network customer you have been selected to receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.\n", 131 | "\n", 132 | "\n", 133 | "9 spam\tHad your mobile 11 months or more? U R entitled to Update to the latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on 08002986030\n", 134 | "\n", 135 | "\n" 136 | ] 137 | } 138 | ], 139 | "source": [ 140 | "for mess_no, messages in enumerate(messages[:10]):\n", 141 | " print(mess_no,messages)\n", 142 | " print('\\n')" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 80, 148 | "metadata": {}, 149 | "outputs": [], 150 | "source": [ 151 | "messages = pd.read_csv(r\".\\data\\SMSSpamCollection\", sep=\"\\t\",\n", 152 | " names=[\"label\",\"message\"])" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 81, 158 | "metadata": {}, 159 | "outputs": [ 160 | { 161 | "data": { 162 | "text/html": [ 163 | "
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labelmessage
0hamGo until jurong point, crazy.. Available only ...
1hamOk lar... Joking wif u oni...
2spamFree entry in 2 a wkly comp to win FA Cup fina...
3hamU dun say so early hor... U c already then say...
4hamNah I don't think he goes to usf, he lives aro...
\n", 213 | "
" 214 | ], 215 | "text/plain": [ 216 | " label message\n", 217 | "0 ham Go until jurong point, crazy.. Available only ...\n", 218 | "1 ham Ok lar... Joking wif u oni...\n", 219 | "2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n", 220 | "3 ham U dun say so early hor... U c already then say...\n", 221 | "4 ham Nah I don't think he goes to usf, he lives aro..." 222 | ] 223 | }, 224 | "execution_count": 81, 225 | "metadata": {}, 226 | "output_type": "execute_result" 227 | } 228 | ], 229 | "source": [ 230 | "messages.head()" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": 82, 236 | "metadata": {}, 237 | "outputs": [ 238 | { 239 | "data": { 240 | "text/html": [ 241 | "
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labelmessage
count55725572
unique25169
tophamSorry, I'll call later
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0hamGo until jurong point, crazy.. Available only ...111
1hamOk lar... Joking wif u oni...29
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4hamNah I don't think he goes to usf, he lives aro...61
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" 494 | ], 495 | "text/plain": [ 496 | " label message length\n", 497 | "0 ham Go until jurong point, crazy.. Available only ... 111\n", 498 | "1 ham Ok lar... Joking wif u oni... 29\n", 499 | "2 spam Free entry in 2 a wkly comp to win FA Cup fina... 155\n", 500 | "3 ham U dun say so early hor... U c already then say... 49\n", 501 | "4 ham Nah I don't think he goes to usf, he lives aro... 61" 502 | ] 503 | }, 504 | "execution_count": 86, 505 | "metadata": {}, 506 | "output_type": "execute_result" 507 | } 508 | ], 509 | "source": [ 510 | "messages.head()" 511 | ] 512 | }, 513 | { 514 | "cell_type": "code", 515 | "execution_count": 87, 516 | "metadata": {}, 517 | "outputs": [ 518 | { 519 | "data": { 520 | "text/plain": [ 521 | "" 522 | ] 523 | }, 524 | "execution_count": 87, 525 | "metadata": {}, 526 | "output_type": "execute_result" 527 | }, 528 | { 529 | "data": { 530 | "image/png": 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" 614 | ], 615 | "text/plain": [ 616 | " label message length\n", 617 | "1085 ham For me the love should start with attraction.i... 910" 618 | ] 619 | }, 620 | "execution_count": 89, 621 | "metadata": {}, 622 | "output_type": "execute_result" 623 | } 624 | ], 625 | "source": [ 626 | "messages[messages['length'] == 910]" 627 | ] 628 | }, 629 | { 630 | "cell_type": "code", 631 | "execution_count": 90, 632 | "metadata": {}, 633 | "outputs": [ 634 | { 635 | "data": { 636 | "text/plain": [ 637 | "\"For me the love should start with attraction.i should feel that I need her every time around me.she should be the first thing which comes in my thoughts.I would start the day and end it with her.she should be there every time I dream.love will be then when my every breath has her name.my life should happen around her.my life will be named to her.I would cry for her.will give all my happiness and take all her sorrows.I will be ready to fight with anyone for her.I will be in love when I will be doing the craziest things for her.love will be when I don't have to proove anyone that my girl is the most beautiful lady on the whole planet.I will always be singing praises for her.love will be when I start up making chicken curry and end up makiing sambar.life will be the most beautiful then.will get every morning and thank god for the day because she is with me.I would like to say a lot..will tell later..\"" 638 | ] 639 | }, 640 | "execution_count": 90, 641 | "metadata": {}, 642 | "output_type": "execute_result" 643 | } 644 | ], 645 | "source": [ 646 | "messages[messages['length'] == 910]['message'].iloc[0]" 647 | ] 648 | }, 649 | { 650 | "cell_type": "code", 651 | "execution_count": 91, 652 | "metadata": {}, 653 | "outputs": [ 654 | { 655 | "data": { 656 | "text/plain": [ 657 | "array([,\n", 658 | " ], dtype=object)" 659 | ] 660 | }, 661 | "execution_count": 91, 662 | "metadata": {}, 663 | "output_type": "execute_result" 664 | }, 665 | { 666 | "data": { 667 | "image/png": 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", 668 | "text/plain": [ 669 | "
" 670 | ] 671 | }, 672 | "metadata": { 673 | "needs_background": "light" 674 | }, 675 | "output_type": "display_data" 676 | } 677 | ], 678 | "source": [ 679 | "messages.hist(column=\"length\", by=\"label\", bins=60, figsize=(12,4))" 680 | ] 681 | }, 682 | { 683 | "cell_type": "code", 684 | "execution_count": 92, 685 | "metadata": {}, 686 | "outputs": [], 687 | "source": [ 688 | "mess = \"Sample message! Notice: it has punctuation.\"" 689 | ] 690 | }, 691 | { 692 | "cell_type": "code", 693 | "execution_count": 93, 694 | "metadata": {}, 695 | "outputs": [], 696 | "source": [ 697 | "nopunc = [c for c in mess if c not in string.punctuation]" 698 | ] 699 | }, 700 | { 701 | "cell_type": "code", 702 | "execution_count": 94, 703 | "metadata": {}, 704 | "outputs": [ 705 | { 706 | "data": { 707 | "text/plain": [ 708 | "['S', 'a', 'm', 'p', 'l']" 709 | ] 710 | }, 711 | "execution_count": 94, 712 | "metadata": {}, 713 | "output_type": "execute_result" 714 | } 715 | ], 716 | "source": [ 717 | "nopunc[:5]" 718 | ] 719 | }, 720 | { 721 | "cell_type": "code", 722 | "execution_count": 95, 723 | "metadata": {}, 724 | "outputs": [ 725 | { 726 | "data": { 727 | "text/plain": [ 728 | "['i',\n", 729 | " 'me',\n", 730 | " 'my',\n", 731 | " 'myself',\n", 732 | " 'we',\n", 733 | " 'our',\n", 734 | " 'ours',\n", 735 | " 'ourselves',\n", 736 | " 'you',\n", 737 | " \"you're\",\n", 738 | " \"you've\",\n", 739 | " \"you'll\",\n", 740 | " \"you'd\",\n", 741 | " 'your',\n", 742 | " 'yours',\n", 743 | " 'yourself',\n", 744 | " 'yourselves',\n", 745 | " 'he',\n", 746 | " 'him',\n", 747 | " 'his',\n", 748 | " 'himself',\n", 749 | " 'she',\n", 750 | " \"she's\",\n", 751 | " 'her',\n", 752 | " 'hers',\n", 753 | " 'herself',\n", 754 | " 'it',\n", 755 | " \"it's\",\n", 756 | " 'its',\n", 757 | " 'itself',\n", 758 | " 'they',\n", 759 | " 'them',\n", 760 | " 'their',\n", 761 | " 'theirs',\n", 762 | " 'themselves',\n", 763 | " 'what',\n", 764 | " 'which',\n", 765 | " 'who',\n", 766 | " 'whom',\n", 767 | " 'this',\n", 768 | " 'that',\n", 769 | " \"that'll\",\n", 770 | " 'these',\n", 771 | " 'those',\n", 772 | " 'am',\n", 773 | " 'is',\n", 774 | " 'are',\n", 775 | " 'was',\n", 776 | " 'were',\n", 777 | " 'be',\n", 778 | " 'been',\n", 779 | " 'being',\n", 780 | " 'have',\n", 781 | " 'has',\n", 782 | " 'had',\n", 783 | " 'having',\n", 784 | " 'do',\n", 785 | " 'does',\n", 786 | " 'did',\n", 787 | " 'doing',\n", 788 | " 'a',\n", 789 | " 'an',\n", 790 | " 'the',\n", 791 | " 'and',\n", 792 | " 'but',\n", 793 | " 'if',\n", 794 | " 'or',\n", 795 | " 'because',\n", 796 | " 'as',\n", 797 | " 'until',\n", 798 | " 'while',\n", 799 | " 'of',\n", 800 | " 'at',\n", 801 | " 'by',\n", 802 | " 'for',\n", 803 | " 'with',\n", 804 | " 'about',\n", 805 | " 'against',\n", 806 | " 'between',\n", 807 | " 'into',\n", 808 | " 'through',\n", 809 | " 'during',\n", 810 | " 'before',\n", 811 | " 'after',\n", 812 | " 'above',\n", 813 | " 'below',\n", 814 | " 'to',\n", 815 | " 'from',\n", 816 | " 'up',\n", 817 | " 'down',\n", 818 | " 'in',\n", 819 | " 'out',\n", 820 | " 'on',\n", 821 | " 'off',\n", 822 | " 'over',\n", 823 | " 'under',\n", 824 | " 'again',\n", 825 | " 'further',\n", 826 | " 'then',\n", 827 | " 'once',\n", 828 | " 'here',\n", 829 | " 'there',\n", 830 | " 'when',\n", 831 | " 'where',\n", 832 | " 'why',\n", 833 | " 'how',\n", 834 | " 'all',\n", 835 | " 'any',\n", 836 | " 'both',\n", 837 | " 'each',\n", 838 | " 'few',\n", 839 | " 'more',\n", 840 | " 'most',\n", 841 | " 'other',\n", 842 | " 'some',\n", 843 | " 'such',\n", 844 | " 'no',\n", 845 | " 'nor',\n", 846 | " 'not',\n", 847 | " 'only',\n", 848 | " 'own',\n", 849 | " 'same',\n", 850 | " 'so',\n", 851 | " 'than',\n", 852 | " 'too',\n", 853 | " 'very',\n", 854 | " 's',\n", 855 | " 't',\n", 856 | " 'can',\n", 857 | " 'will',\n", 858 | " 'just',\n", 859 | " 'don',\n", 860 | " \"don't\",\n", 861 | " 'should',\n", 862 | " \"should've\",\n", 863 | " 'now',\n", 864 | " 'd',\n", 865 | " 'll',\n", 866 | " 'm',\n", 867 | " 'o',\n", 868 | " 're',\n", 869 | " 've',\n", 870 | " 'y',\n", 871 | " 'ain',\n", 872 | " 'aren',\n", 873 | " \"aren't\",\n", 874 | " 'couldn',\n", 875 | " \"couldn't\",\n", 876 | " 'didn',\n", 877 | " \"didn't\",\n", 878 | " 'doesn',\n", 879 | " \"doesn't\",\n", 880 | " 'hadn',\n", 881 | " \"hadn't\",\n", 882 | " 'hasn',\n", 883 | " \"hasn't\",\n", 884 | " 'haven',\n", 885 | " \"haven't\",\n", 886 | " 'isn',\n", 887 | " \"isn't\",\n", 888 | " 'ma',\n", 889 | " 'mightn',\n", 890 | " \"mightn't\",\n", 891 | " 'mustn',\n", 892 | " \"mustn't\",\n", 893 | " 'needn',\n", 894 | " \"needn't\",\n", 895 | " 'shan',\n", 896 | " \"shan't\",\n", 897 | " 'shouldn',\n", 898 | " \"shouldn't\",\n", 899 | " 'wasn',\n", 900 | " \"wasn't\",\n", 901 | " 'weren',\n", 902 | " \"weren't\",\n", 903 | " 'won',\n", 904 | " \"won't\",\n", 905 | " 'wouldn',\n", 906 | " \"wouldn't\"]" 907 | ] 908 | }, 909 | "execution_count": 95, 910 | "metadata": {}, 911 | "output_type": "execute_result" 912 | } 913 | ], 914 | "source": [ 915 | "stopwords.words(\"english\")" 916 | ] 917 | }, 918 | { 919 | "cell_type": "code", 920 | "execution_count": 96, 921 | "metadata": {}, 922 | "outputs": [], 923 | "source": [ 924 | "nopunc = ''.join(nopunc)" 925 | ] 926 | }, 927 | { 928 | "cell_type": "code", 929 | "execution_count": 97, 930 | "metadata": {}, 931 | "outputs": [ 932 | { 933 | "data": { 934 | "text/plain": [ 935 | "'Sample message Notice it has punctuation'" 936 | ] 937 | }, 938 | "execution_count": 97, 939 | "metadata": {}, 940 | "output_type": "execute_result" 941 | } 942 | ], 943 | "source": [ 944 | "nopunc" 945 | ] 946 | }, 947 | { 948 | "cell_type": "code", 949 | "execution_count": 98, 950 | "metadata": {}, 951 | "outputs": [], 952 | "source": [ 953 | "x = ['a','b','c']" 954 | ] 955 | }, 956 | { 957 | "cell_type": "code", 958 | "execution_count": 99, 959 | "metadata": {}, 960 | "outputs": [ 961 | { 962 | "data": { 963 | "text/plain": [ 964 | "'a+++b+++c'" 965 | ] 966 | }, 967 | "execution_count": 99, 968 | "metadata": {}, 969 | "output_type": "execute_result" 970 | } 971 | ], 972 | "source": [ 973 | "'+++'.join(x)" 974 | ] 975 | }, 976 | { 977 | "cell_type": "code", 978 | "execution_count": 100, 979 | "metadata": {}, 980 | "outputs": [ 981 | { 982 | "data": { 983 | "text/plain": [ 984 | "['Sample', 'message', 'Notice', 'it', 'has', 'punctuation']" 985 | ] 986 | }, 987 | "execution_count": 100, 988 | "metadata": {}, 989 | "output_type": "execute_result" 990 | } 991 | ], 992 | "source": [ 993 | "nopunc.split()" 994 | ] 995 | }, 996 | { 997 | "cell_type": "code", 998 | "execution_count": 101, 999 | "metadata": {}, 1000 | "outputs": [], 1001 | "source": [ 1002 | "class_mess = [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]" 1003 | ] 1004 | }, 1005 | { 1006 | "cell_type": "code", 1007 | "execution_count": 102, 1008 | "metadata": {}, 1009 | "outputs": [ 1010 | { 1011 | "data": { 1012 | "text/plain": [ 1013 | "['Sample', 'message', 'Notice', 'punctuation']" 1014 | ] 1015 | }, 1016 | "execution_count": 102, 1017 | "metadata": {}, 1018 | "output_type": "execute_result" 1019 | } 1020 | ], 1021 | "source": [ 1022 | "class_mess" 1023 | ] 1024 | }, 1025 | { 1026 | "cell_type": "markdown", 1027 | "metadata": {}, 1028 | "source": [ 1029 | "Tokenized" 1030 | ] 1031 | }, 1032 | { 1033 | "cell_type": "code", 1034 | "execution_count": 103, 1035 | "metadata": {}, 1036 | "outputs": [], 1037 | "source": [ 1038 | "def text_process(mess):\n", 1039 | " \"\"\"\n", 1040 | " 1. remove punc\n", 1041 | " 2. remove stop word\n", 1042 | " 3. return list of clean text words\n", 1043 | " \"\"\"\n", 1044 | "\n", 1045 | " nopunc = [char for char in mess if char not in string.punctuation]\n", 1046 | " nopunc = \"\".join(nopunc)\n", 1047 | "\n", 1048 | " return [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]\n", 1049 | "\n", 1050 | " " 1051 | ] 1052 | }, 1053 | { 1054 | "cell_type": "code", 1055 | "execution_count": 104, 1056 | "metadata": {}, 1057 | "outputs": [ 1058 | { 1059 | "data": { 1060 | "text/html": [ 1061 | "
\n", 1062 | "\n", 1075 | "\n", 1076 | " \n", 1077 | " \n", 1078 | " \n", 1079 | " \n", 1080 | " \n", 1081 | " \n", 1082 | " \n", 1083 | " \n", 1084 | " \n", 1085 | " \n", 1086 | " \n", 1087 | " \n", 1088 | " \n", 1089 | " \n", 1090 | " \n", 1091 | " \n", 1092 | " \n", 1093 | " \n", 1094 | " \n", 1095 | " \n", 1096 | " \n", 1097 | " \n", 1098 | " \n", 1099 | " \n", 1100 | " \n", 1101 | " \n", 1102 | " \n", 1103 | " \n", 1104 | " \n", 1105 | " \n", 1106 | " \n", 1107 | " \n", 1108 | " \n", 1109 | " \n", 1110 | " \n", 1111 | " \n", 1112 | " \n", 1113 | " \n", 1114 | " \n", 1115 | " \n", 1116 | "
labelmessagelength
0hamGo until jurong point, crazy.. Available only ...111
1hamOk lar... Joking wif u oni...29
2spamFree entry in 2 a wkly comp to win FA Cup fina...155
3hamU dun say so early hor... U c already then say...49
4hamNah I don't think he goes to usf, he lives aro...61
\n", 1117 | "
" 1118 | ], 1119 | "text/plain": [ 1120 | " label message length\n", 1121 | "0 ham Go until jurong point, crazy.. Available only ... 111\n", 1122 | "1 ham Ok lar... Joking wif u oni... 29\n", 1123 | "2 spam Free entry in 2 a wkly comp to win FA Cup fina... 155\n", 1124 | "3 ham U dun say so early hor... U c already then say... 49\n", 1125 | "4 ham Nah I don't think he goes to usf, he lives aro... 61" 1126 | ] 1127 | }, 1128 | "execution_count": 104, 1129 | "metadata": {}, 1130 | "output_type": "execute_result" 1131 | } 1132 | ], 1133 | "source": [ 1134 | "messages.head()" 1135 | ] 1136 | }, 1137 | { 1138 | "cell_type": "code", 1139 | "execution_count": 105, 1140 | "metadata": {}, 1141 | "outputs": [ 1142 | { 1143 | "data": { 1144 | "text/plain": [ 1145 | "0 [Go, jurong, point, crazy, Available, bugis, n...\n", 1146 | "1 [Ok, lar, Joking, wif, u, oni]\n", 1147 | "2 [Free, entry, 2, wkly, comp, win, FA, Cup, fin...\n", 1148 | "3 [U, dun, say, early, hor, U, c, already, say]\n", 1149 | "4 [Nah, dont, think, goes, usf, lives, around, t...\n", 1150 | "Name: message, dtype: object" 1151 | ] 1152 | }, 1153 | "execution_count": 105, 1154 | "metadata": {}, 1155 | "output_type": "execute_result" 1156 | } 1157 | ], 1158 | "source": [ 1159 | "messages['message'].head(5).apply(text_process)" 1160 | ] 1161 | }, 1162 | { 1163 | "cell_type": "code", 1164 | "execution_count": 106, 1165 | "metadata": {}, 1166 | "outputs": [ 1167 | { 1168 | "data": { 1169 | "text/plain": [ 1170 | "CountVectorizer(analyzer=)" 1171 | ] 1172 | }, 1173 | "execution_count": 106, 1174 | "metadata": {}, 1175 | "output_type": "execute_result" 1176 | } 1177 | ], 1178 | "source": [ 1179 | "from sklearn.feature_extraction.text import CountVectorizer\n", 1180 | "bow_transforner = CountVectorizer(analyzer=text_process)\n", 1181 | "bow_transforner.fit(messages[\"message\"])" 1182 | ] 1183 | }, 1184 | { 1185 | "cell_type": "code", 1186 | "execution_count": 107, 1187 | "metadata": {}, 1188 | "outputs": [ 1189 | { 1190 | "name": "stdout", 1191 | "output_type": "stream", 1192 | "text": [ 1193 | "11425\n" 1194 | ] 1195 | } 1196 | ], 1197 | "source": [ 1198 | "print(len(bow_transforner.vocabulary_))" 1199 | ] 1200 | }, 1201 | { 1202 | "cell_type": "code", 1203 | "execution_count": 108, 1204 | "metadata": {}, 1205 | "outputs": [], 1206 | "source": [ 1207 | "mess4 = messages['message'][3]" 1208 | ] 1209 | }, 1210 | { 1211 | "cell_type": "code", 1212 | "execution_count": 109, 1213 | "metadata": {}, 1214 | "outputs": [ 1215 | { 1216 | "name": "stdout", 1217 | "output_type": "stream", 1218 | "text": [ 1219 | "U dun say so early hor... U c already then say...\n" 1220 | ] 1221 | } 1222 | ], 1223 | "source": [ 1224 | "print(mess4)" 1225 | ] 1226 | }, 1227 | { 1228 | "cell_type": "code", 1229 | "execution_count": 110, 1230 | "metadata": {}, 1231 | "outputs": [], 1232 | "source": [ 1233 | "bow4 = bow_transforner.transform([mess4])" 1234 | ] 1235 | }, 1236 | { 1237 | "cell_type": "code", 1238 | "execution_count": 111, 1239 | "metadata": {}, 1240 | "outputs": [ 1241 | { 1242 | "name": "stdout", 1243 | "output_type": "stream", 1244 | "text": [ 1245 | " (0, 4068)\t2\n", 1246 | " (0, 4629)\t1\n", 1247 | " (0, 5261)\t1\n", 1248 | " (0, 6204)\t1\n", 1249 | " (0, 6222)\t1\n", 1250 | " (0, 7186)\t1\n", 1251 | " (0, 9554)\t2\n" 1252 | ] 1253 | } 1254 | ], 1255 | "source": [ 1256 | "print(bow4)" 1257 | ] 1258 | }, 1259 | { 1260 | "cell_type": "code", 1261 | "execution_count": 112, 1262 | "metadata": {}, 1263 | "outputs": [ 1264 | { 1265 | "name": "stdout", 1266 | "output_type": "stream", 1267 | "text": [ 1268 | "(1, 11425)\n" 1269 | ] 1270 | } 1271 | ], 1272 | "source": [ 1273 | "print(bow4.shape)" 1274 | ] 1275 | }, 1276 | { 1277 | "cell_type": "code", 1278 | "execution_count": 113, 1279 | "metadata": {}, 1280 | "outputs": [ 1281 | { 1282 | "name": "stderr", 1283 | "output_type": "stream", 1284 | "text": [ 1285 | "C:\\Users\\hp\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning:\n", 1286 | "\n", 1287 | "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n", 1288 | "\n" 1289 | ] 1290 | }, 1291 | { 1292 | "data": { 1293 | "text/plain": [ 1294 | "'say'" 1295 | ] 1296 | }, 1297 | "execution_count": 113, 1298 | "metadata": {}, 1299 | "output_type": "execute_result" 1300 | } 1301 | ], 1302 | "source": [ 1303 | "bow_transforner.get_feature_names()[9554]" 1304 | ] 1305 | }, 1306 | { 1307 | "cell_type": "code", 1308 | "execution_count": 114, 1309 | "metadata": {}, 1310 | "outputs": [], 1311 | "source": [ 1312 | "messages_bow = bow_transforner.transform(messages['message'])" 1313 | ] 1314 | }, 1315 | { 1316 | "cell_type": "code", 1317 | "execution_count": 115, 1318 | "metadata": {}, 1319 | "outputs": [ 1320 | { 1321 | "name": "stdout", 1322 | "output_type": "stream", 1323 | "text": [ 1324 | "Shape of Sparse Matrix: (5572, 11425)\n" 1325 | ] 1326 | } 1327 | ], 1328 | "source": [ 1329 | "print('Shape of Sparse Matrix: ', messages_bow.shape)" 1330 | ] 1331 | }, 1332 | { 1333 | "cell_type": "code", 1334 | "execution_count": 116, 1335 | "metadata": {}, 1336 | "outputs": [ 1337 | { 1338 | "data": { 1339 | "text/plain": [ 1340 | "50548" 1341 | ] 1342 | }, 1343 | "execution_count": 116, 1344 | "metadata": {}, 1345 | "output_type": "execute_result" 1346 | } 1347 | ], 1348 | "source": [ 1349 | "messages_bow.nnz" 1350 | ] 1351 | }, 1352 | { 1353 | "cell_type": "code", 1354 | "execution_count": 117, 1355 | "metadata": {}, 1356 | "outputs": [ 1357 | { 1358 | "name": "stdout", 1359 | "output_type": "stream", 1360 | "text": [ 1361 | "sparsity: 10364517.229002154\n" 1362 | ] 1363 | } 1364 | ], 1365 | "source": [ 1366 | "sparsity = (100.0 * messages_bow.nnz / messages_bow.shape[0] * messages_bow.shape[1])\n", 1367 | "print('sparsity: {}'.format(sparsity))" 1368 | ] 1369 | }, 1370 | { 1371 | "cell_type": "code", 1372 | "execution_count": 118, 1373 | "metadata": {}, 1374 | "outputs": [ 1375 | { 1376 | "data": { 1377 | "text/plain": [ 1378 | "TfidfTransformer()" 1379 | ] 1380 | }, 1381 | "execution_count": 118, 1382 | "metadata": {}, 1383 | "output_type": "execute_result" 1384 | } 1385 | ], 1386 | "source": [ 1387 | "from sklearn.feature_extraction.text import TfidfTransformer\n", 1388 | "tfidf_transformer = TfidfTransformer()\n", 1389 | "tfidf_transformer.fit(messages_bow)" 1390 | ] 1391 | }, 1392 | { 1393 | "cell_type": "code", 1394 | "execution_count": 119, 1395 | "metadata": {}, 1396 | "outputs": [], 1397 | "source": [ 1398 | "tfidf4 = tfidf_transformer.transform(bow4)" 1399 | ] 1400 | }, 1401 | { 1402 | "cell_type": "code", 1403 | "execution_count": 120, 1404 | "metadata": {}, 1405 | "outputs": [ 1406 | { 1407 | "name": "stdout", 1408 | "output_type": "stream", 1409 | "text": [ 1410 | " (0, 9554)\t0.5385626262927564\n", 1411 | " (0, 7186)\t0.4389365653379857\n", 1412 | " (0, 6222)\t0.3187216892949149\n", 1413 | " (0, 6204)\t0.29953799723697416\n", 1414 | " (0, 5261)\t0.29729957405868723\n", 1415 | " (0, 4629)\t0.26619801906087187\n", 1416 | " (0, 4068)\t0.40832589933384067\n" 1417 | ] 1418 | } 1419 | ], 1420 | "source": [ 1421 | "print(tfidf4)" 1422 | ] 1423 | }, 1424 | { 1425 | "cell_type": "code", 1426 | "execution_count": 121, 1427 | "metadata": {}, 1428 | "outputs": [ 1429 | { 1430 | "data": { 1431 | "text/plain": [ 1432 | "8.527076498901426" 1433 | ] 1434 | }, 1435 | "execution_count": 121, 1436 | "metadata": {}, 1437 | "output_type": "execute_result" 1438 | } 1439 | ], 1440 | "source": [ 1441 | "tfidf_transformer.idf_[bow_transforner.vocabulary_['university']]" 1442 | ] 1443 | }, 1444 | { 1445 | "cell_type": "code", 1446 | "execution_count": 122, 1447 | "metadata": {}, 1448 | "outputs": [], 1449 | "source": [ 1450 | "messages_tfidf = tfidf_transformer.transform(messages_bow)" 1451 | ] 1452 | }, 1453 | { 1454 | "cell_type": "code", 1455 | "execution_count": 123, 1456 | "metadata": {}, 1457 | "outputs": [ 1458 | { 1459 | "data": { 1460 | "text/plain": [ 1461 | "MultinomialNB()" 1462 | ] 1463 | }, 1464 | "execution_count": 123, 1465 | "metadata": {}, 1466 | "output_type": "execute_result" 1467 | } 1468 | ], 1469 | "source": [ 1470 | "from sklearn.naive_bayes import MultinomialNB\n", 1471 | "spam_detect_model = MultinomialNB()\n", 1472 | "spam_detect_model.fit(messages_tfidf, messages['label'])" 1473 | ] 1474 | }, 1475 | { 1476 | "cell_type": "code", 1477 | "execution_count": 124, 1478 | "metadata": {}, 1479 | "outputs": [ 1480 | { 1481 | "data": { 1482 | "text/plain": [ 1483 | "'ham'" 1484 | ] 1485 | }, 1486 | "execution_count": 124, 1487 | "metadata": {}, 1488 | "output_type": "execute_result" 1489 | } 1490 | ], 1491 | "source": [ 1492 | "spam_detect_model.predict(tfidf4)[0]" 1493 | ] 1494 | }, 1495 | { 1496 | "cell_type": "code", 1497 | "execution_count": 125, 1498 | "metadata": {}, 1499 | "outputs": [ 1500 | { 1501 | "data": { 1502 | "text/plain": [ 1503 | "'ham'" 1504 | ] 1505 | }, 1506 | "execution_count": 125, 1507 | "metadata": {}, 1508 | "output_type": "execute_result" 1509 | } 1510 | ], 1511 | "source": [ 1512 | "messages['label'][3]" 1513 | ] 1514 | }, 1515 | { 1516 | "cell_type": "code", 1517 | "execution_count": 126, 1518 | "metadata": {}, 1519 | "outputs": [], 1520 | "source": [ 1521 | "all_pred = spam_detect_model.predict(messages_tfidf)" 1522 | ] 1523 | }, 1524 | { 1525 | "cell_type": "code", 1526 | "execution_count": 127, 1527 | "metadata": {}, 1528 | "outputs": [ 1529 | { 1530 | "data": { 1531 | "text/plain": [ 1532 | "array(['ham', 'ham', 'spam', ..., 'ham', 'ham', 'ham'], dtype=')),\n", 1604 | " ('tfidf', TfidfTransformer()),\n", 1605 | " ('classifier', MultinomialNB())])" 1606 | ] 1607 | }, 1608 | "execution_count": 131, 1609 | "metadata": {}, 1610 | "output_type": "execute_result" 1611 | } 1612 | ], 1613 | "source": [ 1614 | "pipeline_model = Pipeline([\n", 1615 | " ('bow',CountVectorizer(analyzer=text_process)),\n", 1616 | " ('tfidf',TfidfTransformer()),\n", 1617 | " ('classifier',MultinomialNB())\n", 1618 | "])\n", 1619 | "\n", 1620 | "pipeline_model.fit(msg_train,label_train)" 1621 | ] 1622 | }, 1623 | { 1624 | "cell_type": "code", 1625 | "execution_count": 132, 1626 | "metadata": {}, 1627 | "outputs": [], 1628 | "source": [ 1629 | "predictions = pipeline_model.predict(msg_test)" 1630 | ] 1631 | }, 1632 | { 1633 | "cell_type": "code", 1634 | "execution_count": 133, 1635 | "metadata": {}, 1636 | "outputs": [ 1637 | { 1638 | "name": "stdout", 1639 | "output_type": "stream", 1640 | "text": [ 1641 | " precision recall f1-score support\n", 1642 | "\n", 1643 | " ham 0.95 1.00 0.98 1445\n", 1644 | " spam 1.00 0.68 0.81 227\n", 1645 | "\n", 1646 | " accuracy 0.96 1672\n", 1647 | " macro avg 0.98 0.84 0.89 1672\n", 1648 | "weighted avg 0.96 0.96 0.95 1672\n", 1649 | "\n" 1650 | ] 1651 | } 1652 | ], 1653 | "source": [ 1654 | "from sklearn.metrics import classification_report\n", 1655 | "print(classification_report(label_test, predictions))" 1656 | ] 1657 | } 1658 | ], 1659 | "metadata": { 1660 | "interpreter": { 1661 | "hash": "b89b5cfaba6639976dc87ff2fec6d58faec662063367e2c229c520fe71072417" 1662 | }, 1663 | "kernelspec": { 1664 | "display_name": "Python 3.10.1 64-bit", 1665 | "language": "python", 1666 | "name": "python3" 1667 | }, 1668 | "language_info": { 1669 | "codemirror_mode": { 1670 | "name": "ipython", 1671 | "version": 3 1672 | }, 1673 | "file_extension": ".py", 1674 | "mimetype": "text/x-python", 1675 | "name": "python", 1676 | "nbconvert_exporter": "python", 1677 | "pygments_lexer": "ipython3", 1678 | "version": "3.10.1" 1679 | }, 1680 | "orig_nbformat": 4 1681 | }, 1682 | "nbformat": 4, 1683 | "nbformat_minor": 2 1684 | } 1685 | -------------------------------------------------------------------------------- /Natural Language Processing (NLP)/NLP with Python/data/readme: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-ML-/05ad673aadcf1181de762d5af3288e7298043733/Natural Language Processing (NLP)/NLP with Python/data/readme -------------------------------------------------------------------------------- /Principal Component Analysis(PCA)/Note/New Text Document.txt: -------------------------------------------------------------------------------- 1 | hey there 2 | -------------------------------------------------------------------------------- /Principal Component Analysis(PCA)/Principal Component Analysis(PCA) Project with python/output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-ML-/05ad673aadcf1181de762d5af3288e7298043733/Principal Component Analysis(PCA)/Principal Component Analysis(PCA) Project with python/output.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine-Learning-ML- -------------------------------------------------------------------------------- /Random Forest/Note/New Text Document.txt: -------------------------------------------------------------------------------- 1 | hey there 2 | -------------------------------------------------------------------------------- /Random Forest/Random Forest With Python/data/kyphosis.csv: -------------------------------------------------------------------------------- 1 | "Kyphosis","Age","Number","Start" 2 | "absent",71,3,5 3 | "absent",158,3,14 4 | "present",128,4,5 5 | "absent",2,5,1 6 | "absent",1,4,15 7 | "absent",1,2,16 8 | "absent",61,2,17 9 | "absent",37,3,16 10 | "absent",113,2,16 11 | "present",59,6,12 12 | "present",82,5,14 13 | "absent",148,3,16 14 | "absent",18,5,2 15 | "absent",1,4,12 16 | "absent",168,3,18 17 | "absent",1,3,16 18 | "absent",78,6,15 19 | "absent",175,5,13 20 | "absent",80,5,16 21 | "absent",27,4,9 22 | "absent",22,2,16 23 | "present",105,6,5 24 | "present",96,3,12 25 | "absent",131,2,3 26 | "present",15,7,2 27 | "absent",9,5,13 28 | "absent",8,3,6 29 | "absent",100,3,14 30 | "absent",4,3,16 31 | "absent",151,2,16 32 | "absent",31,3,16 33 | "absent",125,2,11 34 | "absent",130,5,13 35 | "absent",112,3,16 36 | "absent",140,5,11 37 | "absent",93,3,16 38 | "absent",1,3,9 39 | "present",52,5,6 40 | "absent",20,6,9 41 | "present",91,5,12 42 | "present",73,5,1 43 | "absent",35,3,13 44 | "absent",143,9,3 45 | "absent",61,4,1 46 | "absent",97,3,16 47 | "present",139,3,10 48 | "absent",136,4,15 49 | "absent",131,5,13 50 | "present",121,3,3 51 | "absent",177,2,14 52 | "absent",68,5,10 53 | "absent",9,2,17 54 | "present",139,10,6 55 | "absent",2,2,17 56 | "absent",140,4,15 57 | "absent",72,5,15 58 | "absent",2,3,13 59 | "present",120,5,8 60 | "absent",51,7,9 61 | "absent",102,3,13 62 | "present",130,4,1 63 | "present",114,7,8 64 | "absent",81,4,1 65 | "absent",118,3,16 66 | "absent",118,4,16 67 | "absent",17,4,10 68 | "absent",195,2,17 69 | "absent",159,4,13 70 | "absent",18,4,11 71 | "absent",15,5,16 72 | "absent",158,5,14 73 | "absent",127,4,12 74 | "absent",87,4,16 75 | "absent",206,4,10 76 | "absent",11,3,15 77 | "absent",178,4,15 78 | "present",157,3,13 79 | "absent",26,7,13 80 | "absent",120,2,13 81 | "present",42,7,6 82 | "absent",36,4,13 83 | -------------------------------------------------------------------------------- /Support Vector Machines(SVM)/SVM Project/output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-ML-/05ad673aadcf1181de762d5af3288e7298043733/Support Vector Machines(SVM)/SVM Project/output.png -------------------------------------------------------------------------------- /Support Vector Machines(SVM)/Support Vector Machines with Python/Support Vector Machines with Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "import seaborn as sns\n", 13 | "import plotly.graph_objs as go \n", 14 | "import cv2\n", 15 | "import cufflinks as cf\n", 16 | "import datetime\n", 17 | "\n", 18 | "\n", 19 | "from chart_studio import plotly as py\n", 20 | "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n", 21 | "from pandas_datareader import data, wb\n", 22 | "\n", 23 | "%matplotlib inline" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "Lodging Data " 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 3, 36 | "metadata": {}, 37 | "outputs": [ 38 | { 39 | "data": { 40 | "text/plain": [ 41 | "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])" 42 | ] 43 | }, 44 | "execution_count": 3, 45 | "metadata": {}, 46 | "output_type": "execute_result" 47 | } 48 | ], 49 | "source": [ 50 | "from sklearn.datasets import load_breast_cancer\n", 51 | "cancer = load_breast_cancer()\n", 52 | "cancer.keys()" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": 4, 58 | "metadata": {}, 59 | "outputs": [ 60 | { 61 | "name": "stdout", 62 | "output_type": "stream", 63 | "text": [ 64 | ".. _breast_cancer_dataset:\n", 65 | "\n", 66 | "Breast cancer wisconsin (diagnostic) dataset\n", 67 | "--------------------------------------------\n", 68 | "\n", 69 | "**Data Set Characteristics:**\n", 70 | "\n", 71 | " :Number of Instances: 569\n", 72 | "\n", 73 | " :Number of Attributes: 30 numeric, predictive attributes and the class\n", 74 | "\n", 75 | " :Attribute Information:\n", 76 | " - radius (mean of distances from center to points on the perimeter)\n", 77 | " - texture (standard deviation of gray-scale values)\n", 78 | " - perimeter\n", 79 | " - area\n", 80 | " - smoothness (local variation in radius lengths)\n", 81 | " - compactness (perimeter^2 / area - 1.0)\n", 82 | " - concavity (severity of concave portions of the contour)\n", 83 | " - concave points (number of concave portions of the contour)\n", 84 | " - symmetry\n", 85 | " - fractal dimension (\"coastline approximation\" - 1)\n", 86 | "\n", 87 | " The mean, standard error, and \"worst\" or largest (mean of the three\n", 88 | " worst/largest values) of these features were computed for each image,\n", 89 | " resulting in 30 features. For instance, field 0 is Mean Radius, field\n", 90 | " 10 is Radius SE, field 20 is Worst Radius.\n", 91 | "\n", 92 | " - class:\n", 93 | " - WDBC-Malignant\n", 94 | " - WDBC-Benign\n", 95 | "\n", 96 | " :Summary Statistics:\n", 97 | "\n", 98 | " ===================================== ====== ======\n", 99 | " Min Max\n", 100 | " ===================================== ====== ======\n", 101 | " radius (mean): 6.981 28.11\n", 102 | " texture (mean): 9.71 39.28\n", 103 | " perimeter (mean): 43.79 188.5\n", 104 | " area (mean): 143.5 2501.0\n", 105 | " smoothness (mean): 0.053 0.163\n", 106 | " compactness (mean): 0.019 0.345\n", 107 | " concavity (mean): 0.0 0.427\n", 108 | " concave points (mean): 0.0 0.201\n", 109 | " symmetry (mean): 0.106 0.304\n", 110 | " fractal dimension (mean): 0.05 0.097\n", 111 | " radius (standard error): 0.112 2.873\n", 112 | " texture (standard error): 0.36 4.885\n", 113 | " perimeter (standard error): 0.757 21.98\n", 114 | " area (standard error): 6.802 542.2\n", 115 | " smoothness (standard error): 0.002 0.031\n", 116 | " compactness (standard error): 0.002 0.135\n", 117 | " concavity (standard error): 0.0 0.396\n", 118 | " concave points (standard error): 0.0 0.053\n", 119 | " symmetry (standard error): 0.008 0.079\n", 120 | " fractal dimension (standard error): 0.001 0.03\n", 121 | " radius (worst): 7.93 36.04\n", 122 | " texture (worst): 12.02 49.54\n", 123 | " perimeter (worst): 50.41 251.2\n", 124 | " area (worst): 185.2 4254.0\n", 125 | " smoothness (worst): 0.071 0.223\n", 126 | " compactness (worst): 0.027 1.058\n", 127 | " concavity (worst): 0.0 1.252\n", 128 | " concave points (worst): 0.0 0.291\n", 129 | " symmetry (worst): 0.156 0.664\n", 130 | " fractal dimension (worst): 0.055 0.208\n", 131 | " ===================================== ====== ======\n", 132 | "\n", 133 | " :Missing Attribute Values: None\n", 134 | "\n", 135 | " :Class Distribution: 212 - Malignant, 357 - Benign\n", 136 | "\n", 137 | " :Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n", 138 | "\n", 139 | " :Donor: Nick Street\n", 140 | "\n", 141 | " :Date: November, 1995\n", 142 | "\n", 143 | "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n", 144 | "https://goo.gl/U2Uwz2\n", 145 | "\n", 146 | "Features are computed from a digitized image of a fine needle\n", 147 | "aspirate (FNA) of a breast mass. They describe\n", 148 | "characteristics of the cell nuclei present in the image.\n", 149 | "\n", 150 | "Separating plane described above was obtained using\n", 151 | "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n", 152 | "Construction Via Linear Programming.\" Proceedings of the 4th\n", 153 | "Midwest Artificial Intelligence and Cognitive Science Society,\n", 154 | "pp. 97-101, 1992], a classification method which uses linear\n", 155 | "programming to construct a decision tree. Relevant features\n", 156 | "were selected using an exhaustive search in the space of 1-4\n", 157 | "features and 1-3 separating planes.\n", 158 | "\n", 159 | "The actual linear program used to obtain the separating plane\n", 160 | "in the 3-dimensional space is that described in:\n", 161 | "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n", 162 | "Programming Discrimination of Two Linearly Inseparable Sets\",\n", 163 | "Optimization Methods and Software 1, 1992, 23-34].\n", 164 | "\n", 165 | "This database is also available through the UW CS ftp server:\n", 166 | "\n", 167 | "ftp ftp.cs.wisc.edu\n", 168 | "cd math-prog/cpo-dataset/machine-learn/WDBC/\n", 169 | "\n", 170 | ".. topic:: References\n", 171 | "\n", 172 | " - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n", 173 | " for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n", 174 | " Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n", 175 | " San Jose, CA, 1993.\n", 176 | " - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n", 177 | " prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n", 178 | " July-August 1995.\n", 179 | " - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n", 180 | " to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n", 181 | " 163-171.\n" 182 | ] 183 | } 184 | ], 185 | "source": [ 186 | "print(cancer[\"DESCR\"])" 187 | ] 188 | }, 189 | { 190 | "cell_type": "code", 191 | "execution_count": 5, 192 | "metadata": {}, 193 | "outputs": [], 194 | "source": [ 195 | "df_feat = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])" 196 | ] 197 | }, 198 | { 199 | "cell_type": "code", 200 | "execution_count": 6, 201 | "metadata": {}, 202 | "outputs": [ 203 | { 204 | "data": { 205 | "text/html": [ 206 | "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst radiusworst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimension
017.9910.38122.81001.00.118400.277600.30010.147100.24190.07871...25.3817.33184.62019.00.16220.66560.71190.26540.46010.11890
120.5717.77132.91326.00.084740.078640.08690.070170.18120.05667...24.9923.41158.81956.00.12380.18660.24160.18600.27500.08902
\n", 298 | "

2 rows × 30 columns

\n", 299 | "
" 300 | ], 301 | "text/plain": [ 302 | " mean radius mean texture mean perimeter mean area mean smoothness \\\n", 303 | "0 17.99 10.38 122.8 1001.0 0.11840 \n", 304 | "1 20.57 17.77 132.9 1326.0 0.08474 \n", 305 | "\n", 306 | " mean compactness mean concavity mean concave points mean symmetry \\\n", 307 | "0 0.27760 0.3001 0.14710 0.2419 \n", 308 | "1 0.07864 0.0869 0.07017 0.1812 \n", 309 | "\n", 310 | " mean fractal dimension ... worst radius worst texture worst perimeter \\\n", 311 | "0 0.07871 ... 25.38 17.33 184.6 \n", 312 | "1 0.05667 ... 24.99 23.41 158.8 \n", 313 | "\n", 314 | " worst area worst smoothness worst compactness worst concavity \\\n", 315 | "0 2019.0 0.1622 0.6656 0.7119 \n", 316 | "1 1956.0 0.1238 0.1866 0.2416 \n", 317 | "\n", 318 | " worst concave points worst symmetry worst fractal dimension \n", 319 | "0 0.2654 0.4601 0.11890 \n", 320 | "1 0.1860 0.2750 0.08902 \n", 321 | "\n", 322 | "[2 rows x 30 columns]" 323 | ] 324 | }, 325 | "execution_count": 6, 326 | "metadata": {}, 327 | "output_type": "execute_result" 328 | } 329 | ], 330 | "source": [ 331 | "df_feat.head(2)" 332 | ] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": 7, 337 | "metadata": {}, 338 | "outputs": [ 339 | { 340 | "name": "stdout", 341 | "output_type": "stream", 342 | "text": [ 343 | "\n", 344 | "RangeIndex: 569 entries, 0 to 568\n", 345 | "Data columns (total 30 columns):\n", 346 | " # Column Non-Null Count Dtype \n", 347 | "--- ------ -------------- ----- \n", 348 | " 0 mean radius 569 non-null float64\n", 349 | " 1 mean texture 569 non-null float64\n", 350 | " 2 mean perimeter 569 non-null float64\n", 351 | " 3 mean area 569 non-null float64\n", 352 | " 4 mean smoothness 569 non-null float64\n", 353 | " 5 mean compactness 569 non-null float64\n", 354 | " 6 mean concavity 569 non-null float64\n", 355 | " 7 mean concave points 569 non-null float64\n", 356 | " 8 mean symmetry 569 non-null float64\n", 357 | " 9 mean fractal dimension 569 non-null float64\n", 358 | " 10 radius error 569 non-null float64\n", 359 | " 11 texture error 569 non-null float64\n", 360 | " 12 perimeter error 569 non-null float64\n", 361 | " 13 area error 569 non-null float64\n", 362 | " 14 smoothness error 569 non-null float64\n", 363 | " 15 compactness error 569 non-null float64\n", 364 | " 16 concavity error 569 non-null float64\n", 365 | " 17 concave points error 569 non-null float64\n", 366 | " 18 symmetry error 569 non-null float64\n", 367 | " 19 fractal dimension error 569 non-null float64\n", 368 | " 20 worst radius 569 non-null float64\n", 369 | " 21 worst texture 569 non-null float64\n", 370 | " 22 worst perimeter 569 non-null float64\n", 371 | " 23 worst area 569 non-null float64\n", 372 | " 24 worst smoothness 569 non-null float64\n", 373 | " 25 worst compactness 569 non-null float64\n", 374 | " 26 worst concavity 569 non-null float64\n", 375 | " 27 worst concave points 569 non-null float64\n", 376 | " 28 worst symmetry 569 non-null float64\n", 377 | " 29 worst fractal dimension 569 non-null float64\n", 378 | "dtypes: float64(30)\n", 379 | "memory usage: 133.5 KB\n" 380 | ] 381 | } 382 | ], 383 | "source": [ 384 | "df_feat.info()" 385 | ] 386 | }, 387 | { 388 | "cell_type": "code", 389 | "execution_count": 8, 390 | "metadata": {}, 391 | "outputs": [ 392 | { 393 | "data": { 394 | "text/plain": [ 395 | "array(['malignant', 'benign'], dtype='