├── README.md ├── test.csv ├── train.csv └── Titanic Predication.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # Titanic-Survival-Prediction 2 | 3 | 4 | 5 | Using Machine learning algorithm on the famous Titanic Disaster Dataset for Predicting the survival of the passenger 6 | 7 | The most famous competition over the kaggle . In this Repository my intention is to basically explain to every beginner but how to begin with Kaggle very first time. So this repository is for those who just begin their Machine Learning Journey. In this notebook i try to take down every single topic with a very naive approach so every beginner can grasp it very easily. 8 | 9 | Dependencies: 10 | 11 | Python3 12 | Numpy 13 | Pandas 14 | Matplotlib 15 | Supervised Learning 16 | Machine Learning Algorithm 17 | Classification Algorithms 18 | 19 | This Notebook will show basic examples of: 20 | 21 | Data Handling 22 | Importing Data with Pandas 23 | Cleaning Data 24 | Exploring Data through Visualizations with Matplotlib 25 | 26 | Data Analysis: 27 | 28 | Supervised Machine learning Techniques: + Logit Regression Model + Plotting results + Support Vector Machine (SVM) using 3 kernels + Basic Random Forest + Plotting results. 29 | 30 | PS: Please do not forget to drop a star if you like it! 31 | -------------------------------------------------------------------------------- /test.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 3 | 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 4 | 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q 5 | 895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S 6 | 896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S 7 | 897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S 8 | 898,3,"Connolly, Miss. 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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 | -------------------------------------------------------------------------------- /Titanic Predication.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 354, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "#Importing All Required Libaries\n", 10 | "import pandas as pd\n", 11 | "import numpy as np\n", 12 | "import matplotlib.pyplot as plt\n", 13 | "\n", 14 | "from warnings import filterwarnings\n", 15 | "filterwarnings(action='ignore')" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 355, 21 | "metadata": {}, 22 | "outputs": [ 23 | { 24 | "data": { 25 | "text/html": [ 26 | "
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PassengerIdSurvivedPclassNameSex...ParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale...0A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female...0PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale...0STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female...011380353.1000C123S
4503Allen, Mr. William Henrymale...03734508.0500NaNS
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5 rows × 12 columns

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" 132 | ], 133 | "text/plain": [ 134 | " PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked\n", 135 | "0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S\n", 136 | "1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C\n", 137 | "2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S\n", 138 | "3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S\n", 139 | "4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S\n", 140 | "\n", 141 | "[5 rows x 12 columns]" 142 | ] 143 | }, 144 | "execution_count": 355, 145 | "metadata": {}, 146 | "output_type": "execute_result" 147 | } 148 | ], 149 | "source": [ 150 | "#Loading Datasets\n", 151 | "pd.set_option('display.max_columns',10,'display.width',1000)\n", 152 | "train = pd.read_csv('train.csv')\n", 153 | "test = pd.read_csv('test.csv')\n", 154 | "train.head()" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 356, 160 | "metadata": {}, 161 | "outputs": [ 162 | { 163 | "data": { 164 | "text/plain": [ 165 | "(891, 12)" 166 | ] 167 | }, 168 | "execution_count": 356, 169 | "metadata": {}, 170 | "output_type": "execute_result" 171 | } 172 | ], 173 | "source": [ 174 | "#Display shape\n", 175 | "train.shape" 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": 357, 181 | "metadata": {}, 182 | "outputs": [ 183 | { 184 | "data": { 185 | "text/plain": [ 186 | "(418, 11)" 187 | ] 188 | }, 189 | "execution_count": 357, 190 | "metadata": {}, 191 | "output_type": "execute_result" 192 | } 193 | ], 194 | "source": [ 195 | "test.shape" 196 | ] 197 | }, 198 | { 199 | "cell_type": "code", 200 | "execution_count": 358, 201 | "metadata": {}, 202 | "outputs": [ 203 | { 204 | "data": { 205 | "text/plain": [ 206 | "PassengerId 0\n", 207 | "Survived 0\n", 208 | "Pclass 0\n", 209 | "Name 0\n", 210 | "Sex 0\n", 211 | "Age 177\n", 212 | "SibSp 0\n", 213 | "Parch 0\n", 214 | "Ticket 0\n", 215 | "Fare 0\n", 216 | "Cabin 687\n", 217 | "Embarked 2\n", 218 | "dtype: int64" 219 | ] 220 | }, 221 | "execution_count": 358, 222 | "metadata": {}, 223 | "output_type": "execute_result" 224 | } 225 | ], 226 | "source": [ 227 | "#Checking for Null values\n", 228 | "train.isnull().sum()" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": 359, 234 | "metadata": {}, 235 | "outputs": [ 236 | { 237 | "data": { 238 | "text/plain": [ 239 | "PassengerId 0\n", 240 | "Pclass 0\n", 241 | "Name 0\n", 242 | "Sex 0\n", 243 | "Age 86\n", 244 | "SibSp 0\n", 245 | "Parch 0\n", 246 | "Ticket 0\n", 247 | "Fare 1\n", 248 | "Cabin 327\n", 249 | "Embarked 0\n", 250 | "dtype: int64" 251 | ] 252 | }, 253 | "execution_count": 359, 254 | "metadata": {}, 255 | "output_type": "execute_result" 256 | } 257 | ], 258 | "source": [ 259 | "test.isnull().sum()" 260 | ] 261 | }, 262 | { 263 | "cell_type": "code", 264 | "execution_count": 360, 265 | "metadata": {}, 266 | "outputs": [ 267 | { 268 | "data": { 269 | "text/html": [ 270 | "
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PassengerIdSurvivedPclassNameSex...ParchTicketFareCabinEmbarked
count891.000000891.000000891.000000891891...891.000000891891.000000204889
uniqueNaNNaNNaN8912...NaN681NaN1473
topNaNNaNNaNSaalfeld, Mr. Adolphemale...NaNCA. 2343NaNB96 B98S
freqNaNNaNNaN1577...NaN7NaN4644
mean446.0000000.3838382.308642NaNNaN...0.381594NaN32.204208NaNNaN
std257.3538420.4865920.836071NaNNaN...0.806057NaN49.693429NaNNaN
min1.0000000.0000001.000000NaNNaN...0.000000NaN0.000000NaNNaN
25%223.5000000.0000002.000000NaNNaN...0.000000NaN7.910400NaNNaN
50%446.0000000.0000003.000000NaNNaN...0.000000NaN14.454200NaNNaN
75%668.5000001.0000003.000000NaNNaN...0.000000NaN31.000000NaNNaN
max891.0000001.0000003.000000NaNNaN...6.000000NaN512.329200NaNNaN
\n", 458 | "

11 rows × 12 columns

\n", 459 | "
" 460 | ], 461 | "text/plain": [ 462 | " PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked\n", 463 | "count 891.000000 891.000000 891.000000 891 891 ... 891.000000 891 891.000000 204 889\n", 464 | "unique NaN NaN NaN 891 2 ... NaN 681 NaN 147 3\n", 465 | "top NaN NaN NaN Saalfeld, Mr. Adolphe male ... NaN CA. 2343 NaN B96 B98 S\n", 466 | "freq NaN NaN NaN 1 577 ... NaN 7 NaN 4 644\n", 467 | "mean 446.000000 0.383838 2.308642 NaN NaN ... 0.381594 NaN 32.204208 NaN NaN\n", 468 | "std 257.353842 0.486592 0.836071 NaN NaN ... 0.806057 NaN 49.693429 NaN NaN\n", 469 | "min 1.000000 0.000000 1.000000 NaN NaN ... 0.000000 NaN 0.000000 NaN NaN\n", 470 | "25% 223.500000 0.000000 2.000000 NaN NaN ... 0.000000 NaN 7.910400 NaN NaN\n", 471 | "50% 446.000000 0.000000 3.000000 NaN NaN ... 0.000000 NaN 14.454200 NaN NaN\n", 472 | "75% 668.500000 1.000000 3.000000 NaN NaN ... 0.000000 NaN 31.000000 NaN NaN\n", 473 | "max 891.000000 1.000000 3.000000 NaN NaN ... 6.000000 NaN 512.329200 NaN NaN\n", 474 | "\n", 475 | "[11 rows x 12 columns]" 476 | ] 477 | }, 478 | "execution_count": 360, 479 | "metadata": {}, 480 | "output_type": "execute_result" 481 | } 482 | ], 483 | "source": [ 484 | "#Description of dataset\n", 485 | "train.describe(include=\"all\")" 486 | ] 487 | }, 488 | { 489 | "cell_type": "code", 490 | "execution_count": 361, 491 | "metadata": {}, 492 | "outputs": [ 493 | { 494 | "data": { 495 | "text/html": [ 496 | "
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PassengerIdPclassAgeSibSpParchFare
Survived
0447.0163932.53187630.6261790.5537340.32969022.117887
1444.3684211.95029228.3436900.4736840.46491248.395408
\n", 552 | "
" 553 | ], 554 | "text/plain": [ 555 | " PassengerId Pclass Age SibSp Parch Fare\n", 556 | "Survived \n", 557 | "0 447.016393 2.531876 30.626179 0.553734 0.329690 22.117887\n", 558 | "1 444.368421 1.950292 28.343690 0.473684 0.464912 48.395408" 559 | ] 560 | }, 561 | "execution_count": 361, 562 | "metadata": {}, 563 | "output_type": "execute_result" 564 | } 565 | ], 566 | "source": [ 567 | "train.groupby('Survived').mean()" 568 | ] 569 | }, 570 | { 571 | "cell_type": "code", 572 | "execution_count": 362, 573 | "metadata": {}, 574 | "outputs": [ 575 | { 576 | "data": { 577 | "text/html": [ 578 | "
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PassengerIdSurvivedPclassAgeSibSpParchFare
PassengerId1.000000-0.005007-0.0351440.036847-0.057527-0.0016520.012658
Survived-0.0050071.000000-0.338481-0.077221-0.0353220.0816290.257307
Pclass-0.035144-0.3384811.000000-0.3692260.0830810.018443-0.549500
Age0.036847-0.077221-0.3692261.000000-0.308247-0.1891190.096067
SibSp-0.057527-0.0353220.083081-0.3082471.0000000.4148380.159651
Parch-0.0016520.0816290.018443-0.1891190.4148381.0000000.216225
Fare0.0126580.257307-0.5495000.0960670.1596510.2162251.000000
\n", 678 | "
" 679 | ], 680 | "text/plain": [ 681 | " PassengerId Survived Pclass Age SibSp Parch Fare\n", 682 | "PassengerId 1.000000 -0.005007 -0.035144 0.036847 -0.057527 -0.001652 0.012658\n", 683 | "Survived -0.005007 1.000000 -0.338481 -0.077221 -0.035322 0.081629 0.257307\n", 684 | "Pclass -0.035144 -0.338481 1.000000 -0.369226 0.083081 0.018443 -0.549500\n", 685 | "Age 0.036847 -0.077221 -0.369226 1.000000 -0.308247 -0.189119 0.096067\n", 686 | "SibSp -0.057527 -0.035322 0.083081 -0.308247 1.000000 0.414838 0.159651\n", 687 | "Parch -0.001652 0.081629 0.018443 -0.189119 0.414838 1.000000 0.216225\n", 688 | "Fare 0.012658 0.257307 -0.549500 0.096067 0.159651 0.216225 1.000000" 689 | ] 690 | }, 691 | "execution_count": 362, 692 | "metadata": {}, 693 | "output_type": "execute_result" 694 | } 695 | ], 696 | "source": [ 697 | "train.corr()" 698 | ] 699 | }, 700 | { 701 | "cell_type": "code", 702 | "execution_count": 363, 703 | "metadata": {}, 704 | "outputs": [ 705 | { 706 | "name": "stdout", 707 | "output_type": "stream", 708 | "text": [ 709 | "No of Males in Titanic: 577\n" 710 | ] 711 | } 712 | ], 713 | "source": [ 714 | "male_ind = len(train[train['Sex'] == 'male'])\n", 715 | "print(\"No of Males in Titanic:\",male_ind)" 716 | ] 717 | }, 718 | { 719 | "cell_type": "code", 720 | "execution_count": 364, 721 | "metadata": {}, 722 | "outputs": [ 723 | { 724 | "name": "stdout", 725 | "output_type": "stream", 726 | "text": [ 727 | "No of Females in Titanic: 314\n" 728 | ] 729 | } 730 | ], 731 | "source": [ 732 | "female_ind = len(train[train['Sex'] == 'female'])\n", 733 | "print(\"No of Females in Titanic:\",female_ind)" 734 | ] 735 | }, 736 | { 737 | "cell_type": "code", 738 | "execution_count": 365, 739 | "metadata": {}, 740 | "outputs": [ 741 | { 742 | "data": { 743 | "image/png": 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\n", 744 | "text/plain": [ 745 | "
" 746 | ] 747 | }, 748 | "metadata": { 749 | "needs_background": "light" 750 | }, 751 | "output_type": "display_data" 752 | } 753 | ], 754 | "source": [ 755 | "#Plotting\n", 756 | "fig = plt.figure()\n", 757 | "ax = fig.add_axes([0,0,1,1])\n", 758 | "gender = ['Male','Female']\n", 759 | "index = [577,314]\n", 760 | "ax.bar(gender,index)\n", 761 | "plt.xlabel(\"Gender\")\n", 762 | "plt.ylabel(\"No of people onboarding ship\")\n", 763 | "plt.show()" 764 | ] 765 | }, 766 | { 767 | "cell_type": "code", 768 | "execution_count": 366, 769 | "metadata": {}, 770 | "outputs": [], 771 | "source": [ 772 | "alive = len(train[train['Survived'] == 1])\n", 773 | "dead = len(train[train['Survived'] == 0])" 774 | ] 775 | }, 776 | { 777 | "cell_type": "code", 778 | "execution_count": 367, 779 | "metadata": {}, 780 | "outputs": [ 781 | { 782 | "data": { 783 | "text/html": [ 784 | "
\n", 785 | "\n", 798 | "\n", 799 | " \n", 800 | " \n", 801 | " \n", 802 | " \n", 803 | " \n", 804 | " \n", 805 | " \n", 806 | " \n", 807 | " \n", 808 | " \n", 809 | " \n", 810 | " \n", 811 | " \n", 812 | " \n", 813 | " \n", 814 | " \n", 815 | " \n", 816 | " \n", 817 | " \n", 818 | " \n", 819 | "
Survived
Sex
female0.742038
male0.188908
\n", 820 | "
" 821 | ], 822 | "text/plain": [ 823 | " Survived\n", 824 | "Sex \n", 825 | "female 0.742038\n", 826 | "male 0.188908" 827 | ] 828 | }, 829 | "execution_count": 367, 830 | "metadata": {}, 831 | "output_type": "execute_result" 832 | } 833 | ], 834 | "source": [ 835 | "train.groupby('Sex')[['Survived']].mean()" 836 | ] 837 | }, 838 | { 839 | "cell_type": "code", 840 | "execution_count": 368, 841 | "metadata": {}, 842 | "outputs": [ 843 | { 844 | "data": { 845 | "image/png": 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\n", 846 | "text/plain": [ 847 | "
" 848 | ] 849 | }, 850 | "metadata": { 851 | "needs_background": "light" 852 | }, 853 | "output_type": "display_data" 854 | } 855 | ], 856 | "source": [ 857 | "fig = plt.figure()\n", 858 | "ax = fig.add_axes([0,0,1,1])\n", 859 | "status = ['Survived','Dead']\n", 860 | "ind = [alive,dead]\n", 861 | "ax.bar(status,ind)\n", 862 | "plt.xlabel(\"Status\")\n", 863 | "plt.show()" 864 | ] 865 | }, 866 | { 867 | "cell_type": "code", 868 | "execution_count": 369, 869 | "metadata": {}, 870 | "outputs": [ 871 | { 872 | "data": { 873 | "text/plain": [ 874 | "Text(0.5, 1.0, \"Bar graph of people accrding to ticket class in which people couldn't survive\")" 875 | ] 876 | }, 877 | "execution_count": 369, 878 | "metadata": {}, 879 | "output_type": "execute_result" 880 | }, 881 | { 882 | "data": { 883 | "image/png": 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\n", 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\n", 896 | "text/plain": [ 897 | "
" 898 | ] 899 | }, 900 | "metadata": { 901 | "needs_background": "light" 902 | }, 903 | "output_type": "display_data" 904 | } 905 | ], 906 | "source": [ 907 | "plt.figure(1)\n", 908 | "train.loc[train['Survived'] == 1, 'Pclass'].value_counts().sort_index().plot.bar()\n", 909 | "plt.title('Bar graph of people accrding to ticket class in which people survived')\n", 910 | "\n", 911 | "\n", 912 | "plt.figure(2)\n", 913 | "train.loc[train['Survived'] == 0, 'Pclass'].value_counts().sort_index().plot.bar()\n", 914 | "plt.title('Bar graph of people accrding to ticket class in which people couldn\\'t survive')" 915 | ] 916 | }, 917 | { 918 | "cell_type": "code", 919 | "execution_count": 370, 920 | "metadata": {}, 921 | "outputs": [ 922 | { 923 | "data": { 924 | "text/plain": [ 925 | "([,\n", 926 | " ,\n", 927 | " ,\n", 928 | " ,\n", 929 | " ,\n", 930 | " ,\n", 931 | " ,\n", 932 | " ,\n", 933 | " ,\n", 934 | " ],\n", 935 | " )" 936 | ] 937 | }, 938 | "execution_count": 370, 939 | "metadata": {}, 940 | "output_type": "execute_result" 941 | }, 942 | { 943 | "data": { 944 | "image/png": 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aLqlfUv/AwECJkMzMrENKXbw/kvRcsIH8q9urSM/BGaO1/5Cm2TOEiIhZEdEXEX09PV3RRWhmZi9RqcTyCHBI/lGbgCNIjz64ibXPu5lKevyDmZkNY6WusdxCev7N7aRfSW8BzCL9r4e/k3Q/6YmYF5Woz8zMulexu8Ii4nOkB/lVPUj6D3hmZvYy4Ue6mJlZUU4sZmZWlBOLmZkVVfKX99Zlemdc3+kQAFg884ROh2BmbeQzFjMzK8qJxczMinJiMTOzopxYzMysKCcWMzMryonFzMyKcmIxM7OinFjMzKwoJxYzMyvKv7y3luuGJwD41/9m7eMzFjMzK8qJxczMinJiMTOzopxYzMysqCKJRdKrJc2v/K2RdIaksZLmSlqUX3cqUZ+ZmXWvIoklIu6LiEkRMQk4CHgWuBqYAcyLiInAvDxsZmbDWCu6wo4AHoiIh4HJwJw8fg4wpQX1mZlZF2lFYjkFuDS/3y0ilgPk111bUJ+ZmXWRoolF0kjgncB3N/Jz0yX1S+ofGBgoGZKZmbVZ6TOW44DbI2JFHl4haRxAfl3Z6EMRMSsi+iKir6enp3BIZmbWTqUTy6ms7QYDuBaYmt9PBa4pXJ+ZmXWZYolF0nbAUcBVldEzgaMkLcrTZpaqz8zMulOxh1BGxLPAznXjVpHuEjMzs5cJ//LezMyKcmIxM7OinFjMzKwoJxYzMyvKicXMzIpyYjEzs6KcWMzMrCgnFjMzK8qJxczMinJiMTOzopxYzMysKCcWMzMryonFzMyKcmIxM7OinFjMzKwoJxYzMyvKicXMzIpyYjEzs6JK/s/7MZKulHSvpIWSDpU0VtJcSYvy606l6jMzs+5U8ozlq8ANEfEa4ABgITADmBcRE4F5edjMzIaxIolF0g7AW4GLACLi9xGxGpgMzMmzzQGmlKjPzMy6V6kzln2AAeCbku6QdKGkUcBuEbEcIL/u2ujDkqZL6pfUPzAwUCgkMzPrhFKJZUvgQOD8iHgD8Awb0e0VEbMioi8i+np6egqFZGZmnVAqsSwFlkbELXn4SlKiWSFpHEB+XVmoPjMz61JFEktEPAYskfTqPOoI4B7gWmBqHjcVuKZEfWZm1r22LFjW3wDfkTQSeBB4PylxXSFpGvAIcHLB+szMrAsVSywRMR/oazDpiFJ1mJlZ9/Mv783MrCgnFjMzK8qJxczMinJiMTOzopxYzMysKCcWMzMryonFzMyKcmIxM7OinFjMzKwoJxYzMyvKicXMzIpyYjEzs6KcWMzMrCgnFjMzK8qJxczMinJiMTOzopxYzMysKCcWMzMrqti/Jpa0GHgKeBF4ISL6JI0FLgd6gcXAn0fEk6XqNDOz7lP6jOXtETEpIvry8AxgXkRMBOblYTMzG8Za3RU2GZiT388BprS4PjMz67CSiSWAH0m6TdL0PG63iFgOkF93bfRBSdMl9UvqHxgYKBiSmZm1W7FrLMBhEbFM0q7AXEn3DvWDETELmAXQ19cXBWMyM7M2K3bGEhHL8utK4GrgYGCFpHEA+XVlqfrMzKw7FUkskkZJ2r72HjgaWABcC0zNs00FrilRn5mZda9SXWG7AVdLqpV5SUTcIOlXwBWSpgGPACcXqs/MzLpUkcQSEQ8CBzQYvwo4okQdZma2efAv783MrCgnFjMzK8qJxczMinJiMTOzopxYzMysKCcWMzMryonFzMyKcmIxM7OinFjMzKwoJxYzMyvKicXMzIpyYjEzs6KcWMzMrCgnFjMzK8qJxczMinJiMTOzopxYzMysKCcWMzMrqmhikTRC0h2SrsvDe0u6RdIiSZdLGlmyPjMz6z5F/ud9xenAQmCHPHwu8JWIuEzSBcA04PzCdXad3hnXdzoEM7OOKXbGImkCcAJwYR4WcDhwZZ5lDjClVH1mZtadSnaFnQd8AvhDHt4ZWB0RL+ThpcD4Rh+UNF1Sv6T+gYGBgiGZmVm7FUkskk4EVkbEbdXRDWaNRp+PiFkR0RcRfT09PSVCMjOzDil1jeUw4J2Sjge2IV1jOQ8YI2nLfNYyAVhWqD4zM+tSRc5YIuLMiJgQEb3AKcCNEXEacBPwrjzbVOCaEvWZmVn3avXvWD4J/J2k+0nXXC5qcX1mZtZhpW83JiJuBm7O7x8EDi5dh5mZda/iiaWT/PsRM7PO8yNdzMysKCcWMzMryonFzMyKcmIxM7OinFjMzKwoJxYzMyvKicXMzIpyYjEzs6KcWMzMrKhh9ct7s2a65akMi2ee0OkQzFrOZyxmZlaUE4uZmRXlxGJmZkU5sZiZWVFOLGZmVpQTi5mZFeXEYmZmRRVJLJK2kXSrpDsl3S3p7Dx+b0m3SFok6XJJI0vUZ2Zm3avUGctzwOERcQAwCThW0iHAucBXImIi8CQwrVB9ZmbWpYoklkiezoNb5b8ADgeuzOPnAFNK1GdmZt2r2DUWSSMkzQdWAnOBB4DVEfFCnmUpML7JZ6dL6pfUPzAwUCokMzPrgGKJJSJejIhJwATgYGDfRrM1+eysiOiLiL6enp5SIZmZWQcUvyssIlYDNwOHAGMk1R50OQFYVro+MzPrLqXuCuuRNCa/3xY4ElgI3AS8K882FbimRH1mZta9Sj02fxwwR9IIUrK6IiKuk3QPcJmkzwN3ABcVqs/MzLpUkcQSEXcBb2gw/kHS9RYzM3uZ8C/vzcysKCcWMzMryonFzMyKcmIxM7OinFjMzKwoJxYzMyvKicXMzIpyYjEzs6KcWMzMrCgnFjMzK8qJxczMinJiMTOzopxYzMysKCcWMzMryonFzMyKcmIxM7OinFjMzKwoJxYzMyuqSGKRtIekmyQtlHS3pNPz+LGS5kpalF93KlGfmZl1r1JnLC8Afx8R+wKHAB+RtB8wA5gXEROBeXnYzMyGsSKJJSKWR8Tt+f1TwEJgPDAZmJNnmwNMKVGfmZl1ry1LFyipF3gDcAuwW0Qsh5R8JO3a5DPTgekAe+65Z+mQzLpG74zrOx0CAItnntDpEGwYK3rxXtJo4HvAGRGxZqifi4hZEdEXEX09PT0lQzIzszYrllgkbUVKKt+JiKvy6BWSxuXp44CVpeozM7PuVOquMAEXAQsj4suVSdcCU/P7qcA1JeozM7PuVeoay2HAe4BfS5qfx30KmAlcIWka8AhwcqH6zMysSxVJLBHxU0BNJh9Rog4zM9s8+Jf3ZmZWlBOLmZkV5cRiZmZFObGYmVlRTixmZlaUE4uZmRXlxGJmZkU5sZiZWVFOLGZmVpQTi5mZFeXEYmZmRTmxmJlZUU4sZmZWlBOLmZkV5cRiZmZFObGYmVlRTixmZlaUE4uZmRVVJLFI+oaklZIWVMaNlTRX0qL8ulOJuszMrLuVOmOZDRxbN24GMC8iJgLz8rCZmQ1zRRJLRPwEeKJu9GRgTn4/B5hSoi4zM+turbzGsltELAfIr7s2m1HSdEn9kvoHBgZaGJKZmbVaV1y8j4hZEdEXEX09PT2dDsfMzDZBKxPLCknjAPLryhbWZWZmXaKVieVaYGp+PxW4poV1mZlZlyh1u/GlwC+AV0taKmkaMBM4StIi4Kg8bGZmw9yWJQqJiFObTDqiRPlmZrb56IqL92ZmNnw4sZiZWVFOLGZmVpQTi5mZFeXEYmZmRRW5K8zMNi+9M67vdAgALJ55QqdDsBbwGYuZmRXlxGJmZkU5sZiZWVFOLGZmVpQTi5mZFeXEYmZmRTmxmJlZUU4sZmZWlBOLmZkV5cRiZmZFObGYmVlRTixmZlZUyx9CKelY4KvACODCiJjZ6jrNzDaGH8pZVkvPWCSNAP4NOA7YDzhV0n6trNPMzDqr1V1hBwP3R8SDEfF74DJgcovrNDOzDmp1V9h4YElleCnwpvqZJE0HpufBpyXd9xLr2wV4/CV+tiTHsa5uiKMbYgDHsQ6d2x1xMDzaY6+SsWyKVicWNRgX642ImAXM2uTKpP6I6NvUchzH8IujG2JwHI5jc4ljU7W6K2wpsEdleAKwrMV1mplZB7U6sfwKmChpb0kjgVOAa1tcp5mZdVBLu8Ii4gVJHwV+SLrd+BsRcXcLq9zk7rRCHMe6uiGObogBHEc9x7GuboljkyhivUseZmZmL5l/eW9mZkU5sZiZWVHDJrFIOlbSfZLulzSjjfV+Q9JKSQsq48ZKmitpUX7dqcUx7CHpJkkLJd0t6fQOxbGNpFsl3ZnjODuP31vSLTmOy/ONHC0naYSkOyRd16k4JC2W9GtJ8yX153FtXS+5zjGSrpR0b95ODm1nHJJendug9rdG0hkdaou/zdvnAkmX5u22E9vG6TmGuyWdkce1vT1aYVgklg4/OmY2cGzduBnAvIiYCMzLw630AvD3EbEvcAjwkbz87Y7jOeDwiDgAmAQcK+kQ4FzgKzmOJ4FpLY6j5nRgYWW4U3G8PSImVX6f0O71Aul5fTdExGuAA0jt0rY4IuK+3AaTgIOAZ4Gr2xkDgKTxwMeAvojYn3RT0Sm0eduQtD/wQdLTSQ4ATpQ0kc5sG+VFxGb/BxwK/LAyfCZwZhvr7wUWVIbvA8bl9+OA+9rcHtcAR3UyDmA74HbSkxYeB7ZstK5aWP8E0o55OHAd6ce6nYhjMbBL3bi2rhdgB+Ah8s06nYqjUu/RwM861Ba1p4GMJd0Vex1wTLu3DeBk0kN5a8OfAT7R6WNHqb9hccZC40fHjO9QLAC7RcRygPy6a7sqltQLvAG4pRNx5O6n+cBKYC7wALA6Il7Is7Rr3ZxH2lH/kId37lAcAfxI0m350UXQ/vWyDzAAfDN3DV4oaVQH4qg5Bbg0v29rDBHxKPAl4BFgOfBb4Dbav20sAN4qaWdJ2wHHk35M3rFjR0nDJbEM6dExw52k0cD3gDMiYk0nYoiIFyN1d0wgnebv22i2VsYg6URgZUTcVh3d7jiywyLiQFI37UckvbUNddbbEjgQOD8i3gA8Q4e6WPK1i3cC3+1Q/TuRHoS7N7A7MIq0buq1dNuIiIWk7re5wA3AnaQu7WFhuCSWbnt0zApJ4wDy68pWVyhpK1JS+U5EXNWpOGoiYjVwM+mazxhJtR/jtmPdHAa8U9Ji0hO1DyedwbQ7DiJiWX5dSbqmcDDtXy9LgaURcUsevpKUaDqxfRwH3B4RK/Jwu2M4EngoIgYi4nngKuDNdGbbuCgiDoyItwJPAIvo4D5b0nBJLN326Jhrgan5/VTSNY+WkSTgImBhRHy5g3H0SBqT329L2okXAjcB72pXHBFxZkRMiIhe0rZwY0Sc1u44JI2StH3tPenawgLavF4i4jFgiaRX51FHAPe0O47sVNZ2g9GBGB4BDpG0Xd5vam3R1m0DQNKu+XVP4CRSu3RinZTX6Ys8pf5IfZS/IfXp/0Mb672U1Ff7POmb4TRSf/480jeQecDYFsfwFtKp+13A/Px3fAfieD1wR45jAfDZPH4f4FbgflIXyNZtXD9vA67rRBy5vjvz39217bLd6yXXOQnoz+vm+8BOHdg+tgNWATtWxnWiLc4G7s3b6MXA1p3YRoH/JiW1O4EjOtUerfjzI13MzKyo4dIVZmZmXcKJxczMinJiMTOzopxYzMysKCcWMzMryonFzMyKcmIxM7Oi/j/WUxKumQKoTAAAAABJRU5ErkJggg==\n", 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957 | "text/plain": [ 958 | "
" 959 | ] 960 | }, 961 | "metadata": { 962 | "needs_background": "light" 963 | }, 964 | "output_type": "display_data" 965 | } 966 | ], 967 | "source": [ 968 | "plt.figure(1)\n", 969 | "age = train.loc[train.Survived == 1, 'Age']\n", 970 | "plt.title('The histogram of the age groups of the people that had survived')\n", 971 | "plt.hist(age, np.arange(0,100,10))\n", 972 | "plt.xticks(np.arange(0,100,10))\n", 973 | "\n", 974 | "\n", 975 | "plt.figure(2)\n", 976 | "age = train.loc[train.Survived == 0, 'Age']\n", 977 | "plt.title('The histogram of the age groups of the people that coudn\\'t survive')\n", 978 | "plt.hist(age, np.arange(0,100,10))\n", 979 | "plt.xticks(np.arange(0,100,10))" 980 | ] 981 | }, 982 | { 983 | "cell_type": "code", 984 | "execution_count": 371, 985 | "metadata": {}, 986 | "outputs": [ 987 | { 988 | "data": { 989 | "text/html": [ 990 | "
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SibSpSurvived
110.535885
220.464286
000.345395
330.250000
440.166667
550.000000
680.000000
\n", 1050 | "
" 1051 | ], 1052 | "text/plain": [ 1053 | " SibSp Survived\n", 1054 | "1 1 0.535885\n", 1055 | "2 2 0.464286\n", 1056 | "0 0 0.345395\n", 1057 | "3 3 0.250000\n", 1058 | "4 4 0.166667\n", 1059 | "5 5 0.000000\n", 1060 | "6 8 0.000000" 1061 | ] 1062 | }, 1063 | "execution_count": 371, 1064 | "metadata": {}, 1065 | "output_type": "execute_result" 1066 | } 1067 | ], 1068 | "source": [ 1069 | "train[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)" 1070 | ] 1071 | }, 1072 | { 1073 | "cell_type": "code", 1074 | "execution_count": 372, 1075 | "metadata": {}, 1076 | "outputs": [ 1077 | { 1078 | "data": { 1079 | "text/html": [ 1080 | "
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PclassSurvived
010.629630
120.472826
230.242363
\n", 1120 | "
" 1121 | ], 1122 | "text/plain": [ 1123 | " Pclass Survived\n", 1124 | "0 1 0.629630\n", 1125 | "1 2 0.472826\n", 1126 | "2 3 0.242363" 1127 | ] 1128 | }, 1129 | "execution_count": 372, 1130 | "metadata": {}, 1131 | "output_type": "execute_result" 1132 | } 1133 | ], 1134 | "source": [ 1135 | "train[[\"Pclass\", \"Survived\"]].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)" 1136 | ] 1137 | }, 1138 | { 1139 | "cell_type": "code", 1140 | "execution_count": 373, 1141 | "metadata": {}, 1142 | "outputs": [ 1143 | { 1144 | "data": { 1145 | "text/html": [ 1146 | "
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AgeSurvived
00.421.0
10.671.0
20.751.0
30.831.0
40.921.0
.........
8370.000.0
8470.500.0
8571.000.0
8674.000.0
8780.001.0
\n", 1226 | "

88 rows × 2 columns

\n", 1227 | "
" 1228 | ], 1229 | "text/plain": [ 1230 | " Age Survived\n", 1231 | "0 0.42 1.0\n", 1232 | "1 0.67 1.0\n", 1233 | "2 0.75 1.0\n", 1234 | "3 0.83 1.0\n", 1235 | "4 0.92 1.0\n", 1236 | ".. ... ...\n", 1237 | "83 70.00 0.0\n", 1238 | "84 70.50 0.0\n", 1239 | "85 71.00 0.0\n", 1240 | "86 74.00 0.0\n", 1241 | "87 80.00 1.0\n", 1242 | "\n", 1243 | "[88 rows x 2 columns]" 1244 | ] 1245 | }, 1246 | "execution_count": 373, 1247 | "metadata": {}, 1248 | "output_type": "execute_result" 1249 | } 1250 | ], 1251 | "source": [ 1252 | "train[[\"Age\", \"Survived\"]].groupby(['Age'], as_index=False).mean().sort_values(by='Age', ascending=True)" 1253 | ] 1254 | }, 1255 | { 1256 | "cell_type": "code", 1257 | "execution_count": 374, 1258 | "metadata": {}, 1259 | "outputs": [ 1260 | { 1261 | "data": { 1262 | "text/html": [ 1263 | "
\n", 1264 | "\n", 1277 | "\n", 1278 | " \n", 1279 | " \n", 1280 | " \n", 1281 | " \n", 1282 | " \n", 1283 | " \n", 1284 | " \n", 1285 | " \n", 1286 | " \n", 1287 | " \n", 1288 | " \n", 1289 | " \n", 1290 | " \n", 1291 | " \n", 1292 | " \n", 1293 | " \n", 1294 | " \n", 1295 | " \n", 1296 | " \n", 1297 | " \n", 1298 | " \n", 1299 | " \n", 1300 | " \n", 1301 | " \n", 1302 | "
EmbarkedSurvived
0C0.553571
1Q0.389610
2S0.336957
\n", 1303 | "
" 1304 | ], 1305 | "text/plain": [ 1306 | " Embarked Survived\n", 1307 | "0 C 0.553571\n", 1308 | "1 Q 0.389610\n", 1309 | "2 S 0.336957" 1310 | ] 1311 | }, 1312 | "execution_count": 374, 1313 | "metadata": {}, 1314 | "output_type": "execute_result" 1315 | } 1316 | ], 1317 | "source": [ 1318 | "train[[\"Embarked\", \"Survived\"]].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)" 1319 | ] 1320 | }, 1321 | { 1322 | "cell_type": "code", 1323 | "execution_count": 375, 1324 | "metadata": {}, 1325 | "outputs": [ 1326 | { 1327 | "data": { 1328 | "image/png": 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PassengerIdPclassNameSexAge...ParchTicketFareCabinEmbarked
count418.000000418.000000418418332.000000...418.000000418417.00000091418
uniqueNaNNaN4182NaN...NaN363NaN763
topNaNNaNPortaluppi, Mr. Emilio Ilario GiuseppemaleNaN...NaNPC 17608NaNB57 B59 B63 B66S
freqNaNNaN1266NaN...NaN5NaN3270
mean1100.5000002.265550NaNNaN30.272590...0.392344NaN35.627188NaNNaN
std120.8104580.841838NaNNaN14.181209...0.981429NaN55.907576NaNNaN
min892.0000001.000000NaNNaN0.170000...0.000000NaN0.000000NaNNaN
25%996.2500001.000000NaNNaN21.000000...0.000000NaN7.895800NaNNaN
50%1100.5000003.000000NaNNaN27.000000...0.000000NaN14.454200NaNNaN
75%1204.7500003.000000NaNNaN39.000000...0.000000NaN31.500000NaNNaN
max1309.0000003.000000NaNNaN76.000000...9.000000NaN512.329200NaNNaN
\n", 1543 | "

11 rows × 11 columns

\n", 1544 | "
" 1545 | ], 1546 | "text/plain": [ 1547 | " PassengerId Pclass Name Sex Age ... Parch Ticket Fare Cabin Embarked\n", 1548 | "count 418.000000 418.000000 418 418 332.000000 ... 418.000000 418 417.000000 91 418\n", 1549 | "unique NaN NaN 418 2 NaN ... NaN 363 NaN 76 3\n", 1550 | "top NaN NaN Portaluppi, Mr. Emilio Ilario Giuseppe male NaN ... NaN PC 17608 NaN B57 B59 B63 B66 S\n", 1551 | "freq NaN NaN 1 266 NaN ... NaN 5 NaN 3 270\n", 1552 | "mean 1100.500000 2.265550 NaN NaN 30.272590 ... 0.392344 NaN 35.627188 NaN NaN\n", 1553 | "std 120.810458 0.841838 NaN NaN 14.181209 ... 0.981429 NaN 55.907576 NaN NaN\n", 1554 | "min 892.000000 1.000000 NaN NaN 0.170000 ... 0.000000 NaN 0.000000 NaN NaN\n", 1555 | "25% 996.250000 1.000000 NaN NaN 21.000000 ... 0.000000 NaN 7.895800 NaN NaN\n", 1556 | "50% 1100.500000 3.000000 NaN NaN 27.000000 ... 0.000000 NaN 14.454200 NaN NaN\n", 1557 | "75% 1204.750000 3.000000 NaN NaN 39.000000 ... 0.000000 NaN 31.500000 NaN NaN\n", 1558 | "max 1309.000000 3.000000 NaN NaN 76.000000 ... 9.000000 NaN 512.329200 NaN NaN\n", 1559 | "\n", 1560 | "[11 rows x 11 columns]" 1561 | ] 1562 | }, 1563 | "execution_count": 376, 1564 | "metadata": {}, 1565 | "output_type": "execute_result" 1566 | } 1567 | ], 1568 | "source": [ 1569 | "test.describe(include=\"all\")" 1570 | ] 1571 | }, 1572 | { 1573 | "cell_type": "code", 1574 | "execution_count": 377, 1575 | "metadata": {}, 1576 | "outputs": [], 1577 | "source": [ 1578 | "#Droping Useless Columns\n", 1579 | "train = train.drop(['Ticket'], axis = 1)\n", 1580 | "test = test.drop(['Ticket'], axis = 1)" 1581 | ] 1582 | }, 1583 | { 1584 | "cell_type": "code", 1585 | "execution_count": 378, 1586 | "metadata": {}, 1587 | "outputs": [], 1588 | "source": [ 1589 | "train = train.drop(['Cabin'], axis = 1)\n", 1590 | "test = test.drop(['Cabin'], axis = 1)" 1591 | ] 1592 | }, 1593 | { 1594 | "cell_type": "code", 1595 | "execution_count": 379, 1596 | "metadata": {}, 1597 | "outputs": [], 1598 | "source": [ 1599 | "train = train.drop(['Name'], axis = 1)\n", 1600 | "test = test.drop(['Name'], axis = 1)" 1601 | ] 1602 | }, 1603 | { 1604 | "cell_type": "code", 1605 | "execution_count": 380, 1606 | "metadata": {}, 1607 | "outputs": [], 1608 | "source": [ 1609 | "#Feature Selection\n", 1610 | "column_train=['Age','Pclass','SibSp','Parch','Fare','Sex','Embarked']\n", 1611 | "#training values\n", 1612 | "X=train[column_train]\n", 1613 | "#target value\n", 1614 | "Y=train['Survived']" 1615 | ] 1616 | }, 1617 | { 1618 | "cell_type": "code", 1619 | "execution_count": 381, 1620 | "metadata": {}, 1621 | "outputs": [ 1622 | { 1623 | "data": { 1624 | "text/plain": [ 1625 | "2" 1626 | ] 1627 | }, 1628 | "execution_count": 381, 1629 | "metadata": {}, 1630 | "output_type": "execute_result" 1631 | } 1632 | ], 1633 | "source": [ 1634 | "X['Age'].isnull().sum()\n", 1635 | "X['Pclass'].isnull().sum()\n", 1636 | "X['SibSp'].isnull().sum()\n", 1637 | "X['Parch'].isnull().sum()\n", 1638 | "X['Fare'].isnull().sum()\n", 1639 | "X['Sex'].isnull().sum()\n", 1640 | "X['Embarked'].isnull().sum()" 1641 | ] 1642 | }, 1643 | { 1644 | "cell_type": "code", 1645 | "execution_count": 382, 1646 | "metadata": {}, 1647 | "outputs": [ 1648 | { 1649 | "data": { 1650 | "text/plain": [ 1651 | "0" 1652 | ] 1653 | }, 1654 | "execution_count": 382, 1655 | "metadata": {}, 1656 | "output_type": "execute_result" 1657 | } 1658 | ], 1659 | "source": [ 1660 | "#now we have to fill all the missing values\n", 1661 | "#age have 177 missing values\n", 1662 | "#either we fill missing values with mean or median form existing values \n", 1663 | "X['Age']=X['Age'].fillna(X['Age'].median())\n", 1664 | "X['Age'].isnull().sum()" 1665 | ] 1666 | }, 1667 | { 1668 | "cell_type": "code", 1669 | "execution_count": 383, 1670 | "metadata": {}, 1671 | "outputs": [ 1672 | { 1673 | "data": { 1674 | "text/plain": [ 1675 | "0" 1676 | ] 1677 | }, 1678 | "execution_count": 383, 1679 | "metadata": {}, 1680 | "output_type": "execute_result" 1681 | } 1682 | ], 1683 | "source": [ 1684 | "X['Embarked'] = train['Embarked'].fillna(method ='pad')\n", 1685 | "X['Embarked'].isnull().sum()" 1686 | ] 1687 | }, 1688 | { 1689 | "cell_type": "code", 1690 | "execution_count": 384, 1691 | "metadata": {}, 1692 | "outputs": [ 1693 | { 1694 | "data": { 1695 | "text/plain": [ 1696 | "0 0\n", 1697 | "1 1\n", 1698 | "2 1\n", 1699 | "3 1\n", 1700 | "4 0\n", 1701 | "Name: Sex, dtype: int64" 1702 | ] 1703 | }, 1704 | "execution_count": 384, 1705 | "metadata": {}, 1706 | "output_type": "execute_result" 1707 | } 1708 | ], 1709 | "source": [ 1710 | "#now we need to convert sex into integer value \n", 1711 | "d={'male':0, 'female':1}\n", 1712 | "X['Sex']=X['Sex'].apply(lambda x:d[x])\n", 1713 | "X['Sex'].head()" 1714 | ] 1715 | }, 1716 | { 1717 | "cell_type": "code", 1718 | "execution_count": 385, 1719 | "metadata": {}, 1720 | "outputs": [ 1721 | { 1722 | "data": { 1723 | "text/plain": [ 1724 | "0 2\n", 1725 | "1 0\n", 1726 | "2 2\n", 1727 | "3 2\n", 1728 | "4 2\n", 1729 | "Name: Embarked, dtype: int64" 1730 | ] 1731 | }, 1732 | "execution_count": 385, 1733 | "metadata": {}, 1734 | "output_type": "execute_result" 1735 | } 1736 | ], 1737 | "source": [ 1738 | "e={'C':0, 'Q':1 ,'S':2}\n", 1739 | "X['Embarked']=X['Embarked'].apply(lambda x:e[x])\n", 1740 | "X['Embarked'].head()" 1741 | ] 1742 | }, 1743 | { 1744 | "cell_type": "code", 1745 | "execution_count": 386, 1746 | "metadata": {}, 1747 | "outputs": [], 1748 | "source": [ 1749 | "#Training Testing and Spliting the model\n", 1750 | "from sklearn.model_selection import train_test_split\n", 1751 | "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.3,random_state=7)" 1752 | ] 1753 | }, 1754 | { 1755 | "cell_type": "code", 1756 | "execution_count": 387, 1757 | "metadata": {}, 1758 | "outputs": [ 1759 | { 1760 | "name": "stdout", 1761 | "output_type": "stream", 1762 | "text": [ 1763 | "Accuracy Score: 0.7574626865671642\n" 1764 | ] 1765 | } 1766 | ], 1767 | "source": [ 1768 | "#Using LogisticRegression\n", 1769 | "from sklearn.linear_model import LogisticRegression\n", 1770 | "model = LogisticRegression()\n", 1771 | "model.fit(X_train,Y_train)\n", 1772 | "Y_pred = model.predict(X_test)\n", 1773 | "\n", 1774 | "from sklearn.metrics import accuracy_score\n", 1775 | "print(\"Accuracy Score:\",accuracy_score(Y_test,Y_pred))" 1776 | ] 1777 | }, 1778 | { 1779 | "cell_type": "code", 1780 | "execution_count": 397, 1781 | "metadata": {}, 1782 | "outputs": [ 1783 | { 1784 | "name": "stdout", 1785 | "output_type": "stream", 1786 | "text": [ 1787 | "[[130 26]\n", 1788 | " [ 39 73]]\n" 1789 | ] 1790 | } 1791 | ], 1792 | "source": [ 1793 | "#Confusion Matrix\n", 1794 | "from sklearn.metrics import accuracy_score,confusion_matrix\n", 1795 | "confusion_mat = confusion_matrix(Y_test,Y_pred)\n", 1796 | "print(confusion_mat)" 1797 | ] 1798 | }, 1799 | { 1800 | "cell_type": "code", 1801 | "execution_count": 415, 1802 | "metadata": {}, 1803 | "outputs": [ 1804 | { 1805 | "name": "stdout", 1806 | "output_type": "stream", 1807 | "text": [ 1808 | "Acc= 0.6604477611940298\n" 1809 | ] 1810 | } 1811 | ], 1812 | "source": [ 1813 | "#Using Support Vector\n", 1814 | "from sklearn.svm import SVC\n", 1815 | "model1 = SVC()\n", 1816 | "model1.fit(X_train,Y_train)\n", 1817 | "\n", 1818 | "pred_y = model1.predict(X_test)\n", 1819 | "\n", 1820 | "from sklearn.metrics import accuracy_score\n", 1821 | "print(\"Acc=\",accuracy_score(Y_test,pred_y))" 1822 | ] 1823 | }, 1824 | { 1825 | "cell_type": "code", 1826 | "execution_count": 416, 1827 | "metadata": {}, 1828 | "outputs": [ 1829 | { 1830 | "name": "stdout", 1831 | "output_type": "stream", 1832 | "text": [ 1833 | "[[149 7]\n", 1834 | " [ 84 28]]\n", 1835 | " precision recall f1-score support\n", 1836 | "\n", 1837 | " 0 0.64 0.96 0.77 156\n", 1838 | " 1 0.80 0.25 0.38 112\n", 1839 | "\n", 1840 | " accuracy 0.66 268\n", 1841 | " macro avg 0.72 0.60 0.57 268\n", 1842 | "weighted avg 0.71 0.66 0.61 268\n", 1843 | "\n" 1844 | ] 1845 | } 1846 | ], 1847 | "source": [ 1848 | "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n", 1849 | "confusion_mat = confusion_matrix(Y_test,pred_y)\n", 1850 | "print(confusion_mat)\n", 1851 | "print(classification_report(Y_test,pred_y))" 1852 | ] 1853 | }, 1854 | { 1855 | "cell_type": "code", 1856 | "execution_count": 412, 1857 | "metadata": {}, 1858 | "outputs": [ 1859 | { 1860 | "name": "stdout", 1861 | "output_type": "stream", 1862 | "text": [ 1863 | "Accuracy Score: 0.6604477611940298\n" 1864 | ] 1865 | } 1866 | ], 1867 | "source": [ 1868 | "#Using KNN Neighbors\n", 1869 | "from sklearn.neighbors import KNeighborsClassifier\n", 1870 | "model2 = KNeighborsClassifier(n_neighbors=5)\n", 1871 | "model2.fit(X_train,Y_train)\n", 1872 | "y_pred2 = model2.predict(X_test)\n", 1873 | "\n", 1874 | "from sklearn.metrics import accuracy_score\n", 1875 | "print(\"Accuracy Score:\",accuracy_score(Y_test,y_pred2))" 1876 | ] 1877 | }, 1878 | { 1879 | "cell_type": "code", 1880 | "execution_count": 414, 1881 | "metadata": {}, 1882 | "outputs": [ 1883 | { 1884 | "name": "stdout", 1885 | "output_type": "stream", 1886 | "text": [ 1887 | "[[126 30]\n", 1888 | " [ 61 51]]\n", 1889 | " precision recall f1-score support\n", 1890 | "\n", 1891 | " 0 0.79 0.83 0.81 156\n", 1892 | " 1 0.74 0.69 0.71 112\n", 1893 | "\n", 1894 | " accuracy 0.77 268\n", 1895 | " macro avg 0.76 0.76 0.76 268\n", 1896 | "weighted avg 0.77 0.77 0.77 268\n", 1897 | "\n" 1898 | ] 1899 | } 1900 | ], 1901 | "source": [ 1902 | "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n", 1903 | "confusion_mat = confusion_matrix(Y_test,y_pred2)\n", 1904 | "print(confusion_mat)\n", 1905 | "print(classification_report(Y_test,y_pred3))" 1906 | ] 1907 | }, 1908 | { 1909 | "cell_type": "code", 1910 | "execution_count": 410, 1911 | "metadata": {}, 1912 | "outputs": [ 1913 | { 1914 | "name": "stdout", 1915 | "output_type": "stream", 1916 | "text": [ 1917 | "Accuracy Score: 0.7686567164179104\n" 1918 | ] 1919 | } 1920 | ], 1921 | "source": [ 1922 | "#Using GaussianNB\n", 1923 | "from sklearn.naive_bayes import GaussianNB\n", 1924 | "model3 = GaussianNB()\n", 1925 | "model3.fit(X_train,Y_train)\n", 1926 | "y_pred3 = model3.predict(X_test)\n", 1927 | "\n", 1928 | "from sklearn.metrics import accuracy_score\n", 1929 | "print(\"Accuracy Score:\",accuracy_score(Y_test,y_pred3))" 1930 | ] 1931 | }, 1932 | { 1933 | "cell_type": "code", 1934 | "execution_count": 411, 1935 | "metadata": {}, 1936 | "outputs": [ 1937 | { 1938 | "name": "stdout", 1939 | "output_type": "stream", 1940 | "text": [ 1941 | "[[129 27]\n", 1942 | " [ 35 77]]\n", 1943 | " precision recall f1-score support\n", 1944 | "\n", 1945 | " 0 0.79 0.83 0.81 156\n", 1946 | " 1 0.74 0.69 0.71 112\n", 1947 | "\n", 1948 | " accuracy 0.77 268\n", 1949 | " macro avg 0.76 0.76 0.76 268\n", 1950 | "weighted avg 0.77 0.77 0.77 268\n", 1951 | "\n" 1952 | ] 1953 | } 1954 | ], 1955 | "source": [ 1956 | "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n", 1957 | "confusion_mat = confusion_matrix(Y_test,y_pred3)\n", 1958 | "print(confusion_mat)\n", 1959 | "print(classification_report(Y_test,y_pred3))" 1960 | ] 1961 | }, 1962 | { 1963 | "cell_type": "code", 1964 | "execution_count": 408, 1965 | "metadata": {}, 1966 | "outputs": [ 1967 | { 1968 | "name": "stdout", 1969 | "output_type": "stream", 1970 | "text": [ 1971 | "Accuracy Score: 0.7425373134328358\n" 1972 | ] 1973 | } 1974 | ], 1975 | "source": [ 1976 | "#Using Decision Tree\n", 1977 | "from sklearn.tree import DecisionTreeClassifier\n", 1978 | "model4 = DecisionTreeClassifier(criterion='entropy',random_state=7)\n", 1979 | "model4.fit(X_train,Y_train)\n", 1980 | "y_pred4 = model4.predict(X_test)\n", 1981 | "\n", 1982 | "from sklearn.metrics import accuracy_score\n", 1983 | "print(\"Accuracy Score:\",accuracy_score(Y_test,y_pred4))" 1984 | ] 1985 | }, 1986 | { 1987 | "cell_type": "code", 1988 | "execution_count": 409, 1989 | "metadata": {}, 1990 | "outputs": [ 1991 | { 1992 | "name": "stdout", 1993 | "output_type": "stream", 1994 | "text": [ 1995 | "[[132 24]\n", 1996 | " [ 45 67]]\n", 1997 | " precision recall f1-score support\n", 1998 | "\n", 1999 | " 0 0.75 0.85 0.79 156\n", 2000 | " 1 0.74 0.60 0.66 112\n", 2001 | "\n", 2002 | " accuracy 0.74 268\n", 2003 | " macro avg 0.74 0.72 0.73 268\n", 2004 | "weighted avg 0.74 0.74 0.74 268\n", 2005 | "\n" 2006 | ] 2007 | } 2008 | ], 2009 | "source": [ 2010 | "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n", 2011 | "confusion_mat = confusion_matrix(Y_test,y_pred4)\n", 2012 | "print(confusion_mat)\n", 2013 | "print(classification_report(Y_test,y_pred4))" 2014 | ] 2015 | }, 2016 | { 2017 | "cell_type": "code", 2018 | "execution_count": 418, 2019 | "metadata": {}, 2020 | "outputs": [ 2021 | { 2022 | "data": { 2023 | "text/html": [ 2024 | "
\n", 2025 | "\n", 2038 | "\n", 2039 | " \n", 2040 | " \n", 2041 | " \n", 2042 | " \n", 2043 | " \n", 2044 | " \n", 2045 | " \n", 2046 | " \n", 2047 | " \n", 2048 | " \n", 2049 | " \n", 2050 | " \n", 2051 | " \n", 2052 | " \n", 2053 | " \n", 2054 | " \n", 2055 | " \n", 2056 | " \n", 2057 | " \n", 2058 | " \n", 2059 | " \n", 2060 | " \n", 2061 | " \n", 2062 | " \n", 2063 | " \n", 2064 | " \n", 2065 | " \n", 2066 | " \n", 2067 | " \n", 2068 | " \n", 2069 | " \n", 2070 | " \n", 2071 | "
Model
Score
0.76Naive Bayes
0.75Logistic Regression
0.74Decision Tree
0.66Support Vector Machines
0.66KNN
\n", 2072 | "
" 2073 | ], 2074 | "text/plain": [ 2075 | " Model\n", 2076 | "Score \n", 2077 | "0.76 Naive Bayes\n", 2078 | "0.75 Logistic Regression\n", 2079 | "0.74 Decision Tree\n", 2080 | "0.66 Support Vector Machines\n", 2081 | "0.66 KNN" 2082 | ] 2083 | }, 2084 | "execution_count": 418, 2085 | "metadata": {}, 2086 | "output_type": "execute_result" 2087 | } 2088 | ], 2089 | "source": [ 2090 | "results = pd.DataFrame({\n", 2091 | " 'Model': ['Logistic Regression','Support Vector Machines', 'Naive Bayes','KNN' ,'Decision Tree'],\n", 2092 | " 'Score': [0.75,0.66,0.76,0.66,0.74]})\n", 2093 | "\n", 2094 | "result_df = results.sort_values(by='Score', ascending=False)\n", 2095 | "result_df = result_df.set_index('Score')\n", 2096 | "result_df.head(9)" 2097 | ] 2098 | }, 2099 | { 2100 | "cell_type": "code", 2101 | "execution_count": null, 2102 | "metadata": {}, 2103 | "outputs": [], 2104 | "source": [ 2105 | "#Hence I will use Naive Bayes algorithms for training my model.\n", 2106 | "#This project was completely made by Amber Kakkar under Project Based Learning at ICT Academy IIT Kanpur" 2107 | ] 2108 | } 2109 | ], 2110 | "metadata": { 2111 | "kernelspec": { 2112 | "display_name": "Python 3", 2113 | "language": "python", 2114 | "name": "python3" 2115 | }, 2116 | "language_info": { 2117 | "codemirror_mode": { 2118 | "name": "ipython", 2119 | "version": 3 2120 | }, 2121 | "file_extension": ".py", 2122 | "mimetype": "text/x-python", 2123 | "name": "python", 2124 | "nbconvert_exporter": "python", 2125 | "pygments_lexer": "ipython3", 2126 | "version": "3.7.6" 2127 | } 2128 | }, 2129 | "nbformat": 4, 2130 | "nbformat_minor": 4 2131 | } 2132 | --------------------------------------------------------------------------------