└── Decision Tree classfication-Copy1.ipynb /Decision Tree classfication-Copy1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "id": "ea84c493", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "name": "stdout", 11 | "output_type": "stream", 12 | "text": [ 13 | "Requirement already satisfied: pandas in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (1.4.2)\n", 14 | "Requirement already satisfied: pytz>=2020.1 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from pandas) (2021.3)\n", 15 | "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n", 16 | "Requirement already satisfied: numpy>=1.18.5 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from pandas) (1.21.5)\n", 17 | "Requirement already satisfied: six>=1.5 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n", 18 | "Note: you may need to restart the kernel to use updated packages.\n" 19 | ] 20 | } 21 | ], 22 | "source": [ 23 | "pip install pandas" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 20, 29 | "id": "71eb3fc0", 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [ 33 | "import pandas as u" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 21, 39 | "id": "ff3d30e4", 40 | "metadata": {}, 41 | "outputs": [], 42 | "source": [ 43 | "import numpy as np" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 22, 49 | "id": "128f6994", 50 | "metadata": {}, 51 | "outputs": [ 52 | { 53 | "data": { 54 | "text/html": [ 55 | "
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ranktitletotal ratingsinstallsaverage ratinggrowth (30 days)growth (60 days)pricecategory5 star ratings4 star ratings3 star ratings2 star ratings1 star ratingspaid
01Garena Free Fire- World Series86273129500.0 M42.16.90GAME ACTION6354676649495073158756212218312495915False
12PUBG MOBILE - Traverse37276732500.0 M41.83.60GAME ACTION28339753216447812531858098214709492False
23Mobile Legends: Bang Bang26663595100.0 M41.53.20GAME ACTION18777988181209410506007139124308998False
34Brawl Stars17971552100.0 M41.44.40GAME ACTION1301861015529507740124061842219794False
45Sniper 3D: Fun Free Online FPS Shooting Game14464235500.0 M40.81.50GAME ACTION9827328212415410477413806701084340False
................................................
9596Bullet Force75600210.0 M30.10.10GAME ACTION434187900785850635311137917False
9697SHADOWGUN: DEADZONE74894510.0 M40.10.30GAME ACTION55416380239381831423162125False
9798Royal Revolt 2: Tower Defense RTS & Castle Bui...72762710.0 M40.10.10GAME ACTION54183391851336691221648055False
9899Lara Croft: Relic Run71890510.0 M40.20.50GAME ACTION44527683143553723583699275False
99100WWE Mayhem70351410.0 M40.61.30GAME ACTION51909068518346581763463612False
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100 rows × 15 columns

\n", 292 | "
" 293 | ], 294 | "text/plain": [ 295 | " rank title total ratings \\\n", 296 | "0 1 Garena Free Fire- World Series 86273129 \n", 297 | "1 2 PUBG MOBILE - Traverse 37276732 \n", 298 | "2 3 Mobile Legends: Bang Bang 26663595 \n", 299 | "3 4 Brawl Stars 17971552 \n", 300 | "4 5 Sniper 3D: Fun Free Online FPS Shooting Game 14464235 \n", 301 | ".. ... ... ... \n", 302 | "95 96 Bullet Force 756002 \n", 303 | "96 97 SHADOWGUN: DEADZONE 748945 \n", 304 | "97 98 Royal Revolt 2: Tower Defense RTS & Castle Bui... 727627 \n", 305 | "98 99 Lara Croft: Relic Run 718905 \n", 306 | "99 100 WWE Mayhem 703514 \n", 307 | "\n", 308 | " installs average rating growth (30 days) growth (60 days) price \\\n", 309 | "0 500.0 M 4 2.1 6.9 0 \n", 310 | "1 500.0 M 4 1.8 3.6 0 \n", 311 | "2 100.0 M 4 1.5 3.2 0 \n", 312 | "3 100.0 M 4 1.4 4.4 0 \n", 313 | "4 500.0 M 4 0.8 1.5 0 \n", 314 | ".. ... ... ... ... ... \n", 315 | "95 10.0 M 3 0.1 0.1 0 \n", 316 | "96 10.0 M 4 0.1 0.3 0 \n", 317 | "97 10.0 M 4 0.1 0.1 0 \n", 318 | "98 10.0 M 4 0.2 0.5 0 \n", 319 | "99 10.0 M 4 0.6 1.3 0 \n", 320 | "\n", 321 | " category 5 star ratings 4 star ratings 3 star ratings \\\n", 322 | "0 GAME ACTION 63546766 4949507 3158756 \n", 323 | "1 GAME ACTION 28339753 2164478 1253185 \n", 324 | "2 GAME ACTION 18777988 1812094 1050600 \n", 325 | "3 GAME ACTION 13018610 1552950 774012 \n", 326 | "4 GAME ACTION 9827328 2124154 1047741 \n", 327 | ".. ... ... ... ... \n", 328 | "95 GAME ACTION 434187 90078 58506 \n", 329 | "96 GAME ACTION 554163 80239 38183 \n", 330 | "97 GAME ACTION 541833 91851 33669 \n", 331 | "98 GAME ACTION 445276 83143 55372 \n", 332 | "99 GAME ACTION 519090 68518 34658 \n", 333 | "\n", 334 | " 2 star ratings 1 star ratings paid \n", 335 | "0 2122183 12495915 False \n", 336 | "1 809821 4709492 False \n", 337 | "2 713912 4308998 False \n", 338 | "3 406184 2219794 False \n", 339 | "4 380670 1084340 False \n", 340 | ".. ... ... ... \n", 341 | "95 35311 137917 False \n", 342 | "96 14231 62125 False \n", 343 | "97 12216 48055 False \n", 344 | "98 35836 99275 False \n", 345 | "99 17634 63612 False \n", 346 | "\n", 347 | "[100 rows x 15 columns]" 348 | ] 349 | }, 350 | "execution_count": 22, 351 | "metadata": {}, 352 | "output_type": "execute_result" 353 | } 354 | ], 355 | "source": [ 356 | "data =u.read_csv(\"C:\\\\Users\\\\Lenovo\\\\Downloads\\\\android-games.csv\")\n", 357 | "data" 358 | ] 359 | }, 360 | { 361 | "cell_type": "code", 362 | "execution_count": 23, 363 | "id": "754834d4", 364 | "metadata": {}, 365 | "outputs": [], 366 | "source": [ 367 | "X = data.drop('total ratings', axis=1)\n", 368 | "X" 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": 24, 374 | "id": "67916bc3", 375 | "metadata": {}, 376 | "outputs": [ 377 | { 378 | "data": { 379 | "text/html": [ 380 | "
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ranktitleinstallsaverage ratinggrowth (30 days)growth (60 days)pricecategory5 star ratings4 star ratings3 star ratings2 star ratings1 star ratingspaid
01Garena Free Fire- World Series500.0 M42.16.90GAME ACTION6354676649495073158756212218312495915False
12PUBG MOBILE - Traverse500.0 M41.83.60GAME ACTION28339753216447812531858098214709492False
23Mobile Legends: Bang Bang100.0 M41.53.20GAME ACTION18777988181209410506007139124308998False
34Brawl Stars100.0 M41.44.40GAME ACTION1301861015529507740124061842219794False
45Sniper 3D: Fun Free Online FPS Shooting Game500.0 M40.81.50GAME ACTION9827328212415410477413806701084340False
.............................................
9596Bullet Force10.0 M30.10.10GAME ACTION434187900785850635311137917False
9697SHADOWGUN: DEADZONE10.0 M40.10.30GAME ACTION55416380239381831423162125False
9798Royal Revolt 2: Tower Defense RTS & Castle Bui...10.0 M40.10.10GAME ACTION54183391851336691221648055False
9899Lara Croft: Relic Run10.0 M40.20.50GAME ACTION44527683143553723583699275False
99100WWE Mayhem10.0 M40.61.30GAME ACTION51909068518346581763463612False
\n", 604 | "

100 rows × 14 columns

\n", 605 | "
" 606 | ], 607 | "text/plain": [ 608 | " rank title installs \\\n", 609 | "0 1 Garena Free Fire- World Series 500.0 M \n", 610 | "1 2 PUBG MOBILE - Traverse 500.0 M \n", 611 | "2 3 Mobile Legends: Bang Bang 100.0 M \n", 612 | "3 4 Brawl Stars 100.0 M \n", 613 | "4 5 Sniper 3D: Fun Free Online FPS Shooting Game 500.0 M \n", 614 | ".. ... ... ... \n", 615 | "95 96 Bullet Force 10.0 M \n", 616 | "96 97 SHADOWGUN: DEADZONE 10.0 M \n", 617 | "97 98 Royal Revolt 2: Tower Defense RTS & Castle Bui... 10.0 M \n", 618 | "98 99 Lara Croft: Relic Run 10.0 M \n", 619 | "99 100 WWE Mayhem 10.0 M \n", 620 | "\n", 621 | " average rating growth (30 days) growth (60 days) price category \\\n", 622 | "0 4 2.1 6.9 0 GAME ACTION \n", 623 | "1 4 1.8 3.6 0 GAME ACTION \n", 624 | "2 4 1.5 3.2 0 GAME ACTION \n", 625 | "3 4 1.4 4.4 0 GAME ACTION \n", 626 | "4 4 0.8 1.5 0 GAME ACTION \n", 627 | ".. ... ... ... ... ... \n", 628 | "95 3 0.1 0.1 0 GAME ACTION \n", 629 | "96 4 0.1 0.3 0 GAME ACTION \n", 630 | "97 4 0.1 0.1 0 GAME ACTION \n", 631 | "98 4 0.2 0.5 0 GAME ACTION \n", 632 | "99 4 0.6 1.3 0 GAME ACTION \n", 633 | "\n", 634 | " 5 star ratings 4 star ratings 3 star ratings 2 star ratings \\\n", 635 | "0 63546766 4949507 3158756 2122183 \n", 636 | "1 28339753 2164478 1253185 809821 \n", 637 | "2 18777988 1812094 1050600 713912 \n", 638 | "3 13018610 1552950 774012 406184 \n", 639 | "4 9827328 2124154 1047741 380670 \n", 640 | ".. ... ... ... ... \n", 641 | "95 434187 90078 58506 35311 \n", 642 | "96 554163 80239 38183 14231 \n", 643 | "97 541833 91851 33669 12216 \n", 644 | "98 445276 83143 55372 35836 \n", 645 | "99 519090 68518 34658 17634 \n", 646 | "\n", 647 | " 1 star ratings paid \n", 648 | "0 12495915 False \n", 649 | "1 4709492 False \n", 650 | "2 4308998 False \n", 651 | "3 2219794 False \n", 652 | "4 1084340 False \n", 653 | ".. ... ... \n", 654 | "95 137917 False \n", 655 | "96 62125 False \n", 656 | "97 48055 False \n", 657 | "98 99275 False \n", 658 | "99 63612 False \n", 659 | "\n", 660 | "[100 rows x 14 columns]" 661 | ] 662 | }, 663 | "execution_count": 24, 664 | "metadata": {}, 665 | "output_type": "execute_result" 666 | } 667 | ], 668 | "source": [ 669 | "X" 670 | ] 671 | }, 672 | { 673 | "cell_type": "code", 674 | "execution_count": 25, 675 | "id": "ef6c9d55", 676 | "metadata": {}, 677 | "outputs": [], 678 | "source": [ 679 | "y = data['total ratings']\n", 680 | "y" 681 | ] 682 | }, 683 | { 684 | "cell_type": "code", 685 | "execution_count": 26, 686 | "id": "5066d69f", 687 | "metadata": {}, 688 | "outputs": [ 689 | { 690 | "data": { 691 | "text/plain": [ 692 | "0 86273129\n", 693 | "1 37276732\n", 694 | "2 26663595\n", 695 | "3 17971552\n", 696 | "4 14464235\n", 697 | " ... \n", 698 | "95 756002\n", 699 | "96 748945\n", 700 | "97 727627\n", 701 | "98 718905\n", 702 | "99 703514\n", 703 | "Name: total ratings, Length: 100, dtype: int64" 704 | ] 705 | }, 706 | "execution_count": 26, 707 | "metadata": {}, 708 | "output_type": "execute_result" 709 | } 710 | ], 711 | "source": [ 712 | "y" 713 | ] 714 | }, 715 | { 716 | "cell_type": "code", 717 | "execution_count": 27, 718 | "id": "b381de5b", 719 | "metadata": {}, 720 | "outputs": [ 721 | { 722 | "data": { 723 | "text/html": [ 724 | "
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ranktotal ratingsaverage ratinggrowth (30 days)growth (60 days)price5 star ratings4 star ratings3 star ratings2 star ratings...title_Temple Run 2title_Tomb of the Masktitle_Vector 2title_WWE Mayhemtitle_War Machines: Tank Battle - Army & Military Gamestitle_War Robots. 6v6 Tactical Multiplayer Battlestitle_Worms Zone .io - Voracious Snaketitle_Zombie Catchers - love the hunt!title_aquapark.iocategory_GAME ACTION
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..................................................................
959675600230.10.10434187900785850635311...0000000001
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100 rows × 113 columns

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91851 33669 12216 \n", 1059 | "98 0 445276 83143 55372 35836 \n", 1060 | "99 0 519090 68518 34658 17634 \n", 1061 | "\n", 1062 | " ... title_Temple Run 2 title_Tomb of the Mask title_Vector 2 \\\n", 1063 | "0 ... 0 0 0 \n", 1064 | "1 ... 0 0 0 \n", 1065 | "2 ... 0 0 0 \n", 1066 | "3 ... 0 0 0 \n", 1067 | "4 ... 0 0 0 \n", 1068 | ".. ... ... ... ... \n", 1069 | "95 ... 0 0 0 \n", 1070 | "96 ... 0 0 0 \n", 1071 | "97 ... 0 0 0 \n", 1072 | "98 ... 0 0 0 \n", 1073 | "99 ... 0 0 0 \n", 1074 | "\n", 1075 | " title_WWE Mayhem title_War Machines: Tank Battle - Army & Military Games \\\n", 1076 | "0 0 0 \n", 1077 | "1 0 0 \n", 1078 | "2 0 0 \n", 1079 | "3 0 0 \n", 1080 | "4 0 0 \n", 1081 | ".. ... ... \n", 1082 | "95 0 0 \n", 1083 | "96 0 0 \n", 1084 | "97 0 0 \n", 1085 | "98 0 0 \n", 1086 | "99 1 0 \n", 1087 | "\n", 1088 | " title_War Robots. 6v6 Tactical Multiplayer Battles \\\n", 1089 | "0 0 \n", 1090 | "1 0 \n", 1091 | "2 0 \n", 1092 | "3 0 \n", 1093 | "4 0 \n", 1094 | ".. ... \n", 1095 | "95 0 \n", 1096 | "96 0 \n", 1097 | "97 0 \n", 1098 | "98 0 \n", 1099 | "99 0 \n", 1100 | "\n", 1101 | " title_Worms Zone .io - Voracious Snake \\\n", 1102 | "0 0 \n", 1103 | "1 0 \n", 1104 | "2 0 \n", 1105 | "3 0 \n", 1106 | "4 0 \n", 1107 | ".. ... \n", 1108 | "95 0 \n", 1109 | "96 0 \n", 1110 | "97 0 \n", 1111 | "98 0 \n", 1112 | "99 0 \n", 1113 | "\n", 1114 | " title_Zombie Catchers - love the hunt! title_aquapark.io \\\n", 1115 | "0 0 0 \n", 1116 | "1 0 0 \n", 1117 | "2 0 0 \n", 1118 | "3 0 0 \n", 1119 | "4 0 0 \n", 1120 | ".. ... ... \n", 1121 | "95 0 0 \n", 1122 | "96 0 0 \n", 1123 | "97 0 0 \n", 1124 | "98 0 0 \n", 1125 | "99 0 0 \n", 1126 | "\n", 1127 | " category_GAME ACTION \n", 1128 | "0 1 \n", 1129 | "1 1 \n", 1130 | "2 1 \n", 1131 | "3 1 \n", 1132 | "4 1 \n", 1133 | ".. ... \n", 1134 | "95 1 \n", 1135 | "96 1 \n", 1136 | "97 1 \n", 1137 | "98 1 \n", 1138 | "99 1 \n", 1139 | "\n", 1140 | "[100 rows x 113 columns]" 1141 | ] 1142 | }, 1143 | "execution_count": 27, 1144 | "metadata": {}, 1145 | "output_type": "execute_result" 1146 | } 1147 | ], 1148 | "source": [ 1149 | "dummi=u.get_dummies(data.drop(\"installs\", axis=1))\n", 1150 | "dummi" 1151 | ] 1152 | }, 1153 | { 1154 | "cell_type": "code", 1155 | "execution_count": 28, 1156 | "id": "4b9b1986", 1157 | "metadata": {}, 1158 | "outputs": [], 1159 | "source": [ 1160 | "X = u.get_dummies(data.drop(\"installs\", axis=1), drop_first=True)\n", 1161 | "y = data[\"installs\"]" 1162 | ] 1163 | }, 1164 | { 1165 | "cell_type": "code", 1166 | "execution_count": 29, 1167 | "id": "32c9c9ab", 1168 | "metadata": {}, 1169 | "outputs": [ 1170 | { 1171 | "data": { 1172 | "text/plain": [ 1173 | "0 500.0 M\n", 1174 | "1 500.0 M\n", 1175 | "2 100.0 M\n", 1176 | "3 100.0 M\n", 1177 | "4 500.0 M\n", 1178 | " ... \n", 1179 | "95 10.0 M\n", 1180 | "96 10.0 M\n", 1181 | "97 10.0 M\n", 1182 | "98 10.0 M\n", 1183 | "99 10.0 M\n", 1184 | "Name: installs, Length: 100, dtype: object" 1185 | ] 1186 | }, 1187 | "execution_count": 29, 1188 | "metadata": {}, 1189 | "output_type": "execute_result" 1190 | } 1191 | ], 1192 | "source": [ 1193 | "y" 1194 | ] 1195 | }, 1196 | { 1197 | "cell_type": "code", 1198 | "execution_count": 30, 1199 | "id": "512bdea4", 1200 | "metadata": {}, 1201 | "outputs": [], 1202 | "source": [ 1203 | "from sklearn.tree import DecisionTreeClassifier\n", 1204 | "model = DecisionTreeClassifier()\n" 1205 | ] 1206 | }, 1207 | { 1208 | "cell_type": "code", 1209 | "execution_count": 31, 1210 | "id": "c9c6517a", 1211 | "metadata": {}, 1212 | "outputs": [ 1213 | { 1214 | "data": { 1215 | "text/plain": [ 1216 | "DecisionTreeClassifier()" 1217 | ] 1218 | }, 1219 | "execution_count": 31, 1220 | "metadata": {}, 1221 | "output_type": "execute_result" 1222 | } 1223 | ], 1224 | "source": [ 1225 | "model.fit(X, y)\n" 1226 | ] 1227 | }, 1228 | { 1229 | "cell_type": "code", 1230 | "execution_count": 32, 1231 | "id": "97a214d4", 1232 | "metadata": {}, 1233 | "outputs": [ 1234 | { 1235 | "data": { 1236 | "text/plain": [ 1237 | "array(['500.0 M', '500.0 M', '100.0 M', '100.0 M', '500.0 M', '100.0 M',\n", 1238 | " '100.0 M', '500.0 M', '100.0 M', '100.0 M', '100.0 M', '100.0 M',\n", 1239 | " '50.0 M', '100.0 M', '50.0 M', '100.0 M', '100.0 M', '50.0 M',\n", 1240 | " '50.0 M', '50.0 M', '100.0 M', '100.0 M', '100.0 M', '100.0 M',\n", 1241 | " '100.0 M', '50.0 M', '100.0 M', '10.0 M', '50.0 M', '10.0 M',\n", 1242 | " '100.0 M', '50.0 M', '50.0 M', '50.0 M', '10.0 M', '100.0 M',\n", 1243 | " '10.0 M', '100.0 M', '10.0 M', '50.0 M', '50.0 M', '100.0 M',\n", 1244 | " '50.0 M', '100.0 M', '50.0 M', '10.0 M', '50.0 M', '10.0 M',\n", 1245 | " '10.0 M', '100.0 M', '100.0 M', '50.0 M', '50.0 M', '10.0 M',\n", 1246 | " '100.0 M', '100.0 M', '100.0 M', '50.0 M', '50.0 M', '100.0 M',\n", 1247 | " '10.0 M', '100.0 M', '50.0 M', '10.0 M', '50.0 M', '50.0 M',\n", 1248 | " '100.0 M', '10.0 M', '50.0 M', '10.0 M', '100.0 M', '10.0 M',\n", 1249 | " '50.0 M', '10.0 M', '100.0 M', '10.0 M', '100.0 M', '10.0 M',\n", 1250 | " '10.0 M', '10.0 M', '10.0 M', '100.0 M', '100.0 M', '10.0 M',\n", 1251 | " '10.0 M', '10.0 M', '100.0 M', '10.0 M', '10.0 M', '50.0 M',\n", 1252 | " '100.0 M', '50.0 M', '50.0 M', '50.0 M', '10.0 M', '10.0 M',\n", 1253 | " '10.0 M', '10.0 M', '10.0 M', '10.0 M'], dtype=object)" 1254 | ] 1255 | }, 1256 | "execution_count": 32, 1257 | "metadata": {}, 1258 | "output_type": "execute_result" 1259 | } 1260 | ], 1261 | "source": [ 1262 | "pred = model.predict(X)\n", 1263 | "pred" 1264 | ] 1265 | }, 1266 | { 1267 | "cell_type": "code", 1268 | "execution_count": 33, 1269 | "id": "3772308f", 1270 | "metadata": {}, 1271 | "outputs": [ 1272 | { 1273 | "data": { 1274 | "image/png": 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\n", 1275 | "text/plain": [ 1276 | "
" 1277 | ] 1278 | }, 1279 | "metadata": { 1280 | "needs_background": "light" 1281 | }, 1282 | "output_type": "display_data" 1283 | } 1284 | ], 1285 | "source": [ 1286 | "from sklearn.tree import plot_tree\n", 1287 | "plot_tree(model, feature_names=X.columns, filled=True);" 1288 | ] 1289 | }, 1290 | { 1291 | "cell_type": "code", 1292 | "execution_count": 34, 1293 | "id": "1dd1f5c3", 1294 | "metadata": {}, 1295 | "outputs": [ 1296 | { 1297 | "data": { 1298 | "text/html": [ 1299 | "
\n", 1300 | "\n", 1313 | "\n", 1314 | " \n", 1315 | " \n", 1316 | " \n", 1317 | " \n", 1318 | " \n", 1319 | " \n", 1320 | " \n", 1321 | " \n", 1322 | " \n", 1323 | " \n", 1324 | " \n", 1325 | " \n", 1326 | " \n", 1327 | " \n", 1328 | " \n", 1329 | " \n", 1330 | " \n", 1331 | " \n", 1332 | " \n", 1333 | " \n", 1334 | " \n", 1335 | " \n", 1336 | " \n", 1337 | " \n", 1338 | " \n", 1339 | " \n", 1340 | " \n", 1341 | " \n", 1342 | " \n", 1343 | " \n", 1344 | " \n", 1345 | " \n", 1346 | " \n", 1347 | " \n", 1348 | " \n", 1349 | " \n", 1350 | " \n", 1351 | " \n", 1352 | " \n", 1353 | " \n", 1354 | " \n", 1355 | " \n", 1356 | " \n", 1357 | " \n", 1358 | " \n", 1359 | " \n", 1360 | " \n", 1361 | " \n", 1362 | " \n", 1363 | " \n", 1364 | " \n", 1365 | " \n", 1366 | " \n", 1367 | " \n", 1368 | " \n", 1369 | " \n", 1370 | " \n", 1371 | " \n", 1372 | " \n", 1373 | " \n", 1374 | " \n", 1375 | " \n", 1376 | " \n", 1377 | " \n", 1378 | "
ActualPredicted
0500.0 M500.0 M
1500.0 M500.0 M
2100.0 M100.0 M
3100.0 M100.0 M
4500.0 M500.0 M
.........
9510.0 M10.0 M
9610.0 M10.0 M
9710.0 M10.0 M
9810.0 M10.0 M
9910.0 M10.0 M
\n", 1379 | "

100 rows × 2 columns

\n", 1380 | "
" 1381 | ], 1382 | "text/plain": [ 1383 | " Actual Predicted\n", 1384 | "0 500.0 M 500.0 M\n", 1385 | "1 500.0 M 500.0 M\n", 1386 | "2 100.0 M 100.0 M\n", 1387 | "3 100.0 M 100.0 M\n", 1388 | "4 500.0 M 500.0 M\n", 1389 | ".. ... ...\n", 1390 | "95 10.0 M 10.0 M\n", 1391 | "96 10.0 M 10.0 M\n", 1392 | "97 10.0 M 10.0 M\n", 1393 | "98 10.0 M 10.0 M\n", 1394 | "99 10.0 M 10.0 M\n", 1395 | "\n", 1396 | "[100 rows x 2 columns]" 1397 | ] 1398 | }, 1399 | "execution_count": 34, 1400 | "metadata": {}, 1401 | "output_type": "execute_result" 1402 | } 1403 | ], 1404 | "source": [ 1405 | "pred= model.predict(X)\n", 1406 | "df =u.DataFrame({'Actual': y, 'Predicted':pred})\n", 1407 | "df" 1408 | ] 1409 | }, 1410 | { 1411 | "cell_type": "code", 1412 | "execution_count": 35, 1413 | "id": "b18e0b66", 1414 | "metadata": {}, 1415 | "outputs": [ 1416 | { 1417 | "name": "stdout", 1418 | "output_type": "stream", 1419 | "text": [ 1420 | "[[31 0 0 0]\n", 1421 | " [ 0 37 0 0]\n", 1422 | " [ 0 0 28 0]\n", 1423 | " [ 0 0 0 4]]\n" 1424 | ] 1425 | } 1426 | ], 1427 | "source": [ 1428 | "from sklearn.metrics import classification_report, confusion_matrix\n", 1429 | "print(confusion_matrix(y, pred))" 1430 | ] 1431 | }, 1432 | { 1433 | "cell_type": "code", 1434 | "execution_count": 36, 1435 | "id": "84f0bf7c", 1436 | "metadata": {}, 1437 | "outputs": [ 1438 | { 1439 | "name": "stdout", 1440 | "output_type": "stream", 1441 | "text": [ 1442 | " precision recall f1-score support\n", 1443 | "\n", 1444 | " 10.0 M 1.00 1.00 1.00 31\n", 1445 | " 100.0 M 1.00 1.00 1.00 37\n", 1446 | " 50.0 M 1.00 1.00 1.00 28\n", 1447 | " 500.0 M 1.00 1.00 1.00 4\n", 1448 | "\n", 1449 | " accuracy 1.00 100\n", 1450 | " macro avg 1.00 1.00 1.00 100\n", 1451 | "weighted avg 1.00 1.00 1.00 100\n", 1452 | "\n" 1453 | ] 1454 | } 1455 | ], 1456 | "source": [ 1457 | "print(classification_report(y, pred))" 1458 | ] 1459 | } 1460 | ], 1461 | "metadata": { 1462 | "kernelspec": { 1463 | "display_name": "Python 3 (ipykernel)", 1464 | "language": "python", 1465 | "name": "python3" 1466 | }, 1467 | "language_info": { 1468 | "codemirror_mode": { 1469 | "name": "ipython", 1470 | "version": 3 1471 | }, 1472 | "file_extension": ".py", 1473 | "mimetype": "text/x-python", 1474 | "name": "python", 1475 | "nbconvert_exporter": "python", 1476 | "pygments_lexer": "ipython3", 1477 | "version": "3.9.12" 1478 | } 1479 | }, 1480 | "nbformat": 4, 1481 | "nbformat_minor": 5 1482 | } 1483 | --------------------------------------------------------------------------------