diff --git a/HML-081_PROJECT.ipynb b/HML-081_PROJECT.ipynb new file mode 100644 index 0000000..759c963 --- /dev/null +++ b/HML-081_PROJECT.ipynb @@ -0,0 +1,2652 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "% pylab inline\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "from sklearn.metrics import r2_score, mean_squared_error\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.ensemble import RandomForestRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomeocean_proximitymedian_house_value
0-122.2337.8841880129.03221268.3252NEAR BAY452600
1-122.2237.862170991106.0240111388.3014NEAR BAY358500
2-122.2437.85521467190.04961777.2574NEAR BAY352100
3-122.2537.85521274235.05582195.6431NEAR BAY341300
4-122.2537.85521627280.05652593.8462NEAR BAY342200
5-122.2537.8552919213.04131934.0368NEAR BAY269700
6-122.2537.84522535489.010945143.6591NEAR BAY299200
7-122.2537.84523104687.011576473.1200NEAR BAY241400
8-122.2637.84422555665.012065952.0804NEAR BAY226700
9-122.2537.84523549707.015517143.6912NEAR BAY261100
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count20640.00000020640.00000020640.00000020640.00000020433.00000020640.00000020640.00000020640.00000020640.000000
mean-119.56970435.63186128.6394862635.763081537.8705531425.476744499.5396803.870671206855.816909
std2.0035322.13595212.5855582181.615252421.3850701132.462122382.3297531.899822115395.615874
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25%-121.80000033.93000018.0000001447.750000296.000000787.000000280.0000002.563400119600.000000
50%-118.49000034.26000029.0000002127.000000435.0000001166.000000409.0000003.534800179700.000000
75%-118.01000037.71000037.0000003148.000000647.0000001725.000000605.0000004.743250264725.000000
max-114.31000041.95000052.00000039320.0000006445.00000035682.0000006082.00000015.000100500001.000000
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomeocean_proximitymedian_house_value
2-122.2437.85521467190.04961777.2574NEAR BAY352100
3-122.2537.85521274235.05582195.6431NEAR BAY341300
4-122.2537.85521627280.05652593.8462NEAR BAY342200
5-122.2537.8552919213.04131934.0368NEAR BAY269700
6-122.2537.84522535489.010945143.6591NEAR BAY299200
7-122.2537.84523104687.011576473.1200NEAR BAY241400
8-122.2637.84422555665.012065952.0804NEAR BAY226700
9-122.2537.84523549707.015517143.6912NEAR BAY261100
10-122.2637.85522202434.09104023.2031NEAR BAY281500
11-122.2637.85523503752.015047343.2705NEAR BAY241800
12-122.2637.85522491474.010984683.0750NEAR BAY213500
13-122.2637.8452696191.03451742.6736NEAR BAY191300
14-122.2637.85522643626.012126201.9167NEAR BAY159200
15-122.2637.85501120283.06972642.1250NEAR BAY140000
16-122.2737.85521966347.07933312.7750NEAR BAY152500
17-122.2737.85521228293.06483032.1202NEAR BAY155500
18-122.2637.84502239455.09904191.9911NEAR BAY158700
19-122.2737.84521503298.06902752.6033NEAR BAY162900
20-122.2737.8540751184.04091661.3578NEAR BAY147500
21-122.2737.85421639367.09293661.7135NEAR BAY159800
22-122.2737.84522436541.010154781.7250NEAR BAY113900
23-122.2737.84521688337.08533252.1806NEAR BAY99700
24-122.2737.84522224437.010064222.6000NEAR BAY132600
25-122.2837.8541535123.03171192.4038NEAR BAY107500
26-122.2837.85491130244.06072392.4597NEAR BAY93800
27-122.2837.85521898421.011023971.8080NEAR BAY105500
28-122.2837.84502082492.011314731.6424NEAR BAY108900
29-122.2837.8452729160.03951551.6875NEAR BAY132000
30-122.2837.84491916447.08633781.9274NEAR BAY122300
31-122.2837.84522153481.011684411.9615NEAR BAY115200
.................................
20610-121.5639.10282130484.011954391.3631INLAND45500
20611-121.5539.10271783441.011634091.2857INLAND47000
20612-121.5639.08261377289.07612671.4934INLAND48300
20613-121.5539.09311728365.011673841.4958INLAND53400
20614-121.5439.08262276460.014554742.4695INLAND58000
20615-121.5439.08231076216.07241972.3598INLAND57500
20616-121.5339.08151810441.011573752.0469INLAND55100
20617-121.5339.0620561109.03081143.3021INLAND70800
20618-121.5539.06251332247.07262262.2500INLAND63400
20619-121.5639.01221891340.010232962.7303INLAND99100
20620-121.4839.054019841.0151484.5625INLAND100000
20621-121.4739.01371244247.04841572.3661INLAND77500
20622-121.4439.0020755147.04571572.4167INLAND67000
20623-121.3739.03321158244.05982272.8235INLAND65500
20624-121.4139.04161698300.07312913.0739INLAND87200
20625-121.5239.123710217.029144.1250INLAND72000
20626-121.4339.18361124184.05041712.1667INLAND93800
20627-121.3239.13535865.0169593.0000INLAND162500
20628-121.4839.10192043421.010183902.5952INLAND92400
20629-121.3939.1228100351856.0691218182.0943INLAND108300
20630-121.3239.29112640505.012574453.5673INLAND112000
20631-121.4039.33152655493.012004323.5179INLAND107200
20632-121.4539.26152319416.010473853.1250INLAND115600
20633-121.5339.19272080412.010823822.5495INLAND98300
20634-121.5639.27282332395.010413443.7125INLAND116800
20635-121.0939.48251665374.08453301.5603INLAND78100
20636-121.2139.4918697150.03561142.5568INLAND77100
20637-121.2239.43172254485.010074331.7000INLAND92300
20638-121.3239.43181860409.07413491.8672INLAND84700
20639-121.2439.37162785616.013875302.3886INLAND89400
\n", + "

19959 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "2 -122.24 37.85 52 1467 190.0 \n", + "3 -122.25 37.85 52 1274 235.0 \n", + "4 -122.25 37.85 52 1627 280.0 \n", + "5 -122.25 37.85 52 919 213.0 \n", + "6 -122.25 37.84 52 2535 489.0 \n", + "7 -122.25 37.84 52 3104 687.0 \n", + "8 -122.26 37.84 42 2555 665.0 \n", + "9 -122.25 37.84 52 3549 707.0 \n", + "10 -122.26 37.85 52 2202 434.0 \n", + "11 -122.26 37.85 52 3503 752.0 \n", + "12 -122.26 37.85 52 2491 474.0 \n", + "13 -122.26 37.84 52 696 191.0 \n", + "14 -122.26 37.85 52 2643 626.0 \n", + "15 -122.26 37.85 50 1120 283.0 \n", + "16 -122.27 37.85 52 1966 347.0 \n", + "17 -122.27 37.85 52 1228 293.0 \n", + "18 -122.26 37.84 50 2239 455.0 \n", + "19 -122.27 37.84 52 1503 298.0 \n", + "20 -122.27 37.85 40 751 184.0 \n", + "21 -122.27 37.85 42 1639 367.0 \n", + "22 -122.27 37.84 52 2436 541.0 \n", + "23 -122.27 37.84 52 1688 337.0 \n", + "24 -122.27 37.84 52 2224 437.0 \n", + "25 -122.28 37.85 41 535 123.0 \n", + "26 -122.28 37.85 49 1130 244.0 \n", + "27 -122.28 37.85 52 1898 421.0 \n", + "28 -122.28 37.84 50 2082 492.0 \n", + "29 -122.28 37.84 52 729 160.0 \n", + "30 -122.28 37.84 49 1916 447.0 \n", + "31 -122.28 37.84 52 2153 481.0 \n", + "... ... ... ... ... ... \n", + "20610 -121.56 39.10 28 2130 484.0 \n", + "20611 -121.55 39.10 27 1783 441.0 \n", + "20612 -121.56 39.08 26 1377 289.0 \n", + "20613 -121.55 39.09 31 1728 365.0 \n", + "20614 -121.54 39.08 26 2276 460.0 \n", + "20615 -121.54 39.08 23 1076 216.0 \n", + "20616 -121.53 39.08 15 1810 441.0 \n", + "20617 -121.53 39.06 20 561 109.0 \n", + "20618 -121.55 39.06 25 1332 247.0 \n", + "20619 -121.56 39.01 22 1891 340.0 \n", + "20620 -121.48 39.05 40 198 41.0 \n", + "20621 -121.47 39.01 37 1244 247.0 \n", + "20622 -121.44 39.00 20 755 147.0 \n", + "20623 -121.37 39.03 32 1158 244.0 \n", + "20624 -121.41 39.04 16 1698 300.0 \n", + "20625 -121.52 39.12 37 102 17.0 \n", + "20626 -121.43 39.18 36 1124 184.0 \n", + "20627 -121.32 39.13 5 358 65.0 \n", + "20628 -121.48 39.10 19 2043 421.0 \n", + "20629 -121.39 39.12 28 10035 1856.0 \n", + "20630 -121.32 39.29 11 2640 505.0 \n", + "20631 -121.40 39.33 15 2655 493.0 \n", + "20632 -121.45 39.26 15 2319 416.0 \n", + "20633 -121.53 39.19 27 2080 412.0 \n", + "20634 -121.56 39.27 28 2332 395.0 \n", + "20635 -121.09 39.48 25 1665 374.0 \n", + "20636 -121.21 39.49 18 697 150.0 \n", + "20637 -121.22 39.43 17 2254 485.0 \n", + "20638 -121.32 39.43 18 1860 409.0 \n", + "20639 -121.24 39.37 16 2785 616.0 \n", + "\n", + " population households median_income ocean_proximity \\\n", + "2 496 177 7.2574 NEAR BAY \n", + "3 558 219 5.6431 NEAR BAY \n", + "4 565 259 3.8462 NEAR BAY \n", + "5 413 193 4.0368 NEAR BAY \n", + "6 1094 514 3.6591 NEAR BAY \n", + "7 1157 647 3.1200 NEAR BAY \n", + "8 1206 595 2.0804 NEAR BAY \n", + "9 1551 714 3.6912 NEAR BAY \n", + "10 910 402 3.2031 NEAR BAY \n", + "11 1504 734 3.2705 NEAR BAY \n", + "12 1098 468 3.0750 NEAR BAY \n", + "13 345 174 2.6736 NEAR BAY \n", + "14 1212 620 1.9167 NEAR BAY \n", + "15 697 264 2.1250 NEAR BAY \n", + "16 793 331 2.7750 NEAR BAY \n", + "17 648 303 2.1202 NEAR BAY \n", + "18 990 419 1.9911 NEAR BAY \n", + "19 690 275 2.6033 NEAR BAY \n", + "20 409 166 1.3578 NEAR BAY \n", + "21 929 366 1.7135 NEAR BAY \n", + "22 1015 478 1.7250 NEAR BAY \n", + "23 853 325 2.1806 NEAR BAY \n", + "24 1006 422 2.6000 NEAR BAY \n", + "25 317 119 2.4038 NEAR BAY \n", + "26 607 239 2.4597 NEAR BAY \n", + "27 1102 397 1.8080 NEAR BAY \n", + "28 1131 473 1.6424 NEAR BAY \n", + "29 395 155 1.6875 NEAR BAY \n", + "30 863 378 1.9274 NEAR BAY \n", + "31 1168 441 1.9615 NEAR BAY \n", + "... ... ... ... ... \n", + "20610 1195 439 1.3631 INLAND \n", + "20611 1163 409 1.2857 INLAND \n", + "20612 761 267 1.4934 INLAND \n", + "20613 1167 384 1.4958 INLAND \n", + "20614 1455 474 2.4695 INLAND \n", + "20615 724 197 2.3598 INLAND \n", + "20616 1157 375 2.0469 INLAND \n", + "20617 308 114 3.3021 INLAND \n", + "20618 726 226 2.2500 INLAND \n", + "20619 1023 296 2.7303 INLAND \n", + "20620 151 48 4.5625 INLAND \n", + "20621 484 157 2.3661 INLAND \n", + "20622 457 157 2.4167 INLAND \n", + "20623 598 227 2.8235 INLAND \n", + "20624 731 291 3.0739 INLAND \n", + "20625 29 14 4.1250 INLAND \n", + "20626 504 171 2.1667 INLAND \n", + "20627 169 59 3.0000 INLAND \n", + "20628 1018 390 2.5952 INLAND \n", + "20629 6912 1818 2.0943 INLAND \n", + "20630 1257 445 3.5673 INLAND \n", + "20631 1200 432 3.5179 INLAND \n", + "20632 1047 385 3.1250 INLAND \n", + "20633 1082 382 2.5495 INLAND \n", + "20634 1041 344 3.7125 INLAND \n", + "20635 845 330 1.5603 INLAND \n", + "20636 356 114 2.5568 INLAND \n", + "20637 1007 433 1.7000 INLAND \n", + "20638 741 349 1.8672 INLAND \n", + "20639 1387 530 2.3886 INLAND \n", + "\n", + " median_house_value \n", + "2 352100 \n", + "3 341300 \n", + "4 342200 \n", + "5 269700 \n", + "6 299200 \n", + "7 241400 \n", + "8 226700 \n", + "9 261100 \n", + "10 281500 \n", + "11 241800 \n", + "12 213500 \n", + "13 191300 \n", + "14 159200 \n", + "15 140000 \n", + "16 152500 \n", + "17 155500 \n", + "18 158700 \n", + "19 162900 \n", + "20 147500 \n", + "21 159800 \n", + "22 113900 \n", + "23 99700 \n", + "24 132600 \n", + "25 107500 \n", + "26 93800 \n", + "27 105500 \n", + "28 108900 \n", + "29 132000 \n", + "30 122300 \n", + "31 115200 \n", + "... ... \n", + "20610 45500 \n", + "20611 47000 \n", + "20612 48300 \n", + "20613 53400 \n", + "20614 58000 \n", + "20615 57500 \n", + "20616 55100 \n", + "20617 70800 \n", + "20618 63400 \n", + "20619 99100 \n", + "20620 100000 \n", + "20621 77500 \n", + "20622 67000 \n", + "20623 65500 \n", + "20624 87200 \n", + "20625 72000 \n", + "20626 93800 \n", + "20627 162500 \n", + "20628 92400 \n", + "20629 108300 \n", + "20630 112000 \n", + "20631 107200 \n", + "20632 115600 \n", + "20633 98300 \n", + "20634 116800 \n", + "20635 78100 \n", + "20636 77100 \n", + "20637 92300 \n", + "20638 84700 \n", + "20639 89400 \n", + "\n", + "[19959 rows x 10 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filt_train" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((19959, 10), (20640, 10))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filt_train.shape,train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(19959, 10)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train = filt_train\n", + "train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.1250 49\n", + "2.8750 46\n", + "4.1250 44\n", + "2.6250 44\n", + "3.8750 41\n", + "3.3750 38\n", + "3.0000 38\n", + "4.0000 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'High_income'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(19959, 11)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['NEAR BAY', '<1H OCEAN', 'INLAND', 'NEAR OCEAN', 'ISLAND'],\n", + " dtype=object)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train['ocean_proximity'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[High_income, medium_income, Low_income]\n", + "Categories (3, object): [Low_income < medium_income < High_income]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train['median_income_rows'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 190., 235., 280., ..., 3008., 1857., 1052.])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train['total_bedrooms'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsocean_proximitymedian_house_valuemedian_income_rows<1H OCEANINLANDISLANDNEAR BAYNEAR OCEAN
2-122.2437.85521467190.0496177NEAR BAY352100000010
3-122.2537.85521274235.0558219NEAR BAY341300200010
4-122.2537.85521627280.0565259NEAR BAY342200200010
5-122.2537.8552919213.0413193NEAR BAY269700200010
6-122.2537.84522535489.01094514NEAR BAY299200200010
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "2 -122.24 37.85 52 1467 190.0 \n", + "3 -122.25 37.85 52 1274 235.0 \n", + "4 -122.25 37.85 52 1627 280.0 \n", + "5 -122.25 37.85 52 919 213.0 \n", + "6 -122.25 37.84 52 2535 489.0 \n", + "\n", + " population households ocean_proximity median_house_value \\\n", + "2 496 177 NEAR BAY 352100 \n", + "3 558 219 NEAR BAY 341300 \n", + "4 565 259 NEAR BAY 342200 \n", + "5 413 193 NEAR BAY 269700 \n", + "6 1094 514 NEAR BAY 299200 \n", + "\n", + " median_income_rows <1H OCEAN INLAND ISLAND NEAR BAY NEAR OCEAN \n", + "2 0 0 0 0 1 0 \n", + "3 2 0 0 0 1 0 \n", + "4 2 0 0 0 1 0 \n", + "5 2 0 0 0 1 0 \n", + "6 2 0 0 0 1 0 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "from sklearn.preprocessing import LabelEncoder\n", + "train_labelencoder = LabelEncoder()\n", + "train['median_income_rows']= train_labelencoder.fit_transform(train['median_income_rows'])\n", + "train.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "train = train.drop(['ocean_proximity'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(19959, 14)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "longitude float64\n", + "latitude float64\n", + "housing_median_age int64\n", + "total_rooms int64\n", + "total_bedrooms float64\n", + "population int64\n", + "households int64\n", + "median_house_value int64\n", + "median_income_rows int64\n", + "<1H OCEAN uint8\n", + "INLAND uint8\n", + "ISLAND uint8\n", + "NEAR BAY uint8\n", + "NEAR OCEAN uint8\n", + "dtype: object" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Shiva Chandra\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", + " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,12))\n", + "sns.distplot(train['median_house_value'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "500001 527\n", + "500000 27\n", + "475000 8\n", + "466700 4\n", + "468800 3\n", + "Name: median_house_value, dtype: int64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train[train['median_house_value']>450000]['median_house_value'].value_counts().head()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "train=train.loc[train['median_house_value']<500001,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Shiva Chandra\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", + " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,12))\n", + "sns.distplot(train['median_house_value'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "train['bed per house']=train['total_bedrooms']/train['total_rooms']\n", + "train['h/p']=train['households']/train['population']" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "x=train.drop('median_house_value',axis=1).values\n", + "y=train['median_house_value'].values" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.2,random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(15545, 15)\n", + "(15545,)\n", + "(3887, 15)\n", + "(3887,)\n" + ] + } + ], + "source": [ + "print(xtrain.shape)\n", + "print(ytrain.shape)\n", + "print(xtest.shape)\n", + "print(ytest.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "lin = LinearRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "lin.fit(xtrain, ytrain)\n", + "predictions = lin.predict(xtest)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "70563.10520125084\n" + ] + } + ], + "source": [ + "print(sqrt(mean_squared_error(ytest, predictions)))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "dtree_reg = DecisionTreeRegressor(max_depth=11)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeRegressor(criterion='mse', max_depth=11, max_features=None,\n", + " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " presort=False, random_state=None, splitter='best')" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dtree_reg.fit(xtrain, ytrain)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse of decision tree is 56078.12770810965\n" + ] + } + ], + "source": [ + "pred = dtree_reg.predict(xtest)\n", + "print(\"rmse of decision tree is\",math.sqrt(mean_squared_error(ytest, pred)))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Accuracy 48.05%\n" + ] + } + ], + "source": [ + "model = DecisionTreeClassifier(max_depth = 100, min_samples_leaf =2, max_features = 'sqrt',min_samples_split = 3,random_state=82)\n", + "model.fit(xtrain,ytrain)\n", + "predicted_train = model.predict(xtrain)\n", + "true_value = ytrain\n", + "print(\"Train Accuracy {:.2%}\".format(accuracy_score(true_value,predicted_train)))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.46001548259958236" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lin.score (xtest, ytest)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Accuracy 63.74%\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "model = RandomForestClassifier(max_depth = 30, min_samples_leaf = 50, max_features = 'sqrt',n_estimators = 1000)\n", + "model.fit(xtrain,ytrain)\n", + "predicted_train = model.predict(xtrain)\n", + "true_value = ytrain\n", + "print(\"Train Accuracy {:.2%}\".format(accuracy_score(true_value,predicted_train)))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "test = pd.DataFrame({'Predicted':pred,'Actual':ytest})\n", + "fig= plt.figure(figsize=(16,8))\n", + "test = test.reset_index()\n", + "test = test.drop(['index'],axis=1)\n", + "plt.plot(test[:50])\n", + "plt.legend(['Actual','Predicted'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Hyderabad-MachineLearning-081_PROJECT_2.ipynb b/Hyderabad-MachineLearning-081_PROJECT_2.ipynb new file mode 100644 index 0000000..bd5b25c --- /dev/null +++ b/Hyderabad-MachineLearning-081_PROJECT_2.ipynb @@ -0,0 +1,2871 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Digit Recognizer" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import pandas as pd \n", + "import random as rn\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg\n", + "import seaborn as sns\n", + "%matplotlib inline\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import cross_val_score, KFold \n", + "import itertools\n", + "import warnings\n", + "from sklearn.exceptions import ConvergenceWarning\n", + "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X:\n", + "\n", + "RangeIndex: 42000 entries, 0 to 41999\n", + "Columns: 784 entries, pixel0 to pixel783\n", + "dtypes: float64(784)\n", + "memory usage: 251.2 MB\n", + "None\n", + "----------------------------------------------------------------------------------------------------\n", + "X_test:\n", + "\n", + "RangeIndex: 28000 entries, 0 to 27999\n", + "Columns: 784 entries, pixel0 to pixel783\n", + "dtypes: float64(784)\n", + "memory usage: 167.5 MB\n", + "None\n", + "----------------------------------------------------------------------------------------------------\n", + "Y:\n", + "(42000,)\n" + ] + } + ], + "source": [ + "print(\"X:\")\n", + "print(X.info())\n", + "print(\"-\"*100)\n", + "print(\"X_test:\")\n", + "print(X_test.info())\n", + "print(\"-\"*100)\n", + "print(\"Y:\")\n", + "print(Y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 0\n", + "2 1\n", + "3 4\n", + "4 0\n", + "Name: label, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 5, sharex=True, sharey=True, figsize=(10,6))\n", + "axs = axs.flatten()\n", + "for i in range(0,5):\n", + " pop= X.iloc[i]\n", + " pop= pop.values.reshape(-1,28,28,1)\n", + " axs[i].imshow(pop[0,:,:,0], cmap=plt.get_cmap('gray'))\n", + " axs[i].set_title(Y[i])\n", + "plt.tight_layout() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distribution of the Labels" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 4684\n", + "7 4401\n", + "3 4351\n", + "9 4188\n", + "2 4177\n", + "6 4137\n", + "0 4132\n", + "4 4072\n", + "8 4063\n", + "5 3795\n", + "Name: label, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(15,5))\n", + "g = sns.countplot(Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get indexes of first 10 occurences for each number" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1,\n", + " 0,\n", + " 16,\n", + " 7,\n", + " 3,\n", + " 8,\n", + " 21,\n", + " 6,\n", + " 10,\n", + " 11,\n", + " 4,\n", + " 2,\n", + " 22,\n", + " 9,\n", + " 32,\n", + " 19,\n", + " 26,\n", + " 18,\n", + " 20,\n", + " 27,\n", + " 5,\n", + " 12,\n", + " 24,\n", + " 13,\n", + " 39,\n", + " 51,\n", + " 45,\n", + " 29,\n", + " 30,\n", + " 28,\n", + " 17,\n", + " 15,\n", + " 34,\n", + " 14,\n", + " 42,\n", + " 62,\n", + " 64,\n", + " 47,\n", + " 67,\n", + " 31,\n", + " 23,\n", + " 35,\n", + " 44,\n", + " 25,\n", + " 43,\n", + " 80,\n", + " 72,\n", + " 48,\n", + " 82,\n", + " 33,\n", + " 54,\n", + " 37,\n", + " 55,\n", + " 36,\n", + " 49,\n", + " 99,\n", + " 74,\n", + " 50,\n", + " 87,\n", + " 40,\n", + " 63,\n", + " 38,\n", + " 56,\n", + " 46,\n", + " 66,\n", + " 107,\n", + " 89,\n", + " 76,\n", + " 105,\n", + " 53,\n", + " 69,\n", + " 41,\n", + " 73,\n", + " 57,\n", + " 75,\n", + " 119,\n", + " 91,\n", + " 102,\n", + " 106,\n", + " 58,\n", + " 98,\n", + " 52,\n", + " 84,\n", + " 65,\n", + " 78,\n", + " 125,\n", + " 93,\n", + " 103,\n", + " 131,\n", + " 71,\n", + " 108,\n", + " 59,\n", + " 94,\n", + " 70,\n", + " 81,\n", + " 128,\n", + " 109,\n", + " 116,\n", + " 135,\n", + " 83]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "li_idxs = []\n", + "for i in range(10):\n", + " for nr in range(10):\n", + " ix = Y[Y==nr].index[i]\n", + " li_idxs.append(ix)\n", + "li_idxs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First 10 image samples for each digit" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(10, 10, sharex=True, sharey=True, figsize=(10,12))\n", + "axs = axs.flatten()\n", + "for n, i in enumerate(li_idxs):\n", + " im = X.iloc[i]\n", + " im = im.values.reshape(-1,28,28,1)\n", + " axs[n].imshow(im[0,:,:,0], cmap=plt.get_cmap('gray'))\n", + " axs[n].set_title(Y[i])\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nr_samples train data: 30000\n", + "start_ix_val: 30000\n", + "end_ix_val: 40000\n" + ] + } + ], + "source": [ + "# for best performance, especially of the NN classfiers,\n", + "# set mode = \"commit\"\n", + "mode = \"edit\"\n", + "mode = \"commit\"\n", + "#\n", + "\n", + "if mode == \"edit\" :\n", + " nr_samples = 1200\n", + "\n", + "if mode == \"commit\" : \n", + " nr_samples = 30000\n", + "\n", + "Y_train=Y[:nr_samples]\n", + "X_train=X[:nr_samples]\n", + "start_ix_val = nr_samples \n", + "end_ix_val = nr_samples + int(nr_samples/3)\n", + "Y_val=Y[start_ix_val:end_ix_val]\n", + "X_val=X[start_ix_val:end_ix_val]\n", + " \n", + "print(\"nr_samples train data:\", nr_samples)\n", + "print(\"start_ix_val:\", start_ix_val)\n", + "print(\"end_ix_val:\", end_ix_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Performing Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n", + " \"this warning.\", FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", + " multi_class='warn', n_jobs=None, penalty='l2',\n", + " random_state=None, solver='warn', tol=0.0001, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "model = LogisticRegression()\n", + "model.fit(X_train,Y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 0, 1, ..., 2, 5, 0], dtype=int64)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "predicted_train = model.predict(X_train)\n", + "true_value = Y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 0, 1, ..., 2, 5, 0], dtype=int64)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predicted_train" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Accuracy 93.31%\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "print(\"Train Accuracy {:.2%}\".format(accuracy_score(true_value,predicted_train)))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n", + " \"this warning.\", FutureWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Accuracy 90.41%\n" + ] + } + ], + "source": [ + "model = LogisticRegression(C = 0.01, tol = 0.001, class_weight = \"balanced\",random_state = 42)\n", + "model.fit(X_train,Y_train)\n", + "predicted_train = model.predict(X_val)\n", + "print(\"Train Accuracy {:.2%}\".format(accuracy_score(Y_val,predicted_train)))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report\n", + " precision recall f1-score support\n", + "\n", + " 0 0.94 0.97 0.96 998\n", + " 1 0.92 0.98 0.95 1127\n", + " 2 0.90 0.88 0.89 967\n", + " 3 0.90 0.88 0.89 1060\n", + " 4 0.89 0.91 0.90 914\n", + " 5 0.90 0.81 0.85 914\n", + " 6 0.93 0.95 0.94 951\n", + " 7 0.92 0.91 0.92 1066\n", + " 8 0.87 0.87 0.87 995\n", + " 9 0.86 0.87 0.87 1008\n", + "\n", + " accuracy 0.90 10000\n", + " macro avg 0.90 0.90 0.90 10000\n", + "weighted avg 0.90 0.90 0.90 10000\n", + "\n", + "Confusion matrix\n", + "[[ 969 0 1 3 1 3 10 1 10 0]\n", + " [ 0 1101 3 1 0 3 1 2 16 0]\n", + " [ 9 11 849 12 16 4 16 19 28 3]\n", + " [ 2 9 42 929 0 28 6 9 23 12]\n", + " [ 0 10 6 1 834 1 8 0 7 47]\n", + " [ 20 8 7 46 23 736 22 6 28 18]\n", + " [ 8 4 6 1 7 11 905 0 9 0]\n", + " [ 10 8 15 5 12 2 0 971 3 40]\n", + " [ 6 36 6 23 5 24 3 5 868 19]\n", + " [ 7 8 4 14 35 8 2 42 9 879]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix,classification_report\n", + "print(\"Classification Report\")\n", + "print (classification_report(Y_val,predicted_train))\n", + "print (\"Confusion matrix\")\n", + "print (confusion_matrix(Y_val,predicted_train))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.matshow(confusion_matrix(Y_val,predicted_train), cmap=plt.cm.binary, interpolation='nearest')\n", + "plt.title('confusion matrix')\n", + "plt.colorbar()\n", + "plt.ylabel('expected label')\n", + "plt.xlabel('predicted label')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Performing KNN\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Accuracy 96.38%\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.metrics import accuracy_score\n", + "model = KNeighborsClassifier(n_neighbors = 10)\n", + "model.fit(X_train,Y_train)\n", + "predicted_train = model.predict(X_val)\n", + "true_value = Y_val\n", + "print(\"Train Accuracy {:.2%}\".format(accuracy_score(true_value,predicted_train)))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report\n", + " precision recall f1-score support\n", + "\n", + " 0 0.97 0.99 0.98 998\n", + " 1 0.93 1.00 0.96 1127\n", + " 2 0.98 0.95 0.97 967\n", + " 3 0.97 0.96 0.97 1060\n", + " 4 0.98 0.96 0.97 914\n", + " 5 0.96 0.96 0.96 914\n", + " 6 0.97 0.99 0.98 951\n", + " 7 0.95 0.97 0.96 1066\n", + " 8 0.99 0.92 0.95 995\n", + " 9 0.95 0.94 0.94 1008\n", + "\n", + " accuracy 0.96 10000\n", + " macro avg 0.97 0.96 0.96 10000\n", + "weighted avg 0.96 0.96 0.96 10000\n", + "\n", + "Confusion matrix\n", + "[[ 990 1 0 0 0 2 5 0 0 0]\n", + " [ 0 1124 1 0 2 0 0 0 0 0]\n", + " [ 14 15 918 2 1 3 1 11 1 1]\n", + " [ 0 9 9 1020 1 7 0 5 4 5]\n", + " [ 0 13 0 0 874 0 5 1 0 21]\n", + " [ 4 3 0 11 3 880 9 0 1 3]\n", + " [ 5 2 2 0 2 2 938 0 0 0]\n", + " [ 0 15 3 0 2 0 0 1037 0 9]\n", + " [ 4 20 1 13 2 22 7 1 913 12]\n", + " [ 6 5 0 6 8 2 0 35 2 944]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix,classification_report\n", + "print(\"Classification Report\")\n", + "print (classification_report(true_value,predicted_train))\n", + "print (\"Confusion matrix\")\n", + "print (confusion_matrix(true_value,predicted_train))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Performing Random Forest Classifier\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Accuracy 95.24%\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "model = RandomForestClassifier(max_depth = 15, min_samples_leaf = 10, max_features = 'sqrt',n_estimators = 50)\n", + "model.fit(X_train,Y_train)\n", + "predicted_train = model.predict(X_val)\n", + "true_value = Y_val\n", + "print(\"Train Accuracy {:.2%}\".format(accuracy_score(true_value,predicted_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Performing SVM\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import svm\n", + "#svc = svm.SVC(kernel='linear', C=1,gamma=1,).fit(X_train, Y_train)\n", + "svc = svm.SVC(C=5, gamma=0.05, kernel='rbf', random_state=0).fit(X_train, Y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Accuracy 95.24%\n" + ] + } + ], + "source": [ + "predicated_train = svc.predict(X_val)\n", + "true_value=Y_val\n", + "print(\"Train Accuracy {:.2%}\".format(accuracy_score(true_value,predicted_train)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# By ANN" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Please use tf.compat.v1.get_default_graph instead.\n", + "\n", + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:4: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=784, activation=\"relu\", units=100, kernel_initializer=\"uniform\")`\n", + " after removing the cwd from sys.path.\n", + "W0926 15:49:13.861601 8036 deprecation_wrapper.py:119] From C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "\n", + "W0926 15:49:13.861601 8036 deprecation_wrapper.py:119] From C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "\n", + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=50, kernel_initializer=\"uniform\")`\n", + " \"\"\"\n", + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:7: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"softmax\", units=10, kernel_initializer=\"uniform\")`\n", + " import sys\n", + "W0926 15:49:13.924086 8036 deprecation_wrapper.py:119] From C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "\n", + "W0926 15:49:13.988602 8036 deprecation_wrapper.py:119] From C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_1 (Dense) (None, 100) 78500 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 50) 5050 \n", + "_________________________________________________________________\n", + "dense_3 (Dense) (None, 10) 510 \n", + "=================================================================\n", + "Total params: 84,060\n", + "Trainable params: 84,060\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "classifier=Sequential()\n", + "\n", + "#1ST Fully connected layer\n", + "classifier.add(Dense(output_dim=100,init='uniform',input_dim=784,activation='relu'))\n", + "classifier.add(Dense(output_dim=50,init='uniform',activation='relu'))\n", + "#output layer\n", + "classifier.add(Dense(output_dim=10,init='uniform',activation='softmax'))\n", + "\n", + "classifier.summary()\n", + "\n", + "classifier.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 33600 samples, validate on 8400 samples\n", + "Epoch 1/50\n", + "33600/33600 [==============================] - 23s 683us/step - loss: 12.6948 - acc: 0.2101 - val_loss: 12.7005 - val_acc: 0.2118\n", + "Epoch 2/50\n", + "33600/33600 [==============================] - 23s 683us/step - loss: 12.6644 - acc: 0.2140 - val_loss: 12.6357 - val_acc: 0.2117\n", + "Epoch 3/50\n", + "33600/33600 [==============================] - 22s 669us/step - loss: 0.3677 - acc: 0.9428 - val_loss: 0.0765 - val_acc: 0.9774\n", + "Epoch 4/50\n", + " 700/33600 [..............................] - ETA: 20s - loss: 0.0487 - acc: 0.9843" + ] + } + ], + "source": [ + "history=classifier.fit(x_train,y_train,epochs=50,batch_size=100,validation_data=(x_test,y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=59)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(33600, 28, 28, 1)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "x_train=x_train.reshape(33600,28,28,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "conv2d_3 (Conv2D) (None, 26, 26, 64) 640 \n", + "_________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 11, 11, 128) 73856 \n", + "_________________________________________________________________\n", + "max_pooling2d_4 (MaxPooling2 (None, 6, 6, 128) 0 \n", + "_________________________________________________________________\n", + "flatten_2 (Flatten) (None, 4608) 0 \n", + "_________________________________________________________________\n", + "dense_4 (Dense) (None, 100) 460900 \n", + "_________________________________________________________________\n", + "dense_5 (Dense) (None, 100) 10100 \n", + "_________________________________________________________________\n", + "dense_6 (Dense) (None, 10) 1010 \n", + "=================================================================\n", + "Total params: 546,506\n", + "Trainable params: 546,506\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), input_shape=(28, 28, 1..., activation=\"relu\")`\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n", + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\")`\n", + " \n", + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:17: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=100)`\n", + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:18: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=100)`\n", + "C:\\Users\\Shiva Chandra\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"softmax\", units=10)`\n" + ] + } + ], + "source": [ + "classifier=Sequential()\n", + "# 1st Convolutional Layer\n", + "classifier.add(Convolution2D(64,3,3,input_shape=(28,28,1),activation='relu'))\n", + "classifier.add(MaxPooling2D(pool_size=(2,2),padding='same'))\n", + "\n", + "\n", + "# 2nd Convolutional Layer\n", + "classifier.add(Convolution2D(128,3,3,activation='relu'))\n", + "classifier.add(MaxPooling2D(pool_size=(2,2),padding='same'))\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "classifier.add(Flatten())\n", + "#1ST Fully connected layer\n", + "classifier.add(Dense(output_dim=100,activation='relu'))\n", + "classifier.add(Dense(output_dim=100,activation='relu'))\n", + "\n", + "#output layer\n", + "classifier.add(Dense(output_dim=10,activation='softmax'))\n", + "\n", + "classifier.summary()\n", + "\n", + "classifier.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tensorflow", + "language": "python", + "name": "tensorflow" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}