diff --git a/BENG 134 Final Project.ipynb b/BENG 134 Final Project.ipynb
index 6834434..f0a59cc 100644
--- a/BENG 134 Final Project.ipynb
+++ b/BENG 134 Final Project.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
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+ "execution_count": 33,
"metadata": {
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},
@@ -38,24 +38,24 @@
},
{
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+ "execution_count": 34,
"metadata": {},
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" \n",
" 71 | \n",
- " 33 | \n",
- " 0 | \n",
- " 56 | \n",
- " 0 | \n",
- " 0.25 | \n",
- " 11 | \n",
- " 4.72 | \n",
- " 11 | \n",
- " 1 | \n",
+ " 33.00 | \n",
+ " 0.0 | \n",
+ " 56.000 | \n",
" 0 | \n",
+ " 0.250 | \n",
+ " 11.000 | \n",
+ " 4.720 | \n",
+ " 11.00 | \n",
+ " 1.000 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 72 | \n",
- " 29 | \n",
- " 0 | \n",
- " 60 | \n",
- " 0 | \n",
- " 0.12 | \n",
- " 10.2 | \n",
- " 4.31 | \n",
- " 15 | \n",
- " 1.67 | \n",
+ " 29.00 | \n",
+ " 0.0 | \n",
+ " 60.000 | \n",
" 0 | \n",
+ " 0.120 | \n",
+ " 10.200 | \n",
+ " 4.310 | \n",
+ " 15.00 | \n",
+ " 1.670 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 73 | \n",
- " 41 | \n",
- " 0 | \n",
- " 59 | \n",
- " 0 | \n",
- " 0.29 | \n",
- " 7.5 | \n",
- " 4.75 | \n",
- " 13 | \n",
- " 1.08 | \n",
+ " 41.00 | \n",
+ " 0.0 | \n",
+ " 59.000 | \n",
" 0 | \n",
+ " 0.290 | \n",
+ " 7.500 | \n",
+ " 4.750 | \n",
+ " 13.00 | \n",
+ " 1.080 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 75 | \n",
- " 15 | \n",
- " 0 | \n",
- " 54 | \n",
+ " 15.00 | \n",
+ " 0.0 | \n",
+ " 54.000 | \n",
" 0 | \n",
" 0.217 | \n",
- " 17.9 | \n",
- " 4.54 | \n",
- " 16.5 | \n",
- " 1.18 | \n",
- " 0 | \n",
+ " 17.900 | \n",
+ " 4.540 | \n",
+ " 16.50 | \n",
+ " 1.180 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 78 | \n",
- " 12 | \n",
- " 0 | \n",
- " 64 | \n",
- " 0 | \n",
- " 0.20 | \n",
- " 7.1 | \n",
- " 4.58 | \n",
- " 14 | \n",
- " 1 | \n",
- " 0 | \n",
+ " 12.00 | \n",
+ " 0.0 | \n",
+ " 64.000 | \n",
+ " 0 | \n",
+ " 0.200 | \n",
+ " 7.100 | \n",
+ " 4.580 | \n",
+ " 14.00 | \n",
+ " 1.000 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 81 | \n",
- " 27 | \n",
- " 0 | \n",
- " 54 | \n",
+ " 27.00 | \n",
+ " 0.0 | \n",
+ " 54.000 | \n",
" 1 | \n",
- " 0.07 | \n",
- " 16.8 | \n",
- " 4.16 | \n",
- " 18 | \n",
- " 1.5 | \n",
- " 0 | \n",
+ " 0.070 | \n",
+ " 16.800 | \n",
+ " 4.160 | \n",
+ " 18.00 | \n",
+ " 1.500 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 83 | \n",
" 0.75 | \n",
- " 1 | \n",
- " 78 | \n",
- " 0 | \n",
- " 0.05 | \n",
- " 10 | \n",
- " 4.44 | \n",
- " 15 | \n",
- " 1.36 | \n",
- " 1 | \n",
+ " 1.0 | \n",
+ " 78.000 | \n",
+ " 0 | \n",
+ " 0.050 | \n",
+ " 10.000 | \n",
+ " 4.440 | \n",
+ " 15.00 | \n",
+ " 1.360 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
" 88 | \n",
- " 55 | \n",
- " 0 | \n",
- " 55 | \n",
- " 0 | \n",
- " 0.28 | \n",
- " 5.5 | \n",
- " 4.48 | \n",
- " 22 | \n",
- " 1.83 | \n",
- " 0 | \n",
+ " 55.00 | \n",
+ " 0.0 | \n",
+ " 55.000 | \n",
+ " 0 | \n",
+ " 0.280 | \n",
+ " 5.500 | \n",
+ " 4.480 | \n",
+ " 22.00 | \n",
+ " 1.830 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 92 | \n",
- " 53 | \n",
- " 0 | \n",
- " 59 | \n",
+ " 53.00 | \n",
+ " 0.0 | \n",
+ " 59.000 | \n",
" 0 | \n",
" 0.344 | \n",
- " 9.1 | \n",
- " 4.04 | \n",
- " 9 | \n",
- " 1 | \n",
- " 0 | \n",
+ " 9.100 | \n",
+ " 4.040 | \n",
+ " 9.00 | \n",
+ " 1.000 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 96 | \n",
- " 40 | \n",
- " 1 | \n",
- " 74 | \n",
- " 0 | \n",
- " 0.20 | \n",
- " 4.8 | \n",
- " 4.56 | \n",
- " 12.5 | \n",
- " 1.04 | \n",
- " 0 | \n",
+ " 40.00 | \n",
+ " 1.0 | \n",
+ " 74.000 | \n",
+ " 0 | \n",
+ " 0.200 | \n",
+ " 4.800 | \n",
+ " 4.560 | \n",
+ " 12.50 | \n",
+ " 1.040 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 98 | \n",
- " 5 | \n",
- " 1 | \n",
- " 65 | \n",
- " 1 | \n",
- " 0.16 | \n",
- " 8.5 | \n",
- " 5.47 | \n",
- " 16 | \n",
- " 1.45 | \n",
+ " 5.00 | \n",
+ " 1.0 | \n",
+ " 65.000 | \n",
" 1 | \n",
+ " 0.160 | \n",
+ " 8.500 | \n",
+ " 5.470 | \n",
+ " 16.00 | \n",
+ " 1.450 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
" 99 | \n",
- " 4 | \n",
- " 1 | \n",
- " 58 | \n",
+ " 4.00 | \n",
+ " 1.0 | \n",
+ " 58.000 | \n",
" 0 | \n",
- " 0.17 | \n",
- " 28.9 | \n",
- " 6.73 | \n",
+ " 0.170 | \n",
+ " 28.900 | \n",
+ " 6.730 | \n",
" 26.08 | \n",
- " 2.01 | \n",
- " 1 | \n",
+ " 2.010 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
" 102 | \n",
- " 22 | \n",
- " 0 | \n",
- " 70 | \n",
- " 0 | \n",
- " 0.38 | \n",
- " 0 | \n",
- " 4.55 | \n",
- " 10 | \n",
- " 1 | \n",
+ " 22.00 | \n",
+ " 0.0 | \n",
+ " 70.000 | \n",
" 0 | \n",
+ " 0.380 | \n",
+ " 0.000 | \n",
+ " 4.550 | \n",
+ " 10.00 | \n",
+ " 1.000 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 104 | \n",
" 1.25 | \n",
- " 1 | \n",
- " 63 | \n",
+ " 1.0 | \n",
+ " 63.000 | \n",
" 0 | \n",
- " 0.30 | \n",
- " 6.9 | \n",
- " 3.52 | \n",
+ " 0.300 | \n",
+ " 6.900 | \n",
+ " 3.520 | \n",
" 18.16 | \n",
- " 1.51 | \n",
- " 1 | \n",
+ " 1.510 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
" 105 | \n",
- " 24 | \n",
- " 0 | \n",
- " 59 | \n",
- " 0 | \n",
- " 0.17 | \n",
- " 14.3 | \n",
- " 5.49 | \n",
- " 13.5 | \n",
- " 1.50 | \n",
+ " 24.00 | \n",
+ " 0.0 | \n",
+ " 59.000 | \n",
" 0 | \n",
+ " 0.170 | \n",
+ " 14.300 | \n",
+ " 5.490 | \n",
+ " 13.50 | \n",
+ " 1.500 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 106 | \n",
- " 25 | \n",
- " 0 | \n",
- " 57 | \n",
+ " 25.00 | \n",
+ " 0.0 | \n",
+ " 57.000 | \n",
" 0 | \n",
" 0.228 | \n",
- " 9.7 | \n",
- " 4.29 | \n",
- " 11 | \n",
- " 1 | \n",
- " 0 | \n",
+ " 9.700 | \n",
+ " 4.290 | \n",
+ " 11.00 | \n",
+ " 1.000 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 108 | \n",
- " .75 | \n",
- " 1 | \n",
- " 78 | \n",
+ " 0.75 | \n",
+ " 1.0 | \n",
+ " 78.000 | \n",
" 0 | \n",
- " 0.23 | \n",
- " 40 | \n",
- " 6.23 | \n",
- " 14 | \n",
- " 1.4 | \n",
- " 1 | \n",
+ " 0.230 | \n",
+ " 40.000 | \n",
+ " 6.230 | \n",
+ " 14.00 | \n",
+ " 1.400 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
" 109 | \n",
- " 3 | \n",
- " 1 | \n",
- " 62 | \n",
+ " 3.00 | \n",
+ " 1.0 | \n",
+ " 62.000 | \n",
" 0 | \n",
- " 0.26 | \n",
- " 7.6 | \n",
- " 4.42 | \n",
- " 14 | \n",
- " 1 | \n",
- " 1 | \n",
+ " 0.260 | \n",
+ " 7.600 | \n",
+ " 4.420 | \n",
+ " 14.00 | \n",
+ " 1.000 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
"
\n",
@@ -874,199 +874,199 @@
"
"
],
"text/plain": [
- " survival still-alive age-at-heart-attack pericardial-effusion \\\n",
- "0 11 0 71 0 \n",
- "1 19 0 72 0 \n",
- "2 16 0 55 0 \n",
- "3 57 0 60 0 \n",
- "4 19 1 57 0 \n",
- "5 26 0 68 0 \n",
- "6 13 0 62 0 \n",
- "7 50 0 60 0 \n",
- "8 19 0 46 0 \n",
- "9 25 0 54 0 \n",
- "10 10 1 77 0 \n",
- "11 52 0 62 1 \n",
- "12 52 0 73 0 \n",
- "13 44 0 60 0 \n",
- "14 0.5 1 62 0 \n",
- "15 24 0 55 1 \n",
- "16 0.5 1 69 1 \n",
- "17 0.5 1 62.529 1 \n",
- "18 22 1 66 0 \n",
- "19 1 1 66 1 \n",
- "20 0.75 1 69 0 \n",
- "21 0.75 1 85 1 \n",
- "22 0.5 1 73 0 \n",
- "23 5 1 71 0 \n",
- "29 36 0 55 1 \n",
- "35 26 0 61 0 \n",
- "40 32 0 54 0 \n",
- "41 16 0 70 1 \n",
- "42 40 0 79 0 \n",
- "47 20 1 59 0 \n",
- ".. ... ... ... ... \n",
- "54 1 1 60 0 \n",
- "55 10 0 66 0 \n",
- "56 45 0 63 0 \n",
- "57 22 0 57 0 \n",
- "58 53 0 70 0 \n",
- "60 26 0 79 0 \n",
- "62 26 0 72 0 \n",
- "65 49 0 51 0 \n",
- "67 49 0 70 1 \n",
- "68 47 0 65 0 \n",
- "69 41 0 78 0 \n",
- "70 .25 1 86 0 \n",
- "71 33 0 56 0 \n",
- "72 29 0 60 0 \n",
- "73 41 0 59 0 \n",
- "75 15 0 54 0 \n",
- "78 12 0 64 0 \n",
- "81 27 0 54 1 \n",
- "83 0.75 1 78 0 \n",
- "88 55 0 55 0 \n",
- "92 53 0 59 0 \n",
- "96 40 1 74 0 \n",
- "98 5 1 65 1 \n",
- "99 4 1 58 0 \n",
- "102 22 0 70 0 \n",
- "104 1.25 1 63 0 \n",
- "105 24 0 59 0 \n",
- "106 25 0 57 0 \n",
- "108 .75 1 78 0 \n",
- "109 3 1 62 0 \n",
+ " survival still-alive age-at-heart-attack pericardial-effusion \\\n",
+ "0 11.00 0.0 71.000 0 \n",
+ "1 19.00 0.0 72.000 0 \n",
+ "2 16.00 0.0 55.000 0 \n",
+ "3 57.00 0.0 60.000 0 \n",
+ "4 19.00 1.0 57.000 0 \n",
+ "5 26.00 0.0 68.000 0 \n",
+ "6 13.00 0.0 62.000 0 \n",
+ "7 50.00 0.0 60.000 0 \n",
+ "8 19.00 0.0 46.000 0 \n",
+ "9 25.00 0.0 54.000 0 \n",
+ "10 10.00 1.0 77.000 0 \n",
+ "11 52.00 0.0 62.000 1 \n",
+ "12 52.00 0.0 73.000 0 \n",
+ "13 44.00 0.0 60.000 0 \n",
+ "14 0.50 1.0 62.000 0 \n",
+ "15 24.00 0.0 55.000 1 \n",
+ "16 0.50 1.0 69.000 1 \n",
+ "17 0.50 1.0 62.529 1 \n",
+ "18 22.00 1.0 66.000 0 \n",
+ "19 1.00 1.0 66.000 1 \n",
+ "20 0.75 1.0 69.000 0 \n",
+ "21 0.75 1.0 85.000 1 \n",
+ "22 0.50 1.0 73.000 0 \n",
+ "23 5.00 1.0 71.000 0 \n",
+ "29 36.00 0.0 55.000 1 \n",
+ "35 26.00 0.0 61.000 0 \n",
+ "40 32.00 0.0 54.000 0 \n",
+ "41 16.00 0.0 70.000 1 \n",
+ "42 40.00 0.0 79.000 0 \n",
+ "47 20.00 1.0 59.000 0 \n",
+ ".. ... ... ... ... \n",
+ "54 1.00 1.0 60.000 0 \n",
+ "55 10.00 0.0 66.000 0 \n",
+ "56 45.00 0.0 63.000 0 \n",
+ "57 22.00 0.0 57.000 0 \n",
+ "58 53.00 0.0 70.000 0 \n",
+ "60 26.00 0.0 79.000 0 \n",
+ "62 26.00 0.0 72.000 0 \n",
+ "65 49.00 0.0 51.000 0 \n",
+ "67 49.00 0.0 70.000 1 \n",
+ "68 47.00 0.0 65.000 0 \n",
+ "69 41.00 0.0 78.000 0 \n",
+ "70 0.25 1.0 86.000 0 \n",
+ "71 33.00 0.0 56.000 0 \n",
+ "72 29.00 0.0 60.000 0 \n",
+ "73 41.00 0.0 59.000 0 \n",
+ "75 15.00 0.0 54.000 0 \n",
+ "78 12.00 0.0 64.000 0 \n",
+ "81 27.00 0.0 54.000 1 \n",
+ "83 0.75 1.0 78.000 0 \n",
+ "88 55.00 0.0 55.000 0 \n",
+ "92 53.00 0.0 59.000 0 \n",
+ "96 40.00 1.0 74.000 0 \n",
+ "98 5.00 1.0 65.000 1 \n",
+ "99 4.00 1.0 58.000 0 \n",
+ "102 22.00 0.0 70.000 0 \n",
+ "104 1.25 1.0 63.000 0 \n",
+ "105 24.00 0.0 59.000 0 \n",
+ "106 25.00 0.0 57.000 0 \n",
+ "108 0.75 1.0 78.000 0 \n",
+ "109 3.00 1.0 62.000 0 \n",
"\n",
- " fractional-shortening epss lvdd wall-motion-score wall-motion-index \\\n",
- "0 0.260 9 4.600 14 1 \n",
- "1 0.380 6 4.100 14 1.700 \n",
- "2 0.260 4 3.420 14 1 \n",
- "3 0.253 12.062 4.603 16 1.450 \n",
- "4 0.160 22 5.750 18 2.250 \n",
- "5 0.260 5 4.310 12 1 \n",
- "6 0.230 31 5.430 22.5 1.875 \n",
- "7 0.330 8 5.250 14 1 \n",
- "8 0.340 0 5.090 16 1.140 \n",
- "9 0.140 13 4.490 15.5 1.190 \n",
- "10 0.130 16 4.230 18 1.800 \n",
- "11 0.450 9 3.600 16 1.140 \n",
- "12 0.330 6 4 14 1 \n",
- "13 0.150 10 3.730 14 1 \n",
- "14 0.120 23 5.800 11.67 2.330 \n",
- "15 0.250 12.063 4.290 14 1 \n",
- "16 0.260 11 4.650 18 1.640 \n",
- "17 0.070 20 5.200 24 2 \n",
- "18 0.090 17 5.819 8 1.333 \n",
- "19 0.220 15 5.400 27 2.250 \n",
- "20 0.150 12 5.390 19.5 1.625 \n",
- "21 0.180 19 5.460 13.83 1.380 \n",
- "22 0.230 12.733 6.060 7.5 1.500 \n",
- "23 0.170 0 4.650 8 1 \n",
- "29 0.210 4.2 4.160 14 1.560 \n",
- "35 0.610 13.100 4.070 13 1.625 \n",
- "40 0.350 9.300 3.630 11 1.222 \n",
- "41 0.270 4.700 4.490 22 2 \n",
- "42 0.150 17.500 4.270 13 1.300 \n",
- "47 0.030 21.300 6.290 17 1.310 \n",
- ".. ... ... ... ... ... \n",
- "54 0.010 24.600 5.650 39 3 \n",
- "55 0.290 15.600 6.150 14 1 \n",
- "56 0.15 13 4.57 13 1.08 \n",
- "57 0.13 18.6 4.37 12.33 1.37 \n",
- "58 0.10 9.8 5.30 23 2.30 \n",
- "60 0.17 11.9 5.15 10.5 1.05 \n",
- "62 0.187 12 5.02 13 1.18 \n",
- "65 0.16 13.2 5.26 11 1 \n",
- "67 0.25 9.7 5.57 5.5 1.10 \n",
- "68 0.36 8.8 5.78 12 1 \n",
- "69 0.06 16.1 5.62 13.67 1.367 \n",
- "70 0.225 12.2 5.20 24 2.18 \n",
- "71 0.25 11 4.72 11 1 \n",
- "72 0.12 10.2 4.31 15 1.67 \n",
- "73 0.29 7.5 4.75 13 1.08 \n",
- "75 0.217 17.9 4.54 16.5 1.18 \n",
- "78 0.20 7.1 4.58 14 1 \n",
- "81 0.07 16.8 4.16 18 1.5 \n",
- "83 0.05 10 4.44 15 1.36 \n",
- "88 0.28 5.5 4.48 22 1.83 \n",
- "92 0.344 9.1 4.04 9 1 \n",
- "96 0.20 4.8 4.56 12.5 1.04 \n",
- "98 0.16 8.5 5.47 16 1.45 \n",
- "99 0.17 28.9 6.73 26.08 2.01 \n",
- "102 0.38 0 4.55 10 1 \n",
- "104 0.30 6.9 3.52 18.16 1.51 \n",
- "105 0.17 14.3 5.49 13.5 1.50 \n",
- "106 0.228 9.7 4.29 11 1 \n",
- "108 0.23 40 6.23 14 1.4 \n",
- "109 0.26 7.6 4.42 14 1 \n",
+ " fractional-shortening epss lvdd wall-motion-score \\\n",
+ "0 0.260 9.000 4.600 14.00 \n",
+ "1 0.380 6.000 4.100 14.00 \n",
+ "2 0.260 4.000 3.420 14.00 \n",
+ "3 0.253 12.062 4.603 16.00 \n",
+ "4 0.160 22.000 5.750 18.00 \n",
+ "5 0.260 5.000 4.310 12.00 \n",
+ "6 0.230 31.000 5.430 22.50 \n",
+ "7 0.330 8.000 5.250 14.00 \n",
+ "8 0.340 0.000 5.090 16.00 \n",
+ "9 0.140 13.000 4.490 15.50 \n",
+ "10 0.130 16.000 4.230 18.00 \n",
+ "11 0.450 9.000 3.600 16.00 \n",
+ "12 0.330 6.000 4.000 14.00 \n",
+ "13 0.150 10.000 3.730 14.00 \n",
+ "14 0.120 23.000 5.800 11.67 \n",
+ "15 0.250 12.063 4.290 14.00 \n",
+ "16 0.260 11.000 4.650 18.00 \n",
+ "17 0.070 20.000 5.200 24.00 \n",
+ "18 0.090 17.000 5.819 8.00 \n",
+ "19 0.220 15.000 5.400 27.00 \n",
+ "20 0.150 12.000 5.390 19.50 \n",
+ "21 0.180 19.000 5.460 13.83 \n",
+ "22 0.230 12.733 6.060 7.50 \n",
+ "23 0.170 0.000 4.650 8.00 \n",
+ "29 0.210 4.200 4.160 14.00 \n",
+ "35 0.610 13.100 4.070 13.00 \n",
+ "40 0.350 9.300 3.630 11.00 \n",
+ "41 0.270 4.700 4.490 22.00 \n",
+ "42 0.150 17.500 4.270 13.00 \n",
+ "47 0.030 21.300 6.290 17.00 \n",
+ ".. ... ... ... ... \n",
+ "54 0.010 24.600 5.650 39.00 \n",
+ "55 0.290 15.600 6.150 14.00 \n",
+ "56 0.150 13.000 4.570 13.00 \n",
+ "57 0.130 18.600 4.370 12.33 \n",
+ "58 0.100 9.800 5.300 23.00 \n",
+ "60 0.170 11.900 5.150 10.50 \n",
+ "62 0.187 12.000 5.020 13.00 \n",
+ "65 0.160 13.200 5.260 11.00 \n",
+ "67 0.250 9.700 5.570 5.50 \n",
+ "68 0.360 8.800 5.780 12.00 \n",
+ "69 0.060 16.100 5.620 13.67 \n",
+ "70 0.225 12.200 5.200 24.00 \n",
+ "71 0.250 11.000 4.720 11.00 \n",
+ "72 0.120 10.200 4.310 15.00 \n",
+ "73 0.290 7.500 4.750 13.00 \n",
+ "75 0.217 17.900 4.540 16.50 \n",
+ "78 0.200 7.100 4.580 14.00 \n",
+ "81 0.070 16.800 4.160 18.00 \n",
+ "83 0.050 10.000 4.440 15.00 \n",
+ "88 0.280 5.500 4.480 22.00 \n",
+ "92 0.344 9.100 4.040 9.00 \n",
+ "96 0.200 4.800 4.560 12.50 \n",
+ "98 0.160 8.500 5.470 16.00 \n",
+ "99 0.170 28.900 6.730 26.08 \n",
+ "102 0.380 0.000 4.550 10.00 \n",
+ "104 0.300 6.900 3.520 18.16 \n",
+ "105 0.170 14.300 5.490 13.50 \n",
+ "106 0.228 9.700 4.290 11.00 \n",
+ "108 0.230 40.000 6.230 14.00 \n",
+ "109 0.260 7.600 4.420 14.00 \n",
"\n",
- " alive-at-1 \n",
- "0 0 \n",
- "1 0 \n",
- "2 0 \n",
- "3 0 \n",
- "4 0 \n",
- "5 0 \n",
- "6 0 \n",
- "7 0 \n",
- "8 0 \n",
- "9 0 \n",
- "10 1 \n",
- "11 0 \n",
- "12 0 \n",
- "13 0 \n",
- "14 1 \n",
- "15 0 \n",
- "16 1 \n",
- "17 1 \n",
- "18 0 \n",
- "19 1 \n",
- "20 1 \n",
- "21 1 \n",
- "22 1 \n",
- "23 1 \n",
- "29 0 \n",
- "35 0 \n",
- "40 0 \n",
- "41 0 \n",
- "42 0 \n",
- "47 0 \n",
- ".. ... \n",
- "54 1 \n",
- "55 0 \n",
- "56 0 \n",
- "57 0 \n",
- "58 0 \n",
- "60 0 \n",
- "62 0 \n",
- "65 0 \n",
- "67 0 \n",
- "68 0 \n",
- "69 0 \n",
- "70 1 \n",
- "71 0 \n",
- "72 0 \n",
- "73 0 \n",
- "75 0 \n",
- "78 0 \n",
- "81 0 \n",
- "83 1 \n",
- "88 0 \n",
- "92 0 \n",
- "96 0 \n",
- "98 1 \n",
- "99 1 \n",
- "102 0 \n",
- "104 1 \n",
- "105 0 \n",
- "106 0 \n",
- "108 1 \n",
- "109 1 \n",
+ " wall-motion-index alive-at-1 \n",
+ "0 1.000 0.0 \n",
+ "1 1.700 0.0 \n",
+ "2 1.000 0.0 \n",
+ "3 1.450 0.0 \n",
+ "4 2.250 0.0 \n",
+ "5 1.000 0.0 \n",
+ "6 1.875 0.0 \n",
+ "7 1.000 0.0 \n",
+ "8 1.140 0.0 \n",
+ "9 1.190 0.0 \n",
+ "10 1.800 1.0 \n",
+ "11 1.140 0.0 \n",
+ "12 1.000 0.0 \n",
+ "13 1.000 0.0 \n",
+ "14 2.330 1.0 \n",
+ "15 1.000 0.0 \n",
+ "16 1.640 1.0 \n",
+ "17 2.000 1.0 \n",
+ "18 1.333 0.0 \n",
+ "19 2.250 1.0 \n",
+ "20 1.625 1.0 \n",
+ "21 1.380 1.0 \n",
+ "22 1.500 1.0 \n",
+ "23 1.000 1.0 \n",
+ "29 1.560 0.0 \n",
+ "35 1.625 0.0 \n",
+ "40 1.222 0.0 \n",
+ "41 2.000 0.0 \n",
+ "42 1.300 0.0 \n",
+ "47 1.310 0.0 \n",
+ ".. ... ... \n",
+ "54 3.000 1.0 \n",
+ "55 1.000 0.0 \n",
+ "56 1.080 0.0 \n",
+ "57 1.370 0.0 \n",
+ "58 2.300 0.0 \n",
+ "60 1.050 0.0 \n",
+ "62 1.180 0.0 \n",
+ "65 1.000 0.0 \n",
+ "67 1.100 0.0 \n",
+ "68 1.000 0.0 \n",
+ "69 1.367 0.0 \n",
+ "70 2.180 1.0 \n",
+ "71 1.000 0.0 \n",
+ "72 1.670 0.0 \n",
+ "73 1.080 0.0 \n",
+ "75 1.180 0.0 \n",
+ "78 1.000 0.0 \n",
+ "81 1.500 0.0 \n",
+ "83 1.360 1.0 \n",
+ "88 1.830 0.0 \n",
+ "92 1.000 0.0 \n",
+ "96 1.040 0.0 \n",
+ "98 1.450 1.0 \n",
+ "99 2.010 1.0 \n",
+ "102 1.000 0.0 \n",
+ "104 1.510 1.0 \n",
+ "105 1.500 0.0 \n",
+ "106 1.000 0.0 \n",
+ "108 1.400 1.0 \n",
+ "109 1.000 1.0 \n",
"\n",
"[61 rows x 10 columns]"
]
},
- "execution_count": 4,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@@ -1099,9 +1099,12 @@
"\n",
"uselessCols = ['mult','name','group']\n",
"table.drop(uselessCols, inplace=True, axis=1)\n",
- " \n",
+ " \n",
+ "\n",
"table\n",
"\n",
+ "table = table.apply(pd.to_numeric, errors='coerce')\n",
+ "\n",
"# Validate the data\n",
"def check_column(data,col):\n",
" data[col] = data[col].replace('?',np.nan)\n",
@@ -1130,15 +1133,30 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "age-at-heart-attack float64\n",
+ "fractional-shortening float64\n",
+ "lvdd float64\n",
+ "wall-motion-score float64\n",
+ "dtype: object\n",
"Accuracy of logistic regression classifier on test set: 0.85\n"
]
+ },
+ {
+ "data": {
+ "image/png": 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q9kAQkXQpKeTKiBHw9ttR5P+cc9KOJjW77QYtW8L228Ptt8Pmm6cdkYhUpjGF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\nAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
@@ -1150,13 +1168,30 @@
"xtrain, xtest = train_test_split(x, test_size=0.2, random_state = 1)\n",
"ytrain, ytest = train_test_split(y, test_size=0.2, random_state = 1)\n",
"\n",
+ "print(xtrain.dtypes)\n",
+ "\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn import metrics\n",
"logreg = LogisticRegression()\n",
"logreg.fit(xtrain,ytrain.values.ravel())\n",
"\n",
"ypred = logreg.predict(xtest)\n",
- "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(xtest,ytest)) )"
+ "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(xtest,ytest)) )\n",
+ "\n",
+ "\n",
+ "#ROC \n",
+ "from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score\n",
+ "fpr, tpr,_=roc_curve(logreg.predict(xtrain),ytrain,drop_intermediate=False)\n",
+ "plt.figure()\n",
+ "plt.plot(fpr, tpr, color='red',\n",
+ " lw=2, label='ROC curve')\n",
+ "##Random FPR and TPR\n",
+ "plt.plot([0, 1], [0, 1], color='blue', lw=2, linestyle='--')\n",
+ "##Title and label\n",
+ "plt.xlabel('FPR')\n",
+ "plt.ylabel('TPR')\n",
+ "plt.title('ROC curve')\n",
+ "plt.show()"
]
},
{
@@ -1170,7 +1205,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -1254,6 +1289,24 @@
"print(y_pca)\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -1280,7 +1333,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.1"
+ "version": "3.6.1"
}
},
"nbformat": 4,