{ "metadata": { "name": "", "signature": "sha256:8b8938eb7092509bfbaf1d7219101cdc7616a222c2bb7110275ca76fe0c218f7" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Tutorial Brief" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Saving your data and model is very handy to continue your work on in-memory objects over long time and multiple machines.\n", "\n", "**Video Tutorial**:\n", "\n", "http://youtu.be/SQDPVSVNq1k" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Import Library" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "joblib library is available in external model inside scikit-learn library. This allows to store in memory objects as pickle files." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.externals import joblib" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Loading data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\"\n", "columns = [\"Sex\", \"Length\", \"Diameter\", \"Height\", \"Whole weight\", \"Shucked weight\",\n", " \"Viscera weight\", \"Shell weight\", \"Rings\"]\n", "csv_data = pd.read_csv(url, names = columns)\n", "csv_data" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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SexLengthDiameterHeightWhole weightShucked weightViscera weightShell weightRings
0 M 0.455 0.365 0.095 0.5140 0.2245 0.1010 0.150 15
1 M 0.350 0.265 0.090 0.2255 0.0995 0.0485 0.070 7
2 F 0.530 0.420 0.135 0.6770 0.2565 0.1415 0.210 9
3 M 0.440 0.365 0.125 0.5160 0.2155 0.1140 0.155 10
4 I 0.330 0.255 0.080 0.2050 0.0895 0.0395 0.055 7
5 I 0.425 0.300 0.095 0.3515 0.1410 0.0775 0.120 8
6 F 0.530 0.415 0.150 0.7775 0.2370 0.1415 0.330 20
7 F 0.545 0.425 0.125 0.7680 0.2940 0.1495 0.260 16
8 M 0.475 0.370 0.125 0.5095 0.2165 0.1125 0.165 9
9 F 0.550 0.440 0.150 0.8945 0.3145 0.1510 0.320 19
10 F 0.525 0.380 0.140 0.6065 0.1940 0.1475 0.210 14
11 M 0.430 0.350 0.110 0.4060 0.1675 0.0810 0.135 10
12 M 0.490 0.380 0.135 0.5415 0.2175 0.0950 0.190 11
13 F 0.535 0.405 0.145 0.6845 0.2725 0.1710 0.205 10
14 F 0.470 0.355 0.100 0.4755 0.1675 0.0805 0.185 10
15 M 0.500 0.400 0.130 0.6645 0.2580 0.1330 0.240 12
16 I 0.355 0.280 0.085 0.2905 0.0950 0.0395 0.115 7
17 F 0.440 0.340 0.100 0.4510 0.1880 0.0870 0.130 10
18 M 0.365 0.295 0.080 0.2555 0.0970 0.0430 0.100 7
19 M 0.450 0.320 0.100 0.3810 0.1705 0.0750 0.115 9
20 M 0.355 0.280 0.095 0.2455 0.0955 0.0620 0.075 11
21 I 0.380 0.275 0.100 0.2255 0.0800 0.0490 0.085 10
22 F 0.565 0.440 0.155 0.9395 0.4275 0.2140 0.270 12
23 F 0.550 0.415 0.135 0.7635 0.3180 0.2100 0.200 9
24 F 0.615 0.480 0.165 1.1615 0.5130 0.3010 0.305 10
25 F 0.560 0.440 0.140 0.9285 0.3825 0.1880 0.300 11
26 F 0.580 0.450 0.185 0.9955 0.3945 0.2720 0.285 11
27 M 0.590 0.445 0.140 0.9310 0.3560 0.2340 0.280 12
28 M 0.605 0.475 0.180 0.9365 0.3940 0.2190 0.295 15
29 M 0.575 0.425 0.140 0.8635 0.3930 0.2270 0.200 11
30 M 0.580 0.470 0.165 0.9975 0.3935 0.2420 0.330 10
31 F 0.680 0.560 0.165 1.6390 0.6055 0.2805 0.460 15
32 M 0.665 0.525 0.165 1.3380 0.5515 0.3575 0.350 18
33 F 0.680 0.550 0.175 1.7980 0.8150 0.3925 0.455 19
34 F 0.705 0.550 0.200 1.7095 0.6330 0.4115 0.490 13
35 M 0.465 0.355 0.105 0.4795 0.2270 0.1240 0.125 8
36 F 0.540 0.475 0.155 1.2170 0.5305 0.3075 0.340 16
37 F 0.450 0.355 0.105 0.5225 0.2370 0.1165 0.145 8
38 F 0.575 0.445 0.135 0.8830 0.3810 0.2035 0.260 11
39 M 0.355 0.290 0.090 0.3275 0.1340 0.0860 0.090 9
40 F 0.450 0.335 0.105 0.4250 0.1865 0.0910 0.115 9
41 F 0.550 0.425 0.135 0.8515 0.3620 0.1960 0.270 14
42 I 0.240 0.175 0.045 0.0700 0.0315 0.0235 0.020 5
43 I 0.205 0.150 0.055 0.0420 0.0255 0.0150 0.012 5
44 I 0.210 0.150 0.050 0.0420 0.0175 0.0125 0.015 4
45 I 0.390 0.295 0.095 0.2030 0.0875 0.0450 0.075 7
46 M 0.470 0.370 0.120 0.5795 0.2930 0.2270 0.140 9
47 F 0.460 0.375 0.120 0.4605 0.1775 0.1100 0.150 7
48 I 0.325 0.245 0.070 0.1610 0.0755 0.0255 0.045 6
49 F 0.525 0.425 0.160 0.8355 0.3545 0.2135 0.245 9
50 I 0.520 0.410 0.120 0.5950 0.2385 0.1110 0.190 8
51 M 0.400 0.320 0.095 0.3030 0.1335 0.0600 0.100 7
52 M 0.485 0.360 0.130 0.5415 0.2595 0.0960 0.160 10
53 F 0.470 0.360 0.120 0.4775 0.2105 0.1055 0.150 10
54 M 0.405 0.310 0.100 0.3850 0.1730 0.0915 0.110 7
55 F 0.500 0.400 0.140 0.6615 0.2565 0.1755 0.220 8
56 M 0.445 0.350 0.120 0.4425 0.1920 0.0955 0.135 8
57 M 0.470 0.385 0.135 0.5895 0.2765 0.1200 0.170 8
58 I 0.245 0.190 0.060 0.0860 0.0420 0.0140 0.025 4
59 F 0.505 0.400 0.125 0.5830 0.2460 0.1300 0.175 7
...........................
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4177 rows \u00d7 9 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ " Sex Length Diameter Height Whole weight Shucked weight \\\n", "0 M 0.455 0.365 0.095 0.5140 0.2245 \n", "1 M 0.350 0.265 0.090 0.2255 0.0995 \n", "2 F 0.530 0.420 0.135 0.6770 0.2565 \n", "3 M 0.440 0.365 0.125 0.5160 0.2155 \n", "4 I 0.330 0.255 0.080 0.2050 0.0895 \n", "5 I 0.425 0.300 0.095 0.3515 0.1410 \n", "6 F 0.530 0.415 0.150 0.7775 0.2370 \n", "7 F 0.545 0.425 0.125 0.7680 0.2940 \n", "8 M 0.475 0.370 0.125 0.5095 0.2165 \n", "9 F 0.550 0.440 0.150 0.8945 0.3145 \n", "10 F 0.525 0.380 0.140 0.6065 0.1940 \n", "11 M 0.430 0.350 0.110 0.4060 0.1675 \n", "12 M 0.490 0.380 0.135 0.5415 0.2175 \n", "13 F 0.535 0.405 0.145 0.6845 0.2725 \n", "14 F 0.470 0.355 0.100 0.4755 0.1675 \n", "15 M 0.500 0.400 0.130 0.6645 0.2580 \n", "16 I 0.355 0.280 0.085 0.2905 0.0950 \n", "17 F 0.440 0.340 0.100 0.4510 0.1880 \n", "18 M 0.365 0.295 0.080 0.2555 0.0970 \n", "19 M 0.450 0.320 0.100 0.3810 0.1705 \n", "20 M 0.355 0.280 0.095 0.2455 0.0955 \n", "21 I 0.380 0.275 0.100 0.2255 0.0800 \n", "22 F 0.565 0.440 0.155 0.9395 0.4275 \n", "23 F 0.550 0.415 0.135 0.7635 0.3180 \n", "24 F 0.615 0.480 0.165 1.1615 0.5130 \n", "25 F 0.560 0.440 0.140 0.9285 0.3825 \n", "26 F 0.580 0.450 0.185 0.9955 0.3945 \n", "27 M 0.590 0.445 0.140 0.9310 0.3560 \n", "28 M 0.605 0.475 0.180 0.9365 0.3940 \n", "29 M 0.575 0.425 0.140 0.8635 0.3930 \n", "30 M 0.580 0.470 0.165 0.9975 0.3935 \n", "31 F 0.680 0.560 0.165 1.6390 0.6055 \n", "32 M 0.665 0.525 0.165 1.3380 0.5515 \n", "33 F 0.680 0.550 0.175 1.7980 0.8150 \n", "34 F 0.705 0.550 0.200 1.7095 0.6330 \n", "35 M 0.465 0.355 0.105 0.4795 0.2270 \n", "36 F 0.540 0.475 0.155 1.2170 0.5305 \n", "37 F 0.450 0.355 0.105 0.5225 0.2370 \n", "38 F 0.575 0.445 0.135 0.8830 0.3810 \n", "39 M 0.355 0.290 0.090 0.3275 0.1340 \n", "40 F 0.450 0.335 0.105 0.4250 0.1865 \n", "41 F 0.550 0.425 0.135 0.8515 0.3620 \n", "42 I 0.240 0.175 0.045 0.0700 0.0315 \n", "43 I 0.205 0.150 0.055 0.0420 0.0255 \n", "44 I 0.210 0.150 0.050 0.0420 0.0175 \n", "45 I 0.390 0.295 0.095 0.2030 0.0875 \n", "46 M 0.470 0.370 0.120 0.5795 0.2930 \n", "47 F 0.460 0.375 0.120 0.4605 0.1775 \n", "48 I 0.325 0.245 0.070 0.1610 0.0755 \n", "49 F 0.525 0.425 0.160 0.8355 0.3545 \n", "50 I 0.520 0.410 0.120 0.5950 0.2385 \n", "51 M 0.400 0.320 0.095 0.3030 0.1335 \n", "52 M 0.485 0.360 0.130 0.5415 0.2595 \n", "53 F 0.470 0.360 0.120 0.4775 0.2105 \n", "54 M 0.405 0.310 0.100 0.3850 0.1730 \n", "55 F 0.500 0.400 0.140 0.6615 0.2565 \n", "56 M 0.445 0.350 0.120 0.4425 0.1920 \n", "57 M 0.470 0.385 0.135 0.5895 0.2765 \n", "58 I 0.245 0.190 0.060 0.0860 0.0420 \n", "59 F 0.505 0.400 0.125 0.5830 0.2460 \n", " ... ... ... ... ... ... \n", "\n", " Viscera weight Shell weight Rings \n", "0 0.1010 0.150 15 \n", "1 0.0485 0.070 7 \n", "2 0.1415 0.210 9 \n", "3 0.1140 0.155 10 \n", "4 0.0395 0.055 7 \n", "5 0.0775 0.120 8 \n", "6 0.1415 0.330 20 \n", "7 0.1495 0.260 16 \n", "8 0.1125 0.165 9 \n", "9 0.1510 0.320 19 \n", "10 0.1475 0.210 14 \n", "11 0.0810 0.135 10 \n", "12 0.0950 0.190 11 \n", "13 0.1710 0.205 10 \n", "14 0.0805 0.185 10 \n", "15 0.1330 0.240 12 \n", "16 0.0395 0.115 7 \n", "17 0.0870 0.130 10 \n", "18 0.0430 0.100 7 \n", "19 0.0750 0.115 9 \n", "20 0.0620 0.075 11 \n", "21 0.0490 0.085 10 \n", "22 0.2140 0.270 12 \n", "23 0.2100 0.200 9 \n", "24 0.3010 0.305 10 \n", "25 0.1880 0.300 11 \n", "26 0.2720 0.285 11 \n", "27 0.2340 0.280 12 \n", "28 0.2190 0.295 15 \n", "29 0.2270 0.200 11 \n", "30 0.2420 0.330 10 \n", "31 0.2805 0.460 15 \n", "32 0.3575 0.350 18 \n", "33 0.3925 0.455 19 \n", "34 0.4115 0.490 13 \n", "35 0.1240 0.125 8 \n", "36 0.3075 0.340 16 \n", "37 0.1165 0.145 8 \n", "38 0.2035 0.260 11 \n", "39 0.0860 0.090 9 \n", "40 0.0910 0.115 9 \n", "41 0.1960 0.270 14 \n", "42 0.0235 0.020 5 \n", "43 0.0150 0.012 5 \n", "44 0.0125 0.015 4 \n", "45 0.0450 0.075 7 \n", "46 0.2270 0.140 9 \n", "47 0.1100 0.150 7 \n", "48 0.0255 0.045 6 \n", "49 0.2135 0.245 9 \n", "50 0.1110 0.190 8 \n", "51 0.0600 0.100 7 \n", "52 0.0960 0.160 10 \n", "53 0.1055 0.150 10 \n", "54 0.0915 0.110 7 \n", "55 0.1755 0.220 8 \n", "56 0.0955 0.135 8 \n", "57 0.1200 0.170 8 \n", "58 0.0140 0.025 4 \n", "59 0.1300 0.175 7 \n", " ... ... ... \n", "\n", "[4177 rows x 9 columns]" ] } ], "prompt_number": 2 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Training Model" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Preparing data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "features = csv_data.drop(\"Sex\", 1)\n", "\n", "sex_dict = {\"M\":0, \"I\":1, \"F\":2}\n", "results = csv_data[\"Sex\"].map(sex_dict)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Training a model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "trained_model = KNeighborsClassifier(n_neighbors=5, weights='uniform')\n", "trained_model.fit(features, results)\n", "trained_model" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " n_neighbors=5, p=2, weights='uniform')" ] } ], "prompt_number": 4 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Saving Data and Model on Disk" ] }, { "cell_type": "code", "collapsed": false, "input": [ "joblib.dump(csv_data, \"data.pkl\")\n", "joblib.dump(trained_model, \"model.pkl\");" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "ls" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2. Pickle Data and Model.ipynb model.pkl model.pkl_06.npy\r\n", "Folder Management.ipynb model.pkl_01.npy model.pkl_07.npy\r\n", "data.pkl model.pkl_02.npy model.pkl_08.npy\r\n", "data.pkl_01.npy model.pkl_03.npy model.pkl_09.npy\r\n", "data.pkl_02.npy model.pkl_04.npy\r\n", "data.pkl_03.npy model.pkl_05.npy\r\n" ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Loading Data and Model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "loaded_data = joblib.load(\"data.pkl\")\n", "loaded_model = joblib.load(\"model.pkl\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "loaded_data" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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SexLengthDiameterHeightWhole weightShucked weightViscera weightShell weightRings
0 M 0.455 0.365 0.095 0.5140 0.2245 0.1010 0.150 15
1 M 0.350 0.265 0.090 0.2255 0.0995 0.0485 0.070 7
2 F 0.530 0.420 0.135 0.6770 0.2565 0.1415 0.210 9
3 M 0.440 0.365 0.125 0.5160 0.2155 0.1140 0.155 10
4 I 0.330 0.255 0.080 0.2050 0.0895 0.0395 0.055 7
5 I 0.425 0.300 0.095 0.3515 0.1410 0.0775 0.120 8
6 F 0.530 0.415 0.150 0.7775 0.2370 0.1415 0.330 20
7 F 0.545 0.425 0.125 0.7680 0.2940 0.1495 0.260 16
8 M 0.475 0.370 0.125 0.5095 0.2165 0.1125 0.165 9
9 F 0.550 0.440 0.150 0.8945 0.3145 0.1510 0.320 19
10 F 0.525 0.380 0.140 0.6065 0.1940 0.1475 0.210 14
11 M 0.430 0.350 0.110 0.4060 0.1675 0.0810 0.135 10
12 M 0.490 0.380 0.135 0.5415 0.2175 0.0950 0.190 11
13 F 0.535 0.405 0.145 0.6845 0.2725 0.1710 0.205 10
14 F 0.470 0.355 0.100 0.4755 0.1675 0.0805 0.185 10
15 M 0.500 0.400 0.130 0.6645 0.2580 0.1330 0.240 12
16 I 0.355 0.280 0.085 0.2905 0.0950 0.0395 0.115 7
17 F 0.440 0.340 0.100 0.4510 0.1880 0.0870 0.130 10
18 M 0.365 0.295 0.080 0.2555 0.0970 0.0430 0.100 7
19 M 0.450 0.320 0.100 0.3810 0.1705 0.0750 0.115 9
20 M 0.355 0.280 0.095 0.2455 0.0955 0.0620 0.075 11
21 I 0.380 0.275 0.100 0.2255 0.0800 0.0490 0.085 10
22 F 0.565 0.440 0.155 0.9395 0.4275 0.2140 0.270 12
23 F 0.550 0.415 0.135 0.7635 0.3180 0.2100 0.200 9
24 F 0.615 0.480 0.165 1.1615 0.5130 0.3010 0.305 10
25 F 0.560 0.440 0.140 0.9285 0.3825 0.1880 0.300 11
26 F 0.580 0.450 0.185 0.9955 0.3945 0.2720 0.285 11
27 M 0.590 0.445 0.140 0.9310 0.3560 0.2340 0.280 12
28 M 0.605 0.475 0.180 0.9365 0.3940 0.2190 0.295 15
29 M 0.575 0.425 0.140 0.8635 0.3930 0.2270 0.200 11
30 M 0.580 0.470 0.165 0.9975 0.3935 0.2420 0.330 10
31 F 0.680 0.560 0.165 1.6390 0.6055 0.2805 0.460 15
32 M 0.665 0.525 0.165 1.3380 0.5515 0.3575 0.350 18
33 F 0.680 0.550 0.175 1.7980 0.8150 0.3925 0.455 19
34 F 0.705 0.550 0.200 1.7095 0.6330 0.4115 0.490 13
35 M 0.465 0.355 0.105 0.4795 0.2270 0.1240 0.125 8
36 F 0.540 0.475 0.155 1.2170 0.5305 0.3075 0.340 16
37 F 0.450 0.355 0.105 0.5225 0.2370 0.1165 0.145 8
38 F 0.575 0.445 0.135 0.8830 0.3810 0.2035 0.260 11
39 M 0.355 0.290 0.090 0.3275 0.1340 0.0860 0.090 9
40 F 0.450 0.335 0.105 0.4250 0.1865 0.0910 0.115 9
41 F 0.550 0.425 0.135 0.8515 0.3620 0.1960 0.270 14
42 I 0.240 0.175 0.045 0.0700 0.0315 0.0235 0.020 5
43 I 0.205 0.150 0.055 0.0420 0.0255 0.0150 0.012 5
44 I 0.210 0.150 0.050 0.0420 0.0175 0.0125 0.015 4
45 I 0.390 0.295 0.095 0.2030 0.0875 0.0450 0.075 7
46 M 0.470 0.370 0.120 0.5795 0.2930 0.2270 0.140 9
47 F 0.460 0.375 0.120 0.4605 0.1775 0.1100 0.150 7
48 I 0.325 0.245 0.070 0.1610 0.0755 0.0255 0.045 6
49 F 0.525 0.425 0.160 0.8355 0.3545 0.2135 0.245 9
50 I 0.520 0.410 0.120 0.5950 0.2385 0.1110 0.190 8
51 M 0.400 0.320 0.095 0.3030 0.1335 0.0600 0.100 7
52 M 0.485 0.360 0.130 0.5415 0.2595 0.0960 0.160 10
53 F 0.470 0.360 0.120 0.4775 0.2105 0.1055 0.150 10
54 M 0.405 0.310 0.100 0.3850 0.1730 0.0915 0.110 7
55 F 0.500 0.400 0.140 0.6615 0.2565 0.1755 0.220 8
56 M 0.445 0.350 0.120 0.4425 0.1920 0.0955 0.135 8
57 M 0.470 0.385 0.135 0.5895 0.2765 0.1200 0.170 8
58 I 0.245 0.190 0.060 0.0860 0.0420 0.0140 0.025 4
59 F 0.505 0.400 0.125 0.5830 0.2460 0.1300 0.175 7
...........................
\n", "

4177 rows \u00d7 9 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ " Sex Length Diameter Height Whole weight Shucked weight \\\n", "0 M 0.455 0.365 0.095 0.5140 0.2245 \n", "1 M 0.350 0.265 0.090 0.2255 0.0995 \n", "2 F 0.530 0.420 0.135 0.6770 0.2565 \n", "3 M 0.440 0.365 0.125 0.5160 0.2155 \n", "4 I 0.330 0.255 0.080 0.2050 0.0895 \n", "5 I 0.425 0.300 0.095 0.3515 0.1410 \n", "6 F 0.530 0.415 0.150 0.7775 0.2370 \n", "7 F 0.545 0.425 0.125 0.7680 0.2940 \n", "8 M 0.475 0.370 0.125 0.5095 0.2165 \n", "9 F 0.550 0.440 0.150 0.8945 0.3145 \n", "10 F 0.525 0.380 0.140 0.6065 0.1940 \n", "11 M 0.430 0.350 0.110 0.4060 0.1675 \n", "12 M 0.490 0.380 0.135 0.5415 0.2175 \n", "13 F 0.535 0.405 0.145 0.6845 0.2725 \n", "14 F 0.470 0.355 0.100 0.4755 0.1675 \n", "15 M 0.500 0.400 0.130 0.6645 0.2580 \n", "16 I 0.355 0.280 0.085 0.2905 0.0950 \n", "17 F 0.440 0.340 0.100 0.4510 0.1880 \n", "18 M 0.365 0.295 0.080 0.2555 0.0970 \n", "19 M 0.450 0.320 0.100 0.3810 0.1705 \n", "20 M 0.355 0.280 0.095 0.2455 0.0955 \n", "21 I 0.380 0.275 0.100 0.2255 0.0800 \n", "22 F 0.565 0.440 0.155 0.9395 0.4275 \n", "23 F 0.550 0.415 0.135 0.7635 0.3180 \n", "24 F 0.615 0.480 0.165 1.1615 0.5130 \n", "25 F 0.560 0.440 0.140 0.9285 0.3825 \n", "26 F 0.580 0.450 0.185 0.9955 0.3945 \n", "27 M 0.590 0.445 0.140 0.9310 0.3560 \n", "28 M 0.605 0.475 0.180 0.9365 0.3940 \n", "29 M 0.575 0.425 0.140 0.8635 0.3930 \n", "30 M 0.580 0.470 0.165 0.9975 0.3935 \n", "31 F 0.680 0.560 0.165 1.6390 0.6055 \n", "32 M 0.665 0.525 0.165 1.3380 0.5515 \n", "33 F 0.680 0.550 0.175 1.7980 0.8150 \n", "34 F 0.705 0.550 0.200 1.7095 0.6330 \n", "35 M 0.465 0.355 0.105 0.4795 0.2270 \n", "36 F 0.540 0.475 0.155 1.2170 0.5305 \n", "37 F 0.450 0.355 0.105 0.5225 0.2370 \n", "38 F 0.575 0.445 0.135 0.8830 0.3810 \n", "39 M 0.355 0.290 0.090 0.3275 0.1340 \n", "40 F 0.450 0.335 0.105 0.4250 0.1865 \n", "41 F 0.550 0.425 0.135 0.8515 0.3620 \n", "42 I 0.240 0.175 0.045 0.0700 0.0315 \n", "43 I 0.205 0.150 0.055 0.0420 0.0255 \n", "44 I 0.210 0.150 0.050 0.0420 0.0175 \n", "45 I 0.390 0.295 0.095 0.2030 0.0875 \n", "46 M 0.470 0.370 0.120 0.5795 0.2930 \n", "47 F 0.460 0.375 0.120 0.4605 0.1775 \n", "48 I 0.325 0.245 0.070 0.1610 0.0755 \n", "49 F 0.525 0.425 0.160 0.8355 0.3545 \n", "50 I 0.520 0.410 0.120 0.5950 0.2385 \n", "51 M 0.400 0.320 0.095 0.3030 0.1335 \n", "52 M 0.485 0.360 0.130 0.5415 0.2595 \n", "53 F 0.470 0.360 0.120 0.4775 0.2105 \n", "54 M 0.405 0.310 0.100 0.3850 0.1730 \n", "55 F 0.500 0.400 0.140 0.6615 0.2565 \n", "56 M 0.445 0.350 0.120 0.4425 0.1920 \n", "57 M 0.470 0.385 0.135 0.5895 0.2765 \n", "58 I 0.245 0.190 0.060 0.0860 0.0420 \n", "59 F 0.505 0.400 0.125 0.5830 0.2460 \n", " ... ... ... ... ... ... \n", "\n", " Viscera weight Shell weight Rings \n", "0 0.1010 0.150 15 \n", "1 0.0485 0.070 7 \n", "2 0.1415 0.210 9 \n", "3 0.1140 0.155 10 \n", "4 0.0395 0.055 7 \n", "5 0.0775 0.120 8 \n", "6 0.1415 0.330 20 \n", "7 0.1495 0.260 16 \n", "8 0.1125 0.165 9 \n", "9 0.1510 0.320 19 \n", "10 0.1475 0.210 14 \n", "11 0.0810 0.135 10 \n", "12 0.0950 0.190 11 \n", "13 0.1710 0.205 10 \n", "14 0.0805 0.185 10 \n", "15 0.1330 0.240 12 \n", "16 0.0395 0.115 7 \n", "17 0.0870 0.130 10 \n", "18 0.0430 0.100 7 \n", "19 0.0750 0.115 9 \n", "20 0.0620 0.075 11 \n", "21 0.0490 0.085 10 \n", "22 0.2140 0.270 12 \n", "23 0.2100 0.200 9 \n", "24 0.3010 0.305 10 \n", "25 0.1880 0.300 11 \n", "26 0.2720 0.285 11 \n", "27 0.2340 0.280 12 \n", "28 0.2190 0.295 15 \n", "29 0.2270 0.200 11 \n", "30 0.2420 0.330 10 \n", "31 0.2805 0.460 15 \n", "32 0.3575 0.350 18 \n", "33 0.3925 0.455 19 \n", "34 0.4115 0.490 13 \n", "35 0.1240 0.125 8 \n", "36 0.3075 0.340 16 \n", "37 0.1165 0.145 8 \n", "38 0.2035 0.260 11 \n", "39 0.0860 0.090 9 \n", "40 0.0910 0.115 9 \n", "41 0.1960 0.270 14 \n", "42 0.0235 0.020 5 \n", "43 0.0150 0.012 5 \n", "44 0.0125 0.015 4 \n", "45 0.0450 0.075 7 \n", "46 0.2270 0.140 9 \n", "47 0.1100 0.150 7 \n", "48 0.0255 0.045 6 \n", "49 0.2135 0.245 9 \n", "50 0.1110 0.190 8 \n", "51 0.0600 0.100 7 \n", "52 0.0960 0.160 10 \n", "53 0.1055 0.150 10 \n", "54 0.0915 0.110 7 \n", "55 0.1755 0.220 8 \n", "56 0.0955 0.135 8 \n", "57 0.1200 0.170 8 \n", "58 0.0140 0.025 4 \n", "59 0.1300 0.175 7 \n", " ... ... ... \n", "\n", "[4177 rows x 9 columns]" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "loaded_model" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " n_neighbors=5, p=2, weights='uniform')" ] } ], "prompt_number": 9 } ], "metadata": {} } ] }