Hello world sonar data.ipynb

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            "file_extension": ".py"
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        "kernelspec": {
            "language": "python", 
            "name": "python2-spark21", 
            "display_name": "Python 2 with Spark 2.1"
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    }, 
    "cells": [
        {
            "execution_count": 1, 
            "metadata": {}, 
            "source": "!rm -f sonar.all-data.csv\n!wget https://goo.gl/NXoJfR -O sonar.all-data.csv", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "--2018-01-29 01:38:52--  https://goo.gl/NXoJfR\nResolving goo.gl (goo.gl)... 172.217.9.174, 2607:f8b0:4000:814::200e\nConnecting to goo.gl (goo.gl)|172.217.9.174|:443... connected.\nHTTP request sent, awaiting response... 301 Moved Permanently\nLocation: https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data [following]\n--2018-01-29 01:38:53--  https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data\nResolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.249\nConnecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.249|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 87776 (86K) [text/plain]\nSaving to: \u2018sonar.all-data.csv\u2019\n\n100%[======================================>] 87,776      --.-K/s   in 0.1s    \n\n2018-01-29 01:38:53 (659 KB/s) - \u2018sonar.all-data.csv\u2019 saved [87776/87776]\n\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 2, 
            "metadata": {}, 
            "source": "import numpy\nfrom matplotlib import pyplot\nfrom pandas import read_csv\nfrom pandas import set_option\nfrom pandas.tools.plotting import scatter_matrix\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import ExtraTreesClassifier", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 3, 
            "metadata": {}, 
            "source": "# Load dataset\nurl = 'sonar.all-data.csv'\ndataset = read_csv(url, header=None)", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 4, 
            "metadata": {}, 
            "source": "#Whaddaya no.. ", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 5, 
            "metadata": {}, 
            "source": "dataset.shape", 
            "outputs": [
                {
                    "execution_count": 5, 
                    "data": {
                        "text/plain": "(208, 61)"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 8, 
            "metadata": {}, 
            "source": "dataset.dtypes", 
            "outputs": [
                {
                    "execution_count": 8, 
                    "data": {
                        "text/plain": "0     float64\n1     float64\n2     float64\n3     float64\n4     float64\n5     float64\n6     float64\n7     float64\n8     float64\n9     float64\n10    float64\n11    float64\n12    float64\n13    float64\n14    float64\n15    float64\n16    float64\n17    float64\n18    float64\n19    float64\n20    float64\n21    float64\n22    float64\n23    float64\n24    float64\n25    float64\n26    float64\n27    float64\n28    float64\n29    float64\n       ...   \n31    float64\n32    float64\n33    float64\n34    float64\n35    float64\n36    float64\n37    float64\n38    float64\n39    float64\n40    float64\n41    float64\n42    float64\n43    float64\n44    float64\n45    float64\n46    float64\n47    float64\n48    float64\n49    float64\n50    float64\n51    float64\n52    float64\n53    float64\n54    float64\n55    float64\n56    float64\n57    float64\n58    float64\n59    float64\n60     object\ndtype: object"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 9, 
            "metadata": {}, 
            "source": "#60 numbers in the range 0.0 to 1.0. \n#Each number represents the energy within a particular frequency band, integrated over a certain period of time. \n#The label associated with each record contains the letter R if the object is a rock and M if it is a mine (metal cylinder)\n# binary classification say it is M or R", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 12, 
            "metadata": {}, 
            "source": "set_option('display.width', 100)# check more of what else you can tinker iwth.. using this.. \ndataset.head()", 
            "outputs": [
                {
                    "execution_count": 12, 
                    "data": {
                        "text/plain": "       0       1       2       3       4       5       6       7       8       9  ...      51  \\\n0  0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109  0.2111 ...  0.0027   \n1  0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337  0.2872 ...  0.0084   \n2  0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598  0.6194 ...  0.0232   \n3  0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598  0.1264 ...  0.0121   \n4  0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564  0.4459 ...  0.0031   \n\n       52      53      54      55      56      57      58      59  60  \n0  0.0065  0.0159  0.0072  0.0167  0.0180  0.0084  0.0090  0.0032   R  \n1  0.0089  0.0048  0.0094  0.0191  0.0140  0.0049  0.0052  0.0044   R  \n2  0.0166  0.0095  0.0180  0.0244  0.0316  0.0164  0.0095  0.0078   R  \n3  0.0036  0.0150  0.0085  0.0073  0.0050  0.0044  0.0040  0.0117   R  \n4  0.0054  0.0105  0.0110  0.0015  0.0072  0.0048  0.0107  0.0094   R  \n\n[5 rows x 61 columns]", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>51</th>\n      <th>52</th>\n      <th>53</th>\n      <th>54</th>\n      <th>55</th>\n      <th>56</th>\n      <th>57</th>\n      <th>58</th>\n      <th>59</th>\n      <th>60</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0200</td>\n      <td>0.0371</td>\n      <td>0.0428</td>\n      <td>0.0207</td>\n      <td>0.0954</td>\n      <td>0.0986</td>\n      <td>0.1539</td>\n      <td>0.1601</td>\n      <td>0.3109</td>\n      <td>0.2111</td>\n      <td>...</td>\n      <td>0.0027</td>\n      <td>0.0065</td>\n      <td>0.0159</td>\n      <td>0.0072</td>\n      <td>0.0167</td>\n      <td>0.0180</td>\n      <td>0.0084</td>\n      <td>0.0090</td>\n      <td>0.0032</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0453</td>\n      <td>0.0523</td>\n      <td>0.0843</td>\n      <td>0.0689</td>\n      <td>0.1183</td>\n      <td>0.2583</td>\n      <td>0.2156</td>\n      <td>0.3481</td>\n      <td>0.3337</td>\n      <td>0.2872</td>\n      <td>...</td>\n      <td>0.0084</td>\n      <td>0.0089</td>\n      <td>0.0048</td>\n      <td>0.0094</td>\n      <td>0.0191</td>\n      <td>0.0140</td>\n      <td>0.0049</td>\n      <td>0.0052</td>\n      <td>0.0044</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0262</td>\n      <td>0.0582</td>\n      <td>0.1099</td>\n      <td>0.1083</td>\n      <td>0.0974</td>\n      <td>0.2280</td>\n      <td>0.2431</td>\n      <td>0.3771</td>\n      <td>0.5598</td>\n      <td>0.6194</td>\n      <td>...</td>\n      <td>0.0232</td>\n      <td>0.0166</td>\n      <td>0.0095</td>\n      <td>0.0180</td>\n      <td>0.0244</td>\n      <td>0.0316</td>\n      <td>0.0164</td>\n      <td>0.0095</td>\n      <td>0.0078</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0100</td>\n      <td>0.0171</td>\n      <td>0.0623</td>\n      <td>0.0205</td>\n      <td>0.0205</td>\n      <td>0.0368</td>\n      <td>0.1098</td>\n      <td>0.1276</td>\n      <td>0.0598</td>\n      <td>0.1264</td>\n      <td>...</td>\n      <td>0.0121</td>\n      <td>0.0036</td>\n      <td>0.0150</td>\n      <td>0.0085</td>\n      <td>0.0073</td>\n      <td>0.0050</td>\n      <td>0.0044</td>\n      <td>0.0040</td>\n      <td>0.0117</td>\n      <td>R</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0762</td>\n      <td>0.0666</td>\n      <td>0.0481</td>\n      <td>0.0394</td>\n      <td>0.0590</td>\n      <td>0.0649</td>\n      <td>0.1209</td>\n      <td>0.2467</td>\n      <td>0.3564</td>\n      <td>0.4459</td>\n      <td>...</td>\n      <td>0.0031</td>\n      <td>0.0054</td>\n      <td>0.0105</td>\n      <td>0.0110</td>\n      <td>0.0015</td>\n      <td>0.0072</td>\n      <td>0.0048</td>\n      <td>0.0107</td>\n      <td>0.0094</td>\n      <td>R</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 61 columns</p>\n</div>"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 14, 
            "metadata": {}, 
            "source": "dataset.describe()", 
            "outputs": [
                {
                    "execution_count": 14, 
                    "data": {
                        "text/plain": "               0           1           2           3           4           5           6   \\\ncount  208.000000  208.000000  208.000000  208.000000  208.000000  208.000000  208.000000   \nmean     0.029164    0.038437    0.043832    0.053892    0.075202    0.104570    0.121747   \nstd      0.022991    0.032960    0.038428    0.046528    0.055552    0.059105    0.061788   \nmin      0.001500    0.000600    0.001500    0.005800    0.006700    0.010200    0.003300   \n25%      0.013350    0.016450    0.018950    0.024375    0.038050    0.067025    0.080900   \n50%      0.022800    0.030800    0.034300    0.044050    0.062500    0.092150    0.106950   \n75%      0.035550    0.047950    0.057950    0.064500    0.100275    0.134125    0.154000   \nmax      0.137100    0.233900    0.305900    0.426400    0.401000    0.382300    0.372900   \n\n               7           8           9      ...              50          51          52  \\\ncount  208.000000  208.000000  208.000000     ...      208.000000  208.000000  208.000000   \nmean     0.134799    0.178003    0.208259     ...        0.016069    0.013420    0.010709   \nstd      0.085152    0.118387    0.134416     ...        0.012008    0.009634    0.007060   \nmin      0.005500    0.007500    0.011300     ...        0.000000    0.000800    0.000500   \n25%      0.080425    0.097025    0.111275     ...        0.008425    0.007275    0.005075   \n50%      0.112100    0.152250    0.182400     ...        0.013900    0.011400    0.009550   \n75%      0.169600    0.233425    0.268700     ...        0.020825    0.016725    0.014900   \nmax      0.459000    0.682800    0.710600     ...        0.100400    0.070900    0.039000   \n\n               53          54          55          56          57          58          59  \ncount  208.000000  208.000000  208.000000  208.000000  208.000000  208.000000  208.000000  \nmean     0.010941    0.009290    0.008222    0.007820    0.007949    0.007941    0.006507  \nstd      0.007301    0.007088    0.005736    0.005785    0.006470    0.006181    0.005031  \nmin      0.001000    0.000600    0.000400    0.000300    0.000300    0.000100    0.000600  \n25%      0.005375    0.004150    0.004400    0.003700    0.003600    0.003675    0.003100  \n50%      0.009300    0.007500    0.006850    0.005950    0.005800    0.006400    0.005300  \n75%      0.014500    0.012100    0.010575    0.010425    0.010350    0.010325    0.008525  \nmax      0.035200    0.044700    0.039400    0.035500    0.044000    0.036400    0.043900  \n\n[8 rows x 60 columns]", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>50</th>\n      <th>51</th>\n      <th>52</th>\n      <th>53</th>\n      <th>54</th>\n      <th>55</th>\n      <th>56</th>\n      <th>57</th>\n      <th>58</th>\n      <th>59</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>...</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n      <td>208.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.029164</td>\n      <td>0.038437</td>\n      <td>0.043832</td>\n      <td>0.053892</td>\n      <td>0.075202</td>\n      <td>0.104570</td>\n      <td>0.121747</td>\n      <td>0.134799</td>\n      <td>0.178003</td>\n      <td>0.208259</td>\n      <td>...</td>\n      <td>0.016069</td>\n      <td>0.013420</td>\n      <td>0.010709</td>\n      <td>0.010941</td>\n      <td>0.009290</td>\n      <td>0.008222</td>\n      <td>0.007820</td>\n      <td>0.007949</td>\n      <td>0.007941</td>\n      <td>0.006507</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.022991</td>\n      <td>0.032960</td>\n      <td>0.038428</td>\n      <td>0.046528</td>\n      <td>0.055552</td>\n      <td>0.059105</td>\n      <td>0.061788</td>\n      <td>0.085152</td>\n      <td>0.118387</td>\n      <td>0.134416</td>\n      <td>...</td>\n      <td>0.012008</td>\n      <td>0.009634</td>\n      <td>0.007060</td>\n      <td>0.007301</td>\n      <td>0.007088</td>\n      <td>0.005736</td>\n      <td>0.005785</td>\n      <td>0.006470</td>\n      <td>0.006181</td>\n      <td>0.005031</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.001500</td>\n      <td>0.000600</td>\n      <td>0.001500</td>\n      <td>0.005800</td>\n      <td>0.006700</td>\n      <td>0.010200</td>\n      <td>0.003300</td>\n      <td>0.005500</td>\n      <td>0.007500</td>\n      <td>0.011300</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000800</td>\n      <td>0.000500</td>\n      <td>0.001000</td>\n      <td>0.000600</td>\n      <td>0.000400</td>\n      <td>0.000300</td>\n      <td>0.000300</td>\n      <td>0.000100</td>\n      <td>0.000600</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.013350</td>\n      <td>0.016450</td>\n      <td>0.018950</td>\n      <td>0.024375</td>\n      <td>0.038050</td>\n      <td>0.067025</td>\n      <td>0.080900</td>\n      <td>0.080425</td>\n      <td>0.097025</td>\n      <td>0.111275</td>\n      <td>...</td>\n      <td>0.008425</td>\n      <td>0.007275</td>\n      <td>0.005075</td>\n      <td>0.005375</td>\n      <td>0.004150</td>\n      <td>0.004400</td>\n      <td>0.003700</td>\n      <td>0.003600</td>\n      <td>0.003675</td>\n      <td>0.003100</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.022800</td>\n      <td>0.030800</td>\n      <td>0.034300</td>\n      <td>0.044050</td>\n      <td>0.062500</td>\n      <td>0.092150</td>\n      <td>0.106950</td>\n      <td>0.112100</td>\n      <td>0.152250</td>\n      <td>0.182400</td>\n      <td>...</td>\n      <td>0.013900</td>\n      <td>0.011400</td>\n      <td>0.009550</td>\n      <td>0.009300</td>\n      <td>0.007500</td>\n      <td>0.006850</td>\n      <td>0.005950</td>\n      <td>0.005800</td>\n      <td>0.006400</td>\n      <td>0.005300</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.035550</td>\n      <td>0.047950</td>\n      <td>0.057950</td>\n      <td>0.064500</td>\n      <td>0.100275</td>\n      <td>0.134125</td>\n      <td>0.154000</td>\n      <td>0.169600</td>\n      <td>0.233425</td>\n      <td>0.268700</td>\n      <td>...</td>\n      <td>0.020825</td>\n      <td>0.016725</td>\n      <td>0.014900</td>\n      <td>0.014500</td>\n      <td>0.012100</td>\n      <td>0.010575</td>\n      <td>0.010425</td>\n      <td>0.010350</td>\n      <td>0.010325</td>\n      <td>0.008525</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>0.137100</td>\n      <td>0.233900</td>\n      <td>0.305900</td>\n      <td>0.426400</td>\n      <td>0.401000</td>\n      <td>0.382300</td>\n      <td>0.372900</td>\n      <td>0.459000</td>\n      <td>0.682800</td>\n      <td>0.710600</td>\n      <td>...</td>\n      <td>0.100400</td>\n      <td>0.070900</td>\n      <td>0.039000</td>\n      <td>0.035200</td>\n      <td>0.044700</td>\n      <td>0.039400</td>\n      <td>0.035500</td>\n      <td>0.044000</td>\n      <td>0.036400</td>\n      <td>0.043900</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows \u00d7 60 columns</p>\n</div>"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 15, 
            "metadata": {}, 
            "source": "dataset.groupby(60).size()", 
            "outputs": [
                {
                    "execution_count": 15, 
                    "data": {
                        "text/plain": "60\nM    111\nR     97\ndtype: int64"
                    }, 
                    "metadata": {}, 
                    "output_type": "execute_result"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 16, 
            "metadata": {}, 
            "source": "# uniform enough.. of samples.. for both M and R", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 18, 
            "metadata": {}, 
            "source": "dataset.hist(sharex=False, sharey=False, xlabelsize=1, ylabelsize=1)\npyplot.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7fe152187910>", 
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9VHp7e2X69Oly4oknloaFuIbiBENvsg/BEpk27RKNoXz4kse6KHAql4a43HTT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\nP80tYTD+scIAAAAAAElFTkSuQmCC\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }, 
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7fe152187f50>", 
                        "image/png": 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9VHp7e2X69Oly4oknloaFuIbiBENvsg/BEpk27RKNoXz4kse6KHAql4a43HTT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\nP80tYTD+scIAAAAAAElFTkSuQmCC\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 19, 
            "metadata": {}, 
            "source": "dataset.plot(kind='density', subplots=True, layout=(8,8), sharex=False, legend=False,fontsize=1)\npyplot.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7fe10f070c10>", 
                        "image/png": 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yXLp0CVVVVUhMTAQRITc3F6dPn0ZlZWV9PEsW/FcoFNyRoqKAuXPZTZLXrWMn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Wp8/vnnvJ25GCuX9B1mqaoCsrLYUQAuffwx29kvWgT07GkcVn2HWdJL09G3c1+t443rPg5nb53F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\n+v/9Mu3fo14MbAAAAABJRU5ErkJggg==\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 20, 
            "metadata": {}, 
            "source": "dataset.plot(kind='box', subplots=True, layout=(8,8), sharex=False, sharey=False,fontsize=1)\npyplot.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7fe10f027e90>", 
                        "image/png": 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6QstuyFAquVQPUFHIQ9HqZheGBE23mirRMvczF2JKWVCnAmJLWQBoo2DaG0WQ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\nyIIgCIHw/wFThoWIERJZVgAAAABJRU5ErkJggg==\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 21, 
            "metadata": {}, 
            "source": "# correlation matrix\nfig = pyplot.figure()\nax = fig.add_subplot(111)\ncax = ax.matshow(dataset.corr(), vmin=-1, vmax=1, interpolation='none')\nfig.colorbar(cax)\npyplot.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7fe10ddf1950>", 
                        "image/png": 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Mdrq5At4PqZDjrHVoZadXhqc69jxvxE63jlOD7PSLK793UOgBp2fZ6SVPH7lq/FqTNsXT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\nGNiCIJh3/F/dRJERllhQbAAAAABJRU5ErkJggg==\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": null, 
            "metadata": {}, 
            "source": "#there are 60.. over laod.. but then there is some stron correlation b/w a set of variables..", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "source": "#The red around the diagonal suggests that attributes that are next to each other are generally more correlated with each other.   \n#The blue patches also suggest some moderate negative correlation the further attributes are away from each other in the ordering.   \n#This makes sense if the order of the attributes refers to the angle of sensors for the sonar chirp.  ", 
            "metadata": {}, 
            "cell_type": "markdown"
        }, 
        {
            "execution_count": 22, 
            "metadata": {}, 
            "source": "# Split-out validation dataset\narray = dataset.values\nX = array[:,0:60].astype(float)\nY = array[:,60]\nvalidation_size = 0.20\nseed = 7\nX_train, X_validation, Y_train, Y_validation = train_test_split(X, Y,test_size=validation_size, random_state=seed)", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 23, 
            "metadata": {}, 
            "source": "# Test options and evaluation metric\nnum_folds = 10\nseed = 7\nscoring = 'accuracy'", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 24, 
            "metadata": {}, 
            "source": "# Spot-Check Algorithms\nmodels = []\nmodels.append(('LR', LogisticRegression()))\nmodels.append(('LDA', LinearDiscriminantAnalysis()))\nmodels.append(('KNN', KNeighborsClassifier()))\nmodels.append(('CART', DecisionTreeClassifier()))\nmodels.append(('NB', GaussianNB()))\nmodels.append(('SVM', SVC()))", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 25, 
            "metadata": {}, 
            "source": "results = []\nnames = []\nfor name, model in models:\n    kfold = KFold(n_splits=num_folds, random_state=seed)\n    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n    results.append(cv_results)\n    names.append(name)\n    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n    print(msg)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "LR: 0.782721 (0.093796)\nLDA: 0.746324 (0.117854)\nKNN: 0.808088 (0.067507)\nCART: 0.693750 (0.083466)\nNB: 0.648897 (0.141868)\nSVM: 0.608824 (0.118656)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 26, 
            "metadata": {}, 
            "source": "# LR and KNN..\n# observe.. how LR is pretty robust... :)", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 27, 
            "metadata": {}, 
            "source": "# Compare Algorithms\nfig = pyplot.figure()\nfig.suptitle('Algorithm Comparison')\nax = fig.add_subplot(111)\npyplot.boxplot(results)\nax.set_xticklabels(names)\npyplot.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7fe10d78d510>", 
                        "image/png": 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kOcBFwCnAscBySUe3lVkErAReHhEvBs6dgVitTZ2eLmJT42NnVek5O0XSYmBVRLw2LZ8P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\nZpYxJ3Ezs4w5iZuZZcxJ3MwsY/8fR1XpF0HyJuAAAAAASUVORK5CYII=\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 28, 
            "metadata": {}, 
            "source": "# much tightness.. KNN", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 29, 
            "metadata": {}, 
            "source": "# Standardize the dataset\n# and reevalutate again...\npipelines = []\npipelines.append(('ScaledLR', Pipeline([('Scaler', StandardScaler()),('LR',LogisticRegression())])))\npipelines.append(('ScaledLDA', Pipeline([('Scaler', StandardScaler()),('LDA',LinearDiscriminantAnalysis())])))\npipelines.append(('ScaledKNN', Pipeline([('Scaler', StandardScaler()),('KNN',KNeighborsClassifier())])))\npipelines.append(('ScaledCART', Pipeline([('Scaler', StandardScaler()),('CART',DecisionTreeClassifier())])))\npipelines.append(('ScaledNB', Pipeline([('Scaler', StandardScaler()),('NB',GaussianNB())])))\npipelines.append(('ScaledSVM', Pipeline([('Scaler', StandardScaler()),('SVM', SVC())])))\n\nresults = []\nnames = []\nfor name, model in pipelines:\n    kfold = KFold(n_splits=num_folds, random_state=seed)\n    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n    results.append(cv_results)\n    names.append(name)\n    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n    print(msg)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "ScaledLR: 0.734191 (0.095885)\nScaledLDA: 0.746324 (0.117854)\nScaledKNN: 0.825735 (0.054511)\nScaledCART: 0.699265 (0.121243)\nScaledNB: 0.648897 (0.141868)\nScaledSVM: 0.836397 (0.088697)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 30, 
            "metadata": {}, 
            "source": "# the score is accuracy..\n# scaling.. helped.. SVM perform much better", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 31, 
            "metadata": {}, 
            "source": "# Compare Algorithms\nfig = pyplot.figure()\nfig.suptitle('Scaled Algorithm Comparison')\nax = fig.add_subplot(111)\npyplot.boxplot(results)\nax.set_xticklabels(names)\npyplot.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7fe10db5f350>", 
                        "image/png": 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cqIytVKUmPgf4WDp1f5iiZvJWitOnlemU+ZcUH+hPAF+RtIyiZ8JDLbb3GYqr\nwhtScLqXoh3xQkkvAzZRtLFd0bTexZLGa25Xpn2VnUnxJZkfEbdkWp7DJF1N0X54D8XFtEb68N0e\nEXeX1l8HPFvSUyPingqVrVwLOh64SNIK4OtN234f8NVS+u3rRcQ3JN2b5lWhTO+l6KHyI0m/Stv9\nO4qa24VN278A+Evg801l2ihpI8Up+XgX0XY14CqUe6JjMu4wFc1Fsyjax99S4fIIeLekT1HU7B+i\nuBg9Xr7Nkh6iaH8v1/zL78FZrea345t9zMwyVonmFDMz642DuJlZxhzEzcwy5iBuZpYxB3Ezs4w5\niJuZZcxB3MwsYw7iZmYZ+/8LtOpF+ldctAAAAABJRU5ErkJggg==\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 32, 
            "metadata": {}, 
            "source": "#hilarious.. the effects of standardization\n# tuning and ensemble .. might give better resutls..", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 34, 
            "metadata": {}, 
            "source": "# Tune scaled KNN\nscaler = StandardScaler().fit(X_train)\nrescaledX = scaler.transform(X_train)\nneighbors = range (1,23)\nparam_grid = dict(n_neighbors=neighbors)\nmodel = KNeighborsClassifier()\nkfold = KFold(n_splits=num_folds, random_state=seed)\ngrid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)\ngrid_result = grid.fit(rescaledX, Y_train)\n\nprint(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n\nmeans = grid_result.cv_results_['mean_test_score']\nstds = grid_result.cv_results_['std_test_score']\nparams = grid_result.cv_results_['params']\n\nfor mean, stdev, param in zip(means, stds, params):\n    print(\"%f (%f) with: %r\" % (mean, stdev, param))", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Best: 0.849398 using {'n_neighbors': 1}\n0.849398 (0.059881) with: {'n_neighbors': 1}\n0.801205 (0.087869) with: {'n_neighbors': 2}\n0.837349 (0.066303) with: {'n_neighbors': 3}\n0.807229 (0.083938) with: {'n_neighbors': 4}\n0.837349 (0.037500) with: {'n_neighbors': 5}\n0.746988 (0.097200) with: {'n_neighbors': 6}\n0.765060 (0.089510) with: {'n_neighbors': 7}\n0.734940 (0.082291) with: {'n_neighbors': 8}\n0.753012 (0.086979) with: {'n_neighbors': 9}\n0.728916 (0.091367) with: {'n_neighbors': 10}\n0.734940 (0.104890) with: {'n_neighbors': 11}\n0.722892 (0.099478) with: {'n_neighbors': 12}\n0.734940 (0.105836) with: {'n_neighbors': 13}\n0.728916 (0.084700) with: {'n_neighbors': 14}\n0.728916 (0.075873) with: {'n_neighbors': 15}\n0.716867 (0.082917) with: {'n_neighbors': 16}\n0.710843 (0.078716) with: {'n_neighbors': 17}\n0.716867 (0.094291) with: {'n_neighbors': 18}\n0.722892 (0.084555) with: {'n_neighbors': 19}\n0.728916 (0.087022) with: {'n_neighbors': 20}\n0.710843 (0.108829) with: {'n_neighbors': 21}\n0.716867 (0.093465) with: {'n_neighbors': 22}\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 35, 
            "metadata": {}, 
            "source": "# funny.. N:neighbours = 1", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 36, 
            "metadata": {}, 
            "source": "# Tune scaled SVM\nscaler = StandardScaler().fit(X_train)\nrescaledX = scaler.transform(X_train)\nc_values = [0.1, 0.3, 0.5, 0.7, 0.9, 1.0, 1.3, 1.5, 1.7, 2.0] # how is this arrived at..? judgement call..?\nkernel_values = ['linear', 'poly', 'rbf', 'sigmoid']\nparam_grid = dict(C=c_values, kernel=kernel_values)\nmodel = SVC()\nkfold = KFold(n_splits=num_folds, random_state=seed)\ngrid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)\ngrid_result = grid.fit(rescaledX, Y_train)\nprint(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\nmeans = grid_result.cv_results_['mean_test_score']\nstds = grid_result.cv_results_['std_test_score']\nparams = grid_result.cv_results_['params']\nfor mean, stdev, param in zip(means, stds, params):\n    print(\"%f (%f) with: %r\" % (mean, stdev, param))", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "Best: 0.867470 using {'kernel': 'rbf', 'C': 1.5}\n0.759036 (0.098863) with: {'kernel': 'linear', 'C': 0.1}\n0.530120 (0.118780) with: {'kernel': 'poly', 'C': 0.1}\n0.572289 (0.130339) with: {'kernel': 'rbf', 'C': 0.1}\n0.704819 (0.066360) with: {'kernel': 'sigmoid', 'C': 0.1}\n0.746988 (0.108913) with: {'kernel': 'linear', 'C': 0.3}\n0.644578 (0.132290) with: {'kernel': 'poly', 'C': 0.3}\n0.765060 (0.092312) with: {'kernel': 'rbf', 'C': 0.3}\n0.734940 (0.054631) with: {'kernel': 'sigmoid', 'C': 0.3}\n0.740964 (0.083035) with: {'kernel': 'linear', 'C': 0.5}\n0.680723 (0.098638) with: {'kernel': 'poly', 'C': 0.5}\n0.789157 (0.064316) with: {'kernel': 'rbf', 'C': 0.5}\n0.746988 (0.059265) with: {'kernel': 'sigmoid', 'C': 0.5}\n0.746988 (0.084525) with: {'kernel': 'linear', 'C': 0.7}\n0.740964 (0.127960) with: {'kernel': 'poly', 'C': 0.7}\n0.813253 (0.084886) with: {'kernel': 'rbf', 'C': 0.7}\n0.753012 (0.058513) with: {'kernel': 'sigmoid', 'C': 0.7}\n0.759036 (0.096940) with: {'kernel': 'linear', 'C': 0.9}\n0.771084 (0.102127) with: {'kernel': 'poly', 'C': 0.9}\n0.837349 (0.087854) with: {'kernel': 'rbf', 'C': 0.9}\n0.753012 (0.073751) with: {'kernel': 'sigmoid', 'C': 0.9}\n0.753012 (0.099230) with: {'kernel': 'linear', 'C': 1.0}\n0.789157 (0.107601) with: {'kernel': 'poly', 'C': 1.0}\n0.837349 (0.087854) with: {'kernel': 'rbf', 'C': 1.0}\n0.753012 (0.070213) with: {'kernel': 'sigmoid', 'C': 1.0}\n0.771084 (0.106063) with: {'kernel': 'linear', 'C': 1.3}\n0.819277 (0.106414) with: {'kernel': 'poly', 'C': 1.3}\n0.849398 (0.079990) with: {'kernel': 'rbf', 'C': 1.3}\n0.710843 (0.076865) with: {'kernel': 'sigmoid', 'C': 1.3}\n0.759036 (0.091777) with: {'kernel': 'linear', 'C': 1.5}\n0.831325 (0.109499) with: {'kernel': 'poly', 'C': 1.5}\n0.867470 (0.090883) with: {'kernel': 'rbf', 'C': 1.5}\n0.740964 (0.063717) with: {'kernel': 'sigmoid', 'C': 1.5}\n0.746988 (0.090228) with: {'kernel': 'linear', 'C': 1.7}\n0.831325 (0.115695) with: {'kernel': 'poly', 'C': 1.7}\n0.861446 (0.087691) with: {'kernel': 'rbf', 'C': 1.7}\n0.710843 (0.088140) with: {'kernel': 'sigmoid', 'C': 1.7}\n0.759036 (0.094276) with: {'kernel': 'linear', 'C': 2.0}\n0.831325 (0.108279) with: {'kernel': 'poly', 'C': 2.0}\n0.867470 (0.094701) with: {'kernel': 'rbf', 'C': 2.0}\n0.728916 (0.095050) with: {'kernel': 'sigmoid', 'C': 2.0}\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 37, 
            "metadata": {}, 
            "source": "# ensembles\nensembles = []\nensembles.append(('AB', AdaBoostClassifier()))\nensembles.append(('GBM', GradientBoostingClassifier()))\nensembles.append(('RF', RandomForestClassifier()))\nensembles.append(('ET', ExtraTreesClassifier()))\nresults = []\nnames = []\nfor name, model in ensembles:\n    kfold = KFold(n_splits=num_folds, random_state=seed)\n    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n    results.append(cv_results)\n    names.append(name)\n    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n    print(msg)", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "AB: 0.813971 (0.066017)\nGBM: 0.847794 (0.100189)\nRF: 0.777206 (0.076753)\nET: 0.746324 (0.086503)\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 38, 
            "metadata": {}, 
            "source": "fig = pyplot.figure()\nfig.suptitle('Ensemble Algorithm Comparison')\nax = fig.add_subplot(111)\npyplot.boxplot(results)\nax.set_xticklabels(names)\npyplot.show()", 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.figure.Figure at 0x7fe10d979410>", 
                        "image/png": 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NfNkLGARuasizEfiTdLGjgb0j4leSDkodskg6ApgDPLKbz8PMJtALwahXVKEuC80tI2kA\n+B9kbwZXRsTFkoaBkYhYmgL65cBLyTpXPxURt0s6HfgssBUYBT4TEbc0Ob/v3HtEWe+Qy1qu6eQ6\n6D8T3bl74jCblLIGkLKWazq5DvqPJw4zM+szDu5mZhXk4G5m1qAKHaoO7mYV4bllOqcv5paZlkK4\nQ7VnlLXTrqzlst7UK68nd6iamfUZB3czswpycDczq6AiE4f1tAMOgC1biuTs9MzEEzfYzZwJTz7Z\n4UtOg0ClnMQ5cv+atasKndOV71Ata8dIWcvVSlnLXdZyTademezKOqevpx8o6x99WcvVSlnLXdZy\nTSfXQf+ZKLhXvlnGzQhm1o8qH9xFlPJuRnJoN7Op49EyZmYV1BfBXSryow7/THy9mTO7XStmNp4q\ndExPZrGOxexYrOOShv2vAK4B9k95zo+I76R95wMfBLYBn4yIZU3O7+kHekRZO+3KWq5Wig/VnV69\nOlS3U3rl9dTWaJm0TN564BTgcbJl9wYjYm0uz2XAyoi4LK3KdEtEHC7pGOBrwPHAocBtwJGNkdzB\nvXeU9UVf1nK1UtZyl7Vc06VXnn+7c8vMAzZExMaI2AosAeY35NkO7Jse7w88lh6/HVgSEdsi4ufA\nhnQ+MzObQkWC+yHAptz25pSWNwy8X9ImYCnw8XGOfazJsWZm1mFFgnuzW/7GDywLgasi4hXAnwHX\nTeJYMzPrsCLj3DcDs3Lbh5K1ved9CDgNICJ+IulFkg4qeCwAQ7nu6VqtRq1WK1A0M7MdOtlBrQ59\n+bGTndP1ep16vV4ob5EO1T2AdWQdqk8AdwELI2JNLs//Ab4eEdekDtXvRcShuQ7V15E1x3wPd6j2\ntLJ2NJW1XK2UtdxlLVcrZSz3VJaprekHImJU0lnAMnYMhVwjaRgYiYilwDnA5ZL+C1nn6pnp2Ick\nfR14CNgKfMxR3Mxs6lV+4jDrrDLeGUF5y9VKWctd1nK1UsZyd+vOvS++oWpm1m8c3M3MKsjB3cys\nghzczcwqyMHdzKyCHNzNzCrIwd3MrIIqv8yedV6nvpbdSV78xGxnDu42KZ38MkYZv3BiVhVuljEz\nqyAHdzOzCnJwNzOrIAd3M7MKcnC3rlm0qNslMKsuT/lr1kVlHTFU1nK1UsZye8pfMzPrmELBXdKA\npLWS1ks6t8n+L0haJWmlpHWSnsztG03pqyTd2MnCm5lZc0XWUJ0BrCdbQ/VxYAQYjIi14+Q/Czgu\nIj6ctp+JiH1bXMPNMtafyvh13zG9+DdZ1vqcorpsaw1VYB6wISI2ppMtAeYDTYM7sBD4TP76kyir\nWV8RUcoYKkEJi9VSGeuzW3VZpFnmEGBTbntzStuFpFnAYcDyXPLeku6SdKek+btbUKueoaFul8Cs\nuooE92Z33uO9EQ0C32hoY5kVEfOA9wGLJR0+yTJaRQ0Pd7sEZtVVpFlmMzArt30oWdt7M4PAx/IJ\nEfGL9PtRSXXgtcCjjQcO5W7jarUatVqtQNHMzPpHvV6nXq8XylukQ3UPYB1Zh+oTwF3AwohY05Dv\nKOA7EXFELm1/4LmIeF7SQcCPgPmNnbHuUO1PZRyTPN3KWgdlLVcrZSx3t8a5t7xzj4jRNAJmGVkz\nzpURsUbSMDASEUtT1kFgScPhRwOXSRpNx1403igbMzPrHH9D1bqmjHdZ062sdVDWcrVSxnL7G6rW\ndzy3jNnU8Z27WReV8U4TyluuVspYbt+5m5lZxzi4m5lVkIO7mVkFObibmVWQg7t1jeeWMZs6Hi1j\nXVPGkQ3Trax1UNZytVLGcnu0jJmZdYyDu5lZBTm4m5lVkIO7mVkFFZnP3WxKeG6ZTBmX/Zw5s9sl\n2H1lq89u1aVHy5hVRBlHivSqXqlLj5bpkMWLF3e7CGZmhTi4T8KNN97Y7SKYmRVSKLhLGpC0VtJ6\nSec22f8FSaskrZS0TtKTuX1npuPWSTqjk4U3M7PmiqyhOgNYT7aG6uPACDA43nJ5aUm+4yLiw5Jm\nAncDcwEB9wBzI+LphmNK2+a+ePHiF+7Yv//973PSSScBsGDBAs4+++xuFs1sJ73STtwLeqUu21pD\nFZgHbIiIjelkS4D5wHhroS4EPpMenwYsGwvmkpYBA8D1xYvfXWefffYLQbxWqxVeedxaGxry/DKd\n5NFHnVOFuizSLHMIsCm3vTml7ULSLOAwYPk4xz423rHWf4aHu12CavEbZedUoS6L3Lk3u+Uf7wPL\nIPCNXBtL4WOHcrVZq9Wo1WoFija9FixY0O0imFkfq9frhVsPirS5nwAMRcRA2j4PiIi4pEnelcDH\nIuInaXsQqEXER9P2l4EVEXF9w3GlbXO3qdMr7ZpmZdXuOPcRYI6k2ZL2Irs7v6nJRY4C9h8L7Mmt\nwKmS9kudq6emNDMzm0Itm2UiYjSNgFlG9mZwZUSskTQMjETE0pR1EFjScOwWSReSjZgJYDginuro\nMzAzs114+gHrGo+W6SzXZ+f0Sl1O1Czj4G5WEe7D6JxeqUvPLWNm1mc85a9NCXVw3tV+/1Q3mbos\nkrXf67NfOLjblHAA6RzXpe0ON8uYmVWQg7uZWYMqzC3j0TJmZj3Ko2XMzPqMg7uZWQU5uJuZVZCD\nu5lZBTm4m5k16IV5ZVrxaBkzswaeW8bMzErJwd3MrIIc3M3MKqhQcJc0IGmtpPWSzh0nz7slrZb0\ngKTrcumjklZKWiXpxk4V3MzMxtdyVkhJM4BLgVOAx4ERSd+OiLW5PHOAc4HXR8Qzkg7KneLZiJjb\n4XKbmU2ZKswtU+TOfR6wISI2RsRWsnVS5zfk+QjwxYh4BiAifpXb17mJvc3MpkEVhkIWCe6HAJty\n25tTWt4rgaMk3SHpTkmn5fbtLemulN74pmBmZlOgyGIdze68G0eA7gnMAU4EZgE/lHRsupOfFRG/\nkHQ4sFzS/RHxaOMJh3JvlbVajVqtVuwZmJn1iXq9Tr1eL5S35ZeYJJ0ADEXEQNo+D4iIuCSX50vA\njyPi2rR9G3BuRNzTcK6rgJsj4oaGdH+Jycxsktr9EtMIMEfSbEl7AYPATQ15bgROThc7CDgSeETS\n/umYsfQ3AA/t3tMwM2ufpI7+lFXLZpmIGJV0FrCM7M3gyohYI2kYGImIpRFxq6S3SFoNbAPOiYgt\nkl4PXCZpNB17UX6UjZnZdOuXVgLPLWNm1qM8t4yZWZ9xcDczqyAHdzOzCnJwNzOrIAd3M7MKcnA3\nM6sgB3czswpycDczqyAHdzOzCnJwNzOrIAd3M7MKcnA3M6sgB3czswpycDczqyAHdzOzCnJwNzOr\noELBXdKApLWS1ks6d5w875a0WtIDkq7LpZ+Zjlsn6YxOFdzMzMZXZIHsGcB64BTgcbI1VQfzy+VJ\nmgNcD7w5Ip6RdFBE/ErSTOBuYC4g4B5gbkQ83XANr8RkZjZJ7a7ENA/YEBEbI2IrsASY35DnI8AX\nI+IZgIj4VUo/DVgWEU9HxFNk67AO7M6TMDOz4ooE90OATbntzSkt75XAUZLukHSnpNPGOfaxJsea\nmVmH7VkgT7Nb/sY2lD2BOcCJwCzgh5KOLXgsAENDQy88rtVq1Gq1AkUzM+sf9Xqder1eKG+RNvcT\ngKGIGEjb5wEREZfk8nwJ+HFEXJu2bwPOBY4EahHx0ZT+ZWBFRFzfcA23uZuZTVK7be4jwBxJsyXt\nBQwCNzXkuRE4OV3sILKg/ghwK3CqpP1S5+qpKc3MzKZQy2aZiBiVdBZZZ+gM4MqIWCNpGBiJiKUR\ncaukt0haDWwDzomILQCSLiQbMRPAcOpYNTOzKdSyWWZaCuFmGTOzSWu3WcbMzHqMg7uZWQU5uJuZ\nVZCDu5lZBTm4m5lVkIO7mVkFObibmVWQg7uZWQU5uJuZVZCDu5lZBTm4m5lVkIO7mVmDonOml5mD\nu5lZAwd3MzMrpSLL7JmZVV5+Cbvh4eEX0nt12c9CwV3SALCYHYt1XNKw/0zg78gWzwa4NCK+mvaN\nAveRrae6MSIWdKjsZmYd0xjE8+s696KWzTKSZgCXAqcBxwILJb2qSdYlETE3/Xw1l/5sSnttrwf2\nKrTDlYnrs7Ncn53z85//vNtFaFuRNvd5wIaI2BgRW4ElwPwm+ZquBjJBes/xH09nuT47y/VpeUWC\n+yHAptz25pTW6HRJ90r6uqRDc+l7S7pL0p2Smr0pmJmVymGHHdbtIrStSHBvdufduODpTcBhEXEc\ncDtwTW7frIiYB7wPWCzp8N0qqZmZFdZygWxJJwBDETGQts8DorFTNZd/BvBkROzfZN9VwM0RcUND\nulfHNjPbDeMtkF1ktMwIMEfSbOAJYBBYmM8g6fci4hdpcz7wUErfH3guIp6XdBDwBmCXN4XxCmdm\nZrunZXCPiFFJZwHL2DEUco2kYWAkIpYCn5D0dmAr8CTwgXT40cBlaTjkDOCiiFg7Bc/DzMxyWjbL\nmJlZ7/H0AxOQ9A5J2yW9Mm3PlvScpJVpZNAdko7sdjnLRtLBkr4m6WFJI5J+JGm+pJMkPZXq7z5J\ny1JzHZLOTHX95tx5xur/9O49m/KRNJrq8AFJ35a0b0rPvz5Xpd/+FnoLufocq7NPS7ohbW/IvWZX\npj7InuDgPrFB4Ifp95iH05eyjgOuBf5rV0pWbjcC9YiYExHHk9Xf2PDYH6T6ew1wN/Cfc8fdz879\nOe8B7p2OAveYsS8GvhrYws51+HDuS4NzI2Jbl8rYS55tqLPPRcTpEfFa4MPseM3OjYifdLuwRTm4\nj0PSPmQdwB9i54CT7/zdl6yPwRJJJwO/jYjLx9IiYlNEfHEsS8on4HfJgtOYO4B5kvZI9T8HB/dW\nfszO3zvx4ITJq2Sd+SPb+BYA342IhyX9WtJxZIHoDyStJAvsLwZe181CltCxwMoJ9r8p1d9BwL8C\n5+f2BXAbMADsB3wb8PcidjX2BrkHcApwRW7f2OsT4EcR8fHpLlwPenGqM5G9Bi+KiH/qcpna5jv3\n8S0km2oB4Hrgvenx2MfeOcDZwOXNDraMpEtT/8RdKWnsI+4s4CqyCefGBFmdD5I1yfxvKnpX1aax\nYPQEcDDwvdy+h3NNCA7sxTzX0CzT84EdHNybknQAcDJwhaRHgHOAd7NroLkZOHGai1d2q4E/GtuI\niLPI7i5fxq7fbL4ZeFM+ISLuBv4QODAiHp7aovas5yJiLjCL7DV5VpfLYyXk4N7cu4BrIuLwiDgi\nImYDj5J1CuYD/JsAB6CciFhONp/QX+aS92FHYG+sv581Oc15uKN6IgKIiH8DPgmck5poXthnk1LJ\nOnObe3PvAS5uSPsmcAFwRPpIPAP4LVlvuu1sAdk8Qp8G/i/wLHAu2R/RG3P19xRN6i8ibs1vTn1x\ne84LdRIR90q6l6wp6w5cX7vjRQ1t7t+NiAu6XKa2+UtMZmYV5GYZM7MKcnA3M6sgB3czswpycDcz\nqyAHdzOzCnJwNzOrIAd3M7MKcnA3M6ug/w/84k7rwsFrywAAAABJRU5ErkJggg==\n"
                    }, 
                    "metadata": {}, 
                    "output_type": "display_data"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": null, 
            "metadata": {}, 
            "source": "#GBM.. mean.. and skews upwards.. of 90+ for accuracy", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": null, 
            "metadata": {}, 
            "source": "#SVM still better.. low complexity.. and just a standaridization", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": 39, 
            "metadata": {}, 
            "source": "# prepare the model\nscaler = StandardScaler().fit(X_train)\nrescaledX = scaler.transform(X_train)\nmodel = SVC(C=1.5)\nmodel.fit(rescaledX, Y_train)\n# estimate accuracy on validation dataset\nrescaledValidationX = scaler.transform(X_validation)\npredictions = model.predict(rescaledValidationX)\nprint(accuracy_score(Y_validation, predictions))\nprint(confusion_matrix(Y_validation, predictions))\nprint(classification_report(Y_validation, predictions))", 
            "outputs": [
                {
                    "name": "stdout", 
                    "output_type": "stream", 
                    "text": "0.857142857143\n[[23  4]\n [ 2 13]]\n             precision    recall  f1-score   support\n\n          M       0.92      0.85      0.88        27\n          R       0.76      0.87      0.81        15\n\navg / total       0.86      0.86      0.86        42\n\n"
                }
            ], 
            "cell_type": "code"
        }, 
        {
            "execution_count": null, 
            "metadata": {}, 
            "source": "", 
            "outputs": [], 
            "cell_type": "code"
        }, 
        {
            "execution_count": null, 
            "metadata": {}, 
            "source": "", 
            "outputs": [], 
            "cell_type": "code"
        }
    ], 
    "nbformat_minor": 1
}