DiabetesPrediction.ipynb - machinelearning

DiabetesPrediction.ipynb (217299B)
      1 {
      2  "cells": [
      3   {
      4    "cell_type": "markdown",
      5    "metadata": {},
      6    "source": [
      7     "https://www.kaggle.com/datasets/ayatollahabdelhamed/diabetes"
      8    ]
      9   },
     10   {
     11    "cell_type": "markdown",
     12    "metadata": {},
     13    "source": [
     14     "Logistic regression was almost 80% accurate followed closely at 77% by a neural network. Adaboost, Random Forest, and Polynomial feature logistic regression were in the low 70s."
     15    ]
     16   },
     17   {
     18    "cell_type": "code",
     19    "execution_count": 374,
     20    "metadata": {},
     21    "outputs": [],
     22    "source": [
     23     "import pandas as pd\n",
     24     "import numpy as np \n",
     25     "\n",
     26     "df = pd.read_csv('../datasets/diabetes/diabetes.csv')"
     27    ]
     28   },
     29   {
     30    "cell_type": "markdown",
     31    "metadata": {},
     32    "source": [
     33     "Exploration"
     34    ]
     35   },
     36   {
     37    "cell_type": "code",
     38    "execution_count": 375,
     39    "metadata": {},
     40    "outputs": [
     41     {
     42      "data": {
     43       "text/html": [
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     53        "\n",
     54        "    .dataframe thead th {\n",
     55        "        text-align: right;\n",
     56        "    }\n",
     57        "</style>\n",
     58        "<table border=\"1\" class=\"dataframe\">\n",
     59        "  <thead>\n",
     60        "    <tr style=\"text-align: right;\">\n",
     61        "      <th></th>\n",
     62        "      <th>Pregnancies</th>\n",
     63        "      <th>Glucose</th>\n",
     64        "      <th>BloodPressure</th>\n",
     65        "      <th>SkinThickness</th>\n",
     66        "      <th>Insulin</th>\n",
     67        "      <th>BMI</th>\n",
     68        "      <th>DiabetesPedigreeFunction</th>\n",
     69        "      <th>Age</th>\n",
     70        "      <th>Outcome</th>\n",
     71        "    </tr>\n",
     72        "  </thead>\n",
     73        "  <tbody>\n",
     74        "    <tr>\n",
     75        "      <th>Pregnancies</th>\n",
     76        "      <td>1.000000</td>\n",
     77        "      <td>0.129459</td>\n",
     78        "      <td>0.141282</td>\n",
     79        "      <td>-0.081672</td>\n",
     80        "      <td>-0.073535</td>\n",
     81        "      <td>0.017683</td>\n",
     82        "      <td>-0.033523</td>\n",
     83        "      <td>0.544341</td>\n",
     84        "      <td>0.221898</td>\n",
     85        "    </tr>\n",
     86        "    <tr>\n",
     87        "      <th>Glucose</th>\n",
     88        "      <td>0.129459</td>\n",
     89        "      <td>1.000000</td>\n",
     90        "      <td>0.152590</td>\n",
     91        "      <td>0.057328</td>\n",
     92        "      <td>0.331357</td>\n",
     93        "      <td>0.221071</td>\n",
     94        "      <td>0.137337</td>\n",
     95        "      <td>0.263514</td>\n",
     96        "      <td>0.466581</td>\n",
     97        "    </tr>\n",
     98        "    <tr>\n",
     99        "      <th>BloodPressure</th>\n",
    100        "      <td>0.141282</td>\n",
    101        "      <td>0.152590</td>\n",
    102        "      <td>1.000000</td>\n",
    103        "      <td>0.207371</td>\n",
    104        "      <td>0.088933</td>\n",
    105        "      <td>0.281805</td>\n",
    106        "      <td>0.041265</td>\n",
    107        "      <td>0.239528</td>\n",
    108        "      <td>0.065068</td>\n",
    109        "    </tr>\n",
    110        "    <tr>\n",
    111        "      <th>SkinThickness</th>\n",
    112        "      <td>-0.081672</td>\n",
    113        "      <td>0.057328</td>\n",
    114        "      <td>0.207371</td>\n",
    115        "      <td>1.000000</td>\n",
    116        "      <td>0.436783</td>\n",
    117        "      <td>0.392573</td>\n",
    118        "      <td>0.183928</td>\n",
    119        "      <td>-0.113970</td>\n",
    120        "      <td>0.074752</td>\n",
    121        "    </tr>\n",
    122        "    <tr>\n",
    123        "      <th>Insulin</th>\n",
    124        "      <td>-0.073535</td>\n",
    125        "      <td>0.331357</td>\n",
    126        "      <td>0.088933</td>\n",
    127        "      <td>0.436783</td>\n",
    128        "      <td>1.000000</td>\n",
    129        "      <td>0.197859</td>\n",
    130        "      <td>0.185071</td>\n",
    131        "      <td>-0.042163</td>\n",
    132        "      <td>0.130548</td>\n",
    133        "    </tr>\n",
    134        "    <tr>\n",
    135        "      <th>BMI</th>\n",
    136        "      <td>0.017683</td>\n",
    137        "      <td>0.221071</td>\n",
    138        "      <td>0.281805</td>\n",
    139        "      <td>0.392573</td>\n",
    140        "      <td>0.197859</td>\n",
    141        "      <td>1.000000</td>\n",
    142        "      <td>0.140647</td>\n",
    143        "      <td>0.036242</td>\n",
    144        "      <td>0.292695</td>\n",
    145        "    </tr>\n",
    146        "    <tr>\n",
    147        "      <th>DiabetesPedigreeFunction</th>\n",
    148        "      <td>-0.033523</td>\n",
    149        "      <td>0.137337</td>\n",
    150        "      <td>0.041265</td>\n",
    151        "      <td>0.183928</td>\n",
    152        "      <td>0.185071</td>\n",
    153        "      <td>0.140647</td>\n",
    154        "      <td>1.000000</td>\n",
    155        "      <td>0.033561</td>\n",
    156        "      <td>0.173844</td>\n",
    157        "    </tr>\n",
    158        "    <tr>\n",
    159        "      <th>Age</th>\n",
    160        "      <td>0.544341</td>\n",
    161        "      <td>0.263514</td>\n",
    162        "      <td>0.239528</td>\n",
    163        "      <td>-0.113970</td>\n",
    164        "      <td>-0.042163</td>\n",
    165        "      <td>0.036242</td>\n",
    166        "      <td>0.033561</td>\n",
    167        "      <td>1.000000</td>\n",
    168        "      <td>0.238356</td>\n",
    169        "    </tr>\n",
    170        "    <tr>\n",
    171        "      <th>Outcome</th>\n",
    172        "      <td>0.221898</td>\n",
    173        "      <td>0.466581</td>\n",
    174        "      <td>0.065068</td>\n",
    175        "      <td>0.074752</td>\n",
    176        "      <td>0.130548</td>\n",
    177        "      <td>0.292695</td>\n",
    178        "      <td>0.173844</td>\n",
    179        "      <td>0.238356</td>\n",
    180        "      <td>1.000000</td>\n",
    181        "    </tr>\n",
    182        "  </tbody>\n",
    183        "</table>\n",
    184        "</div>"
    185       ],
    186       "text/plain": [
    187        "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
    188        "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
    189        "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
    190        "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
    191        "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
    192        "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
    193        "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
    194        "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
    195        "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
    196        "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
    197        "\n",
    198        "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
    199        "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
    200        "Glucose                   0.331357  0.221071                  0.137337   \n",
    201        "BloodPressure             0.088933  0.281805                  0.041265   \n",
    202        "SkinThickness             0.436783  0.392573                  0.183928   \n",
    203        "Insulin                   1.000000  0.197859                  0.185071   \n",
    204        "BMI                       0.197859  1.000000                  0.140647   \n",
    205        "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
    206        "Age                      -0.042163  0.036242                  0.033561   \n",
    207        "Outcome                   0.130548  0.292695                  0.173844   \n",
    208        "\n",
    209        "                               Age   Outcome  \n",
    210        "Pregnancies               0.544341  0.221898  \n",
    211        "Glucose                   0.263514  0.466581  \n",
    212        "BloodPressure             0.239528  0.065068  \n",
    213        "SkinThickness            -0.113970  0.074752  \n",
    214        "Insulin                  -0.042163  0.130548  \n",
    215        "BMI                       0.036242  0.292695  \n",
    216        "DiabetesPedigreeFunction  0.033561  0.173844  \n",
    217        "Age                       1.000000  0.238356  \n",
    218        "Outcome                   0.238356  1.000000  "
    219       ]
    220      },
    221      "execution_count": 375,
    222      "metadata": {},
    223      "output_type": "execute_result"
    224     }
    225    ],
    226    "source": [
    227     "df.corr()"
    228    ]
    229   },
    230   {
    231    "cell_type": "code",
    232    "execution_count": 376,
    233    "metadata": {},
    234    "outputs": [
    235     {
    236      "data": {
    237       "text/html": [
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    252        "<table border=\"1\" class=\"dataframe\">\n",
    253        "  <thead>\n",
    254        "    <tr style=\"text-align: right;\">\n",
    255        "      <th></th>\n",
    256        "      <th>Pregnancies</th>\n",
    257        "      <th>Glucose</th>\n",
    258        "      <th>BloodPressure</th>\n",
    259        "      <th>SkinThickness</th>\n",
    260        "      <th>Insulin</th>\n",
    261        "      <th>BMI</th>\n",
    262        "      <th>DiabetesPedigreeFunction</th>\n",
    263        "      <th>Age</th>\n",
    264        "      <th>Outcome</th>\n",
    265        "    </tr>\n",
    266        "  </thead>\n",
    267        "  <tbody>\n",
    268        "    <tr>\n",
    269        "      <th>count</th>\n",
    270        "      <td>768.000000</td>\n",
    271        "      <td>768.000000</td>\n",
    272        "      <td>768.000000</td>\n",
    273        "      <td>768.000000</td>\n",
    274        "      <td>768.000000</td>\n",
    275        "      <td>768.000000</td>\n",
    276        "      <td>768.000000</td>\n",
    277        "      <td>768.000000</td>\n",
    278        "      <td>768.000000</td>\n",
    279        "    </tr>\n",
    280        "    <tr>\n",
    281        "      <th>mean</th>\n",
    282        "      <td>3.845052</td>\n",
    283        "      <td>120.894531</td>\n",
    284        "      <td>69.105469</td>\n",
    285        "      <td>20.536458</td>\n",
    286        "      <td>79.799479</td>\n",
    287        "      <td>31.992578</td>\n",
    288        "      <td>0.471876</td>\n",
    289        "      <td>33.240885</td>\n",
    290        "      <td>0.348958</td>\n",
    291        "    </tr>\n",
    292        "    <tr>\n",
    293        "      <th>std</th>\n",
    294        "      <td>3.369578</td>\n",
    295        "      <td>31.972618</td>\n",
    296        "      <td>19.355807</td>\n",
    297        "      <td>15.952218</td>\n",
    298        "      <td>115.244002</td>\n",
    299        "      <td>7.884160</td>\n",
    300        "      <td>0.331329</td>\n",
    301        "      <td>11.760232</td>\n",
    302        "      <td>0.476951</td>\n",
    303        "    </tr>\n",
    304        "    <tr>\n",
    305        "      <th>min</th>\n",
    306        "      <td>0.000000</td>\n",
    307        "      <td>0.000000</td>\n",
    308        "      <td>0.000000</td>\n",
    309        "      <td>0.000000</td>\n",
    310        "      <td>0.000000</td>\n",
    311        "      <td>0.000000</td>\n",
    312        "      <td>0.078000</td>\n",
    313        "      <td>21.000000</td>\n",
    314        "      <td>0.000000</td>\n",
    315        "    </tr>\n",
    316        "    <tr>\n",
    317        "      <th>25%</th>\n",
    318        "      <td>1.000000</td>\n",
    319        "      <td>99.000000</td>\n",
    320        "      <td>62.000000</td>\n",
    321        "      <td>0.000000</td>\n",
    322        "      <td>0.000000</td>\n",
    323        "      <td>27.300000</td>\n",
    324        "      <td>0.243750</td>\n",
    325        "      <td>24.000000</td>\n",
    326        "      <td>0.000000</td>\n",
    327        "    </tr>\n",
    328        "    <tr>\n",
    329        "      <th>50%</th>\n",
    330        "      <td>3.000000</td>\n",
    331        "      <td>117.000000</td>\n",
    332        "      <td>72.000000</td>\n",
    333        "      <td>23.000000</td>\n",
    334        "      <td>30.500000</td>\n",
    335        "      <td>32.000000</td>\n",
    336        "      <td>0.372500</td>\n",
    337        "      <td>29.000000</td>\n",
    338        "      <td>0.000000</td>\n",
    339        "    </tr>\n",
    340        "    <tr>\n",
    341        "      <th>75%</th>\n",
    342        "      <td>6.000000</td>\n",
    343        "      <td>140.250000</td>\n",
    344        "      <td>80.000000</td>\n",
    345        "      <td>32.000000</td>\n",
    346        "      <td>127.250000</td>\n",
    347        "      <td>36.600000</td>\n",
    348        "      <td>0.626250</td>\n",
    349        "      <td>41.000000</td>\n",
    350        "      <td>1.000000</td>\n",
    351        "    </tr>\n",
    352        "    <tr>\n",
    353        "      <th>max</th>\n",
    354        "      <td>17.000000</td>\n",
    355        "      <td>199.000000</td>\n",
    356        "      <td>122.000000</td>\n",
    357        "      <td>99.000000</td>\n",
    358        "      <td>846.000000</td>\n",
    359        "      <td>67.100000</td>\n",
    360        "      <td>2.420000</td>\n",
    361        "      <td>81.000000</td>\n",
    362        "      <td>1.000000</td>\n",
    363        "    </tr>\n",
    364        "  </tbody>\n",
    365        "</table>\n",
    366        "</div>"
    367       ],
    368       "text/plain": [
    369        "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
    370        "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
    371        "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
    372        "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
    373        "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
    374        "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
    375        "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
    376        "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
    377        "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
    378        "\n",
    379        "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
    380        "count  768.000000                768.000000  768.000000  768.000000  \n",
    381        "mean    31.992578                  0.471876   33.240885    0.348958  \n",
    382        "std      7.884160                  0.331329   11.760232    0.476951  \n",
    383        "min      0.000000                  0.078000   21.000000    0.000000  \n",
    384        "25%     27.300000                  0.243750   24.000000    0.000000  \n",
    385        "50%     32.000000                  0.372500   29.000000    0.000000  \n",
    386        "75%     36.600000                  0.626250   41.000000    1.000000  \n",
    387        "max     67.100000                  2.420000   81.000000    1.000000  "
    388       ]
    389      },
    390      "execution_count": 376,
    391      "metadata": {},
    392      "output_type": "execute_result"
    393     }
    394    ],
    395    "source": [
    396     "df.describe()"
    397    ]
    398   },
    399   {
    400    "cell_type": "code",
    401    "execution_count": 377,
    402    "metadata": {},
    403    "outputs": [
    404     {
    405      "data": {
    406       "text/plain": [
    407        "array([[<Axes: title={'center': 'Pregnancies'}>,\n",
    408        "        <Axes: title={'center': 'Glucose'}>,\n",
    409        "        <Axes: title={'center': 'BloodPressure'}>],\n",
    410        "       [<Axes: title={'center': 'SkinThickness'}>,\n",
    411        "        <Axes: title={'center': 'Insulin'}>,\n",
    412        "        <Axes: title={'center': 'BMI'}>],\n",
    413        "       [<Axes: title={'center': 'DiabetesPedigreeFunction'}>,\n",
    414        "        <Axes: title={'center': 'Age'}>,\n",
    415        "        <Axes: title={'center': 'Outcome'}>]], dtype=object)"
    416       ]
    417      },
    418      "execution_count": 377,
    419      "metadata": {},
    420      "output_type": "execute_result"
    421     },
    422     {
    423      "data": {
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",
    425       "text/plain": [
    426        "<Figure size 640x480 with 9 Axes>"
    427       ]
    428      },
    429      "metadata": {},
    430      "output_type": "display_data"
    431     }
    432    ],
    433    "source": [
    434     "df.hist()"
    435    ]
    436   },
    437   {
    438    "cell_type": "markdown",
    439    "metadata": {},
    440    "source": [
    441     "Preprocessing"
    442    ]
    443   },
    444   {
    445    "cell_type": "code",
    446    "execution_count": 378,
    447    "metadata": {},
    448    "outputs": [
    449     {
    450      "data": {
    451       "text/plain": [
    452        "array([[<Axes: title={'center': 'Pregnancies'}>,\n",
    453        "        <Axes: title={'center': 'Glucose'}>,\n",
    454        "        <Axes: title={'center': 'BloodPressure'}>],\n",
    455        "       [<Axes: title={'center': 'SkinThickness'}>,\n",
    456        "        <Axes: title={'center': 'Insulin'}>,\n",
    457        "        <Axes: title={'center': 'BMI'}>],\n",
    458        "       [<Axes: title={'center': 'DiabetesPedigreeFunction'}>,\n",
    459        "        <Axes: title={'center': 'Age'}>,\n",
    460        "        <Axes: title={'center': 'Outcome'}>]], dtype=object)"
    461       ]
    462      },
    463      "execution_count": 378,
    464      "metadata": {},
    465      "output_type": "execute_result"
    466     },
    467     {
    468      "data": {
    469       "image/png": 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    470       "text/plain": [
    471        "<Figure size 640x480 with 9 Axes>"
    472       ]
    473      },
    474      "metadata": {},
    475      "output_type": "display_data"
    476     }
    477    ],
    478    "source": [
    479     "df['DiabetesPedigreeFunction'] = np.log(df['DiabetesPedigreeFunction'])\n",
    480     "df['Age'] = np.log2(df['Age'])\n",
    481     "df['Age'] = np.log2(df['Age'])\n",
    482     "df['Insulin'] = df['Insulin'] + 50\n",
    483     "df['Insulin'] = np.log(df['Insulin'])\n",
    484     "df['Pregnancies'] = df['Pregnancies'] + 1\n",
    485     "df['Pregnancies'] = np.log(df['Pregnancies'])\n",
    486     "df.hist()"
    487    ]
    488   },
    489   {
    490    "cell_type": "code",
    491    "execution_count": 379,
    492    "metadata": {},
    493    "outputs": [],
    494    "source": [
    495     "from sklearn.preprocessing import StandardScaler\n",
    496     "std_scl = StandardScaler()\n",
    497     "\n",
    498     "y = df['Outcome']\n",
    499     "\n",
    500     "df = df.drop('Outcome' , axis=1)\n",
    501     "df = std_scl.fit_transform(df)\n",
    502     "\n",
    503     "df = pd.DataFrame(data=df, columns=std_scl.feature_names_in_)"
    504    ]
    505   },
    506   {
    507    "cell_type": "code",
    508    "execution_count": 380,
    509    "metadata": {},
    510    "outputs": [],
    511    "source": [
    512     "X = df"
    513    ]
    514   },
    515   {
    516    "cell_type": "code",
    517    "execution_count": 381,
    518    "metadata": {},
    519    "outputs": [],
    520    "source": [
    521     "from sklearn.model_selection import train_test_split\n",
    522     "X_train, X_test, y_train, y_test = train_test_split(X,y)\n",
    523     "X_test, X_val, y_test, y_val = train_test_split(X_test,y_test, test_size=.5)"
    524    ]
    525   },
    526   {
    527    "cell_type": "code",
    528    "execution_count": 382,
    529    "metadata": {},
    530    "outputs": [
    531     {
    532      "data": {
    533       "application/vnd.plotly.v1+json": {
    534        "config": {
    535         "plotlyServerURL": "https://plot.ly"
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    540          "legendgroup": "",
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   4488            "aaxis": {
   4489             "gridcolor": "white",
   4490             "linecolor": "white",
   4491             "ticks": ""
   4492            },
   4493            "baxis": {
   4494             "gridcolor": "white",
   4495             "linecolor": "white",
   4496             "ticks": ""
   4497            },
   4498            "bgcolor": "#E5ECF6",
   4499            "caxis": {
   4500             "gridcolor": "white",
   4501             "linecolor": "white",
   4502             "ticks": ""
   4503            }
   4504           },
   4505           "title": {
   4506            "x": 0.05
   4507           },
   4508           "xaxis": {
   4509            "automargin": true,
   4510            "gridcolor": "white",
   4511            "linecolor": "white",
   4512            "ticks": "",
   4513            "title": {
   4514             "standoff": 15
   4515            },
   4516            "zerolinecolor": "white",
   4517            "zerolinewidth": 2
   4518           },
   4519           "yaxis": {
   4520            "automargin": true,
   4521            "gridcolor": "white",
   4522            "linecolor": "white",
   4523            "ticks": "",
   4524            "title": {
   4525             "standoff": 15
   4526            },
   4527            "zerolinecolor": "white",
   4528            "zerolinewidth": 2
   4529           }
   4530          }
   4531         }
   4532        }
   4533       }
   4534      },
   4535      "metadata": {},
   4536      "output_type": "display_data"
   4537     }
   4538    ],
   4539    "source": [
   4540     "from sklearn.manifold import TSNE\n",
   4541     "import plotly.express as px\n",
   4542     "\n",
   4543     "tsne = TSNE(n_components=3)\n",
   4544     "tsneOut = tsne.fit_transform(X=X)\n",
   4545     "tsneOut.shape\n",
   4546     "\n",
   4547     "fig = px.scatter_3d(x=tsneOut[:,0], y=tsneOut[:,1], z=tsneOut[:,2], color=y)\n",
   4548     "fig.update_traces(marker=dict(size=2))"
   4549    ]
   4550   },
   4551   {
   4552    "cell_type": "markdown",
   4553    "metadata": {},
   4554    "source": [
   4555     "Start Model Training\n",
   4556     "\n",
   4557     "1. Logistic Regression\n",
   4558     "2. AdaBoost\n",
   4559     "3. Random Forest\n",
   4560     "4. Neural Network\n",
   4561     "5. Polynomial Feature Logistic Regression"
   4562    ]
   4563   },
   4564   {
   4565    "cell_type": "code",
   4566    "execution_count": 383,
   4567    "metadata": {},
   4568    "outputs": [
   4569     {
   4570      "name": "stdout",
   4571      "output_type": "stream",
   4572      "text": [
   4573       "0.7708333333333334 0.7916666666666666\n",
   4574       "0.8090277777777778 0.71875\n",
   4575       "0.8263888888888888 0.7604166666666666\n"
   4576      ]
   4577     }
   4578    ],
   4579    "source": [
   4580     "from sklearn.linear_model import LogisticRegression\n",
   4581     "from sklearn.ensemble import RandomForestClassifier\n",
   4582     "from sklearn.ensemble import AdaBoostClassifier\n",
   4583     "\n",
   4584     "models = [\n",
   4585     "    LogisticRegression(),\n",
   4586     "    AdaBoostClassifier(n_estimators=40, algorithm='SAMME'),\n",
   4587     "    RandomForestClassifier(max_depth=4, n_jobs=-1, max_features=7, n_estimators=100),\n",
   4588     "]\n",
   4589     "\n",
   4590     "for model in models:\n",
   4591     "    model.fit(X=X_train,y=y_train)\n",
   4592     "    print(model.score(X_train,y_train), model.score(X_val,y_val))\n"
   4593    ]
   4594   },
   4595   {
   4596    "cell_type": "code",
   4597    "execution_count": 384,
   4598    "metadata": {},
   4599    "outputs": [],
   4600    "source": [
   4601     "import keras\n",
   4602     "\n",
   4603     "model = keras.Sequential(\n",
   4604     "    layers=[\n",
   4605     "        keras.layers.Input(shape=(8,)),\n",
   4606     "        keras.layers.Dense(32, activation='relu'),\n",
   4607     "        keras.layers.Dense(64, activation='relu'),\n",
   4608     "        keras.layers.BatchNormalization(),\n",
   4609     "        keras.layers.Dense(64, activation='relu'),\n",
   4610     "        keras.layers.Dense(32, activation='relu'),\n",
   4611     "        keras.layers.Dropout(.5),\n",
   4612     "        keras.layers.Dense(1, activation='sigmoid')\n",
   4613     "    ]\n",
   4614     ")"
   4615    ]
   4616   },
   4617   {
   4618    "cell_type": "code",
   4619    "execution_count": 385,
   4620    "metadata": {},
   4621    "outputs": [],
   4622    "source": [
   4623     "model.compile(loss=keras.losses.binary_crossentropy, optimizer='adam', metrics=['accuracy'])"
   4624    ]
   4625   },
   4626   {
   4627    "cell_type": "code",
   4628    "execution_count": 386,
   4629    "metadata": {},
   4630    "outputs": [
   4631     {
   4632      "name": "stdout",
   4633      "output_type": "stream",
   4634      "text": [
   4635       "Epoch 1/13\n",
   4636       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.5114 - loss: 0.7966 - val_accuracy: 0.6771 - val_loss: 0.6707\n",
   4637       "Epoch 2/13\n",
   4638       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6403 - loss: 0.6296 - val_accuracy: 0.7396 - val_loss: 0.6437\n",
   4639       "Epoch 3/13\n",
   4640       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7108 - loss: 0.5425 - val_accuracy: 0.7292 - val_loss: 0.6204\n",
   4641       "Epoch 4/13\n",
   4642       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7841 - loss: 0.4895 - val_accuracy: 0.7396 - val_loss: 0.6035\n",
   4643       "Epoch 5/13\n",
   4644       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7335 - loss: 0.5056 - val_accuracy: 0.7500 - val_loss: 0.5940\n",
   4645       "Epoch 6/13\n",
   4646       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7647 - loss: 0.4612 - val_accuracy: 0.7812 - val_loss: 0.5836\n",
   4647       "Epoch 7/13\n",
   4648       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7974 - loss: 0.4371 - val_accuracy: 0.7917 - val_loss: 0.5755\n",
   4649       "Epoch 8/13\n",
   4650       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7866 - loss: 0.4563 - val_accuracy: 0.7708 - val_loss: 0.5627\n",
   4651       "Epoch 9/13\n",
   4652       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8228 - loss: 0.4207 - val_accuracy: 0.7812 - val_loss: 0.5584\n",
   4653       "Epoch 10/13\n",
   4654       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8226 - loss: 0.3962 - val_accuracy: 0.7812 - val_loss: 0.5629\n",
   4655       "Epoch 11/13\n",
   4656       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8265 - loss: 0.4075 - val_accuracy: 0.7708 - val_loss: 0.5577\n",
   4657       "Epoch 12/13\n",
   4658       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8353 - loss: 0.3923 - val_accuracy: 0.7396 - val_loss: 0.5593\n",
   4659       "Epoch 13/13\n",
   4660       "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8152 - loss: 0.4005 - val_accuracy: 0.7500 - val_loss: 0.5643\n"
   4661      ]
   4662     },
   4663     {
   4664      "data": {
   4665       "text/plain": [
   4666        "<keras.src.callbacks.history.History at 0x7f55979ff350>"
   4667       ]
   4668      },
   4669      "execution_count": 386,
   4670      "metadata": {},
   4671      "output_type": "execute_result"
   4672     }
   4673    ],
   4674    "source": [
   4675     "model.fit(X_train,y_train, validation_data=(X_val,y_val), epochs=13, batch_size=32)"
   4676    ]
   4677   },
   4678   {
   4679    "cell_type": "markdown",
   4680    "metadata": {},
   4681    "source": [
   4682     "Accuracy Metrics"
   4683    ]
   4684   },
   4685   {
   4686    "cell_type": "code",
   4687    "execution_count": 393,
   4688    "metadata": {},
   4689    "outputs": [
   4690     {
   4691      "name": "stdout",
   4692      "output_type": "stream",
   4693      "text": [
   4694       "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n"
   4695      ]
   4696     },
   4697     {
   4698      "data": {
   4699       "text/plain": [
   4700        "['LogisticRegression: 0.7916666666666666',\n",
   4701        " 'AdaBoostClassifier: 0.7083333333333334',\n",
   4702        " 'RandomForestClassifier: 0.75',\n",
   4703        " 'Neural Network: 0.7708333333333334']"
   4704       ]
   4705      },
   4706      "execution_count": 393,
   4707      "metadata": {},
   4708      "output_type": "execute_result"
   4709     }
   4710    ],
   4711    "source": [
   4712     "from sklearn.metrics import accuracy_score\n",
   4713     "accuracies = []\n",
   4714     "\n",
   4715     "for m in models:\n",
   4716     "    y_pred = m.predict(X_test)\n",
   4717     "    accuracies.append(m.__class__.__name__ + \": \" + str(accuracy_score(y_true=y_test,y_pred=y_pred)))\n",
   4718     "\n",
   4719     "y_pred = np.round(model.predict(X_test))\n",
   4720     "\n",
   4721     "accuracies.append(\"Neural Network: \" + str(accuracy_score(y_true=y_test,y_pred=y_pred)))\n",
   4722     "accuracies"
   4723    ]
   4724   },
   4725   {
   4726    "cell_type": "code",
   4727    "execution_count": 414,
   4728    "metadata": {},
   4729    "outputs": [],
   4730    "source": [
   4731     "from sklearn.preprocessing import PolynomialFeatures\n",
   4732     "\n",
   4733     "poly = PolynomialFeatures(degree=2)\n",
   4734     "X_train_poly = poly.fit_transform(X_train)\n",
   4735     "X_val_poly = poly.transform(X_val)\n",
   4736     "X_test_poly = poly.transform(X_test)\n",
   4737     "\n",
   4738     "\n",
   4739     "log_reg = LogisticRegression()\n",
   4740     "log_reg = log_reg.fit(X_train_poly,y_train)"
   4741    ]
   4742   },
   4743   {
   4744    "cell_type": "code",
   4745    "execution_count": 415,
   4746    "metadata": {},
   4747    "outputs": [
   4748     {
   4749      "data": {
   4750       "text/plain": [
   4751        "0.7604166666666666"
   4752       ]
   4753      },
   4754      "execution_count": 415,
   4755      "metadata": {},
   4756      "output_type": "execute_result"
   4757     }
   4758    ],
   4759    "source": [
   4760     "y_pred = log_reg.predict(X_test_poly)\n",
   4761     "\n",
   4762     "accuracy_score(y_true=y_test,y_pred=y_pred)"
   4763    ]
   4764   }
   4765  ],
   4766  "metadata": {
   4767   "kernelspec": {
   4768    "display_name": ".venv",
   4769    "language": "python",
   4770    "name": "python3"
   4771   },
   4772   "language_info": {
   4773    "codemirror_mode": {
   4774     "name": "ipython",
   4775     "version": 3
   4776    },
   4777    "file_extension": ".py",
   4778    "mimetype": "text/x-python",
   4779    "name": "python",
   4780    "nbconvert_exporter": "python",
   4781    "pygments_lexer": "ipython3",
   4782    "version": "3.11.2"
   4783   }
   4784  },
   4785  "nbformat": 4,
   4786  "nbformat_minor": 2
   4787 }
	machinelearning Machine learning code
	git clone git://git.laack.co/machinelearning.git
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