dermatology.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Author : Amir Shokri\n",
    "# github link : https://github.com/amirshnll/dermatology\n",
    "# dataset link : http://archive.ics.uci.edu/ml/datasets/Dermatology\n",
    "# email : amirsh.nll@gmail.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dermatology_data.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.replace(to_replace=\"?\", method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>34</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.068306</td>\n",
       "      <td>1.795082</td>\n",
       "      <td>1.549180</td>\n",
       "      <td>1.366120</td>\n",
       "      <td>0.633880</td>\n",
       "      <td>0.448087</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.377049</td>\n",
       "      <td>0.614754</td>\n",
       "      <td>0.519126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.393443</td>\n",
       "      <td>0.464481</td>\n",
       "      <td>0.456284</td>\n",
       "      <td>0.953552</td>\n",
       "      <td>0.453552</td>\n",
       "      <td>0.103825</td>\n",
       "      <td>0.114754</td>\n",
       "      <td>1.866120</td>\n",
       "      <td>0.554645</td>\n",
       "      <td>2.803279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.664753</td>\n",
       "      <td>0.701527</td>\n",
       "      <td>0.907525</td>\n",
       "      <td>1.138299</td>\n",
       "      <td>0.908016</td>\n",
       "      <td>0.957327</td>\n",
       "      <td>0.570588</td>\n",
       "      <td>0.834147</td>\n",
       "      <td>0.982979</td>\n",
       "      <td>0.905639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.849406</td>\n",
       "      <td>0.864899</td>\n",
       "      <td>0.954873</td>\n",
       "      <td>1.130172</td>\n",
       "      <td>0.954744</td>\n",
       "      <td>0.450433</td>\n",
       "      <td>0.488723</td>\n",
       "      <td>0.726108</td>\n",
       "      <td>1.105908</td>\n",
       "      <td>1.597803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0           1           2           3           4           5   \\\n",
       "count  366.000000  366.000000  366.000000  366.000000  366.000000  366.000000   \n",
       "mean     2.068306    1.795082    1.549180    1.366120    0.633880    0.448087   \n",
       "std      0.664753    0.701527    0.907525    1.138299    0.908016    0.957327   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      2.000000    1.000000    1.000000    0.000000    0.000000    0.000000   \n",
       "50%      2.000000    2.000000    2.000000    1.000000    0.000000    0.000000   \n",
       "75%      2.000000    2.000000    2.000000    2.000000    1.000000    0.000000   \n",
       "max      3.000000    3.000000    3.000000    3.000000    3.000000    3.000000   \n",
       "\n",
       "               6           7           8           9   ...          24  \\\n",
       "count  366.000000  366.000000  366.000000  366.000000  ...  366.000000   \n",
       "mean     0.166667    0.377049    0.614754    0.519126  ...    0.393443   \n",
       "std      0.570588    0.834147    0.982979    0.905639  ...    0.849406   \n",
       "min      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
       "75%      0.000000    0.000000    1.000000    1.000000  ...    0.000000   \n",
       "max      3.000000    3.000000    3.000000    3.000000  ...    3.000000   \n",
       "\n",
       "               25          26          27          28          29          30  \\\n",
       "count  366.000000  366.000000  366.000000  366.000000  366.000000  366.000000   \n",
       "mean     0.464481    0.456284    0.953552    0.453552    0.103825    0.114754   \n",
       "std      0.864899    0.954873    1.130172    0.954744    0.450433    0.488723   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%      1.000000    0.000000    2.000000    0.000000    0.000000    0.000000   \n",
       "max      3.000000    3.000000    3.000000    3.000000    3.000000    3.000000   \n",
       "\n",
       "               31          32          34  \n",
       "count  366.000000  366.000000  366.000000  \n",
       "mean     1.866120    0.554645    2.803279  \n",
       "std      0.726108    1.105908    1.597803  \n",
       "min      0.000000    0.000000    1.000000  \n",
       "25%      1.000000    0.000000    1.000000  \n",
       "50%      2.000000    0.000000    3.000000  \n",
       "75%      2.000000    0.000000    4.000000  \n",
       "max      3.000000    3.000000    6.000000  \n",
       "\n",
       "[8 rows x 34 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "properties = data[data.columns[:34]]\n",
    "target = data[data.columns[34]]\n",
    "scaler = MinMaxScaler()\n",
    "scaled_x = scaler.fit_transform(properties)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23b944ca7f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target.value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=15)\n",
    "reduced_x = pca.fit_transform(scaled_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(reduced_x, target, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(100, 100))\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "dt = DecisionTreeClassifier()\n",
    "regressor = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Amirshnll\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "gnb.fit(X_train, y_train)\n",
    "y_predgnb = gnb.predict(X_test)\n",
    "\n",
    "mlp.fit(X_train, y_train)\n",
    "y_predmlp = mlp.predict(X_test)\n",
    "\n",
    "knn.fit(X_train, y_train)\n",
    "y_predknn = knn.predict(X_test)\n",
    "\n",
    "dt.fit(X_train, y_train)\n",
    "y_preddt = dt.predict(X_test)\n",
    "\n",
    "regressor.fit(X_train, y_train)\n",
    "y_predregressor = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gnb f1:  0.9727272727272728\n",
      "gnb accuracy:  0.9727272727272728\n",
      "mlp f1:  1.0\n",
      "mlp accuracy:  1.0\n",
      "knn f1:  0.9727272727272728\n",
      "knn accuracy:  0.9818181818181818\n",
      "decision tree f1:  0.9727272727272728\n",
      "decision tree accuracy:  0.9090909090909091\n",
      "logistic regression f1:  0.9727272727272728\n",
      "logistic regression accuracy:  0.990909090909091\n"
     ]
    }
   ],
   "source": [
    "print('gnb f1: ', f1_score(y_test, y_predgnb, average='micro'))\n",
    "print('gnb accuracy: ', accuracy_score(y_test, y_predgnb))\n",
    "\n",
    "print('mlp f1: ', f1_score(y_test, y_predmlp, average='micro'))\n",
    "print('mlp accuracy: ', accuracy_score(y_test, y_predmlp))\n",
    "\n",
    "print('knn f1: ', f1_score(y_test, y_predgnb, average='micro'))\n",
    "print('knn accuracy: ', accuracy_score(y_test, y_predknn))\n",
    "\n",
    "print('decision tree f1: ', f1_score(y_test, y_predgnb, average='micro'))\n",
    "print('decision tree accuracy: ', accuracy_score(y_test, y_preddt))\n",
    "\n",
    "print('logistic regression f1: ', f1_score(y_test, y_predgnb, average='micro'))\n",
    "print('logistic regression accuracy: ', accuracy_score(y_test, y_predregressor))"
   ]
  }
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