diabetes.ipynb

{
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    "colab_type": "code",
    "id": "-_cBE0XiO9Tq",
    "outputId": "0d21347f-4e67-4acf-d431-4c63747478df"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "import pickle\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "colab_type": "code",
    "id": "mQGAJ-ILPrn7",
    "outputId": "b02dae5d-3b4a-410a-a7d8-aaa5637f0383"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Polyuria</th>\n",
       "      <th>Polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>Polyphagia</th>\n",
       "      <th>Genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>Itching</th>\n",
       "      <th>Irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>Alopecia</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Gender Polyuria Polydipsia sudden weight loss weakness Polyphagia  \\\n",
       "0   40   Male       No        Yes                 No      Yes         No   \n",
       "1   58   Male       No         No                 No      Yes         No   \n",
       "2   41   Male      Yes         No                 No      Yes        Yes   \n",
       "3   45   Male       No         No                Yes      Yes        Yes   \n",
       "4   60   Male      Yes        Yes                Yes      Yes        Yes   \n",
       "\n",
       "  Genital thrush visual blurring Itching Irritability delayed healing  \\\n",
       "0             No              No     Yes           No             Yes   \n",
       "1             No             Yes      No           No              No   \n",
       "2             No              No     Yes           No             Yes   \n",
       "3            Yes              No     Yes           No             Yes   \n",
       "4             No             Yes     Yes          Yes             Yes   \n",
       "\n",
       "  partial paresis muscle stiffness Alopecia Obesity     class  \n",
       "0              No              Yes      Yes     Yes  Positive  \n",
       "1             Yes               No      Yes      No  Positive  \n",
       "2              No              Yes      Yes      No  Positive  \n",
       "3              No               No       No      No  Positive  \n",
       "4             Yes              Yes      Yes     Yes  Positive  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing the dataset\n",
    "data = pd.read_csv('diabetes_data_upload.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "Wz-k2m_mQHSn",
    "outputId": "af3e5a0c-4a27-4b9e-bdaa-26786e40e0fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Polyuria', 'Polydipsia', 'sudden weight loss',\n",
       "       'weakness', 'Polyphagia', 'Genital thrush', 'visual blurring',\n",
       "       'Itching', 'Irritability', 'delayed healing', 'partial paresis',\n",
       "       'muscle stiffness', 'Alopecia', 'Obesity', 'class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "gCw9UUayQdIA",
    "outputId": "374ecf6e-5904-4085-c6c3-355179dbe52f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(520, 17)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "avSgPaw4Qfma",
    "outputId": "077b6596-665c-462b-bead-5806374cda03"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      Age  Gender Polyuria Polydipsia sudden weight loss weakness Polyphagia  \\\n",
       "0     40    Male       No        Yes                 No      Yes         No   \n",
       "1     58    Male       No         No                 No      Yes         No   \n",
       "2     41    Male      Yes         No                 No      Yes        Yes   \n",
       "3     45    Male       No         No                Yes      Yes        Yes   \n",
       "4     60    Male      Yes        Yes                Yes      Yes        Yes   \n",
       "..   ...     ...      ...        ...                ...      ...        ...   \n",
       "515   39  Female      Yes        Yes                Yes       No        Yes   \n",
       "516   48  Female      Yes        Yes                Yes      Yes        Yes   \n",
       "517   58  Female      Yes        Yes                Yes      Yes        Yes   \n",
       "518   32  Female       No         No                 No      Yes         No   \n",
       "519   42    Male       No         No                 No       No         No   \n",
       "\n",
       "    Genital thrush visual blurring Itching Irritability delayed healing  \\\n",
       "0               No              No     Yes           No             Yes   \n",
       "1               No             Yes      No           No              No   \n",
       "2               No              No     Yes           No             Yes   \n",
       "3              Yes              No     Yes           No             Yes   \n",
       "4               No             Yes     Yes          Yes             Yes   \n",
       "..             ...             ...     ...          ...             ...   \n",
       "515             No              No     Yes           No             Yes   \n",
       "516             No              No     Yes          Yes             Yes   \n",
       "517             No             Yes      No           No              No   \n",
       "518             No             Yes     Yes           No             Yes   \n",
       "519             No              No      No           No              No   \n",
       "\n",
       "    partial paresis muscle stiffness Alopecia Obesity     class  \n",
       "0                No              Yes      Yes     Yes  Positive  \n",
       "1               Yes               No      Yes      No  Positive  \n",
       "2                No              Yes      Yes      No  Positive  \n",
       "3                No               No       No      No  Positive  \n",
       "4               Yes              Yes      Yes     Yes  Positive  \n",
       "..              ...              ...      ...     ...       ...  \n",
       "515             Yes               No       No      No  Positive  \n",
       "516             Yes               No       No      No  Positive  \n",
       "517             Yes              Yes       No     Yes  Positive  \n",
       "518              No               No      Yes      No  Negative  \n",
       "519              No               No       No      No  Negative  \n",
       "\n",
       "[520 rows x 17 columns]>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "JGErYismQiil",
    "outputId": "851fd289-6156-4a5a-9d02-ab0b8947d549"
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.028846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.151466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "              Age\n",
       "count  520.000000\n",
       "mean    48.028846\n",
       "std     12.151466\n",
       "min     16.000000\n",
       "25%     39.000000\n",
       "50%     47.500000\n",
       "75%     57.000000\n",
       "max     90.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "id": "FcV2l0PoQmt2",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                   0\n",
       "Gender                0\n",
       "Polyuria              0\n",
       "Polydipsia            0\n",
       "sudden weight loss    0\n",
       "weakness              0\n",
       "Polyphagia            0\n",
       "Genital thrush        0\n",
       "visual blurring       0\n",
       "Itching               0\n",
       "Irritability          0\n",
       "delayed healing       0\n",
       "partial paresis       0\n",
       "muscle stiffness      0\n",
       "Alopecia              0\n",
       "Obesity               0\n",
       "class                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "execution_count": 8,
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the data\n",
    "data.hist()\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 258
    },
    "colab_type": "code",
    "id": "pAQjXi-MQ354",
    "outputId": "db78a477-3ced-467a-8f72-480599229647"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Polyuria</th>\n",
       "      <th>Polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>Polyphagia</th>\n",
       "      <th>Genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>Itching</th>\n",
       "      <th>Irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>Alopecia</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Gender Polyuria Polydipsia  ... muscle stiffness Alopecia Obesity     class\n",
       "0   40   Male       No        Yes  ...              Yes      Yes     Yes  Positive\n",
       "1   58   Male       No         No  ...               No      Yes      No  Positive\n",
       "2   41   Male      Yes         No  ...              Yes      Yes      No  Positive\n",
       "3   45   Male       No         No  ...               No       No      No  Positive\n",
       "4   60   Male      Yes        Yes  ...              Yes      Yes     Yes  Positive\n",
       "\n",
       "[5 rows x 17 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DOJNDczsRWG5"
   },
   "outputs": [],
   "source": [
    "label = LabelEncoder()\n",
    "data[\"Gender\"] = label.fit_transform(data[\"Gender\"])\n",
    "data[\"Polyuria\"] = label.fit_transform(data[\"Polyuria\"])\n",
    "data[\"Polydipsia\"] = label.fit_transform(data[\"Polydipsia\"])\n",
    "data[\"sudden weight loss\"] = label.fit_transform(data[\"sudden weight loss\"])\n",
    "data[\"weakness\"] = label.fit_transform(data[\"weakness\"])\n",
    "data[\"Polyphagia\"] = label.fit_transform(data[\"Polyphagia\"])\n",
    "data[\"Genital thrush\"] = label.fit_transform(data[\"Genital thrush\"])\n",
    "data[\"visual blurring\"] = label.fit_transform(data[\"visual blurring\"])\n",
    "data[\"Itching\"] = label.fit_transform(data[\"Itching\"])\n",
    "data[\"Irritability\"] = label.fit_transform(data[\"Irritability\"])\n",
    "data[\"delayed healing\"] = label.fit_transform(data[\"delayed healing\"])\n",
    "data[\"partial paresis\"] = label.fit_transform(data[\"partial paresis\"])\n",
    "data[\"muscle stiffness\"] = label.fit_transform(data[\"muscle stiffness\"])\n",
    "data[\"Alopecia\"] = label.fit_transform(data[\"Alopecia\"])\n",
    "data[\"Obesity\"] = label.fit_transform(data[\"Obesity\"])\n",
    "data[\"class\"] = label.fit_transform(data[\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 165
    },
    "colab_type": "code",
    "id": "C0-a_2E8W4Dt",
    "outputId": "5ef8d1f1-1085-470b-ddc6-8a82c457d19c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    320\n",
       "0    200\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "YnE8wVySXCcD",
    "outputId": "e06ce878-d027-4b5d-cd41-f8ec1600c1d5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Polyuria</th>\n",
       "      <th>Polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>Polyphagia</th>\n",
       "      <th>Genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>Itching</th>\n",
       "      <th>Irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>Alopecia</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Polyuria  Polydipsia  sudden weight loss  weakness  \\\n",
       "0   40       1         0           1                   0         1   \n",
       "1   58       1         0           0                   0         1   \n",
       "2   41       1         1           0                   0         1   \n",
       "3   45       1         0           0                   1         1   \n",
       "4   60       1         1           1                   1         1   \n",
       "\n",
       "   Polyphagia  Genital thrush  visual blurring  Itching  Irritability  \\\n",
       "0           0               0                0        1             0   \n",
       "1           0               0                1        0             0   \n",
       "2           1               0                0        1             0   \n",
       "3           1               1                0        1             0   \n",
       "4           1               0                1        1             1   \n",
       "\n",
       "   delayed healing  partial paresis  muscle stiffness  Alopecia  Obesity  \\\n",
       "0                1                0                 1         1        1   \n",
       "1                0                1                 0         1        0   \n",
       "2                1                0                 1         1        0   \n",
       "3                1                0                 0         0        0   \n",
       "4                1                1                 1         1        1   \n",
       "\n",
       "   class  \n",
       "0      1  \n",
       "1      1  \n",
       "2      1  \n",
       "3      1  \n",
       "4      1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "id": "1oKxARb7ZGDN",
    "outputId": "aeb8f6e4-88a4-4f65-f5c2-4006090b397d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Splitting the datasets\n",
    "#Plotting the histogram\n",
    "data.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "2Y1Vj8EMZV5j",
    "outputId": "ec68bfa1-2696-4ae6-9a7a-de3b8a241af9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Polyuria</th>\n",
       "      <th>Polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>Polyphagia</th>\n",
       "      <th>Genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>Itching</th>\n",
       "      <th>Irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>Alopecia</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062872</td>\n",
       "      <td>0.199781</td>\n",
       "      <td>0.137382</td>\n",
       "      <td>0.064808</td>\n",
       "      <td>0.224596</td>\n",
       "      <td>0.315577</td>\n",
       "      <td>0.096519</td>\n",
       "      <td>0.402729</td>\n",
       "      <td>0.296559</td>\n",
       "      <td>0.201625</td>\n",
       "      <td>0.257501</td>\n",
       "      <td>0.232742</td>\n",
       "      <td>0.307703</td>\n",
       "      <td>0.321691</td>\n",
       "      <td>0.140458</td>\n",
       "      <td>0.108679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>0.062872</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.268894</td>\n",
       "      <td>-0.312262</td>\n",
       "      <td>-0.281840</td>\n",
       "      <td>-0.124490</td>\n",
       "      <td>-0.219968</td>\n",
       "      <td>0.208961</td>\n",
       "      <td>-0.208092</td>\n",
       "      <td>-0.052496</td>\n",
       "      <td>-0.013735</td>\n",
       "      <td>-0.101978</td>\n",
       "      <td>-0.332288</td>\n",
       "      <td>-0.090542</td>\n",
       "      <td>0.327871</td>\n",
       "      <td>-0.005396</td>\n",
       "      <td>-0.449233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polyuria</th>\n",
       "      <td>0.199781</td>\n",
       "      <td>-0.268894</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.598609</td>\n",
       "      <td>0.447207</td>\n",
       "      <td>0.263000</td>\n",
       "      <td>0.373873</td>\n",
       "      <td>0.087273</td>\n",
       "      <td>0.235095</td>\n",
       "      <td>0.088289</td>\n",
       "      <td>0.237740</td>\n",
       "      <td>0.149873</td>\n",
       "      <td>0.441664</td>\n",
       "      <td>0.152938</td>\n",
       "      <td>-0.144192</td>\n",
       "      <td>0.126567</td>\n",
       "      <td>0.665922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polydipsia</th>\n",
       "      <td>0.137382</td>\n",
       "      <td>-0.312262</td>\n",
       "      <td>0.598609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.405965</td>\n",
       "      <td>0.332453</td>\n",
       "      <td>0.316839</td>\n",
       "      <td>0.028081</td>\n",
       "      <td>0.331250</td>\n",
       "      <td>0.128716</td>\n",
       "      <td>0.203446</td>\n",
       "      <td>0.115691</td>\n",
       "      <td>0.442249</td>\n",
       "      <td>0.180723</td>\n",
       "      <td>-0.310964</td>\n",
       "      <td>0.098691</td>\n",
       "      <td>0.648734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sudden weight loss</th>\n",
       "      <td>0.064808</td>\n",
       "      <td>-0.281840</td>\n",
       "      <td>0.447207</td>\n",
       "      <td>0.405965</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.282884</td>\n",
       "      <td>0.243511</td>\n",
       "      <td>0.089858</td>\n",
       "      <td>0.068754</td>\n",
       "      <td>-0.004516</td>\n",
       "      <td>0.140340</td>\n",
       "      <td>0.088140</td>\n",
       "      <td>0.264014</td>\n",
       "      <td>0.109756</td>\n",
       "      <td>-0.202727</td>\n",
       "      <td>0.169294</td>\n",
       "      <td>0.436568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weakness</th>\n",
       "      <td>0.224596</td>\n",
       "      <td>-0.124490</td>\n",
       "      <td>0.263000</td>\n",
       "      <td>0.332453</td>\n",
       "      <td>0.282884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.180266</td>\n",
       "      <td>0.027780</td>\n",
       "      <td>0.301043</td>\n",
       "      <td>0.309440</td>\n",
       "      <td>0.146698</td>\n",
       "      <td>0.335507</td>\n",
       "      <td>0.272982</td>\n",
       "      <td>0.263164</td>\n",
       "      <td>0.090490</td>\n",
       "      <td>0.045665</td>\n",
       "      <td>0.243275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polyphagia</th>\n",
       "      <td>0.315577</td>\n",
       "      <td>-0.219968</td>\n",
       "      <td>0.373873</td>\n",
       "      <td>0.316839</td>\n",
       "      <td>0.243511</td>\n",
       "      <td>0.180266</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.063712</td>\n",
       "      <td>0.293545</td>\n",
       "      <td>0.144390</td>\n",
       "      <td>0.239466</td>\n",
       "      <td>0.263980</td>\n",
       "      <td>0.373569</td>\n",
       "      <td>0.320031</td>\n",
       "      <td>-0.053498</td>\n",
       "      <td>0.029785</td>\n",
       "      <td>0.342504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genital thrush</th>\n",
       "      <td>0.096519</td>\n",
       "      <td>0.208961</td>\n",
       "      <td>0.087273</td>\n",
       "      <td>0.028081</td>\n",
       "      <td>0.089858</td>\n",
       "      <td>0.027780</td>\n",
       "      <td>-0.063712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.148408</td>\n",
       "      <td>0.125336</td>\n",
       "      <td>0.160551</td>\n",
       "      <td>0.136111</td>\n",
       "      <td>-0.195612</td>\n",
       "      <td>-0.100188</td>\n",
       "      <td>0.204847</td>\n",
       "      <td>0.053828</td>\n",
       "      <td>0.110288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual blurring</th>\n",
       "      <td>0.402729</td>\n",
       "      <td>-0.208092</td>\n",
       "      <td>0.235095</td>\n",
       "      <td>0.331250</td>\n",
       "      <td>0.068754</td>\n",
       "      <td>0.301043</td>\n",
       "      <td>0.293545</td>\n",
       "      <td>-0.148408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.291191</td>\n",
       "      <td>0.077095</td>\n",
       "      <td>0.177767</td>\n",
       "      <td>0.364156</td>\n",
       "      <td>0.412369</td>\n",
       "      <td>0.014604</td>\n",
       "      <td>0.109005</td>\n",
       "      <td>0.251300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Itching</th>\n",
       "      <td>0.296559</td>\n",
       "      <td>-0.052496</td>\n",
       "      <td>0.088289</td>\n",
       "      <td>0.128716</td>\n",
       "      <td>-0.004516</td>\n",
       "      <td>0.309440</td>\n",
       "      <td>0.144390</td>\n",
       "      <td>0.125336</td>\n",
       "      <td>0.291191</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114006</td>\n",
       "      <td>0.453316</td>\n",
       "      <td>0.116669</td>\n",
       "      <td>0.215575</td>\n",
       "      <td>0.266506</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>-0.013384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irritability</th>\n",
       "      <td>0.201625</td>\n",
       "      <td>-0.013735</td>\n",
       "      <td>0.237740</td>\n",
       "      <td>0.203446</td>\n",
       "      <td>0.140340</td>\n",
       "      <td>0.146698</td>\n",
       "      <td>0.239466</td>\n",
       "      <td>0.160551</td>\n",
       "      <td>0.077095</td>\n",
       "      <td>0.114006</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.126877</td>\n",
       "      <td>0.151571</td>\n",
       "      <td>0.201637</td>\n",
       "      <td>0.043708</td>\n",
       "      <td>0.127801</td>\n",
       "      <td>0.299467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delayed healing</th>\n",
       "      <td>0.257501</td>\n",
       "      <td>-0.101978</td>\n",
       "      <td>0.149873</td>\n",
       "      <td>0.115691</td>\n",
       "      <td>0.088140</td>\n",
       "      <td>0.335507</td>\n",
       "      <td>0.263980</td>\n",
       "      <td>0.136111</td>\n",
       "      <td>0.177767</td>\n",
       "      <td>0.453316</td>\n",
       "      <td>0.126877</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187382</td>\n",
       "      <td>0.250078</td>\n",
       "      <td>0.290179</td>\n",
       "      <td>-0.066339</td>\n",
       "      <td>0.046980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial paresis</th>\n",
       "      <td>0.232742</td>\n",
       "      <td>-0.332288</td>\n",
       "      <td>0.441664</td>\n",
       "      <td>0.442249</td>\n",
       "      <td>0.264014</td>\n",
       "      <td>0.272982</td>\n",
       "      <td>0.373569</td>\n",
       "      <td>-0.195612</td>\n",
       "      <td>0.364156</td>\n",
       "      <td>0.116669</td>\n",
       "      <td>0.151571</td>\n",
       "      <td>0.187382</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.232634</td>\n",
       "      <td>-0.221576</td>\n",
       "      <td>-0.009401</td>\n",
       "      <td>0.432288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>muscle stiffness</th>\n",
       "      <td>0.307703</td>\n",
       "      <td>-0.090542</td>\n",
       "      <td>0.152938</td>\n",
       "      <td>0.180723</td>\n",
       "      <td>0.109756</td>\n",
       "      <td>0.263164</td>\n",
       "      <td>0.320031</td>\n",
       "      <td>-0.100188</td>\n",
       "      <td>0.412369</td>\n",
       "      <td>0.215575</td>\n",
       "      <td>0.201637</td>\n",
       "      <td>0.250078</td>\n",
       "      <td>0.232634</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.040758</td>\n",
       "      <td>0.158910</td>\n",
       "      <td>0.122474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alopecia</th>\n",
       "      <td>0.321691</td>\n",
       "      <td>0.327871</td>\n",
       "      <td>-0.144192</td>\n",
       "      <td>-0.310964</td>\n",
       "      <td>-0.202727</td>\n",
       "      <td>0.090490</td>\n",
       "      <td>-0.053498</td>\n",
       "      <td>0.204847</td>\n",
       "      <td>0.014604</td>\n",
       "      <td>0.266506</td>\n",
       "      <td>0.043708</td>\n",
       "      <td>0.290179</td>\n",
       "      <td>-0.221576</td>\n",
       "      <td>0.040758</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029229</td>\n",
       "      <td>-0.267512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Obesity</th>\n",
       "      <td>0.140458</td>\n",
       "      <td>-0.005396</td>\n",
       "      <td>0.126567</td>\n",
       "      <td>0.098691</td>\n",
       "      <td>0.169294</td>\n",
       "      <td>0.045665</td>\n",
       "      <td>0.029785</td>\n",
       "      <td>0.053828</td>\n",
       "      <td>0.109005</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.127801</td>\n",
       "      <td>-0.066339</td>\n",
       "      <td>-0.009401</td>\n",
       "      <td>0.158910</td>\n",
       "      <td>0.029229</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.072173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>0.108679</td>\n",
       "      <td>-0.449233</td>\n",
       "      <td>0.665922</td>\n",
       "      <td>0.648734</td>\n",
       "      <td>0.436568</td>\n",
       "      <td>0.243275</td>\n",
       "      <td>0.342504</td>\n",
       "      <td>0.110288</td>\n",
       "      <td>0.251300</td>\n",
       "      <td>-0.013384</td>\n",
       "      <td>0.299467</td>\n",
       "      <td>0.046980</td>\n",
       "      <td>0.432288</td>\n",
       "      <td>0.122474</td>\n",
       "      <td>-0.267512</td>\n",
       "      <td>0.072173</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Age    Gender  Polyuria  Polydipsia  \\\n",
       "Age                 1.000000  0.062872  0.199781    0.137382   \n",
       "Gender              0.062872  1.000000 -0.268894   -0.312262   \n",
       "Polyuria            0.199781 -0.268894  1.000000    0.598609   \n",
       "Polydipsia          0.137382 -0.312262  0.598609    1.000000   \n",
       "sudden weight loss  0.064808 -0.281840  0.447207    0.405965   \n",
       "weakness            0.224596 -0.124490  0.263000    0.332453   \n",
       "Polyphagia          0.315577 -0.219968  0.373873    0.316839   \n",
       "Genital thrush      0.096519  0.208961  0.087273    0.028081   \n",
       "visual blurring     0.402729 -0.208092  0.235095    0.331250   \n",
       "Itching             0.296559 -0.052496  0.088289    0.128716   \n",
       "Irritability        0.201625 -0.013735  0.237740    0.203446   \n",
       "delayed healing     0.257501 -0.101978  0.149873    0.115691   \n",
       "partial paresis     0.232742 -0.332288  0.441664    0.442249   \n",
       "muscle stiffness    0.307703 -0.090542  0.152938    0.180723   \n",
       "Alopecia            0.321691  0.327871 -0.144192   -0.310964   \n",
       "Obesity             0.140458 -0.005396  0.126567    0.098691   \n",
       "class               0.108679 -0.449233  0.665922    0.648734   \n",
       "\n",
       "                    sudden weight loss  weakness  Polyphagia  Genital thrush  \\\n",
       "Age                           0.064808  0.224596    0.315577        0.096519   \n",
       "Gender                       -0.281840 -0.124490   -0.219968        0.208961   \n",
       "Polyuria                      0.447207  0.263000    0.373873        0.087273   \n",
       "Polydipsia                    0.405965  0.332453    0.316839        0.028081   \n",
       "sudden weight loss            1.000000  0.282884    0.243511        0.089858   \n",
       "weakness                      0.282884  1.000000    0.180266        0.027780   \n",
       "Polyphagia                    0.243511  0.180266    1.000000       -0.063712   \n",
       "Genital thrush                0.089858  0.027780   -0.063712        1.000000   \n",
       "visual blurring               0.068754  0.301043    0.293545       -0.148408   \n",
       "Itching                      -0.004516  0.309440    0.144390        0.125336   \n",
       "Irritability                  0.140340  0.146698    0.239466        0.160551   \n",
       "delayed healing               0.088140  0.335507    0.263980        0.136111   \n",
       "partial paresis               0.264014  0.272982    0.373569       -0.195612   \n",
       "muscle stiffness              0.109756  0.263164    0.320031       -0.100188   \n",
       "Alopecia                     -0.202727  0.090490   -0.053498        0.204847   \n",
       "Obesity                       0.169294  0.045665    0.029785        0.053828   \n",
       "class                         0.436568  0.243275    0.342504        0.110288   \n",
       "\n",
       "                    visual blurring   Itching  Irritability  delayed healing  \\\n",
       "Age                        0.402729  0.296559      0.201625         0.257501   \n",
       "Gender                    -0.208092 -0.052496     -0.013735        -0.101978   \n",
       "Polyuria                   0.235095  0.088289      0.237740         0.149873   \n",
       "Polydipsia                 0.331250  0.128716      0.203446         0.115691   \n",
       "sudden weight loss         0.068754 -0.004516      0.140340         0.088140   \n",
       "weakness                   0.301043  0.309440      0.146698         0.335507   \n",
       "Polyphagia                 0.293545  0.144390      0.239466         0.263980   \n",
       "Genital thrush            -0.148408  0.125336      0.160551         0.136111   \n",
       "visual blurring            1.000000  0.291191      0.077095         0.177767   \n",
       "Itching                    0.291191  1.000000      0.114006         0.453316   \n",
       "Irritability               0.077095  0.114006      1.000000         0.126877   \n",
       "delayed healing            0.177767  0.453316      0.126877         1.000000   \n",
       "partial paresis            0.364156  0.116669      0.151571         0.187382   \n",
       "muscle stiffness           0.412369  0.215575      0.201637         0.250078   \n",
       "Alopecia                   0.014604  0.266506      0.043708         0.290179   \n",
       "Obesity                    0.109005  0.001894      0.127801        -0.066339   \n",
       "class                      0.251300 -0.013384      0.299467         0.046980   \n",
       "\n",
       "                    partial paresis  muscle stiffness  Alopecia   Obesity  \\\n",
       "Age                        0.232742          0.307703  0.321691  0.140458   \n",
       "Gender                    -0.332288         -0.090542  0.327871 -0.005396   \n",
       "Polyuria                   0.441664          0.152938 -0.144192  0.126567   \n",
       "Polydipsia                 0.442249          0.180723 -0.310964  0.098691   \n",
       "sudden weight loss         0.264014          0.109756 -0.202727  0.169294   \n",
       "weakness                   0.272982          0.263164  0.090490  0.045665   \n",
       "Polyphagia                 0.373569          0.320031 -0.053498  0.029785   \n",
       "Genital thrush            -0.195612         -0.100188  0.204847  0.053828   \n",
       "visual blurring            0.364156          0.412369  0.014604  0.109005   \n",
       "Itching                    0.116669          0.215575  0.266506  0.001894   \n",
       "Irritability               0.151571          0.201637  0.043708  0.127801   \n",
       "delayed healing            0.187382          0.250078  0.290179 -0.066339   \n",
       "partial paresis            1.000000          0.232634 -0.221576 -0.009401   \n",
       "muscle stiffness           0.232634          1.000000  0.040758  0.158910   \n",
       "Alopecia                  -0.221576          0.040758  1.000000  0.029229   \n",
       "Obesity                   -0.009401          0.158910  0.029229  1.000000   \n",
       "class                      0.432288          0.122474 -0.267512  0.072173   \n",
       "\n",
       "                       class  \n",
       "Age                 0.108679  \n",
       "Gender             -0.449233  \n",
       "Polyuria            0.665922  \n",
       "Polydipsia          0.648734  \n",
       "sudden weight loss  0.436568  \n",
       "weakness            0.243275  \n",
       "Polyphagia          0.342504  \n",
       "Genital thrush      0.110288  \n",
       "visual blurring     0.251300  \n",
       "Itching            -0.013384  \n",
       "Irritability        0.299467  \n",
       "delayed healing     0.046980  \n",
       "partial paresis     0.432288  \n",
       "muscle stiffness    0.122474  \n",
       "Alopecia           -0.267512  \n",
       "Obesity             0.072173  \n",
       "class               1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Forming the correlation matrix\n",
    "corr = data.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "jvFb_n_6ZfkO",
    "outputId": "49936dfd-1989-49fd-f7d1-38303ab8c17e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Forming the heatmaps\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(corr, cbar=True, square=True, fmt='.1f' , annot= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Polyuria', 'Polydipsia', 'sudden weight loss',\n",
       "       'weakness', 'Polyphagia', 'Genital thrush', 'visual blurring',\n",
       "       'Itching', 'Irritability', 'delayed healing', 'partial paresis',\n",
       "       'muscle stiffness', 'Alopecia', 'Obesity', 'class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FPnCLiKrZudC"
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Age  Gender  Polyuria  Polydipsia  sudden weight loss  weakness  \\\n",
       "0   40       1         0           1                   0         1   \n",
       "1   58       1         0           0                   0         1   \n",
       "2   41       1         1           0                   0         1   \n",
       "3   45       1         0           0                   1         1   \n",
       "4   60       1         1           1                   1         1   \n",
       "\n",
       "   Polyphagia  Genital thrush  visual blurring  Itching  Irritability  \\\n",
       "0           0               0                0        1             0   \n",
       "1           0               0                1        0             0   \n",
       "2           1               0                0        1             0   \n",
       "3           1               1                0        1             0   \n",
       "4           1               0                1        1             1   \n",
       "\n",
       "   delayed healing  partial paresis  muscle stiffness  Alopecia  Obesity  \n",
       "0                1                0                 1         1        1  \n",
       "1                0                1                 0         1        0  \n",
       "2                1                0                 1         1        0  \n",
       "3                1                0                 0         0        0  \n",
       "4                1                1                 1         1        1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.loc[:,['Age', 'Gender', 'Polyuria', 'Polydipsia', 'sudden weight loss',\n",
    "       'weakness', 'Polyphagia', 'Genital thrush', 'visual blurring',\n",
    "       'Itching', 'Irritability', 'delayed healing', 'partial paresis',\n",
    "       'muscle stiffness', 'Alopecia', 'Obesity']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "colab_type": "code",
    "id": "bb_OitS1aAEW",
    "outputId": "6ebea5ff-de16-407d-849c-f41b447489d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: class, dtype: int32"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data['class']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BiFMWxi7aO4N"
   },
   "outputs": [
    {
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      "text/plain": [
       "     Age  Gender  Polyuria  Polydipsia  sudden weight loss  weakness  \\\n",
       "374   27       1         0           0                   0         0   \n",
       "390   47       1         0           0                   0         0   \n",
       "56    66       0         1           1                   1         1   \n",
       "272   47       1         0           0                   0         0   \n",
       "60    65       0         1           1                   0         1   \n",
       "\n",
       "     Polyphagia  Genital thrush  visual blurring  Itching  Irritability  \\\n",
       "374           0               0                0        0             0   \n",
       "390           0               0                0        0             1   \n",
       "56            1               1                1        1             0   \n",
       "272           0               0                0        1             0   \n",
       "60            1               0                0        1             0   \n",
       "\n",
       "     delayed healing  partial paresis  muscle stiffness  Alopecia  Obesity  \n",
       "374                0                0                 0         0        0  \n",
       "390                0                1                 0         0        1  \n",
       "56                 1                1                 1         0        0  \n",
       "272                0                0                 0         1        0  \n",
       "60                 0                1                 1         0        0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X , y, test_size=0.2, random_state=0)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "ZlkmVEmBcuAx",
    "outputId": "f19a73e4-a86d-4a13-bdeb-d9841732bd11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of class before fit \n",
      " 1    256\n",
      "0    160\n",
      "Name: class, dtype: int64\n",
      "\n",
      "The number of class after fit \n",
      " 1    256\n",
      "0    256\n",
      "Name: class, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abhis\\anaconda3\\envs\\solo\\lib\\site-packages\\imblearn\\utils\\_validation.py:635: FutureWarning: Pass sampling_strategy=1 as keyword args. From version 0.9 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.9 \"\n"
     ]
    }
   ],
   "source": [
    "os=RandomOverSampler(1)\n",
    "X_train_ns,y_train_ns=os.fit_sample(X_train,y_train)\n",
    "print(\"The number of class before fit \\n\",y_train.value_counts())\n",
    "print(\"\\nThe number of class after fit \\n\",y_train_ns.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "rgW4N7TWbMmQ",
    "outputId": "745849ac-35d4-435b-d593-63828f5358b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.921946 (0.041885)\n",
      "LDA: 0.880845 (0.057723)\n",
      "KNN: 0.867195 (0.035900)\n",
      "DTC: 0.972700 (0.019910)\n",
      "SVM: 0.937519 (0.022789)\n",
      "RFC: 0.986388 (0.017359)\n",
      "ABC: 0.931674 (0.050509)\n",
      "XGB: 0.978582 (0.018276)\n"
     ]
    }
   ],
   "source": [
    "models = []\n",
    "models.append(('LR', LogisticRegression(solver='liblinear')))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('DTC', DecisionTreeClassifier()))\n",
    "models.append(('SVM', SVC(gamma='auto')))\n",
    "models.append(('RFC', RandomForestClassifier()))\n",
    "models.append(('ABC', AdaBoostClassifier()))\n",
    "models.append(('XGB', XGBClassifier()))\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "    kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)\n",
    "    cv_results = cross_val_score(model, X_train_ns, y_train_ns, cv=kfold, scoring='accuracy')\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "QTQOrCCcbMi-",
    "outputId": "dbb14dd0-6f1f-43b7-a1f9-e3c4087f2cf7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt', 'log2'], 'criterion': ['gini', 'entropy'], 'max_depth': [3, 9, 16, 23, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "max_features = ['auto', 'sqrt', 'log2']\n",
    "max_depth = [int(x) for x in np.linspace(3, 30, num = 5)]\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "min_samples_leaf = [1, 2, 5, 10]\n",
    "\n",
    "rf_params = {'n_estimators' : n_estimators,\n",
    "             'max_features' : max_features,\n",
    "             'criterion' : ['gini', 'entropy'],\n",
    "             'max_depth' : max_depth,\n",
    "             'min_samples_split' : min_samples_split,\n",
    "             'min_samples_leaf' : min_samples_leaf}\n",
    "\n",
    "print(rf_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 578
    },
    "colab_type": "code",
    "id": "A7V_NTTDabZS",
    "outputId": "3dec9988-7bc1-4cec-838b-dd9d7bef4729"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    8.0s\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:   19.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [3, 9, 16, 23, 30],\n",
       "                                        'max_features': ['auto', 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random_search = RandomizedSearchCV(RandomForestClassifier(), param_distributions = rf_params,\n",
    "                                       scoring= 'roc_auc',\n",
    "                                       n_jobs= -1, verbose= 3)\n",
    "\n",
    "rf_random_search.fit(X_train_ns, y_train_ns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 900,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'log2',\n",
       " 'max_depth': 16,\n",
       " 'criterion': 'gini'}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9903846153846154\n"
     ]
    }
   ],
   "source": [
    "tuned_rf_classifier = RandomForestClassifier(n_estimators = 900,\n",
    "                                             min_samples_split = 2,\n",
    "                                             min_samples_leaf = 1,\n",
    "                                             max_features = 'log2',\n",
    "                                             max_depth = 16, \n",
    "                                             criterion = 'gini')\n",
    "\n",
    "tuned_rf_classifier.fit(X_train_ns, y_train_ns)\n",
    "y_pred_tuned_rf = tuned_rf_classifier.predict(X_test)\n",
    "accuracy_tuned_rf = accuracy_score(y_test, y_pred_tuned_rf)\n",
    "print(accuracy_tuned_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99        40\n",
      "           1       1.00      0.98      0.99        64\n",
      "\n",
      "    accuracy                           0.99       104\n",
      "   macro avg       0.99      0.99      0.99       104\n",
      "weighted avg       0.99      0.99      0.99       104\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_tuned_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[40  0]\n",
      " [ 1 63]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred_tuned_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(tuned_rf_classifier, open('model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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