Water_Quality_Classification.ipynb

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    "# Water Quality Classification"
   ]
  },
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   "source": [
    "👉 This study will examine the dataset named as **\"Water Quality\"** under the 'water-potability' file at Kaggle website [external link text](https://www.kaggle.com/adityakadiwal/water-potability).\n",
    "\n",
    "Context of this dataset is about access to safe drinking-water which is essential to health, a basic human right and a component of effective policy for health protection.\n",
    "\n",
    "Features' Descriptions:\n",
    "\n",
    "1. ph: pH of 1. water (0 to 14).\n",
    "\n",
    "2. Hardness: Capacity of water to precipitate soap in mg/L.\n",
    "\n",
    "3. Solids: Total dissolved solids in ppm.\n",
    "\n",
    "4. Chloramines: Amount of Chloramines in ppm.\n",
    "\n",
    "5. Sulfate: Amount of Sulfates dissolved in mg/L.\n",
    "\n",
    "6. Conductivity: Electrical conductivity of water in μS/cm.\n",
    "\n",
    "7. Organic_carbon: Amount of organic carbon in ppm.\n",
    "\n",
    "8. Trihalomethanes: Amount of Trihalomethanes in μg/L.\n",
    "\n",
    "9. Turbidity: Measure of light emiting property of water in NTU.\n",
    "\n",
    "10. Potability: Indicates if water is safe for human consumption. Potable -1 and Not potable -0"
   ]
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   "source": [
    "## 1. EXPLORATORY DATA ANALYSIS (EDA) & VISUALIZATION"
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    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats"
   ]
  },
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   "source": [
    "df = pd.read_csv(\"water_potability.csv\")"
   ]
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       "         ph    Hardness        Solids  Chloramines     Sulfate  Conductivity  \\\n",
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       "   Organic_carbon  Trihalomethanes  Turbidity  Potability  \n",
       "0       10.379783        86.990970   2.963135           0  \n",
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      "<class 'pandas.core.frame.DataFrame'>\n",
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      " #   Column           Non-Null Count  Dtype  \n",
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      " 2   Solids           3276 non-null   float64\n",
      " 3   Chloramines      3276 non-null   float64\n",
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      " 5   Conductivity     3276 non-null   float64\n",
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       "      <td>3.308162</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>12.065801</td>\n",
       "      <td>14.218338</td>\n",
       "      <td>16.557652</td>\n",
       "      <td>28.300000</td>\n",
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       "      <td>0.738000</td>\n",
       "      <td>55.844536</td>\n",
       "      <td>66.622485</td>\n",
       "      <td>77.337473</td>\n",
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       "      <td>0.780382</td>\n",
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       "      <td>4.500320</td>\n",
       "      <td>6.739000</td>\n",
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       "      <th>Potability</th>\n",
       "      <td>3276.0</td>\n",
       "      <td>0.390110</td>\n",
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       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-eacbe761-6847-4e31-a3da-b7aa6fd19427 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-eacbe761-6847-4e31-a3da-b7aa6fd19427');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                  count          mean          std         min           25%  \\\n",
       "ph               2785.0      7.080795     1.594320    0.000000      6.093092   \n",
       "Hardness         3276.0    196.369496    32.879761   47.432000    176.850538   \n",
       "Solids           3276.0  22014.092526  8768.570828  320.942611  15666.690297   \n",
       "Chloramines      3276.0      7.122277     1.583085    0.352000      6.127421   \n",
       "Sulfate          2495.0    333.775777    41.416840  129.000000    307.699498   \n",
       "Conductivity     3276.0    426.205111    80.824064  181.483754    365.734414   \n",
       "Organic_carbon   3276.0     14.284970     3.308162    2.200000     12.065801   \n",
       "Trihalomethanes  3114.0     66.396293    16.175008    0.738000     55.844536   \n",
       "Turbidity        3276.0      3.966786     0.780382    1.450000      3.439711   \n",
       "Potability       3276.0      0.390110     0.487849    0.000000      0.000000   \n",
       "\n",
       "                          50%           75%           max  \n",
       "ph                   7.036752      8.062066     14.000000  \n",
       "Hardness           196.967627    216.667456    323.124000  \n",
       "Solids           20927.833607  27332.762127  61227.196008  \n",
       "Chloramines          7.130299      8.114887     13.127000  \n",
       "Sulfate            333.073546    359.950170    481.030642  \n",
       "Conductivity       421.884968    481.792304    753.342620  \n",
       "Organic_carbon      14.218338     16.557652     28.300000  \n",
       "Trihalomethanes     66.622485     77.337473    124.000000  \n",
       "Turbidity            3.955028      4.500320      6.739000  \n",
       "Potability           0.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 300,
     "status": "ok",
     "timestamp": 1674687732667,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "6UKhJupRIMDZ",
    "outputId": "bd29c139-7c06-4bee-8507-794c027ea683"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3276, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 411,
     "status": "ok",
     "timestamp": 1674687734524,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "aKsYuvgaKb_m",
    "outputId": "f74c7062-f6f4-41fb-c1db-a211e44a2011"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    3276\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 281,
     "status": "ok",
     "timestamp": 1674690251608,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "lGb9Zn2YKcCZ",
    "outputId": "4e26d5b5-91a7-40a9-91cf-eca3c8a1963c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ph                 0\n",
       "Hardness           0\n",
       "Solids             0\n",
       "Chloramines        0\n",
       "Sulfate            0\n",
       "Conductivity       0\n",
       "Organic_carbon     0\n",
       "Trihalomethanes    0\n",
       "Turbidity          0\n",
       "Potability         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 312,
     "status": "ok",
     "timestamp": 1674692168906,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "5QcT8mW3TMjF"
   },
   "outputs": [],
   "source": [
    "for i in df.columns:\n",
    "    df[i]=df[i].fillna(df[i].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 518
    },
    "executionInfo": {
     "elapsed": 618,
     "status": "ok",
     "timestamp": 1674688197035,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "GV4GFVJQMdob",
    "outputId": "623cfdf8-bb97-4fb6-d092-ad72cf87023b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1998\n",
      "1    1278\n",
      "Name: Potability, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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BWo0jz/p6HxanpPQIVG+503M48qyTbL7UDTwA7Bw7iyTVUQLsUy7m7owdpJU48qyf47A4JaWPo8/1cORZB9l8KQB3Ay+NnUWSGmS/cjF3W+wQrcKRZ328BYtTUrr9v9gBWonlWR+fjh1AkhrsLdl8aafYIVqF5TlG2Xzp9cA+sXNIUoN1AafEDtEqLM+xOy12AElqkg9k86VJsUO0AstzDLL50iHAq2LnkKQmmYSnRQGW51g5hSGp08zI5ksdf2KU5bmZsvnStsCbYueQpCbbCXhr7BCxWZ6b7wSgO3YISYrgY7EDxOYmCZthZMpiPrBj7CySFMnB5WLultghYnHkuXlyWJySOtuM2AFisjw3z/TYASQpsjdn86WtY4eIxfKsUTZf2hl4bewckhRZH9UDMTqS5Vm7f8f/3SQJ4L2xA8TigqEaZPOlXuBRYGrsLJLUIl5WLubuiR2i2RxB1ebNWJyStKb3xg4Qg+VZm46d35ekDXhXJ+44ZHmO0shmyP8SO4cktZhtgdfFDtFslufovYnq6jJJ0treGztAs1meo/f22AEkqUW9sdPu+bQ8R2HkL4X3dkrS+vXRYZvFW56jcwzQEzuEJLWwjjplyvIcHadsJWnjjszmSxNih2gWy3MTsvnSVOCI2DkkqcWNo4Mub1mem/ZWPLdTkkajY6ZuLc9N66iL4JI0Bkd1yoYJludGZPOlicChsXNIUpuYAhwcO0QzWJ4bdwTQGzuEJLWRjpi6tTw3zu34JKk2lqd4fewAktRmXpzNl14WO0SjWZ4bkM2XdgeysXNIUhtK/cDD8tywI2MHkKQ2lfqFlpbnhlmekrR5DsnmSyF2iEayPNdj5D6lw2PnkKQ2tTWwV+wQjWR5rt++wFaxQ0hSGzssdoBGsjzX7/DYASSpzaX6uqfluX4Hxg4gSW3O8uxA+8cOIElt7nnZfOklsUM0iuX5HNl8aTtg+9g5JCkFUnvd0/Jcl6NOSaqP1E7dWp7rsjwlqT5eGTtAo1ie67I8Jak+stl8aVLsEI1gea5hZEeM/WLnkKSUCKR0swTLc227A/2xQ0hSikyLHaARLM+1OWUrSfX18tgBGsHyXJtTtpJUX448O8AesQNIUsrslcYTVizPte0WO4AkpcwE4MWxQ9Sb5Tkimy+NB3aInUOSUih1U7eW52q7Ul1WLUmqr9QtGrI8V3PKVpIaY8/YAerN8lzN8pSkxsjGDlBvludqlqckNcYLYweoN8tzNctTkhpjm2y+tEXsEPVkea5meUpS4+wUO0A9WZ5ANl/aGpgSO4ckpVjnlWcI4bYQwskhhK0bHSiS1M3HS1KL6bzyBN4ObAfMCSFcEkJ4XQghTfdEPj92AElKuc4rzyRJ5iVJchrV64IXARcCD4UQzgghTG5kwCZ5XuwAkpRynVeeACGElwNfAc4EfgYcCzwN/LYx0ZrKkackNVaqLo91j+Y/CiHcBjwFXADkkyRZMfJLs0MIBzcqXBM58pSkxkrVyHNU5QkcmyTJg2s+EULYOUmS+UmSvKUBuZrNkackNVaq7mgY7bTtT0f5XLuyPCWpsbbI5ks9sUPUy0ZHniGElwAvA/pDCGuOMCcB4xoZrMmctpWkxusHFsUOUQ+bmrbdHTgK2Ao4eo3nlwIfbFSoCBx5SlLjdUZ5JklyOXB5COGVSZL8rkmZYpgaO4AkdYCtYgeol01N234ySZL/Af4thHDcc389SZIZDUvWJNl8aUugN3YOSeoA/bED1Mumpm3vHfk4t9FBIkrTtVtJamWdUZ5Jklwx8vEHzYkTRV/sAJLUITpm2vYKINnQrydJ8sa6J2o+y1OSmqMzRp7Al5uSIi7LU5KaozNGnkmSzGxWkIgsT0lqji1iB6iXTU3b/jhJkn8NIfyRtadvA5AkSfLyhqZrDstTkpqjK3aAetnUtO0pIx+PanSQiCxPSWqO1JTnRve2TZJk4cjHh4AVwDTg5cCKkefSwFtVJKk5Rn0MZqsb1Q8SQjgB+D3wFuBtwK0hhPc3MlgTOfKUpOZIzchztEeSfQLYO0mSxQAhhCnALcCFjQrWRO4upDaTJF/u+fbMl4e/+HdXbeWJZKtFkIsdoy5GW56LqW4G/3dLR55Lg8HYAaTRmsDyZdf0ffKe7cPiw2NnkWq1G3+9PXaGetnUatuPjXw6D5gdQric6qrbNwF/aHC2ZhmIHUAajWxY+MhVvfmBcWHV/rGzSJtpKHaAetnUyHPLkY9/GXn83eWNiROF5amWd0Tm9rvO7/nKDpmQ7Bg7izQGqZnp29QmCWc0K0hElqda2qndl950ctflB4ZAT+ws0hh1zMgTgBDCVOCTwMtY49aOJEmOaFCuZloeO4C0PhmGhy7u/cKsAzL3HRo7i1QnqSnP0d5z8yPgPmBn4AygDMxpUKZmc+SpljOJZZXZfSffaXEqZVIzbTva8pySJMkFwKokSWYmSfJ+IA2jTrA81WJ2Dw/Pn9t30pKpobJv7CxSnT0bO0C9jPZWlVUjHxeGEHLAAmByYyI1neWplnF05pa5Z/d8Y9cQ0nN0k7SGRbED1Mtoy/MLIYR+4OPAOcAk4CMNS9Vclqdawue7L5z5rq5rXxVCenZhkZ6j48rzySRJKkAF+GeAEMLBDUvVXJanouphcOUvek+fvWemfFjsLFKDpWVznVFf8zxnlM+1nXIxtwILVJFMobJoTt/0+/bMlA+JnUVqgs4YeYYQXgkcBExdY7chqE7bpmlq6XHghbFDqLNMC/Me+FlvYYvuMJyGc3Gl0UhNeW5q5NkLTKRasluu8Xia6ukqafFY7ADqLMd1XTf7st7Tt+8OwzvEziI1SQIsiR2iXja1w9BMYGYI4ftJkjwUQpg48vyypqRrnr/FDqDO8bWeb8x8U+aWQ0MgxM4iNdFTFCqp2SRhtAuGtgwh3MHI7SkhhEXAe5Ik+VPDkjWXI0813DhWLL+y97Q7dskscGGQOlFqpmxh9OX5HeBjSZJcDxBCOHzkuYMalKvZHHmqobZl8WO/6fvEkolhIC3/n5FqlaryHO1q2wl/L06AJEluACY0JFEcjjzVMAdm7r775r4ZYWIY2CN2FimiVJXnaEeeD4YQPgv838jX7wIebEykKBx5qiE+1HXlrE91X7RvCKsPVJA6VGru8YTRl+f7qW4I/3OqK6ZuGnkuLSxP1VmSXNDz5Rtf3XWH1zelqs4ZeYYQxgEnArsAfwQ+niTJqo29pk05bau6mcDyZdf0ffKe7cNii1NaLVXvs5saef6A6qbwNwH/AryU9Oxpu6YFsQMoHbJh4SNX9eYHxoVV+8fOIrWYB2IHqKdNLRjaI0mSdyVJ8m2qmyKk8mzBcjH3DBaoxuiIzO13/bb31C3GhVW7xs4itaD7Yweop02NPP8xRZskyWAIqb6n+35gu9gh1J5O7b70ppO7Lj8wBHpiZ5Fa0ErStch0k+U5LYTw9MjnARg/8nUAkiRJJjU0XXPdz8iJMdJoZRgeurj3C7MOyNyXylkZqU7+QqEyGDtEPW1qe740bf6+KamaUlDjTWJZ5bq+T8ybGioWp7RxqXt/He2tKp0gdX+4apzdw8Pzr+j9DL1hcN/YWaQ2cF/sAPU22h2GOoHlqVE5OnPL3Kt685N7w+DOsbNIbSJ176+OPFcrAyuAvsg51MI+333hzHd1XfuqEFJ1nq3UaI4806pczA0D82LnUGvqYXDllb2fvund3dceZnFKNbM8Uy51UwsauylUFs3pm37fnpnyIbGzSG3ocQqVp2KHqDfLc22p+9eRxmZamPfA7L6TB7YKz7w8dhapTaXyfdXyXNudsQOodRzXdd3sy3pP3647DO8QO4vUxlJZni4YWtvc2AHUGs7qOfeGN2dmHRYCqd5WS2qCe2MHaARHnmsoF3PzSdmxOarNOFYsv7b31FuO6Zp1uMUp1cXvYgdoBMtzXbfFDqA4tmXxY3P7ps/fJbPgoNhZpJR4Frg9dohGsDzXNSd2ADXfgZm77765b0aYGAb2iJ1FSpHfU6ik8Qxor3mux+zYAdRcH+q6ctanui/aNwTGxc4ipczNsQM0iuW5rlTOz2t9kuSCni/f+OquOw6LnURKqdSWp9O2z1Eu5haTshPPta4JLF82q2/GHItTapgh4JbYIRrF8ly/1P6BC3YKjz06t2/6wu3D4v1jZ5FS7I8UKktjh2gUy3P9LM+UOiJz+13X9358/PiwctfYWaSUS+2ULXjNc0NuiB1A9Xdq96U3ndx1+YEh0BM7i9QBUl2ejjzXo1zM/Rl4MHYO1UeG4aFLez9344e7Lz/E4pSaxvLsUFfHDqCxm8Syyuy+k+88IHPfobGzSB2kTKHy19ghGsny3DDLs83tHh6eP7fvpCVTQ2Xf2FmkDpPqUSdYnhvzWyCVO2N0gqMzt8y9qjc/uTcM7hw7i9SBZsYO0GiW5waUi7mluOq2LX2++8KZZ/d8Y+8Q6I+dRepAw8AVsUM0mqttN+5qwJvo20QPgyt/0Xv67D0zZf/MpHh+R6Hyt9ghGs2R58Z53bNNTKGyaE7f9Pv2zJQPiZ1F6nC/iB2gGSzPjbsDeDx2CG3ctDDvgdl9Jw9sFZ55eewskizPjlcu5hLgmtg5tGHHdV03+7Le07frDsM7xM4iiT9QqHTEPfKW56ZdFjuA1u+snnNv+FL3BfuHwMTYWSQBHTLqBBcMjcavgKXAlrGDqGocK5Zf2XvaHbtkFhweO4uktXRMeTry3IRyMbcc+GXsHKralsWPze2bPn+XzIKDYmeRtJb5FCp3xQ7RLJbn6FwSO4DgwMzdd9/cNyNMDAN7xM4iaR0dM+oEy3O0rgaejB2ik32o68pZF/d88cVdIXl+7CyS1svy1NrKxdwq4Oexc3SmJLmg58yZn+656OAQGBc7jaT1epwO25HN8hw9p26bbALLl83qmzHn1V13uGOQ1Noup1AZjh2imSzP0bseSP2WU61ip/DYo3P7pi/cPizeP3YWSZt0cewAzWZ5jlK5mBsCfho7Ryc4InP7Xdf3fnz8+LBy19hZJG3SAxQq18cO0WyWZ2067l9XzXZq96U3XdDz5T0yIZkSO4ukUflO7AAxhCRJYmdoK9l86V7gJbFzpE2G4aGLe78w64DMfYfGziJp1FYA21OoLI4dpNkcedbum7EDpM0kllVm9518p8UptZ2fdWJxguW5OX4APBs7RFrsHh6eP7fvpCVTQ2Xf2Fkk1ezbsQPEYnnWqFzMVYCLYudIg6Mzt8y9qjc/uTcM7hw7i6Sa3UOhcmPsELFYnpvnvNgB2t3nuy+ceXbPN/YOgf7YWSRtlo5cKPR3ludmKBdzdwCzY+doRz0Mrryi99M3vbv72sNCoCt2HkmbZQD439ghYrI8N5+jzxpNobJoTt/0e/fKlA+JnUXSmPyYQqWj9/u2PDffj4GOXGW2OaaFeQ/M7jt5YKvwzLTYWSSNWccuFPo7y3MzlYu5AeB7sXO0g+O6rpt9We/p23WH4R1iZ5E0Zn+iUOmoTeDXx/Icm28CQ7FDtLKzes694UvdF+wfAhNjZ5FUF16ywvIck3Ix9yBwaewcrWgcK5Zf23vqLcd0zTo8BELsPJLqYgFwYewQrcDyHLsvAu5xuIZtWfzY3L7p83fJLDgodhZJdfXfFCorYodoBZbnGJWLuXvosBPUN+bAzN1339w3I0wMA3vEziKprh6jw+/tXJPlWR9fiB2gFXyo68pZF/d88cVdIXl+7CyS6u5/KFQGYodoFZZnHYxsmvCr2DniSZLze8684dM9Fx0cAuNip5FUd38DvhU7RCuxPOvn87EDxDCB5ctu7jvl90d23XF47CySGubLFCrLY4doJZZnnZSLuVuB38bO0Uw7hccends3feEOYdEBsbNIapgn8PaUdVie9dUx1z6PyNx+1/W9Hx8/PqzcNXYWSQ31FQoVj2F8DsuzjsrF3PXArNg5Gu3U7ktvuqDny3tkQjIldhZJDbUYODd2iFbUHTtACuWBm2KHaIQMw0MX935h1gGZ+w6NnUVSU3yVQmVZ7BCtyJFnnZWLuZuBn8XOUW+TWFaZ3XfynRan1DGWAOfEDtGqLM/G+H/Aytgh6mX38PD8uX0nLZkaKvvGziKpac6kUFkaO0SrsoVs0xAAAA6DSURBVDwboFzM/YWUXCc4OnPL3Kt685N7w+DOsbNIapo/A1+NHaKVWZ6N83mq0x5t64zu7914ds839g6B/thZJDXVKRQqqZk9a4SQJO5p3ijZfOkU4Guxc9Sqh8GVP+89ffZemfIhsbNIarorKFTeGDtEq3Pk2VjnUZ3+aBtTqCya0zf9XotT6kgDwEdih2gHlmcDlYu5VcAnY+cYrWlh3gOz+04e2Co8My12FklRnEmh8mDsEO3A8mywcjF3GTAzdo5NOa7rutmX9Z6+XXcY3iF2FklRPAT8V+wQ7cJNEprjJOAOoDd2kPU5q+fcG96cmXVYCITYWSRF8zE3fx89R55NMHJgdjF2jucax4rl1/aeessxXbMOtziljnYNhcrPY4doJ5Zn83wRuCd2iL/blsWPze2bPn+XzIKDYmeRFNUqYEbsEO3G8myScjG3EjgBGI6d5YBwzz03981gYhjYI3YWSdF9jULl/tgh2o3l2UTlYu53wDdjZvhgV+mWS3q/8KKukLwgZg61noHBhP2/u4xp31rGy85bxn9ePwDAb+cPss+3l7Hnect4z2XLGRxe/73hP7hzJbues4xdz1nGD+6s3l+/YjDh9T98hj3PW8Z5c1bfc/+hK5Zz+8Khxv9Q2pQFwOdih2hHlmfzfQp4pPnfNknO7znzhtN6fnRQCIxr/vdXq+vrgt++ZwJ3nTiRO/99Alf9ZZBbHhnkPZct55K3jedPJ01kp/7AD+5ctc5rlyxPOGPmCmafMIHfnzCBM2au4MnlCVf/ZZBXvbCbP0yfwP/9ofq6ux4bYmgY9tm2q9k/otb1IU9N2TyWZ5OVi7mlwPRmfs8JLF92c98pvz+y647Dm/l91V5CCEzsra4bWzUMq4agK0BvF+w2pVp0r3lRNz+7d3Cd1149b5DXvKibyeMDW48PvOZF3Vw1b5CeDDy7KmHVEPx9M7PPXr+Czx/R17SfSxt0AYVKKXaIdmV5RlAu5krAJc34XjuFxx6d2zd94Q5h0QHN+H5qb0PDCa/41jKed+ZSXvOibvbfvovBYZi7oDrF+tN7Bnnk6XUv2/916TA79q9+O9lhUoa/Lh3mNS/upvzUMAde8AwzDujll/evYp9tM2y3pW89kZWBj8YO0c68zzOeGcCRwDaN+gZHZG6/6/yer+yQCYkbH2hUujKBO0+cyFMDCcdc+ix3PzHMJW8dz0evHmDFYMJrX9xNVw03NXVnAhe9dQsAVg0lvO6Hz3L5O7bgY1cP8HBlmOOn9fDG3Xsa9NNoAxLgfR43Njb+8y+ScjH3BPC+Rv3+p3ZfetMFPV/eIxOSKY36HkqvrcYF/jlbnXp95Y7d3PS+Cfz+gxM5dKdudpuy7tvG9ltmeKSyekT66NPDbP+c0eV5c1Zy/LQebn10iP6+wKVvG89XfufBHRF8nULlhtgh2p3lGVG5mLsS+Ho9f88Mw0OX9n7uxg93X35ICPhPeo3aE88M89RA9cLk8lUJv3lwkJdsk+HxZ6qluGIw4b9nreDE/dbdKOt1u3RzzYODPLk84cnlCdc8OMjrdlk9sfXk8oQr/zzI8dN6eHZVQiZACNXvo6a6h+qiRY2R07bxfRI4FNh7rL/RJJZVruv7xLypoXLo2GOp0yxclvCey55laBiGE/jXl/Vw1G49fOKaAa788yDDCUzfr4cjdq6+bcxdMMS35q7k/DeOZ/L4wGcP7eOfvltduHn6oX1MHr96fvdzM1dw2iF9ZELgdbt0c+6cZ9nrm6s4cd+W3LEyrVYAx1GoDMQOkgae59kCsvnSbsDtwITN/T12Dw/Pv6L3M/SGwZ3rl0xSisygUDkndoi0cNq2BZSLuQeAD2/u64/K/O62q3rzky1OSRtwpcVZX5ZniygXc98HLqr1dWd0f+/Gc3rOeUUI9Nc/laQUWEgDFyd2Kq95tpYTgQOAF2/qP+xhcOXPe0+fvVem7PVNSRsyDBxPobIodpC0ceTZQkZ2HzqO6ikHGzSFyqI5fdPv3StTPqQ5ySS1qdMoVK6NHSKNLM8WUy7m5rCRnT+mhXkPzO47eWCr8My0JsaS1H5+SKHScucIp4Xl2YLKxdy5wAXPff64rutmX9Z7+nbdYdgdgyRtzO+BD8YOkWZe82xdJwF7AK8EOKvn3BvenJl1WAjUsDmapA70V+DN3s/ZWN7n2cKy+dK241lx8xW9pz22S2bBQbHzSGp5y4FDKVTmxg6SdpZni1vynzvsOzksvQkYHzuLpJb3bxQqF8cO0Qm85tniJp/x6G3A8VRPQpCkDfmSxdk8lmc7KFR+CnwmdgxJLesyfI9oKqdt20mh/3vAe2PHkNRS/ggcRKGyLHaQTuLIs738O3Bj7BCSWsYTwBstzuazPNtJobISOAb4U+wokqJbRrU4y7GDdCLLs90UKkuAI4H7Y0eRFM1y4CgKlVtjB+lUlmc7KlT+BrwaeDB2FElNt4LqJggzYwfpZJZnuypU/gocATwSO4qkplkFHEuhck3sIJ3O8mxnhcpDVAt0YewokhpuCHgnhcoVsYPI8mx/hco8qlO4T8SOIqlhEuB9FCo/iR1EVZZnGhQq9wKvAZ6MHUVSQ5xIofJ/sUNoNcszLQqVu4DXAk/HjiKprj5CofKd2CG0NsszTaonKbwBeCZ2FEl18SkKla/HDqF1WZ5pU6jMAo6meh+YpPZ1BoVKMXYIrZ9726ZVof9g4JfA5NhRJNVkGPgohcrZsYNowyzPNCv0vwS4CtgpdhRJo7ICeLeraluf5Zl2hf5tgV8Br4gdRdJGPQW8iULFwx/agNc8065QWQgcClwbO4qkDXoUeJXF2T4sz05QqCylugr3h7GjSFrHn4BXUqjcHTuIRs/y7BSFyirgeMDVe1LrmEl1xPlo7CCqjdc8O1Gh/2TgbPzHkxTTj4HjKVRWxA6i2vnm2YkKlXOBtwEDsaNIHerrwDsszvblyLOTFfoPAi4HtokdReoQQ8AnKVS+GjuIxsby7HSF/h2AS4GDYkeRUu4xqqNND7FOAadtO111ocLhgP8SlhrneuAVFmd6OPLUaoX+NwPfB/ojJ5HSIgG+BPwnhcpQ7DCqH8tTayv0vwj4CbBP7ChSm1tCdau9X8UOovpz2lZrK1QepHr989uxo0htbDawt8WZXo48tWGF/ndSLdEJsaNIbeRs4BMUKitjB1HjWJ7auEL/S4GfAnvEjiK1uKXABzwRpTM4bauNK1TuBfYH/jd2FKmF3QHsZ3F2DkeeGr1C/9HAN4HtY0eRWsQA8DngTAqVwdhh1DyWp2pT6J8E/A/wISBETiPFdDNwAoXK/bGDqPksT22eQv9hwHeBXWNHkZpsKfAp4DwKFd9AO5Tlqc1X6B8HFICPA91xw0hN8WvgRAqVh2MHUVyWp8au0L8PcAHwithRpAZZDHyUQuX/YgdRa3C1rcauULkd+Cfg03jMmdLnx8AeFqfW5MhT9VXo3w04HzgkdhRpjBYAJ1GoXB47iFqPI0/VV6HyAHAY8A7gwchppM2xFPgssKvFqQ1x5KnGKfT3ACdSfSOaGjmNtCmrqG5H+TkKlSdih1FrszzVeNV7Qz8JfBTYInIaaX1+AnyaQmVe7CBqD5anmqfQvx3VW1veD3TFDSMBcCPwSQqV2bGDqL1Ynmq+6mbz/wW8KXYUdax7gDyFyhWxg6g9WZ6Kp9D/Kqpb/b0ydhR1jAXA6cD3KVSGYodR+7I8FV+h/41Ur4keHDuKUqsMfA34LoXKs5GzKAUsT7WOQv8BwMeAt+I1UdXHbOArwM8daaqeLE+1nkL/TsApwAnAlpHTqP0MA5cDX6FQmRU7jNLJ8lTrKvT3Ax8EZgA7Rk6j1vcs8D3ga95yokazPNX6Cv3dwLFUT2/ZN3IatZ7HgHOAb1GoLIkdRp3B8lR7KfQfSnWzhaPwGLROdzvV0ryIQmVl7DDqLJan2lOhfyrwduBdwAGR06h5/gb8iOqtJn+MHUady/JU+yv070K1RN8J7BI5jepvJXAF8APg1xQqg5HzSJanUqZ6u8u7qI5K3Yy+fQ0DNwAXU73NxGuZaimWp9KpusjodVSL9E3A+LiBNEq/Ay4Bfkyh8ljsMNKGWJ5Kv0L/llQXGL2BaqE6Im0dS6mOMK8GShQq5ahppFGyPNVZCv0ZYD+qRfqGkc9D1EydZZjqKtlrqBbm7yhUVsWNJNXO8lRnq67aPWLk8WrgxXEDpdJfqZblNcBvKFQWR84jjZnlKa2pujXgq6mW6SHAC+MGaktLqO4p+xvgagqVeyLnkerO8pQ2ptA/Bdh7jcc+wK5AJmasFrKQ6jTs7cAdwO0UKg/FjSQ1nuUp1arQPwF4BavLdG/gZUBPzFhNMJ81S7JalH+LG0mKw/KU6qHQ3wvsOfLYkep0745rfD4pXrhRGwAeBR4GHlnj4zzgDgqVpyJmk1qK5Sk1Q6F/EmuX6Y5rPLYHJgJbUL0fdVwdv/NKqqeN/P2xiNXFuHZJFiqP1/H7SqlmeUqtptAfqJboFmt83OI5z40HBlm7GNd9FCrDzY4vdQLLU5KkGrliUFJbCyFcGEJ4PITwp9hZ1DksT0nt7vvA62OHUGexPCW1tSRJbqS6MYPUNJanJEk1sjwlSaqR5SlJUo0sT0mSamR5SmprIYSLgd8Bu4cQHg0hfCB2JqWfmyRIklQjR56SJNXI8pQkqUaWpyRJNbI8JUmqkeUpSVKNLE9JkmpkeUqSVCPLU5KkGlmekiTVyPKUJKlGlqckSTWyPCVJqpHlKUlSjSxPSZJqZHlKklQjy1OSpBpZnpIk1cjylCSpRpanJEk1sjwlSaqR5SlJUo0sT0mSamR5SpJUI8tTkqQaWZ6SJNXI8pQkqUaWpyRJNbI8JUmqkeUpSVKNLE9JkmpkeUqSVCPLU5KkGlmekiTVyPKUJKlGlqckSTWyPCVJqpHlKUlSjSxPSZJqZHlKklQjy1OSpBr9f2XiIgpfMOfQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df[\"Potability\"].value_counts())\n",
    "df[\"Potability\"].value_counts().plot(kind=\"pie\", autopct='%1.1f%%',figsize=(8,8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "executionInfo": {
     "elapsed": 1622,
     "status": "ok",
     "timestamp": 1674689031825,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "iacBms6qMefD",
    "outputId": "b1b36dd0-3f8d-4696-de2a-0ac8bb05b069"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ND5RiW2ZQgLb"
   },
   "source": [
    "There is not multicollniarity on this data.That means high correlation btw the features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 2584,
     "status": "ok",
     "timestamp": 1674691779018,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "LHeBt9HXTHeU"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas_profiling import ProfileReport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yGNFaoeTTHhE"
   },
   "outputs": [],
   "source": [
    "#!pip install pandas_profiling  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hGP6fY0gTHjN"
   },
   "outputs": [],
   "source": [
    "#pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 917,
     "output_embedded_package_id": "1nuC9dCKbDgM38DwpEd70UppBqvGhf7cH",
     "referenced_widgets": [
      "5182ca030bc54e939badcebea3dbdb7f",
      "474a7cd2035e4f37b0d715556ee800ed",
      "54974d7f54204653899fc8bf6771ab6d",
      "efcffa7b068142eda70ee251f7df056f",
      "a2b19495f7fc41c9a2437110dcf55efa",
      "2d7ec6e8c53a4fee98c90500cf887b76",
      "6866499508c54372b3ec043c0327baec",
      "500c6d60340b43a1b0de3ef20ce29ce8",
      "2b533dd2b0f84089a0cccd39d23c6a81",
      "05317e0c729f491dbcb1c2649cf2a984",
      "e5ded8e082b645aeb6ed0b68d428507a",
      "f3fc454586874871b8ff08d2fd4b3a5b",
      "161982e93b074f94aa86ccc66b2be395",
      "e577632aaee94560b10e5d01fdc2c46a",
      "bb52429349894258a82c791e35f6cfb4",
      "610a4494e3e34c29824db4f4e62166a3",
      "5cf042d877954dbf8152be917e654fa0",
      "9d38ec5e6f2e49b9acc3dc9d7323fe5d",
      "870a7209fa0c4a64a89c0aa45c9e2ab0",
      "23100ff8272244ad8260beb037ea083d",
      "8f72a193898d41a587aae073593d2572",
      "86a969f17df34c47a9bed9505b010938",
      "fee4b8afd636488f9c646f5a533fe79f",
      "f2f98a2486204f0a9a0c992f319003c5",
      "784d0606db6244508dec6eb49cccab12",
      "fa8e934623014a0dbef39dddfe9857e6",
      "e2bc2df319044a6fafe0943511220fb0",
      "8a99390273cf47e08ed7eb7e46196d85",
      "cf6e6e486d2146d19aaa4e5be9ba7311",
      "fae5240bf6ad41859253ccc298a65e14",
      "4f0d1569cab244b8b2bfba47bb2eef48",
      "484192f0e6054a8fb368a8f38514800b",
      "81ac5676c61a4a7884f92c2d9318dd0c"
     ]
    },
    "executionInfo": {
     "elapsed": 28377,
     "status": "ok",
     "timestamp": 1674691817962,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "3KK9n-z2THn8",
    "outputId": "a1c1fae4-3af3-41b7-dd33-0b9327e0f82a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Output hidden; open in https://colab.research.google.com to view."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profile = ProfileReport(df)\n",
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LdN576hVahLZ"
   },
   "outputs": [],
   "source": [
    "#!pip install pycaret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 2854,
     "status": "ok",
     "timestamp": 1674692403055,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "3jl7vb4KahTA"
   },
   "outputs": [],
   "source": [
    "from pycaret.classification import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "ac2f6b26326045b893470009a5565ae0",
      "04e5f0714c9548239d019551eff21281",
      "83dc5f7095344ac09255be2235de480e",
      "333d0659281440818c4c888e7825d3cd",
      "3812cbf99a0743499c838d96d32e74fa",
      "4df2bb26f82d4bbca61e62a30237d504"
     ]
    },
    "executionInfo": {
     "elapsed": 1201744,
     "status": "ok",
     "timestamp": 1674693658467,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "clbJ86S9ahWA",
    "outputId": "3da8aa9a-164b-4a4f-995d-cb3e12fa0764"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-025b5ac8-01e7-4929-81bb-e01d98c1bf30\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Description</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>session_id</td>\n",
       "      <td>1229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Target</td>\n",
       "      <td>Potability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Target Type</td>\n",
       "      <td>Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Label Encoded</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Original Data</td>\n",
       "      <td>(3276, 10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Missing Values</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Numeric Features</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Categorical Features</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ordinal Features</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>High Cardinality Features</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>High Cardinality Method</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Transformed Train Set</td>\n",
       "      <td>(2293, 9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Transformed Test Set</td>\n",
       "      <td>(983, 9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shuffle Train-Test</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Stratify Train-Test</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Fold Generator</td>\n",
       "      <td>StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Fold Number</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CPU Jobs</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Use GPU</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Log Experiment</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Experiment Name</td>\n",
       "      <td>clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>USI</td>\n",
       "      <td>3a4c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Imputation Type</td>\n",
       "      <td>simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Iterative Imputation Iteration</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Numeric Imputer</td>\n",
       "      <td>mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Iterative Imputation Numeric Model</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Categorical Imputer</td>\n",
       "      <td>constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Iterative Imputation Categorical Model</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Unknown Categoricals Handling</td>\n",
       "      <td>least_frequent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Normalize</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Normalize Method</td>\n",
       "      <td>zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Transformation</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Transformation Method</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PCA</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PCA Method</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>PCA Components</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Ignore Low Variance</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Combine Rare Levels</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Rare Level Threshold</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Numeric Binning</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Remove Outliers</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Outliers Threshold</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Remove Multicollinearity</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Multicollinearity Threshold</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Remove Perfect Collinearity</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Clustering</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Clustering Iteration</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Polynomial Features</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Polynomial Degree</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Trignometry Features</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Polynomial Threshold</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Group Features</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Feature Selection</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Feature Selection Method</td>\n",
       "      <td>classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Features Selection Threshold</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Feature Interaction</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Feature Ratio</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Interaction Threshold</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Fix Imbalance</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Fix Imbalance Method</td>\n",
       "      <td>SMOTE</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                               Description             Value\n",
       "0                               session_id              1229\n",
       "1                                   Target        Potability\n",
       "2                              Target Type            Binary\n",
       "3                            Label Encoded              None\n",
       "4                            Original Data        (3276, 10)\n",
       "5                           Missing Values             False\n",
       "6                         Numeric Features                 9\n",
       "7                     Categorical Features                 0\n",
       "8                         Ordinal Features             False\n",
       "9                High Cardinality Features             False\n",
       "10                 High Cardinality Method              None\n",
       "11                   Transformed Train Set         (2293, 9)\n",
       "12                    Transformed Test Set          (983, 9)\n",
       "13                      Shuffle Train-Test              True\n",
       "14                     Stratify Train-Test             False\n",
       "15                          Fold Generator   StratifiedKFold\n",
       "16                             Fold Number                10\n",
       "17                                CPU Jobs                -1\n",
       "18                                 Use GPU             False\n",
       "19                          Log Experiment             False\n",
       "20                         Experiment Name  clf-default-name\n",
       "21                                     USI              3a4c\n",
       "22                         Imputation Type            simple\n",
       "23          Iterative Imputation Iteration              None\n",
       "24                         Numeric Imputer              mean\n",
       "25      Iterative Imputation Numeric Model              None\n",
       "26                     Categorical Imputer          constant\n",
       "27  Iterative Imputation Categorical Model              None\n",
       "28           Unknown Categoricals Handling    least_frequent\n",
       "29                               Normalize              True\n",
       "30                        Normalize Method            zscore\n",
       "31                          Transformation             False\n",
       "32                   Transformation Method              None\n",
       "33                                     PCA             False\n",
       "34                              PCA Method              None\n",
       "35                          PCA Components              None\n",
       "36                     Ignore Low Variance             False\n",
       "37                     Combine Rare Levels             False\n",
       "38                    Rare Level Threshold              None\n",
       "39                         Numeric Binning             False\n",
       "40                         Remove Outliers             False\n",
       "41                      Outliers Threshold              None\n",
       "42                Remove Multicollinearity             False\n",
       "43             Multicollinearity Threshold              None\n",
       "44             Remove Perfect Collinearity              True\n",
       "45                              Clustering             False\n",
       "46                    Clustering Iteration              None\n",
       "47                     Polynomial Features             False\n",
       "48                       Polynomial Degree              None\n",
       "49                    Trignometry Features             False\n",
       "50                    Polynomial Threshold              None\n",
       "51                          Group Features             False\n",
       "52                       Feature Selection             False\n",
       "53                Feature Selection Method           classic\n",
       "54            Features Selection Threshold              None\n",
       "55                     Feature Interaction             False\n",
       "56                           Feature Ratio             False\n",
       "57                   Interaction Threshold              None\n",
       "58                           Fix Imbalance             False\n",
       "59                    Fix Imbalance Method             SMOTE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:logs:create_model_container: 0\n",
      "INFO:logs:master_model_container: 0\n",
      "INFO:logs:display_container: 1\n",
      "INFO:logs:Pipeline(memory=None,\n",
      "         steps=[('dtypes',\n",
      "                 DataTypes_Auto_infer(categorical_features=[],\n",
      "                                      display_types=True, features_todrop=[],\n",
      "                                      id_columns=[],\n",
      "                                      ml_usecase='classification',\n",
      "                                      numerical_features=[],\n",
      "                                      target='Potability', time_features=[])),\n",
      "                ('imputer',\n",
      "                 Simple_Imputer(categorical_strategy='not_available',\n",
      "                                fill_value_categorical=None,\n",
      "                                fill_value_numerical=None,\n",
      "                                numeric_s...\n",
      "                ('P_transform', 'passthrough'), ('binn', 'passthrough'),\n",
      "                ('rem_outliers', 'passthrough'), ('cluster_all', 'passthrough'),\n",
      "                ('dummy', Dummify(target='Potability')),\n",
      "                ('fix_perfect', Remove_100(target='Potability')),\n",
      "                ('clean_names', Clean_Colum_Names()),\n",
      "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
      "                ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
      "         verbose=False)\n",
      "INFO:logs:setup() succesfully completed......................................\n"
     ]
    }
   ],
   "source": [
    "clf=setup(data=df,target='Potability',session_id=1229,normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
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     "height": 627,
     "referenced_widgets": [
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    "executionInfo": {
     "elapsed": 28169,
     "status": "ok",
     "timestamp": 1674693814605,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
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     "user_tz": -180
    },
    "id": "_C_Evn_cahbz",
    "outputId": "993f187c-0c68-4aef-cff4-3dd4f5f4c7ec"
   },
   "outputs": [
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       "      <td>0.6388</td>\n",
       "      <td>0.4411</td>\n",
       "      <td>0.2343</td>\n",
       "      <td>0.2589</td>\n",
       "      <td>0.699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lightgbm</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>0.6607</td>\n",
       "      <td>0.6708</td>\n",
       "      <td>0.4219</td>\n",
       "      <td>0.5937</td>\n",
       "      <td>0.4916</td>\n",
       "      <td>0.2484</td>\n",
       "      <td>0.2573</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gbc</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>0.6411</td>\n",
       "      <td>0.6391</td>\n",
       "      <td>0.2566</td>\n",
       "      <td>0.6017</td>\n",
       "      <td>0.3568</td>\n",
       "      <td>0.1606</td>\n",
       "      <td>0.1901</td>\n",
       "      <td>0.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>K Neighbors Classifier</td>\n",
       "      <td>0.6310</td>\n",
       "      <td>0.6264</td>\n",
       "      <td>0.3795</td>\n",
       "      <td>0.5431</td>\n",
       "      <td>0.4460</td>\n",
       "      <td>0.1819</td>\n",
       "      <td>0.1891</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.6271</td>\n",
       "      <td>0.5889</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>0.5583</td>\n",
       "      <td>0.3331</td>\n",
       "      <td>0.1285</td>\n",
       "      <td>0.1519</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.6145</td>\n",
       "      <td>0.5114</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.0282</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge</th>\n",
       "      <td>Ridge Classifier</td>\n",
       "      <td>0.6145</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.0282</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda</th>\n",
       "      <td>Linear Discriminant Analysis</td>\n",
       "      <td>0.6145</td>\n",
       "      <td>0.5115</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.0282</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>Dummy Classifier</td>\n",
       "      <td>0.6092</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ada</th>\n",
       "      <td>Ada Boost Classifier</td>\n",
       "      <td>0.5975</td>\n",
       "      <td>0.5621</td>\n",
       "      <td>0.2232</td>\n",
       "      <td>0.4778</td>\n",
       "      <td>0.3019</td>\n",
       "      <td>0.0676</td>\n",
       "      <td>0.0796</td>\n",
       "      <td>0.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.5848</td>\n",
       "      <td>0.5658</td>\n",
       "      <td>0.4789</td>\n",
       "      <td>0.4693</td>\n",
       "      <td>0.4731</td>\n",
       "      <td>0.1310</td>\n",
       "      <td>0.1313</td>\n",
       "      <td>0.036</td>\n",
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       "    <tr>\n",
       "      <th>svm</th>\n",
       "      <td>SVM - Linear Kernel</td>\n",
       "      <td>0.5752</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.4303</td>\n",
       "      <td>0.2508</td>\n",
       "      <td>0.0196</td>\n",
       "      <td>0.0256</td>\n",
       "      <td>0.023</td>\n",
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      ],
      "text/plain": [
       "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
       "qda       Quadratic Discriminant Analysis    0.6729  0.6889  0.3382  0.6632   \n",
       "et                 Extra Trees Classifier    0.6707  0.6833  0.3115  0.6729   \n",
       "rf               Random Forest Classifier    0.6659  0.6785  0.3394  0.6388   \n",
       "lightgbm  Light Gradient Boosting Machine    0.6607  0.6708  0.4219  0.5937   \n",
       "gbc          Gradient Boosting Classifier    0.6411  0.6391  0.2566  0.6017   \n",
       "knn                K Neighbors Classifier    0.6310  0.6264  0.3795  0.5431   \n",
       "nb                            Naive Bayes    0.6271  0.5889  0.2400  0.5583   \n",
       "lr                    Logistic Regression    0.6145  0.5114  0.0145  0.6667   \n",
       "ridge                    Ridge Classifier    0.6145  0.0000  0.0145  0.6667   \n",
       "lda          Linear Discriminant Analysis    0.6145  0.5115  0.0145  0.6667   \n",
       "dummy                    Dummy Classifier    0.6092  0.5000  0.0000  0.0000   \n",
       "ada                  Ada Boost Classifier    0.5975  0.5621  0.2232  0.4778   \n",
       "dt               Decision Tree Classifier    0.5848  0.5658  0.4789  0.4693   \n",
       "svm                   SVM - Linear Kernel    0.5752  0.0000  0.1980  0.4303   \n",
       "\n",
       "              F1   Kappa     MCC  TT (Sec)  \n",
       "qda       0.4459  0.2473  0.2766     0.017  \n",
       "et        0.4239  0.2351  0.2701     0.379  \n",
       "rf        0.4411  0.2343  0.2589     0.699  \n",
       "lightgbm  0.4916  0.2484  0.2573     0.135  \n",
       "gbc       0.3568  0.1606  0.1901     0.550  \n",
       "knn       0.4460  0.1819  0.1891     0.047  \n",
       "nb        0.3331  0.1285  0.1519     0.016  \n",
       "lr        0.0282  0.0166  0.0724     0.021  \n",
       "ridge     0.0282  0.0166  0.0724     0.016  \n",
       "lda       0.0282  0.0166  0.0724     0.018  \n",
       "dummy     0.0000  0.0000  0.0000     0.013  \n",
       "ada       0.3019  0.0676  0.0796     0.204  \n",
       "dt        0.4731  0.1310  0.1313     0.036  \n",
       "svm       0.2508  0.0196  0.0256     0.023  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:logs:create_model_container: 28\n",
      "INFO:logs:master_model_container: 28\n",
      "INFO:logs:display_container: 3\n",
      "INFO:logs:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
      "                              store_covariance=False, tol=0.0001)\n",
      "INFO:logs:compare_models() succesfully completed......................................\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
       "                              store_covariance=False, tol=0.0001)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models(sort='Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1674694747278,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "VpQL67dIj22L"
   },
   "outputs": [],
   "source": [
    "x=df.drop(\"Potability\",axis=1)\n",
    "y=df.Potability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1674694811393,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "N7Namff9ahen"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "x_scaled = scaler.fit_transform(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1674694871186,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "252gSshJjnKR"
   },
   "outputs": [],
   "source": [
    "def classification_funct(x,y):\n",
    "    from sklearn.naive_bayes import GaussianNB\n",
    "    from sklearn.naive_bayes import BernoulliNB\n",
    "    from sklearn.svm import SVC\n",
    "    from sklearn.neighbors import KNeighborsClassifier\n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    from xgboost import XGBClassifier\n",
    "    from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score\n",
    "    from sklearn.metrics import confusion_matrix,classification_report\n",
    "    from sklearn.model_selection import train_test_split   \n",
    "    x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)\n",
    "\n",
    "    g=GaussianNB()\n",
    "    b=BernoulliNB()\n",
    "    KN=KNeighborsClassifier()\n",
    "    SVC=SVC() \n",
    "    D=DecisionTreeClassifier()\n",
    "    R=RandomForestClassifier()\n",
    "    Log=LogisticRegression()\n",
    "    XGB=XGBClassifier()\n",
    "    \n",
    "    algos=[g,b,KN,SVC,D,R,Log,XGB]\n",
    "    algo_names=['GaussianNB','BernoulliNB','KNeighborsClassifier','SVC','DecisionTreeClassifier','RandomForestClassifier','LogisticRegression','XGBClassifier']\n",
    "   \n",
    "    accuracy_scored=[]\n",
    "\n",
    "    \n",
    "    for item in algos:\n",
    "        item.fit(x_train,y_train)\n",
    "        item.predict(x_test)\n",
    "        \n",
    "        accuracy_scored.append(accuracy_score(y_test,item.predict(x_test)))\n",
    "       \n",
    "    result=pd.DataFrame(columns=['accuracy_score'],index=algo_names)\n",
    "    result['accuracy_score']=accuracy_scored\n",
    "    return result.sort_values('accuracy_score',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
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    "executionInfo": {
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   "outputs": [
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-b2cf424b-1258-4f32-b4a1-3afad64cfaa5 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-b2cf424b-1258-4f32-b4a1-3afad64cfaa5');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                        accuracy_score\n",
       "SVC                           0.695122\n",
       "RandomForestClassifier        0.682927\n",
       "XGBClassifier                 0.676829\n",
       "GaussianNB                    0.631098\n",
       "BernoulliNB                   0.628049\n",
       "LogisticRegression            0.628049\n",
       "KNeighborsClassifier          0.626524\n",
       "DecisionTreeClassifier        0.571646"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_funct(x_scaled,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 8455,
     "status": "ok",
     "timestamp": 1674695107012,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "O83D3UVCahiW"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "executionInfo": {
     "elapsed": 312,
     "status": "ok",
     "timestamp": 1674695162390,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "vqHiBsjUahkA"
   },
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(8, activation=\"relu\"))\n",
    "model.add(Dense(12, activation=\"relu\"))   \n",
    "model.add(Dense(10, activation=\"relu\"))   \n",
    "model.add(Dense(8, activation=\"relu\"))   \n",
    "model.add(Dense(6, activation=\"relu\"))  \n",
    "model.add(Dense(1, activation=\"sigmoid\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "executionInfo": {
     "elapsed": 291,
     "status": "ok",
     "timestamp": 1674695167299,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "vPB9JKlWm7qm"
   },
   "outputs": [],
   "source": [
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 116784,
     "status": "ok",
     "timestamp": 1674695513606,
     "user": {
      "displayName": "Humeyra Armutlu",
      "userId": "12643211298338865125"
     },
     "user_tz": -180
    },
    "id": "LVl-NYjFm7tL",
    "outputId": "1bd016aa-a9e9-40ff-cd83-cb3cdf1405a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "328/328 [==============================] - 4s 6ms/step - loss: 0.6672 - accuracy: 0.6099\n",
      "Epoch 2/150\n",
      "328/328 [==============================] - 1s 4ms/step - loss: 0.6586 - accuracy: 0.6099\n",
      "Epoch 3/150\n",
      "328/328 [==============================] - 1s 4ms/step - loss: 0.6503 - accuracy: 0.6099\n",
      "Epoch 4/150\n",
      "328/328 [==============================] - 1s 4ms/step - loss: 0.6425 - accuracy: 0.6099\n",
      "Epoch 5/150\n",
      "328/328 [==============================] - 1s 4ms/step - loss: 0.6381 - accuracy: 0.6215\n",
      "Epoch 6/150\n",
      "328/328 [==============================] - 1s 4ms/step - loss: 0.6337 - accuracy: 0.6313\n",
      "Epoch 7/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.6281 - accuracy: 0.6465\n",
      "Epoch 8/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.6244 - accuracy: 0.6526\n",
      "Epoch 9/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.6205 - accuracy: 0.6575\n",
      "Epoch 10/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.6153 - accuracy: 0.6575\n",
      "Epoch 11/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.6131 - accuracy: 0.6621\n",
      "Epoch 12/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.6097 - accuracy: 0.6667\n",
      "Epoch 13/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.6052 - accuracy: 0.6722\n",
      "Epoch 14/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.6033 - accuracy: 0.6758\n",
      "Epoch 15/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5999 - accuracy: 0.6807\n",
      "Epoch 16/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5989 - accuracy: 0.6740\n",
      "Epoch 17/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5968 - accuracy: 0.6804\n",
      "Epoch 18/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5956 - accuracy: 0.6807\n",
      "Epoch 19/150\n",
      "328/328 [==============================] - 1s 3ms/step - loss: 0.5943 - accuracy: 0.6874\n",
      "Epoch 20/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5926 - accuracy: 0.6862\n",
      "Epoch 21/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5909 - accuracy: 0.6902\n",
      "Epoch 22/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5902 - accuracy: 0.6905\n",
      "Epoch 23/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5885 - accuracy: 0.6947\n",
      "Epoch 24/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5875 - accuracy: 0.6957\n",
      "Epoch 25/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5880 - accuracy: 0.6944\n",
      "Epoch 26/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5873 - accuracy: 0.6954\n",
      "Epoch 27/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5861 - accuracy: 0.7018\n",
      "Epoch 28/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5854 - accuracy: 0.6999\n",
      "Epoch 29/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5853 - accuracy: 0.6947\n",
      "Epoch 30/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5849 - accuracy: 0.6978\n",
      "Epoch 31/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5836 - accuracy: 0.6975\n",
      "Epoch 32/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5829 - accuracy: 0.6990\n",
      "Epoch 33/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5822 - accuracy: 0.7015\n",
      "Epoch 34/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5821 - accuracy: 0.6954\n",
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      "328/328 [==============================] - 1s 2ms/step - loss: 0.5816 - accuracy: 0.7042\n",
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      "Epoch 38/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5793 - accuracy: 0.7015\n",
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      "Epoch 40/150\n",
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      "Epoch 41/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5780 - accuracy: 0.7073\n",
      "Epoch 42/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5760 - accuracy: 0.7088\n",
      "Epoch 43/150\n",
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      "Epoch 44/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5752 - accuracy: 0.7067\n",
      "Epoch 45/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5762 - accuracy: 0.7033\n",
      "Epoch 46/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5757 - accuracy: 0.7121\n",
      "Epoch 47/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5744 - accuracy: 0.7088\n",
      "Epoch 48/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5742 - accuracy: 0.7103\n",
      "Epoch 49/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5732 - accuracy: 0.7131\n",
      "Epoch 50/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5750 - accuracy: 0.7118\n",
      "Epoch 51/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5728 - accuracy: 0.7161\n",
      "Epoch 52/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5738 - accuracy: 0.7042\n",
      "Epoch 53/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5730 - accuracy: 0.7103\n",
      "Epoch 54/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5722 - accuracy: 0.7115\n",
      "Epoch 55/150\n",
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      "Epoch 56/150\n",
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      "Epoch 57/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5720 - accuracy: 0.7121\n",
      "Epoch 58/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5697 - accuracy: 0.7198\n",
      "Epoch 59/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5717 - accuracy: 0.7149\n",
      "Epoch 60/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5709 - accuracy: 0.7189\n",
      "Epoch 61/150\n",
      "328/328 [==============================] - 1s 3ms/step - loss: 0.5708 - accuracy: 0.7140\n",
      "Epoch 62/150\n",
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      "Epoch 63/150\n",
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      "Epoch 64/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5703 - accuracy: 0.7170\n",
      "Epoch 65/150\n",
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      "Epoch 66/150\n",
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      "Epoch 67/150\n",
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      "Epoch 68/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5668 - accuracy: 0.7183\n",
      "Epoch 69/150\n",
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      "Epoch 70/150\n",
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      "Epoch 72/150\n",
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      "Epoch 73/150\n",
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      "Epoch 75/150\n",
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      "Epoch 76/150\n",
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      "Epoch 77/150\n",
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      "Epoch 78/150\n",
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      "Epoch 79/150\n",
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      "Epoch 80/150\n",
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      "Epoch 81/150\n",
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      "Epoch 82/150\n",
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      "Epoch 83/150\n",
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      "Epoch 84/150\n",
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      "Epoch 85/150\n",
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      "Epoch 86/150\n",
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      "Epoch 87/150\n",
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      "Epoch 88/150\n",
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      "Epoch 89/150\n",
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      "Epoch 90/150\n",
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      "Epoch 91/150\n",
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      "Epoch 92/150\n",
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      "Epoch 93/150\n",
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      "Epoch 94/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5610 - accuracy: 0.7231\n",
      "Epoch 95/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5627 - accuracy: 0.7222\n",
      "Epoch 96/150\n",
      "328/328 [==============================] - 2s 5ms/step - loss: 0.5600 - accuracy: 0.7286\n",
      "Epoch 97/150\n",
      "328/328 [==============================] - 1s 3ms/step - loss: 0.5608 - accuracy: 0.7210\n",
      "Epoch 98/150\n",
      "328/328 [==============================] - 1s 4ms/step - loss: 0.5594 - accuracy: 0.7231\n",
      "Epoch 99/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5584 - accuracy: 0.7289\n",
      "Epoch 100/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5591 - accuracy: 0.7274\n",
      "Epoch 101/150\n",
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      "Epoch 102/150\n",
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      "Epoch 103/150\n",
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      "Epoch 104/150\n",
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      "Epoch 105/150\n",
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      "Epoch 106/150\n",
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      "Epoch 107/150\n",
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      "Epoch 108/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5574 - accuracy: 0.7265\n",
      "Epoch 109/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5567 - accuracy: 0.7237\n",
      "Epoch 110/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5578 - accuracy: 0.7228\n",
      "Epoch 111/150\n",
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      "Epoch 112/150\n",
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      "Epoch 113/150\n",
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      "Epoch 114/150\n",
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      "Epoch 115/150\n",
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      "Epoch 116/150\n",
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      "Epoch 117/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5533 - accuracy: 0.7295\n",
      "Epoch 118/150\n",
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      "Epoch 119/150\n",
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      "Epoch 120/150\n",
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      "Epoch 121/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5515 - accuracy: 0.7305\n",
      "Epoch 122/150\n",
      "328/328 [==============================] - 1s 3ms/step - loss: 0.5534 - accuracy: 0.7250\n",
      "Epoch 123/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5510 - accuracy: 0.7323\n",
      "Epoch 124/150\n",
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      "Epoch 125/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5506 - accuracy: 0.7292\n",
      "Epoch 126/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5535 - accuracy: 0.7289\n",
      "Epoch 127/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5527 - accuracy: 0.7280\n",
      "Epoch 128/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5516 - accuracy: 0.7283\n",
      "Epoch 129/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5517 - accuracy: 0.7256\n",
      "Epoch 130/150\n",
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      "Epoch 131/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5507 - accuracy: 0.7311\n",
      "Epoch 132/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5505 - accuracy: 0.7302\n",
      "Epoch 133/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5503 - accuracy: 0.7338\n",
      "Epoch 134/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5490 - accuracy: 0.7350\n",
      "Epoch 135/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5498 - accuracy: 0.7265\n",
      "Epoch 136/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5489 - accuracy: 0.7311\n",
      "Epoch 137/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5492 - accuracy: 0.7292\n",
      "Epoch 138/150\n",
      "328/328 [==============================] - 1s 3ms/step - loss: 0.5486 - accuracy: 0.7338\n",
      "Epoch 139/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5483 - accuracy: 0.7305\n",
      "Epoch 140/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5473 - accuracy: 0.7341\n",
      "Epoch 141/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5503 - accuracy: 0.7289\n",
      "Epoch 142/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5476 - accuracy: 0.7344\n",
      "Epoch 143/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5470 - accuracy: 0.7347\n",
      "Epoch 144/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5481 - accuracy: 0.7341\n",
      "Epoch 145/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5470 - accuracy: 0.7338\n",
      "Epoch 146/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5484 - accuracy: 0.7347\n",
      "Epoch 147/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5458 - accuracy: 0.7366\n",
      "Epoch 148/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5467 - accuracy: 0.7335\n",
      "Epoch 149/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5450 - accuracy: 0.7344\n",
      "Epoch 150/150\n",
      "328/328 [==============================] - 1s 2ms/step - loss: 0.5493 - accuracy: 0.7332\n"
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    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc2bc5a8d60>"
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     },
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   "source": [
    "# For high level forecasting I need more data for this issue."
   ]
  },
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