diabetes_prediction_tf_neuralnet.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/abidshafee/predictionModel/blob/master/diabetes_prediction_tf_neuralnet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "HYWH_gBoXCFg"
      },
      "outputs": [],
      "source": [
        "csv_data = 'https://raw.githubusercontent.com/abidshafee/predictionModel/master/diabetes.csv'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "pzkUV7ZZXilH"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "-290DGbzdmZw"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(csv_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Bn7DEH6Xhufe",
        "outputId": "8ed6ac0c-06d9-4657-b0a3-75ba423ed13e"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
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              "  </thead>\n",
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              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>66</td>\n",
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              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
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            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4h2D83Ibhy-7",
        "outputId": "dd850035-86b8-480a-9306-8fd04a002d42"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
              "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SQ_QKDEikju",
        "outputId": "62a7d32b-81e2-4208-9744-d92f721d3dcd"
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        {
          "data": {
            "text/plain": [
              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
              "       'BMI', 'DiabetesPedigreeFunction', 'Age'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns[:-1] #all columns excluding the Outcome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDR4bZdwiRGE",
        "outputId": "5e37fde9-7a35-406e-b4f3-ef1b682c0cb4"
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        {
          "data": {
            "text/plain": [
              "8"
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          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "len(df.columns[:-1])"
      ]
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      "cell_type": "code",
      "execution_count": 8,
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        "id": "cy9emsoria3V",
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              "  <thead>\n",
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              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "<p>268 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6      148             72             35        0  33.6   \n",
              "2              8      183             64              0        0  23.3   \n",
              "4              0      137             40             35      168  43.1   \n",
              "6              3       78             50             32       88  31.0   \n",
              "8              2      197             70             45      543  30.5   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
              "755            1      128             88             39      110  36.5   \n",
              "757            0      123             72              0        0  36.3   \n",
              "759            6      190             92              0        0  35.5   \n",
              "761            9      170             74             31        0  44.0   \n",
              "766            1      126             60              0        0  30.1   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                       0.627   50        1  \n",
              "2                       0.672   32        1  \n",
              "4                       2.288   33        1  \n",
              "6                       0.248   26        1  \n",
              "8                       0.158   53        1  \n",
              "..                        ...  ...      ...  \n",
              "755                     1.057   37        1  \n",
              "757                     0.258   52        1  \n",
              "759                     0.278   66        1  \n",
              "761                     0.403   43        1  \n",
              "766                     0.349   47        1  \n",
              "\n",
              "[268 rows x 9 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[df['Outcome']==1] # return a dataframe where Outcome is 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Detecting Univariate Outliar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='BloodPressure', ylabel='Density'>"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#distribution plot\n",
        "sns.distplot(df[df.columns[2]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Python38\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 24.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='SkinThickness'>"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x338.4 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Univariate outliers boxplot\n",
        "ax = plt.subplots(figsize=(12, 4.7))\n",
        "sns.boxplot(x=df[df.columns[3]], data=df, hue='Outcome', linewidth=2.5, orient=\"h\", palette=\"Blues\")\n",
        "sns.swarmplot(x=df[df.columns[3]], color=\".25\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x1e6c174a4c0>"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.displot(df[df.columns[2]], legend=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pcguPKr5odrh"
      },
      "source": [
        "### Class-wise Data Distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xb3N6CuQjXaN",
        "outputId": "47de414b-6ec8-42bc-d391-91708339b323"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for i in range(len(df.columns[:-1])):\n",
        "  label = df.columns[i]\n",
        "  plt.hist(df[df['Outcome']==1][label], color='red', label='Diabetes', alpha=0.8)\n",
        "  plt.hist(df[df['Outcome']==0][label], color='yellow', label='No Diabetes', alpha = 0.9)\n",
        "  plt.title(label)\n",
        "  plt.xlabel(label)\n",
        "  plt.ylabel('Probability')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QyRTnKxAkxfI",
        "outputId": "91a1ee86-f665-47f4-8b93-cd35b4d76d8f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(268, 500)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df[df['Outcome']==1]), len(df[df['Outcome']==0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "mf2-rZaWpygf"
      },
      "outputs": [],
      "source": [
        "X = df.iloc[:, 0:-1].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbHrciaPsfBi",
        "outputId": "274d982f-5b71-4732-f6de-561beeec75ba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[  6.   , 148.   ,  72.   , ...,  33.6  ,   0.627,  50.   ],\n",
              "       [  1.   ,  85.   ,  66.   , ...,  26.6  ,   0.351,  31.   ],\n",
              "       [  8.   , 183.   ,  64.   , ...,  23.3  ,   0.672,  32.   ],\n",
              "       ...,\n",
              "       [  5.   , 121.   ,  72.   , ...,  26.2  ,   0.245,  30.   ],\n",
              "       [  1.   , 126.   ,  60.   , ...,  30.1  ,   0.349,  47.   ],\n",
              "       [  1.   ,  93.   ,  70.   , ...,  30.4  ,   0.315,  23.   ]])"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "pabFdXBEshLt"
      },
      "outputs": [],
      "source": [
        "y = df.iloc[:,-1].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2DnLkiPZtHFW",
        "outputId": "8f1f8b89-0859-4983-a370-a85a8beff655"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
              "       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
              "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
              "       1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
              "       1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n",
              "       0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
              "       1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n",
              "       0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
              "       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
              "       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
              "       1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0,\n",
              "       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n",
              "       1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0],\n",
              "      dtype=int64)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "vjvSNS8PJPwS"
      },
      "outputs": [],
      "source": [
        "# Scaling X into Standard deviation range\n",
        "scalar = StandardScaler()\n",
        "X = scalar.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwJY0ATzJhw4",
        "outputId": "3f4d4304-e7f4-4d0b-9f83-2198e7efb7ae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,\n",
              "         0.46849198,  1.4259954 ],\n",
              "       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,\n",
              "        -0.36506078, -0.19067191],\n",
              "       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,\n",
              "         0.60439732, -0.10558415],\n",
              "       ...,\n",
              "       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,\n",
              "        -0.68519336, -0.27575966],\n",
              "       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,\n",
              "        -0.37110101,  1.17073215],\n",
              "       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,\n",
              "        -0.47378505, -0.87137393]])"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjvqtb8vbub9"
      },
      "source": [
        "### Creating Scalled DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ocLFvQrbzDp",
        "outputId": "80e2af6e-b3fb-4c56-8d6d-0af994b178df"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((768, 8), (768,))"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.shape, y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "05ZHI3gpb7MA"
      },
      "outputs": [],
      "source": [
        "data = np.hstack((X, np.reshape(y, (-1, 1))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Sp8v6xoHcbjR"
      },
      "outputs": [],
      "source": [
        "scaled_df = pd.DataFrame(data, columns=df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "s6YTCgxyc37T",
        "outputId": "d4a5001d-1cfc-4543-a561-b1e6c724850c"
      },
      "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.639947</td>\n",
              "      <td>0.848324</td>\n",
              "      <td>0.149641</td>\n",
              "      <td>0.907270</td>\n",
              "      <td>-0.692891</td>\n",
              "      <td>0.204013</td>\n",
              "      <td>0.468492</td>\n",
              "      <td>1.425995</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.844885</td>\n",
              "      <td>-1.123396</td>\n",
              "      <td>-0.160546</td>\n",
              "      <td>0.530902</td>\n",
              "      <td>-0.692891</td>\n",
              "      <td>-0.684422</td>\n",
              "      <td>-0.365061</td>\n",
              "      <td>-0.190672</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.233880</td>\n",
              "      <td>1.943724</td>\n",
              "      <td>-0.263941</td>\n",
              "      <td>-1.288212</td>\n",
              "      <td>-0.692891</td>\n",
              "      <td>-1.103255</td>\n",
              "      <td>0.604397</td>\n",
              "      <td>-0.105584</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.844885</td>\n",
              "      <td>-0.998208</td>\n",
              "      <td>-0.160546</td>\n",
              "      <td>0.154533</td>\n",
              "      <td>0.123302</td>\n",
              "      <td>-0.494043</td>\n",
              "      <td>-0.920763</td>\n",
              "      <td>-1.041549</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.141852</td>\n",
              "      <td>0.504055</td>\n",
              "      <td>-1.504687</td>\n",
              "      <td>0.907270</td>\n",
              "      <td>0.765836</td>\n",
              "      <td>1.409746</td>\n",
              "      <td>5.484909</td>\n",
              "      <td>-0.020496</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
              "0     0.639947  0.848324       0.149641       0.907270 -0.692891  0.204013   \n",
              "1    -0.844885 -1.123396      -0.160546       0.530902 -0.692891 -0.684422   \n",
              "2     1.233880  1.943724      -0.263941      -1.288212 -0.692891 -1.103255   \n",
              "3    -0.844885 -0.998208      -0.160546       0.154533  0.123302 -0.494043   \n",
              "4    -1.141852  0.504055      -1.504687       0.907270  0.765836  1.409746   \n",
              "\n",
              "   DiabetesPedigreeFunction       Age  Outcome  \n",
              "0                  0.468492  1.425995      1.0  \n",
              "1                 -0.365061 -0.190672      0.0  \n",
              "2                  0.604397 -0.105584      1.0  \n",
              "3                 -0.920763 -1.041549      0.0  \n",
              "4                  5.484909 -0.020496      1.0  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scaled_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pg5XpxTJeFuu"
      },
      "source": [
        "### ploting Scalled data distribution in standard deviation range"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KSxuF4AjdpOh",
        "outputId": "29cac726-ea22-4fab-83eb-9a941c4a493b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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YcZmZWdOKOVKYBhzWqG02MDAiBgN/By4GkLQPMBbYN9nmZkldihibmZnlUbSkEBGPA+80ansoIj5OFv8G9E2+Hw3cGREfRsTLwHJgv2LFZmZm+WV5T2Ec8Kfk+27Aaw3W1SZtnyFpvKQaSTV1dXVFDtHMrLxkkhQkXQp8DEyvb8rTLfJtGxFTIqIqIqp69+5drBDNzMpSyQviSaoGjgRGR0T9L/5aoF+Dbn2BN0odm5lZuSvpSEHSYcCPgKMi4oMGq+4DxkrqJqk/MAB4upSxmZlZEUcKku4ARgK9JNUCPyY326gbMFsSwN8i4syIWCxpJvACuctKZ0fEhmLFZmZm+RUtKUTECXmab91E/4nAxGLFY2ZmzfMTzWZmlnJSMDOzlJOCmZmlnBTMzCzlpGBmZiknBTMzSzkpmJlZyknBzMxSTgpmZpZyUjAzs1RBSUHSkZKcQMzMOrlCf9GPBZZJ+rmkvYsZkJmZZaegpBAR3wWGAv8AfiPpyeQNaNsUNTozMyupgi8JRcR7wCzgTmAX4JvAs5LOLVJsZmZWYoXeUzhK0j3AI0AFsF9EHA58CfhBEeMzM7MSKvR9Ct8Gro+Ixxs2RsQHksa1fVhmZpaFQi8fvdk4IUj6GUBEzMm3gaSpklZKWtSgbUdJsyUtSz53aLDuYknLJS2VNKYV52JmZpup0KRwSJ62w5vZZhpwWKO2CcCciBgAzEmWkbQPuRlO+ybb3CypS4GxmZlZG9lkUpD035KeB74oaWGDfy8DCze1bTKyeKdR89HAbcn324BjGrTfGREfRsTLwHJgv5adipmZba7m7in8HvgT8FOSv+oT70dE41/4hdgpIt4EiIg3JfVJ2ncD/tagX23S9hmSxgPjAT7/+c+3IgQzM2tKc0khIuIVSWc3XiFpx1YmhnyU79hNBDQFmAJQVVWVt49Zedo1o+O+kdFxrRgKGSkcCcwj90u64S/vAPZo4fHekrRLMkrYBViZtNcC/Rr064v/l2ZmVnKbvKcQEUcmn/0jYo/ks/5fSxMCwH1AdfK9Gri3QftYSd0k9QcGAE+3Yv9mZrYZNjlSkDRsU+sj4tlNbHsHMBLoJakW+DEwCZgp6XTgVeA7yX4WS5oJvAB8DJwdERtacB5mZtYGmrt89MtNrAtgVJMrI05oYtXoJvpPBCY2E4+ZmRXRJpNCRBxcqkDMzCx7zV0+GhURj0g6Nt/6iLi7OGGZmVkWmrt89DVyRfC+kWddAE4KZmadSHOXj36cfJ5WmnDMzCxLhZbO7inpBknPSpon6VeSehY7ODMzK61CC+LdCdQB3yJXRrsOmFGsoMzMLBuFvk9hx4j4SYPlqyUdU4R4zMwsQ4WOFB6VNFbSFsm/44AHihmYmZmVXnNTUt/nk5pH3wduT1ZtAfyb3FPKZmbWSTQ3+2ibUgViZmbZK/SeAsmrMwcA3evbGr+i08zMOraCkoKk/wLOJ1fSegHwFeBJNlH7yMzMOp5CbzSfDwwHViT1kIaSm5ZqZmadSKFJYV1ErAOQ1C0iXgT2Kl5YZmaWhULvKdRK2h74AzBb0r/wm9HMzDqdgpJCRHwz+XqFpEeB7YA/t/agkv4n8F/kprs+D5wGbEXuKelK4BXguIj4V2uPYWZmLVfo5SMkDZN0HjAYqI2Ij1pzQEm7AecBVRExEOgCjAUmAHMiYgAwJ1k2M7MSKrQg3uXAbUBPoBfwG0n/azOO2xXoIakruRHCG8DRyTFIPo/ZjP2bmVkrFHpP4QRgaIObzZOAZ4GrW3rAiHhd0i/IvaN5LfBQRDwkaaeIeDPp86akPi3dt5mZbZ5CLx+9QoOH1oBuwD9ac8DkIbijgf7ArsDWkr7bgu3HS6qRVFNX51mxZmZtqbnaRzeSuxn8IbBY0uxk+RBgbiuP+XXg5YioS45xNzACeEvSLskoYRdgZb6NI2IKMAWgqqoqWhmDmZnl0dzlo5rkcx5wT4P2xzbjmK8CX5G0FbnLR6OT46wBqoFJyee9m3EMMzNrheYK4tXf+EXSlsCeyeLSiFjfmgNGxFOS7iJ3T+JjYD65v/w/B8yUdDq5xPGd1uzfzMxar9DaRyPJzQh6hVwZ7X6SqltbEC9593Pjstsfkhs1mJlZRgqdffRL4NCIWAogaU/gDuDLxQrMzMxKr9DZRxX1CQEgIv4OVBQnJDMzy0qhI4V5km4Ffpcsn0Tu5rOZmXUihSaFM4GzyZWnEPA4cHOxgjIzs2w0mxQkbQHMS+oUXVf8kMzMLCvN3lOIiI3Ac5I+X4J4zMwsQ4VePtqF3BPNT5N7yAyAiDiqKFGZmVkmCk0KVxY1CjMzaxeaq33UndxN5v8g9zKcWyPi41IEZmZmpdfcPYXbgCpyCeFwcg+xmZlZJ9Xc5aN9ImIQQPKcwtPFD8nMzLLS3EghLXrny0ZmZp1fcyOFL0l6L/kucq/QfC/5HhGxbVGjMzOzkmqudHaXUgViZmbZK7QgnpmZlQEnBTMzSxX68FqbkrQ9cAswkNw7n8cBS4EZQCW5l/kcFxH/yiI+sw6tblVpj3d41Sffa2qa7mcdQlYjhV8Bf46ILwJfApYAE4A5ETEAmJMsm5lZCZU8KUjaFvgqcCtARHwUEe8CR5N7WI7k85hSx2ZmVu6yGCnsAdQBv5E0X9ItkrYGdoqINwGSzz75NpY0XlKNpJq6urrSRW1mVgaySApdgWHA/4mIoeSqrhZ8qSgipkREVURU9e7du1gxmpmVpSySQi1QGxFPJct3kUsSb0naBSD5XJlBbGZmZa3kSSEi/gm8JmmvpGk08AJwH1CdtFUD95Y6NjOzcpfJlFTgXGC6pC2Bl4DTyCWomZJOB14FvpNRbGZmZSuTpBARC8iV5G5sdIlDMTOzBvxEs5mZpZwUzMwsldU9BbPOr9TlJszagEcKZmaWclIwM7OUk4KZmaV8T8HM2k5VvpnmJeCS3W3GIwUzM0t5pGBmm+dPC7M79uGDszt2J+WRgpmZpZwUzMws5aRgZmYpJwUzM0v5RrOZdXyeCttmPFIwM7OUk4KZmaUySwqSukiaL+n+ZHlHSbMlLUs+d8gqNjOzcpXlSOF8YEmD5QnAnIgYAMxJls3MrIQySQqS+gJHALc0aD4auC35fhtwTInDMjMre1mNFP43cBGwsUHbThHxJkDy2SffhpLGS6qRVFNXV1f0QM3MyknJp6RKOhJYGRHzJI1s6fYRMQWYAlBVVRVtG52ZWQtkNRUWijYdNovnFA4EjpL0n0B3YFtJtwNvSdolIt6UtAuwMoPYzMzKWskvH0XExRHRNyIqgbHAIxHxXeA+oDrpVg3cW+rYzMzKXXt6TmEScIikZcAhybKZmZVQpmUuIuIx4LHk+9vA6CzjMTMrd+1ppGBmZhlzUjAzs5STgpmZpZwUzMws5aRgZmYpJwUzM0s5KZiZWcpJwczMUk4KZmaWyvSJZisXu2YdgJkVyCMFMzNLOSmYmVnKScHMzFJOCmZmlnJSMDOzlJOCmZmlSp4UJPWT9KikJZIWSzo/ad9R0mxJy5LPHUodm5lZuctipPAxcGFE7A18BThb0j7ABGBORAwA5iTLZmZWQiVPChHxZkQ8m3x/H1gC7AYcDdyWdLsNOKbUsZmZlbtM7ylIqgSGAk8BO0XEm5BLHECfJrYZL6lGUk1dXV3JYjUzKweZJQVJnwNmARdExHuFbhcRUyKiKiKqevfuXbwAzczKUCZJQVIFuYQwPSLuTprfkrRLsn4XYGUWsZmZlbMsZh8JuBVYEhHXNVh1H1CdfK8G7i11bGZm5S6LKqkHAicDz0takLRdAkwCZko6HXgV+E4GsZmZlbWSJ4WImAuoidWjSxmLmZl9mp9oNjOzlJOCmZmlnBTMzCzl13Fa51a3KusIzDoUJwUz67j+tDCb4x4+OJvjloAvH5mZWcpJwczMUk4KZmaW8j0FM7OW6sT3MjxSMDOzlJOCmZmlnBTMzCzlpGBmZiknBTMzS3n2UVnZNesAzKydK/OkkNUvyTcyOq6Z2aa1u6Qg6TDgV0AX4JaImFS0g2VVLK13Noc1M2tOu7qnIKkLcBNwOLAPcIKkfbKNysysfLS3kcJ+wPKIeAlA0p3A0cALmUZlm88lrM06hPaWFHYDXmuwXAvs37CDpPHA+GTx35KWNtpHL6Cd/wZq6hXVqQ5wDs3yOWSvo8cPPodG5n3yVc3+HtmU3Zta0d6SQr6zjE8tREwBpjS5A6kmIqraOrBS8jm0Dx39HDp6/OBzyEK7uqdAbmTQr8FyXzxVx8ysZNpbUngGGCCpv6QtgbHAfRnHZGZWNtrV5aOI+FjSOcCD5KakTo2IxS3cTZOXljoQn0P70NHPoaPHDz6HklNENN/LzMzKQnu7fGRmZhlyUjAzs1SnTgqSfiApJPXKOpaWkvQTSQslLZD0kKQOVc1O0rWSXkzO4R5J22cdU0tJ+o6kxZI2SuowUwohVy5G0lJJyyVNyDqelpI0VdJKSYuyjqW1JPWT9KikJcn/js7POqZCdNqkIKkfcAjwataxtNK1ETE4IoYA9wOXZxxPS80GBkbEYODvwMUZx9Mai4BjgcezDqQlOkm5mGnAYVkHsZk+Bi6MiL2BrwBnd4T/Dp02KQDXAxfR6OG3jiIi3muwuDUd7Dwi4qGI+DhZ/Bu5Z046lIhYEhGNn5jvCNJyMRHxEVBfLqbDiIjHgXeyjmNzRMSbEfFs8v19YAm5qg3tWruaktpWJB0FvB4Rz2nzHgXPlKSJwCnAauDgjMPZHOOAGVkHUUaaLRdjpSWpEhgKPJVxKM3qsElB0sPAznlWXQpcAhxa2ohablPnEBH3RsSlwKWSLgbOAX5c0gCb0Vz8SZ9LyQ2jp5cytkIVcg4dULPlYqx0JH0OmAVc0OgKQLvUYZNCRHw9X7ukQUB/oH6U0Bd4VtJ+EfHPEobYrKbOIY/fAw/QzpJCc/FLqgaOBEZHO30gpgX/DToSl4tpJyRVkEsI0yPi7qzjKUSHTQpNiYjngT71y5JeAaoiokNVWpQ0ICKWJYtHAS9mGU9LJS9L+hHwtYj4IOt4ykxaLgZ4nVy5mBOzDan8KPdX6a3Akoi4Lut4CtWZbzR3dJMkLZK0kNylsA4xna2BycA2wOxkWu2vsw6opSR9U1ItcADwgKQHs46pEMkN/vpyMUuAma0oF5MpSXcATwJ7SaqVdHrWMbXCgcDJwKjk/wMLJP1n1kE1x2UuzMws5ZGCmZmlnBTMzCzlpGBmZiknBTMzSzkpmJlZyknBrAFJO0n6vaSXJM2T9GQyNXWkpPuzjs+s2JwUzBLJw0Z/AB6PiD0i4svkHvzqcMX8zFrLScHsE6OAjyIifdAuIlZExI0NO0m6QtIPGiwvSgqeIemU5B0Sz0n6XdK2u6Q5SfscSZ9P2r+TbPucpMeTti7JuyieSfp/r/inbfaJTlfmwmwz7As829qNJe1LriDjgRGxStKOyarJwG8j4jZJ44AbgGPIvSNjTES83uAlRKcDqyNiuKRuwF8lPRQRL7c2LrOW8EjBrAmSbkr+in+mwE1GAXfV19mKiPr3ARxArqghwO+Ag5LvfwWmSToD6JK0HQqcImkBuTLLPYEBm3UiZi3gkYLZJxYD36pfiIizk1e51jTq9zGf/oOqe/IpCitRHcn+z5S0P3AEsEDSkGQf50ZEh6izZJ2PRwpmn3gE6C7pvxu0bZWn3yvAMABJw8iVageYAxwnqWeyrv7y0RPkblgDnATMTdZ/ISKeiojLgVXkyl0/CPx3UnIZSXtK2rptTs+seR4pmCUiIiQdA1wv6SKgDlhDrgR4Q7P45BLPM+TeQU1ELE7elvcXSRuA+cCpwHnAVEk/TPZ5WrKfayUNIDc6mAM8BywEKsm9A0RJ/2OKcLpmeblKqpmZpXz5yMzMUk4KZmaWclIwM7OUk4KZmaWcFMzMLOWkYGZmKScFMzNL/X/Sjr/11taMzQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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q1FfUv/4j4mvAUOAPwN2SXkhO+HZrxTrNzCxjRR8SiohN5EYKs4HewD8DiyV9q0S1mZlZmRV7TmGcpAeBp4EK4OiIOAX4LPCdEtZnZmZlVOwlqf8C3JzcpZyKiL9JmtT6ZZmZWRaKPXy0tnEgSPohQEQsaPWqzMwsE8WGwugCbae0ZiFmZpa9XR4+kvR/gAuAf5C0LK+rG/DrUhZmZmbl19w5hf8EHgd+QDLNdeK9iGg8A6qZmbVzzYVCRMTrki5s3CHpIAeDmdnepZiRwlhgEblZS/NnMw3gsBLVZWZmGdhlKETE2OS1f3nKMTOzLDV3onnYrvojYnHrlmNmZllq7vDRj3fRF8CJrViLmZllrLnDRyeUqxAzM8tec4ePToyIpyWdXqg/Ih4oTVlmZpaF5g4ffYHcJHhfKtAXgEPBzGwv0tzho+8nr+eVpxwzM8tSsVNn95B0i6TFkhZJ+qmkHqUuzszMyqvYCfFmAw3AV8hNo90AzClVUWZmlo1iQ+GgiLg2Iv6Y/FwHHNjSjUr6V0krJC2X9HNJXSQdJGm+pJXJa/eWfr+ZmbVMsaHwjKQJkvZJfr4KPNaSDUo6FLgIqIqIgUAnYAK5CfcWRMQAYAE7T8BnZmZlsMtQkPSepE3AN8jNg/RB8jMb+Nc92G5noKukzsB+wJ+A04B7k/57gS/vwfebmVkLNHf1UbfW3mBEvCXpR8AbwGbgyYh4UtInI2Jtss5aSb0KfV5SDVAD8KlPfaq1yzMz69CKPXyEpO6Sjpb0+R0/Ldlgcq7gNKA/cAjwcUlfK/bzETEjIqoioqqysrIlJZiZWROau3kNAEn/G7gY6AMsBT4HvEDL5j76IvDHiGhIvvsBYATwtqTeySihN7CuBd9tZmZ7oNiRwsXAcGBNMh/SUHKXpbbEG8DnJO0nScAooA54BKhO1qkGHm7h95uZWQsVNVIAtkTEFklI2jciXpV0eEs2GBEvSrofWAx8CCwBZgCfAOZK+jq54DijJd9vZmYtV2wo1Es6EHgImC/pL+SuGGqRZPqM7zdq/ju5UYOZmWWkqFCIiH9O3l4l6RngAOCJklVlZmaZKHaksOMpbMeTmx311xHxQcmqMjOzTBQ7Id6V5G4o6wH0BO6W9O+lLMzMzMqv2JHCmcDQiNgCIGkauRPF15WqMDMzK79iL0l9HeiSt7wv8IdWr8bMzDLV3OM4byV3DuHvwApJ85Pl0cDC0pdnZmbl1Nzho9rkdRHwYF77syWpxszMMtXchHg7Zi1F0seAf0wWX4uIraUszMzMyq/YuY9Gkrv66HVAQF9J1RHxXMkqMzOzsiv26qMfAydFxGsAkv4R+DnwP0tVmJmZlV+xVx9V7AgEgIj4PVBRmpLMzCwrxY4UFkm6E/h/yfLZ5E4+m5nZXqTYUPgmcCG5ZysLeA64vVRFmZlZNpoNBUn7AIsiYiBwU+lLMjOzrDR7TiEitgOvSPIDkc3M9nLFHj7qTe6O5peA93c0RsS4klRlZmaZKDYUri5pFWZm1iY0N/dRF3Inmf8H8Fvgzoj4sByFmZlZ+TV3TuFeoIpcIJxC7ia2PSbpQEn3S3pVUp2kYyUdJGm+pJXJa/fW2JaZmRWvuVA4MiK+FhH/F/gX4H+10nZ/CjwREZ8BPgvUAVOBBRExAFiQLJuZWRk1FwrppHetddhI0v7A54E7k+/9ICLeBU4jNzIhef1ya2zPzMyK19yJ5s9K2pS8F9A1WRYQEbF/C7Z5GNBA7pGenyV3Z/TFwCcjYi25L14rqVehD0uqAWoAPvUpXyVrZtaadjlSiIhOEbF/8tMtIjrnvW9JIEAuiIYB/xERQ8ld4lr0oaKImBERVRFRVVlZ2cISzMyskGInxGtN9UB9RLyYLN9PLiTeltQbIHldl0FtZmYdWtlDISL+DLwp6fCkaRTwO+ARoDppqwYeLndtZmYdXbE3r7W2bwGzkqe5rQbOIxdQcyV9HXgDOCOj2szMOqxMQiEilpK7/6GxUWUuxczM8mRxTsHMzNooh4KZmaUcCmZmlnIomJlZyqFgZmYph4KZmaUcCmZmlnIomJlZyqFgZmaprKa5sHbtkKwLMLMS8UjBzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0s5FMzMLOVQMDOzVGahIKmTpCWSHk2WD5I0X9LK5LV7VrWZmXVUWY4ULgbq8panAgsiYgCwIFk2M7MyymSaC0l9gFOB64FvJ82nASOT9/cCzwL/Vu7azNq9qqrstl1bm922rVVkNVL4CXApsD2v7ZMRsRYgee1V6IOSaiTVSqptaGgoeaFmZh1J2UNB0lhgXUQsasnnI2JGRFRFRFVlZWUrV2dm1rFlcfjoOGCcpH8CugD7S5oJvC2pd0SsldQbWJdBbWZmHVrZRwoRcVlE9ImIfsAE4OmI+BrwCFCdrFYNPFzu2szMOrq2dJ/CNGC0pJXA6GTZzMzKKNOH7ETEs+SuMiIi1gOjsqzHzKyja0sjBTMzy5hDwczMUg4FMzNLORTMzCzlUDAzs1SmVx+ZWSt7fFl5tnPK4PJsx8rOIwUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSZQ8FSX0lPSOpTtIKSRcn7QdJmi9pZfLavdy1mZl1dFmMFD4ELomII4DPARdKOhKYCiyIiAHAgmTZzMzKqOyhEBFrI2Jx8v49oA44FDgNuDdZ7V7gy+Wuzcyso8v0nIKkfsBQ4EXgkxGxFnLBAfTKsDQzsw4ps1CQ9AlgHjAlIjbtxudqJNVKqm1oaChdgWZmHVAmoSCpglwgzIqIB5LmtyX1Tvp7A+sKfTYiZkREVURUVVZWlqdgM7MOIourjwTcCdRFxE15XY8A1cn7auDhctdmZtbRZfGM5uOAicBvJS1N2i4HpgFzJX0deAM4I4PazMw6tLKHQkQsBNRE96hy1mJmZjvLYqRgHVHDO1lXYGZF8DQXZmaWciiYmVnKoWBmZimHgpmZpRwKZmaWciiYmVnKoWBmZinfp2BmraeqKpvt1tZms929kEcKZmaW6uAjhUPKtJ0/lWk7ZmZ7xiMFMzNLdfCRgpm1yOPLyrOdUwaXZzuW8kjBzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0u1uUtSJZ0M/BToBNwREdNKtrFyPSLylEa3/vuWfLPiFHvpa8PH9mw7Lb30dS/8u9ymQkFSJ+A2YDRQD7ws6ZGI+F22lZmZFZDVXE9QskBqa4ePjgZWRcTqiPgAmA2clnFNZmYdRpsaKQCHAm/mLdcDx+SvIKkGqEkW/yrptVbYbk+ghMeSFu28KJVuU62vxPum3fP+aVo72jeLml+l9e3Z/tmz3yOfbqqjrYVCoT9l7LQQMQOY0aoblWojIsNxYNvlfbNr3j9N877Ztba6f9ra4aN6oG/ech88xaiZWdm0tVB4GRggqb+kjwETgEcyrsnMrMNoU4ePIuJDSZOBX5K7JPWuiFhRhk236uGovYz3za55/zTN+2bX2uT+UUQ0v5aZmXUIbe3wkZmZZcihYGZmKYdCHknfkRSSemZdS1si6UZJr0paJulBSQdmXVPWJJ0s6TVJqyRNzbqetkRSX0nPSKqTtELSxVnX1NZI6iRpiaRHs66lMYdCQlJfctNrvJF1LW3QfGBgRAwGfg9clnE9mcqbjuUU4EjgTElHZltVm/IhcElEHAF8DrjQ++cjLgbqsi6iEIfCf7sZuJRGN8sZRMSTEfFhsvgbcvePdGSejmUXImJtRCxO3r9H7pffodlW1XZI6gOcCtyRdS2FOBQASeOAtyLilaxraQcmAY9nXUTGCk3H4l96BUjqBwwFXsy4lLbkJ+T+Abo94zoKalP3KZSSpKeAgwt0fQ+4HDipvBW1LbvaPxHxcLLO98gdGphVztraoGanYzGQ9AlgHjAlIjZlXU9bIGkssC4iFkkamXE5BXWYUIiILxZqlzQI6A+8otwEU32AxZKOjog/l7HETDW1f3aQVA2MBUaFb27xdCzNkFRBLhBmRcQDWdfThhwHjJP0T0AXYH9JMyPiaxnXlfLNa41Ieh2oioh2Mrtj6SUPProJ+EJENGRdT9YkdSZ3wn0U8Ba56VnOKtPd922ecv+6uhfYEBFTMi6nzUpGCt+JiLEZl7ITn1OwYkwHugHzJS2V9LOsC8pSctJ9x3QsdcBcB8JOjgMmAicm/78sTf5lbO2ARwpmZpbySMHMzFIOBTMzSzkUzMws5VAwM7OUQ8HMzFIOBbMWkrQtudzyFUmLJY1I2vsls+1em7duT0lbJU1Plq+S9J2sajdrikPBrOU2R8SQiPgsuZljf5DXt5rcHeA7nAH4XgZr8xwKZq1jf+AvecubgTpJVcnyeGBu2asy200dZu4jsxLoKmkpuTlsegMnNuqfDUyQ9GdgG7n5kQ4pa4Vmu8mhYNZymyNiCICkY4H7JA3M638CuBZ4G5hT/vLMdp8PH5m1goh4AegJVOa1fQAsAi4hN2OoWZvnkYJZK5D0GaATsB7YL6/rx8CvImJ9MjW7WZvmUDBruR3nFCD34J3qiNiW/8s/mT3VVx1Zu+FZUs3MLOVzCmZmlnIomJlZyqFgZmYph4KZmaUcCmZmlnIomJlZyqFgZmap/w+0hUkyytbx/AAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for i in range(len(df.columns[:-1])):\n",
        "  label = scaled_df.columns[i]\n",
        "  plt.hist(scaled_df[scaled_df['Outcome']==1][label], color='red', label='Diabetes', alpha=0.8)\n",
        "  plt.hist(scaled_df[scaled_df['Outcome']==0][label], color='yellow', label='No Diabetes', alpha = 0.9)\n",
        "  plt.title(label)\n",
        "  plt.xlabel(label)\n",
        "  plt.ylabel('Probability')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VefhGf8Xvt7J"
      },
      "source": [
        "### tf neural net"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "sctUzpAbtJ6B"
      },
      "outputs": [],
      "source": [
        "x_train, x_temp, y_train, y_temp = train_test_split(X, y, test_size=0.2, random_state=11)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "QbTAyBj0uvHO"
      },
      "outputs": [],
      "source": [
        "x_val, x_test, y_val, y_test = train_test_split(x_temp, y_temp, test_size=0.5, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "WXa7sxlZvkIu"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "                             tf.keras.layers.Dense(16, activation= 'relu'),\n",
        "                             tf.keras.layers.Dense(16, activation = 'relu'),\n",
        "                             tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "vUKdOQdRx4To"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), \n",
        "              loss = tf.keras.losses.BinaryCrossentropy(), \n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enraiKTGzAzZ",
        "outputId": "62c70ab7-7d1d-4237-e2ca-273d6bf18d67"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "20/20 [==============================] - 2s 4ms/step - loss: 0.6637 - accuracy: 0.6645\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.663699209690094, 0.6644951105117798]"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(x_train, y_train)\n",
        "# model.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h5ZN9-oSzo6Y",
        "outputId": "f9aac785-c634-43d6-edfa-0de7890f4902"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "77/77 [==============================] - 2s 2ms/step - loss: 0.6247 - accuracy: 0.6954 - val_loss: 0.6126 - val_accuracy: 0.5974\n",
            "Epoch 2/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.5676 - accuracy: 0.7166 - val_loss: 0.5839 - val_accuracy: 0.6494\n",
            "Epoch 3/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.5336 - accuracy: 0.7378 - val_loss: 0.5628 - val_accuracy: 0.6883\n",
            "Epoch 4/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.5093 - accuracy: 0.7541 - val_loss: 0.5530 - val_accuracy: 0.6883\n",
            "Epoch 5/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4942 - accuracy: 0.7541 - val_loss: 0.5471 - val_accuracy: 0.6623\n",
            "Epoch 6/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4806 - accuracy: 0.7704 - val_loss: 0.5471 - val_accuracy: 0.6753\n",
            "Epoch 7/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4710 - accuracy: 0.7752 - val_loss: 0.5462 - val_accuracy: 0.7013\n",
            "Epoch 8/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4630 - accuracy: 0.7769 - val_loss: 0.5429 - val_accuracy: 0.7013\n",
            "Epoch 9/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4580 - accuracy: 0.7785 - val_loss: 0.5423 - val_accuracy: 0.7013\n",
            "Epoch 10/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4525 - accuracy: 0.7834 - val_loss: 0.5444 - val_accuracy: 0.7273\n",
            "Epoch 11/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4475 - accuracy: 0.7850 - val_loss: 0.5416 - val_accuracy: 0.7273\n",
            "Epoch 12/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4440 - accuracy: 0.7883 - val_loss: 0.5481 - val_accuracy: 0.7273\n",
            "Epoch 13/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4419 - accuracy: 0.7834 - val_loss: 0.5465 - val_accuracy: 0.7143\n",
            "Epoch 14/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4397 - accuracy: 0.7850 - val_loss: 0.5409 - val_accuracy: 0.7143\n",
            "Epoch 15/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4363 - accuracy: 0.7899 - val_loss: 0.5496 - val_accuracy: 0.7143\n",
            "Epoch 16/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4351 - accuracy: 0.7899 - val_loss: 0.5535 - val_accuracy: 0.7143\n",
            "Epoch 17/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4327 - accuracy: 0.7899 - val_loss: 0.5468 - val_accuracy: 0.7143\n",
            "Epoch 18/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4313 - accuracy: 0.7915 - val_loss: 0.5444 - val_accuracy: 0.7013\n",
            "Epoch 19/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4306 - accuracy: 0.7915 - val_loss: 0.5512 - val_accuracy: 0.7013\n",
            "Epoch 20/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4273 - accuracy: 0.7932 - val_loss: 0.5471 - val_accuracy: 0.6883\n",
            "Epoch 21/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4270 - accuracy: 0.7850 - val_loss: 0.5648 - val_accuracy: 0.7013\n",
            "Epoch 22/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4245 - accuracy: 0.7899 - val_loss: 0.5445 - val_accuracy: 0.7013\n",
            "Epoch 23/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4250 - accuracy: 0.7980 - val_loss: 0.5424 - val_accuracy: 0.6883\n",
            "Epoch 24/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4207 - accuracy: 0.8029 - val_loss: 0.5553 - val_accuracy: 0.7013\n",
            "Epoch 25/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4204 - accuracy: 0.7948 - val_loss: 0.5493 - val_accuracy: 0.7013\n",
            "Epoch 26/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4190 - accuracy: 0.8046 - val_loss: 0.5605 - val_accuracy: 0.7013\n",
            "Epoch 27/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4171 - accuracy: 0.7980 - val_loss: 0.5484 - val_accuracy: 0.6883\n",
            "Epoch 28/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4160 - accuracy: 0.7997 - val_loss: 0.5513 - val_accuracy: 0.6883\n",
            "Epoch 29/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.4163 - accuracy: 0.8029 - val_loss: 0.5647 - val_accuracy: 0.7013\n",
            "Epoch 30/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4126 - accuracy: 0.7964 - val_loss: 0.5454 - val_accuracy: 0.6883\n",
            "Epoch 31/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4129 - accuracy: 0.8046 - val_loss: 0.5549 - val_accuracy: 0.6883\n",
            "Epoch 32/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4124 - accuracy: 0.7964 - val_loss: 0.5559 - val_accuracy: 0.6883\n",
            "Epoch 33/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4104 - accuracy: 0.7980 - val_loss: 0.5433 - val_accuracy: 0.6883\n",
            "Epoch 34/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4089 - accuracy: 0.8029 - val_loss: 0.5508 - val_accuracy: 0.6883\n",
            "Epoch 35/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4083 - accuracy: 0.7980 - val_loss: 0.5497 - val_accuracy: 0.6883\n",
            "Epoch 36/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4069 - accuracy: 0.8029 - val_loss: 0.5556 - val_accuracy: 0.6883\n",
            "Epoch 37/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4055 - accuracy: 0.8078 - val_loss: 0.5541 - val_accuracy: 0.6883\n",
            "Epoch 38/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4050 - accuracy: 0.8046 - val_loss: 0.5548 - val_accuracy: 0.7013\n",
            "Epoch 39/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4042 - accuracy: 0.8046 - val_loss: 0.5633 - val_accuracy: 0.7013\n",
            "Epoch 40/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4042 - accuracy: 0.8046 - val_loss: 0.5643 - val_accuracy: 0.6883\n",
            "Epoch 41/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4022 - accuracy: 0.8094 - val_loss: 0.5579 - val_accuracy: 0.6883\n",
            "Epoch 42/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.4006 - accuracy: 0.8143 - val_loss: 0.5519 - val_accuracy: 0.6883\n",
            "Epoch 43/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3996 - accuracy: 0.8029 - val_loss: 0.5620 - val_accuracy: 0.6883\n",
            "Epoch 44/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3994 - accuracy: 0.8094 - val_loss: 0.5659 - val_accuracy: 0.6883\n",
            "Epoch 45/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3988 - accuracy: 0.8111 - val_loss: 0.5726 - val_accuracy: 0.7013\n",
            "Epoch 46/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.3976 - accuracy: 0.8111 - val_loss: 0.5549 - val_accuracy: 0.6883\n",
            "Epoch 47/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.3962 - accuracy: 0.8127 - val_loss: 0.5664 - val_accuracy: 0.7013\n",
            "Epoch 48/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3957 - accuracy: 0.8094 - val_loss: 0.5727 - val_accuracy: 0.7013\n",
            "Epoch 49/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3951 - accuracy: 0.8111 - val_loss: 0.5561 - val_accuracy: 0.6883\n",
            "Epoch 50/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.3953 - accuracy: 0.8127 - val_loss: 0.5598 - val_accuracy: 0.6883\n",
            "Epoch 51/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.3953 - accuracy: 0.8078 - val_loss: 0.5648 - val_accuracy: 0.6883\n",
            "Epoch 52/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3924 - accuracy: 0.8111 - val_loss: 0.5689 - val_accuracy: 0.6883\n",
            "Epoch 53/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3914 - accuracy: 0.8176 - val_loss: 0.5844 - val_accuracy: 0.6753\n",
            "Epoch 54/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3926 - accuracy: 0.8192 - val_loss: 0.5755 - val_accuracy: 0.6883\n",
            "Epoch 55/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3896 - accuracy: 0.8225 - val_loss: 0.5632 - val_accuracy: 0.7013\n",
            "Epoch 56/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3893 - accuracy: 0.8127 - val_loss: 0.5681 - val_accuracy: 0.6883\n",
            "Epoch 57/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3880 - accuracy: 0.8176 - val_loss: 0.5752 - val_accuracy: 0.6883\n",
            "Epoch 58/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3853 - accuracy: 0.8176 - val_loss: 0.5663 - val_accuracy: 0.6883\n",
            "Epoch 59/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3857 - accuracy: 0.8160 - val_loss: 0.5723 - val_accuracy: 0.6883\n",
            "Epoch 60/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.3859 - accuracy: 0.8274 - val_loss: 0.5711 - val_accuracy: 0.6883\n",
            "Epoch 61/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.3843 - accuracy: 0.8192 - val_loss: 0.5792 - val_accuracy: 0.6883\n",
            "Epoch 62/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3836 - accuracy: 0.8143 - val_loss: 0.5731 - val_accuracy: 0.6883\n",
            "Epoch 63/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3803 - accuracy: 0.8257 - val_loss: 0.5680 - val_accuracy: 0.6883\n",
            "Epoch 64/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3798 - accuracy: 0.8192 - val_loss: 0.5823 - val_accuracy: 0.6883\n",
            "Epoch 65/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.3798 - accuracy: 0.8208 - val_loss: 0.5624 - val_accuracy: 0.6883\n",
            "Epoch 66/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3794 - accuracy: 0.8160 - val_loss: 0.5726 - val_accuracy: 0.6883\n",
            "Epoch 67/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3779 - accuracy: 0.8257 - val_loss: 0.5833 - val_accuracy: 0.6883\n",
            "Epoch 68/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3748 - accuracy: 0.8290 - val_loss: 0.5640 - val_accuracy: 0.6883\n",
            "Epoch 69/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3760 - accuracy: 0.8225 - val_loss: 0.5624 - val_accuracy: 0.6883\n",
            "Epoch 70/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3773 - accuracy: 0.8225 - val_loss: 0.5659 - val_accuracy: 0.6883\n",
            "Epoch 71/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3735 - accuracy: 0.8306 - val_loss: 0.5876 - val_accuracy: 0.6883\n",
            "Epoch 72/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3740 - accuracy: 0.8257 - val_loss: 0.5873 - val_accuracy: 0.6883\n",
            "Epoch 73/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3711 - accuracy: 0.8339 - val_loss: 0.5749 - val_accuracy: 0.6883\n",
            "Epoch 74/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3718 - accuracy: 0.8322 - val_loss: 0.5685 - val_accuracy: 0.6883\n",
            "Epoch 75/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3705 - accuracy: 0.8290 - val_loss: 0.5839 - val_accuracy: 0.6883\n",
            "Epoch 76/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3688 - accuracy: 0.8355 - val_loss: 0.5789 - val_accuracy: 0.6883\n",
            "Epoch 77/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3661 - accuracy: 0.8388 - val_loss: 0.5871 - val_accuracy: 0.6883\n",
            "Epoch 78/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3663 - accuracy: 0.8290 - val_loss: 0.5691 - val_accuracy: 0.6753\n",
            "Epoch 79/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3658 - accuracy: 0.8257 - val_loss: 0.5818 - val_accuracy: 0.6883\n",
            "Epoch 80/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.3660 - accuracy: 0.8306 - val_loss: 0.6028 - val_accuracy: 0.6623\n",
            "Epoch 81/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3661 - accuracy: 0.8306 - val_loss: 0.5839 - val_accuracy: 0.7013\n",
            "Epoch 82/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3608 - accuracy: 0.8339 - val_loss: 0.5918 - val_accuracy: 0.6753\n",
            "Epoch 83/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3636 - accuracy: 0.8306 - val_loss: 0.6017 - val_accuracy: 0.6623\n",
            "Epoch 84/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3594 - accuracy: 0.8404 - val_loss: 0.5825 - val_accuracy: 0.6883\n",
            "Epoch 85/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.3614 - accuracy: 0.8339 - val_loss: 0.5696 - val_accuracy: 0.6883\n",
            "Epoch 86/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3592 - accuracy: 0.8469 - val_loss: 0.5807 - val_accuracy: 0.6883\n",
            "Epoch 87/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3585 - accuracy: 0.8453 - val_loss: 0.6018 - val_accuracy: 0.6623\n",
            "Epoch 88/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3575 - accuracy: 0.8420 - val_loss: 0.5848 - val_accuracy: 0.6753\n",
            "Epoch 89/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3554 - accuracy: 0.8388 - val_loss: 0.5982 - val_accuracy: 0.6753\n",
            "Epoch 90/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3540 - accuracy: 0.8420 - val_loss: 0.5928 - val_accuracy: 0.6753\n",
            "Epoch 91/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3523 - accuracy: 0.8404 - val_loss: 0.5858 - val_accuracy: 0.6883\n",
            "Epoch 92/100\n",
            "77/77 [==============================] - 0s 2ms/step - loss: 0.3525 - accuracy: 0.8436 - val_loss: 0.6036 - val_accuracy: 0.6883\n",
            "Epoch 93/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3522 - accuracy: 0.8453 - val_loss: 0.6156 - val_accuracy: 0.6623\n",
            "Epoch 94/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3491 - accuracy: 0.8550 - val_loss: 0.5880 - val_accuracy: 0.6883\n",
            "Epoch 95/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3495 - accuracy: 0.8485 - val_loss: 0.6025 - val_accuracy: 0.6623\n",
            "Epoch 96/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3481 - accuracy: 0.8485 - val_loss: 0.5898 - val_accuracy: 0.6883\n",
            "Epoch 97/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3459 - accuracy: 0.8502 - val_loss: 0.5865 - val_accuracy: 0.6883\n",
            "Epoch 98/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3468 - accuracy: 0.8502 - val_loss: 0.6012 - val_accuracy: 0.6753\n",
            "Epoch 99/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3439 - accuracy: 0.8567 - val_loss: 0.6004 - val_accuracy: 0.6753\n",
            "Epoch 100/100\n",
            "77/77 [==============================] - 0s 1ms/step - loss: 0.3435 - accuracy: 0.8534 - val_loss: 0.5994 - val_accuracy: 0.6883\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x1e6bfea9dc0>"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(x_train, y_train, batch_size=8, epochs=100, validation_data=(x_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [],
      "source": [
        "pred =model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(77, 1) (77,)\n"
          ]
        }
      ],
      "source": [
        "print(pred.shape, y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_test = np.expand_dims(y_test, axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [],
      "source": [
        "pred = np.expand_dims(pred, axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Converting NNumpy two dimentional array to one dimetional"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [],
      "source": [
        "pred = np.squeeze(pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(77,) (1, 77)\n"
          ]
        }
      ],
      "source": [
        "print(pred.shape, y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "        0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
              "        0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n",
              "        0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0]], dtype=int64)"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# pred\n",
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_test = y_test.astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "        0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
              "        0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n",
              "        0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0]])"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.00828803, 0.82313263, 0.36038506, 0.26705748, 0.26960394,\n",
              "       0.08020091, 0.093072  , 0.297287  , 0.01453862, 0.11739472,\n",
              "       0.78442764, 0.00805056, 0.0466381 , 0.0163182 , 0.01097336,\n",
              "       0.10438982, 0.01745623, 0.4857557 , 0.87243986, 0.00771552,\n",
              "       0.81023693, 0.3045416 , 0.47129935, 0.46859646, 0.22393733,\n",
              "       0.74476135, 0.14459923, 0.10184723, 0.15377551, 0.13866267,\n",
              "       0.15350911, 0.9633641 , 0.5278729 , 0.581449  , 0.29691723,\n",
              "       0.00267291, 0.0038802 , 0.34070322, 0.07422635, 0.00467771,\n",
              "       0.84195614, 0.16069984, 0.56309235, 0.90508294, 0.00557625,\n",
              "       0.94086254, 0.01152146, 0.01975226, 0.01452091, 0.00986221,\n",
              "       0.00666645, 0.20363522, 0.7196103 , 0.00574642, 0.07472414,\n",
              "       0.13648114, 0.7514651 , 0.32119194, 0.00428528, 0.02037677,\n",
              "       0.5229499 , 0.81896216, 0.02484858, 0.03388467, 0.98818064,\n",
              "       0.617926  , 0.08585066, 0.33858007, 0.81909263, 0.08915073,\n",
              "       0.08963898, 0.04904881, 0.24954683, 0.9443144 , 0.08761409,\n",
              "       0.75198627, 0.22735325], dtype=float32)"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [],
      "source": [
        "pred = pred.astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9N9QIcN6HQ7I",
        "outputId": "c1942d42-28d0-4882-82db-8cbc121c8058"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "Data cardinality is ambiguous:\n  x sizes: 77\n  y sizes: 1\nMake sure all arrays contain the same number of samples.",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-40-d5f7b36966e1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[1;32mc:\\Python38\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mevaluate\u001b[1;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict, **kwargs)\u001b[0m\n\u001b[0;32m   1464\u001b[0m       \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1465\u001b[0m         \u001b[1;31m# Creates a `tf.data.Dataset` and handles batch and epoch iteration.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1466\u001b[1;33m         data_handler = data_adapter.get_data_handler(\n\u001b[0m\u001b[0;32m   1467\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1468\u001b[0m             \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\Python38\\lib\\site-packages\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36mget_data_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1381\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"model\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"_cluster_coordinator\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1382\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_ClusterCoordinatorDataHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1383\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mDataHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1384\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1385\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\Python38\\lib\\site-packages\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)\u001b[0m\n\u001b[0;32m   1136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1137\u001b[0m     \u001b[0madapter_cls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mselect_data_adapter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1138\u001b[1;33m     self._adapter = adapter_cls(\n\u001b[0m\u001b[0;32m   1139\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1140\u001b[0m         \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\Python38\\lib\\site-packages\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)\u001b[0m\n\u001b[0;32m    239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m     \u001b[0mnum_samples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m     \u001b[0m_check_data_cardinality\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    243\u001b[0m     \u001b[1;31m# If batch_size is not passed but steps is, calculate from the input data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\Python38\\lib\\site-packages\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m_check_data_cardinality\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m   1647\u001b[0m           label, \", \".join(str(i.shape[0]) for i in tf.nest.flatten(single_data)))\n\u001b[0;32m   1648\u001b[0m     \u001b[0mmsg\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;34m\"Make sure all arrays contain the same number of samples.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1649\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1651\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;31mValueError\u001b[0m: Data cardinality is ambiguous:\n  x sizes: 77\n  y sizes: 1\nMake sure all arrays contain the same number of samples."
          ]
        }
      ],
      "source": [
        "model.evaluate(pred, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(116, 8)"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#model.predict(x_test[5])\n",
        "# x_test[1]\n",
        "x_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ua8FWX2cM0SY",
        "outputId": "0e664888-23e5-480a-9ca7-d05474e324c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 16)                144       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 433\n",
            "Trainable params: 433\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lq9T0SJFUJJO"
      },
      "source": [
        "### Save the trained model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0I0_x1TCNWI1"
      },
      "outputs": [],
      "source": [
        "model.save(\"diabetes_dnnmodel2.h5\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oz9D8SVda2tS"
      },
      "source": [
        "### Load the trained Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ePZEdqLeTfea"
      },
      "outputs": [],
      "source": [
        "new_model = tf.keras.models.load_model(\"diabetes_dnnmodel2.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-meHo8TUoEB",
        "outputId": "e8bfcfab-3979-4032-8f22-325b01d34f41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4/4 - 0s - loss: 0.4851 - accuracy: 0.7931\n"
          ]
        }
      ],
      "source": [
        "evlr = new_model.evaluate(x_test, y_test, verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7931034564971924"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "evlr[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kdd40TstU1cH"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0]])"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pred = new_model.predict(x_test[5:29])\n",
        "# new_model = new_model.astype(int)\n",
        "pred.dtype\n",
        "pred = pred.astype(int)\n",
        "pred"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyOvT0w8J93j/X9vP5sRlhHr",
      "include_colab_link": true,
      "name": "diabetes-prediction-tf-neuralnet.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.8.3 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    },
    "vscode": {
      "interpreter": {
        "hash": "9650cb4e16cdd4a8e8e2d128bf38d875813998db22a3c986335f89e0cb4d7bb2"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}