Rock_vs_Mine.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/redom13/ML/blob/main/Rock_vs_Mine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Importing librarys"
      ],
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        "id": "znW6igZr34w8"
      }
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      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "K9Dsffyu2QHJ"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Collection and Data Preprocessing"
      ],
      "metadata": {
        "id": "fpNuZtV54E8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_data=pd.read_csv('/content/Copy of sonar data.csv',header=None)\n",
        "sonar_data.head()"
      ],
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        "id": "Y5Q0Q7pG32bC",
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          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "37b1f7b8-5725-4184-860c-ca64d590e8b8"
      },
      "execution_count": 8,
      "outputs": [
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sonar_data"
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          "metadata": {},
          "execution_count": 8
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    {
      "cell_type": "code",
      "source": [
        "sonar_data.shape"
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      "metadata": {
        "id": "pqZ4l-bZ4pDt"
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      "execution_count": null,
      "outputs": []
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        "sonar_data.describe()"
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        "id": "DYTCOdOc41Ut"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_data[60].value_counts()"
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      "metadata": {
        "id": "WRsyTaVq5Gl3"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_data.groupby(60).mean()"
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      "metadata": {
        "id": "VXmrG-S55rOV"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Separating Data and Labels"
      ],
      "metadata": {
        "id": "xhR1LaoI66Xx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=sonar_data.drop(columns=60,axis=1)\n",
        "y=sonar_data[60]"
      ],
      "metadata": {
        "id": "DLUuV1AX6iBX"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)\n",
        "print(y)"
      ],
      "metadata": {
        "id": "pBUHQmca7Na2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Training* Data and *Test* Data"
      ],
      "metadata": {
        "id": "7DrO2iI07vRu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,stratify=y,random_state=1)"
      ],
      "metadata": {
        "id": "RXPMRKTo7Rvr"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Training"
      ],
      "metadata": {
        "id": "5s8WQnxJ8N7K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=LogisticRegression()\n",
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "M03VPBSu8CoE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Accuracy"
      ],
      "metadata": {
        "id": "BhSWd1-dAMNM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_prediction=model.predict(X_train)\n",
        "training_accuracy=accuracy_score(training_prediction,y_train)\n",
        "print('Accuracy on training data:',training_accuracy)"
      ],
      "metadata": {
        "id": "mVn9vCvV84Kk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction=model.predict(X_test)\n",
        "test_accuracy=accuracy_score(test_prediction,y_test)\n",
        "print('Accuracy on test data:',test_accuracy)"
      ],
      "metadata": {
        "id": "ViIu8xJg9ufB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_prediction"
      ],
      "metadata": {
        "id": "1AufgwEM9qQH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction using **Neural Network**"
      ],
      "metadata": {
        "id": "yxlYBPhQPEb9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing diff libraries"
      ],
      "metadata": {
        "id": "ozCbmHUYRE3J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.losses import BinaryCrossentropy\n",
        "from keras.optimizers import Adam\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "j4vpo0XJ-wP_"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining model"
      ],
      "metadata": {
        "id": "4Es4B3ZERO2I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nn_model=Sequential([\n",
        "    Dense(60,activation='relu'),\n",
        "    Dense(30,activation='relu'),\n",
        "    Dense(1,activation='linear') # will use z directly\n",
        "])"
      ],
      "metadata": {
        "id": "EqaBwzRXQp0u"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compiling the model"
      ],
      "metadata": {
        "id": "V1GkESoyR4KQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nn_model.compile(\n",
        "    loss=BinaryCrossentropy(from_logits=True),\n",
        "    optimizer=Adam(learning_rate=0.001),\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "J0DSOa7BR3Li"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bit of preprocessing"
      ],
      "metadata": {
        "id": "EFHyhmcjUaJH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder=LabelEncoder()\n",
        "y_train=label_encoder.fit_transform(y_train)\n",
        "y_test=label_encoder.fit_transform(y_test)"
      ],
      "metadata": {
        "id": "GowGDHgEUdQx"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6V1LMn1XVnRr",
        "outputId": "b8206dfe-51c1-40ba-862d-8f7f36a4720c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fitting the data"
      ],
      "metadata": {
        "id": "KCXA3zIvSg_z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nn_model.fit(X_train,y_train,epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zd3aOYVQSgCm",
        "outputId": "e1269356-0cbe-42cd-c184-e75840fe9958"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "6/6 [==============================] - 1s 3ms/step - loss: 0.6870 - accuracy: 0.5348\n",
            "Epoch 2/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.6633 - accuracy: 0.5348\n",
            "Epoch 3/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.6526 - accuracy: 0.5348\n",
            "Epoch 4/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.6397 - accuracy: 0.5401\n",
            "Epoch 5/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.6291 - accuracy: 0.5722\n",
            "Epoch 6/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.6163 - accuracy: 0.5829\n",
            "Epoch 7/100\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.6053 - accuracy: 0.5829\n",
            "Epoch 8/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.5935 - accuracy: 0.5829\n",
            "Epoch 9/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.5792 - accuracy: 0.6043\n",
            "Epoch 10/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.5657 - accuracy: 0.6310\n",
            "Epoch 11/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.5524 - accuracy: 0.6471\n",
            "Epoch 12/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.5379 - accuracy: 0.6578\n",
            "Epoch 13/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.5264 - accuracy: 0.6471\n",
            "Epoch 14/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.5113 - accuracy: 0.6898\n",
            "Epoch 15/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.4964 - accuracy: 0.7005\n",
            "Epoch 16/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.4827 - accuracy: 0.7059\n",
            "Epoch 17/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.4676 - accuracy: 0.7326\n",
            "Epoch 18/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.4598 - accuracy: 0.7754\n",
            "Epoch 19/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.4517 - accuracy: 0.7059\n",
            "Epoch 20/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.4426 - accuracy: 0.7647\n",
            "Epoch 21/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.4258 - accuracy: 0.7861\n",
            "Epoch 22/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.4174 - accuracy: 0.7380\n",
            "Epoch 23/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.4083 - accuracy: 0.7754\n",
            "Epoch 24/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.3995 - accuracy: 0.7807\n",
            "Epoch 25/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3954 - accuracy: 0.7861\n",
            "Epoch 26/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3854 - accuracy: 0.7861\n",
            "Epoch 27/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.3784 - accuracy: 0.7807\n",
            "Epoch 28/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3733 - accuracy: 0.8235\n",
            "Epoch 29/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3666 - accuracy: 0.8128\n",
            "Epoch 30/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3588 - accuracy: 0.7968\n",
            "Epoch 31/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3546 - accuracy: 0.8449\n",
            "Epoch 32/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3501 - accuracy: 0.8021\n",
            "Epoch 33/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.3395 - accuracy: 0.8182\n",
            "Epoch 34/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3377 - accuracy: 0.8663\n",
            "Epoch 35/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.3363 - accuracy: 0.8128\n",
            "Epoch 36/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.3248 - accuracy: 0.8556\n",
            "Epoch 37/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3211 - accuracy: 0.8663\n",
            "Epoch 38/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3139 - accuracy: 0.8556\n",
            "Epoch 39/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.3081 - accuracy: 0.8610\n",
            "Epoch 40/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.3041 - accuracy: 0.8556\n",
            "Epoch 41/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.3089 - accuracy: 0.8770\n",
            "Epoch 42/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.2888 - accuracy: 0.8824\n",
            "Epoch 43/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2923 - accuracy: 0.8663\n",
            "Epoch 44/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2838 - accuracy: 0.8877\n",
            "Epoch 45/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2754 - accuracy: 0.8770\n",
            "Epoch 46/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2732 - accuracy: 0.8717\n",
            "Epoch 47/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2674 - accuracy: 0.8877\n",
            "Epoch 48/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2606 - accuracy: 0.8984\n",
            "Epoch 49/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2582 - accuracy: 0.8877\n",
            "Epoch 50/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2554 - accuracy: 0.8930\n",
            "Epoch 51/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2542 - accuracy: 0.9091\n",
            "Epoch 52/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2507 - accuracy: 0.8717\n",
            "Epoch 53/100\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.2451 - accuracy: 0.9037\n",
            "Epoch 54/100\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.2356 - accuracy: 0.9144\n",
            "Epoch 55/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2413 - accuracy: 0.8663\n",
            "Epoch 56/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2319 - accuracy: 0.9198\n",
            "Epoch 57/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2239 - accuracy: 0.9037\n",
            "Epoch 58/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2210 - accuracy: 0.9144\n",
            "Epoch 59/100\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.2172 - accuracy: 0.9144\n",
            "Epoch 60/100\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.2135 - accuracy: 0.9091\n",
            "Epoch 61/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2213 - accuracy: 0.8984\n",
            "Epoch 62/100\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.2038 - accuracy: 0.9091\n",
            "Epoch 63/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2067 - accuracy: 0.9305\n",
            "Epoch 64/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.2056 - accuracy: 0.9251\n",
            "Epoch 65/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1991 - accuracy: 0.9091\n",
            "Epoch 66/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1978 - accuracy: 0.9305\n",
            "Epoch 67/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1966 - accuracy: 0.9091\n",
            "Epoch 68/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1870 - accuracy: 0.9305\n",
            "Epoch 69/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1841 - accuracy: 0.9358\n",
            "Epoch 70/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1793 - accuracy: 0.9251\n",
            "Epoch 71/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1780 - accuracy: 0.9358\n",
            "Epoch 72/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1784 - accuracy: 0.9198\n",
            "Epoch 73/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1727 - accuracy: 0.9305\n",
            "Epoch 74/100\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.1670 - accuracy: 0.9305\n",
            "Epoch 75/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1652 - accuracy: 0.9305\n",
            "Epoch 76/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1631 - accuracy: 0.9358\n",
            "Epoch 77/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1613 - accuracy: 0.9305\n",
            "Epoch 78/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1570 - accuracy: 0.9358\n",
            "Epoch 79/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1537 - accuracy: 0.9412\n",
            "Epoch 80/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1524 - accuracy: 0.9251\n",
            "Epoch 81/100\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.1492 - accuracy: 0.9358\n",
            "Epoch 82/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1481 - accuracy: 0.9412\n",
            "Epoch 83/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1444 - accuracy: 0.9412\n",
            "Epoch 84/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1401 - accuracy: 0.9412\n",
            "Epoch 85/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1415 - accuracy: 0.9465\n",
            "Epoch 86/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1392 - accuracy: 0.9358\n",
            "Epoch 87/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1329 - accuracy: 0.9412\n",
            "Epoch 88/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1285 - accuracy: 0.9465\n",
            "Epoch 89/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1304 - accuracy: 0.9465\n",
            "Epoch 90/100\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.1273 - accuracy: 0.9465\n",
            "Epoch 91/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1237 - accuracy: 0.9465\n",
            "Epoch 92/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.1216 - accuracy: 0.9465\n",
            "Epoch 93/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.1179 - accuracy: 0.9412\n",
            "Epoch 94/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.1207 - accuracy: 0.9519\n",
            "Epoch 95/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.1169 - accuracy: 0.9519\n",
            "Epoch 96/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.1122 - accuracy: 0.9733\n",
            "Epoch 97/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1090 - accuracy: 0.9626\n",
            "Epoch 98/100\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.1093 - accuracy: 0.9572\n",
            "Epoch 99/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.1065 - accuracy: 0.9679\n",
            "Epoch 100/100\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.1047 - accuracy: 0.9626\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7f3176d29e10>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=nn_model.predict(X_test)\n",
        "y_pred=tf.nn.sigmoid(y_pred)\n",
        "y_pred=np.where(y_pred>0.5,1,0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-21-vFJ6SmHJ",
        "outputId": "65844a95-3e56-4edd-ccb1-72ae6ed40475"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 53ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred.reshape(-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bve76t9YV2bv",
        "outputId": "59fed5ed-8f8b-434c-81c9-df8cd6aaff57"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy:',accuracy_score(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qtZInkgVKFa",
        "outputId": "2fc1eed8-2c5e-4042-c275-578a274009b9"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7619047619047619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This accuracy is almost same as the one found with simple Logistic regression model. So **No Real Improvement** in this particular case"
      ],
      "metadata": {
        "id": "_uw0d8SqWnuq"
      }
    }
  ]
}