1. Perceptron for Phish Detection.ipynb

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        "# **Perceptron for Phish Detection**\n",
        "\n",
        "**Task:** Train a single-layer perceptron model to create a phish detector. You will use the Phishing Website dataset ( https://archive.ics.uci.edu/ml/datasets/phishing+websites ) for training and evaluating your model. The description of features can be found in the Data Folder. "
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Creating dataframe for the phishing dataset\n",
        "df = pd.read_csv('phishing_dataset.csv',header = None)\n",
        "df"
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      "execution_count": 0,
      "outputs": [
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Creating target & sample variables\n",
        "\n",
        "y = df.iloc[:, 30].values\n",
        "X = df.iloc[:, :30].values\n",
        "y.shape, X.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((11055,), (11055, 30))"
            ]
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      "metadata": {
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Splitting the data into train and test datasets\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "         X, y, test_size=0.2, random_state= 12) \n",
        "X_test.shape, X_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2211, 30), (8844, 30))"
            ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "hTNLIRq4J1Je",
        "colab_type": "code",
        "outputId": "088575c5-2e00-423d-da57-aa04490fd806",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "# Fitting the train data to perceptron model\n",
        "from sklearn.linear_model import Perceptron\n",
        "\n",
        "p = Perceptron(max_iter=5000, eta0=0.0003, random_state=4)\n",
        "p.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=0.0003,\n",
              "           fit_intercept=True, max_iter=5000, n_iter_no_change=5, n_jobs=None,\n",
              "           penalty=None, random_state=4, shuffle=True, tol=0.001,\n",
              "           validation_fraction=0.1, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9u5U55fDJ3tc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predicting the target value for the test dataset\n",
        "\n",
        "y_pred = p.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcZzFY3SJ8Jr",
        "colab_type": "code",
        "outputId": "a17c947d-6a92-4aca-9f40-03274423c2bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Calculating accuracy and number of mispredicted samples\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "print('Misclassified samples: %d' % (y_test != y_pred).sum())\n",
        "print('Accuracy: %.5f' % accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Misclassified samples: 162\n",
            "Accuracy: 0.92673\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNFK4J5KFe2B",
        "colab_type": "text"
      },
      "source": [
        "* max_iter - No dependency.\n",
        "* validation_fraction - No dependency"
      ]
    }
  ]
}