7. Naive_Bayes_Classifier.ipynb

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  "metadata": {
    "colab": {
      "name": "Naive Bayes Classifier.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMc1J+BpCu3Ft2UjQ7iL06L",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MainakRepositor/ML-Algorithms/blob/master/Naive_Bayes_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ObmWHGPUp_Lu"
      },
      "source": [
        "# Naive Bayes Classifier\n",
        "\n",
        "<hr>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rimqjjBiqEeN"
      },
      "source": [
        "## 1. Importing the datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BKDGFsOrp5YL",
        "outputId": "112870cc-76da-4cfb-fd1a-eb4159c64af0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "print(\"Basic packages included successfully!\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Basic packages included successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7fxlkwaEqV8R"
      },
      "source": [
        "## 2. Importing the datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DKffXCwuqUnY",
        "outputId": "e8a054aa-16e4-4c95-ff3e-d78048776977",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Social_Network_Ads.csv'\n",
        "df = pd.read_csv(url,error_bad_lines=False)\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  EstimatedSalary  Purchased\n",
              "0   19            19000          0\n",
              "1   35            20000          0\n",
              "2   26            43000          0\n",
              "3   27            57000          0\n",
              "4   19            76000          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cNDCdIo2tZLL"
      },
      "source": [
        "## 3. Splitting the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuJAfS-GsqEf"
      },
      "source": [
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0sQbOvCtb1e"
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddW-XNdXtlj3"
      },
      "source": [
        "## 4. Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVtjnyL4tiNl"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9oJ0yPfktuQv"
      },
      "source": [
        "## 5. Training the Naive Bayes model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0srIoNCBtp_1",
        "outputId": "de1428f7-60eb-41e4-a77f-80db3dc14b36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w69yqC01t5Rm"
      },
      "source": [
        "## 6. Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghmABKl4t0Hh",
        "outputId": "052ffbb0-9d9a-4ac9-e22f-8ce8d107ed7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(classifier.predict(sc.transform([[30,87000]])))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kpwBb1xouDQg"
      },
      "source": [
        "## 7. Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrwOdCFNt9CP",
        "outputId": "c9b14fd8-9550-425a-ef0c-a9b6cbcf72b3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqJzk1OQuFSz",
        "outputId": "e533fe3a-a226-4abd-edfc-4b01ab3fb093",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion matrix :\")\n",
        "print(cm)\n",
        "print(\"Accuracy of the model :\",accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix :\n",
            "[[65  3]\n",
            " [ 7 25]]\n",
            "Accuracy of the model : 0.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqCqGPL2ulxA"
      },
      "source": [
        "## 8. Visualizing results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMuGXdcxuQBs",
        "outputId": "69c7e58e-aeda-4d50-b797-855b279d1e26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Training set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = sc.inverse_transform(X_train), y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
        "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Naive Bayes (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEvKSI1GuWOt",
        "outputId": "15331155-9902-49d4-f812-c85a6b27628d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Test set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = sc.inverse_transform(X_test), y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
        "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Naive Bayes (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luO3U7Yquj5r"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}