6. Random_Forest_Classifier.ipynb

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  "metadata": {
    "colab": {
      "name": "Random Forest Classifier.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMXzoZzKYXduyK089m6QMJP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "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/Random_Forest_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": "HbFWauEpY-ir"
      },
      "source": [
        "# Random Forest Classifier\n",
        "\n",
        "<hr>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-orZ8mWhXzx"
      },
      "source": [
        "## 1. Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Rq08N8bY67W",
        "outputId": "5d5e90d7-c15e-4fd3-ee00-ddab5c765b2a",
        "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 libraries included successfully!\")\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Basic libraries included successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oIJ_7hFCh-nQ"
      },
      "source": [
        "## 2. Importing the datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bATpyKIoh3wt",
        "outputId": "babbaaee-e515-463a-c09b-b66b1db6436c",
        "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": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwwOvPQflRPi"
      },
      "source": [
        "## 3.Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dk-ddwiHjnd3"
      },
      "source": [
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CeTjRGeHlXAb"
      },
      "source": [
        "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": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CGjaIY6ZlilX"
      },
      "source": [
        "## 4. Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MygBmy5leNQ"
      },
      "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": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EY6855Drlsv2"
      },
      "source": [
        "## 5.Training the Random Forest Classification model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0D70fVIolomy",
        "outputId": "64989478-1bc4-433a-acc5-18e0fa540309",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='entropy', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jPoS8I6Nl1L8"
      },
      "source": [
        "## 6. Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VY1G33YAly1L",
        "outputId": "5106cc55-b5a5-48c3-b668-eff294c81281",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(classifier.predict(sc.transform([[30,87000]])))\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9h4DSwTgl-0w"
      },
      "source": [
        "## 7. Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEtxWlfVl9Rn",
        "outputId": "61292dbe-8902-47fb-db48-cfd48c2206c9",
        "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": 10,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGh_BpsJmLOR"
      },
      "source": [
        "## 8. Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGUlXEwFmFuW",
        "outputId": "fa672c5e-2fb7-493d-dc83-74e91e027165",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "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(\"\\nAccuracy of the model is :\",accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix :\n",
            "[[63  5]\n",
            " [ 4 28]]\n",
            "\n",
            "Accuracy of the model is : 0.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9kENTQRmRHc",
        "outputId": "a116512a-398a-4c46-a89e-096972f65ae8",
        "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('Random Forest Classification (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "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": "N658g7b2mjfn",
        "outputId": "fdfcd4ef-769e-4689-8bad-d2d23a28d9af",
        "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('Random Forest Classification (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "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": "5s1OUMMUo6vn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}