Random_Forest.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Random_Forest.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMMipGFl9KAjaYehSXtWcyV",
      "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/Chandradithya8/Machine-Learning/blob/master/Random_Forest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hEfEZ0puWUr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.datasets import load_digits\n",
        "digits=load_digits()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmfrOzW4vzqQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5a45d127-d88d-485a-e9cd-6ed473963615"
      },
      "source": [
        "dir(digits)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DESCR', 'data', 'images', 'target', 'target_names']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MRQYgvPJveZb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2c990c43-9893-4849-af3a-a8f17907c3c4"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "plt.gray\n",
        "for i in range(5):\n",
        "  plt.matshow(digits.images[i])"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ia--xEyav22f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "aa7057bb-14cf-40d5-b667-d5f486f20841"
      },
      "source": [
        "df=pd.DataFrame(digits.data)\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>15.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>14.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>13.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
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              "      <td>5.0</td>\n",
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              "      <td>10.0</td>\n",
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              "      <td>16.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>10.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    0    1    2     3     4     5    6   ...   57   58    59    60    61   62   63\n",
              "0  0.0  0.0  5.0  13.0   9.0   1.0  0.0  ...  0.0  6.0  13.0  10.0   0.0  0.0  0.0\n",
              "1  0.0  0.0  0.0  12.0  13.0   5.0  0.0  ...  0.0  0.0  11.0  16.0  10.0  0.0  0.0\n",
              "2  0.0  0.0  0.0   4.0  15.0  12.0  0.0  ...  0.0  0.0   3.0  11.0  16.0  9.0  0.0\n",
              "3  0.0  0.0  7.0  15.0  13.0   1.0  0.0  ...  0.0  7.0  13.0  13.0   9.0  0.0  0.0\n",
              "4  0.0  0.0  0.0   1.0  11.0   0.0  0.0  ...  0.0  0.0   2.0  16.0   4.0  0.0  0.0\n",
              "\n",
              "[5 rows x 64 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JOwQjVezRL_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "a8f79914-765c-4bba-8a99-2380939cbc03"
      },
      "source": [
        "df['target']=digits.target\n",
        "df.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     0    1    2     3     4     5  ...    59    60    61   62   63  target\n",
              "0  0.0  0.0  5.0  13.0   9.0   1.0  ...  13.0  10.0   0.0  0.0  0.0       0\n",
              "1  0.0  0.0  0.0  12.0  13.0   5.0  ...  11.0  16.0  10.0  0.0  0.0       1\n",
              "2  0.0  0.0  0.0   4.0  15.0  12.0  ...   3.0  11.0  16.0  9.0  0.0       2\n",
              "3  0.0  0.0  7.0  15.0  13.0   1.0  ...  13.0  13.0   9.0  0.0  0.0       3\n",
              "4  0.0  0.0  0.0   1.0  11.0   0.0  ...   2.0  16.0   4.0  0.0  0.0       4\n",
              "\n",
              "[5 rows x 65 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LefIl9rB05HC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x=df.drop('target',axis='columns')\n",
        "y=df['target']"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LfbxHp9x0rkC",
        "colab_type": "code",
        "colab": {}
      },
      "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=0.2)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a428e67G1UfT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "454bb42b-4b63-4b61-baf8-543c77fbc166"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model=RandomForestClassifier(n_estimators=110)\n",
        "model.fit(x_train,y_train)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', 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=110,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X33HBCFP1gEa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "875c4c2a-3461-4e0a-9473-2374811cd2bf"
      },
      "source": [
        "model.score(x_test,y_test)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9833333333333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ub0C84tf2O_m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_predicted=model.predict(x_test)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwUZ0UPC2O-G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "1af47087-aa02-4550-c20a-198d8686eb15"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(y_test,y_predicted)\n",
        "cm"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[36,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0, 41,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 1,  0, 40,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0, 47,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0, 29,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0, 27,  1,  0,  1,  1],\n",
              "       [ 0,  0,  0,  0,  0,  0, 36,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0, 36,  0,  0],\n",
              "       [ 0,  1,  0,  0,  0,  0,  0,  0, 30,  1],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0, 32]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCA_cuow2iI4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "38f92cc1-cb2d-4282-ce66-733bc566e44d"
      },
      "source": [
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "sn.heatmap(cm,annot=True)\n",
        "plt.xlabel('Original')\n",
        "plt.ylabel('Predicted')\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Predicted')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}