Decision_Tree.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kartikshastrakar/kartik/blob/main/Decision_Tree.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6YV_cLPKBS_4"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z_TxbI8qBS_-"
      },
      "outputs": [],
      "source": [
        "iris=pd.read_csv('E:\\iris dataset.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bcgqhTkCBS__",
        "outputId": "f6449cd6-aa07-44dc-e1d7-ea6ba29fe03f"
      },
      "outputs": [
        {
          "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>Sepal.Length</th>\n",
              "      <th>Sepal.Width</th>\n",
              "      <th>Petal.Length</th>\n",
              "      <th>Petal.Width</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "iris.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rAvrNWWvBTAB"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KvJUtqtEBTAB",
        "outputId": "c0d32378-6017-4280-fee1-6da74aa20cb8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Making a histogram for Sepal length\n",
        "plt.figure(figsize=(5,7))\n",
        "plt.hist(iris['Sepal.Length'])\n",
        "plt.title(\"Distribution of Sepal.Length\")\n",
        "plt.xlabel(\"Sepal.Length\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aEYIbgD5BTAC",
        "outputId": "f3514f78-766c-4b40-987b-c4de9de0d7c5"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 360x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Making a histogram for petal length\n",
        "plt.figure(figsize=(5,7))\n",
        "plt.hist(iris['Petal.Length'],color=\"g\")\n",
        "plt.title(\"Distribution of Petal.Length\")\n",
        "plt.xlabel(\"Petal.Length\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7_NWBXrlBTAD"
      },
      "outputs": [],
      "source": [
        "y = iris[['Species']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9NkZd6sJBTAE"
      },
      "outputs": [],
      "source": [
        "x = iris[['Sepal.Length']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iYusF-8dBTAF"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jguCWSOxBTAG"
      },
      "outputs": [],
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nf9VN0V7BTAH",
        "outputId": "dc917852-d7cb-41b2-af00-fa641c3a9bdc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(     Sepal.Length\n",
              " 34            4.9\n",
              " 2             4.7\n",
              " 100           6.3\n",
              " 74            6.4\n",
              " 129           7.2\n",
              " ..            ...\n",
              " 91            6.1\n",
              " 37            4.9\n",
              " 132           6.4\n",
              " 94            5.6\n",
              " 35            5.0\n",
              " \n",
              " [90 rows x 1 columns],\n",
              "         Species\n",
              " 34       setosa\n",
              " 2        setosa\n",
              " 100   virginica\n",
              " 74   versicolor\n",
              " 129   virginica\n",
              " ..          ...\n",
              " 91   versicolor\n",
              " 37       setosa\n",
              " 132   virginica\n",
              " 94   versicolor\n",
              " 35       setosa\n",
              " \n",
              " [90 rows x 1 columns])"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train,y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YknxqmSPBTAI"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S-FhKaC-BTAJ"
      },
      "outputs": [],
      "source": [
        "dtc = DecisionTreeClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xizqNO5OBTAJ",
        "outputId": "f06a1c75-8953-4ae9-d87c-bfd96b3d53ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dtc.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "klpKJy-oBTAK"
      },
      "outputs": [],
      "source": [
        "y_pred=dtc.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WwwVrpIdBTAK"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vEx3CfIXBTAL",
        "outputId": "b05f7673-c006-46fd-e5c2-9d52e6aa5ef7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[18,  2,  0],\n",
              "       [ 4,  9,  7],\n",
              "       [ 0, 10, 10]], dtype=int64)"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "confusion_matrix(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u83cPYByBTAL",
        "outputId": "09a7c53f-7f42-4ad6-f3a8-88224d75aebc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6333333333333333"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(22+7+9)/(22+2+0+7+7+11+1+1+9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lmmxZWwkBTAM"
      },
      "outputs": [],
      "source": [
        "#decision tree regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h-wNjUHxBTAM",
        "outputId": "d27e647a-4cd4-43fc-9f4d-ab940e6430d4"
      },
      "outputs": [
        {
          "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>Sepal.Length</th>\n",
              "      <th>Sepal.Width</th>\n",
              "      <th>Petal.Length</th>\n",
              "      <th>Petal.Width</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "iris.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b6qQRscrBTAN"
      },
      "outputs": [],
      "source": [
        "x= iris[['Petal.Length']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vJdKtxPSBTAN"
      },
      "outputs": [],
      "source": [
        "y = iris[['Sepal.Length']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4vuvdcvZBTAN"
      },
      "outputs": [],
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "koVxaH2YBTAO"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K3saefsGBTAO"
      },
      "outputs": [],
      "source": [
        "dtr = DecisionTreeRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "45tn9-n9BTAO",
        "outputId": "704ce481-37d2-463a-cffe-26801057a21f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DecisionTreeRegressor()"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dtr.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yZyxdDDMBTAP"
      },
      "outputs": [],
      "source": [
        "y_pred=dtr.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jn06ZYtTBTAP",
        "outputId": "2a8b2e4b-ef01-41b8-ae4f-5e950cb7b5ae"
      },
      "outputs": [
        {
          "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>Sepal.Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>6.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>4.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Sepal.Length\n",
              "73           6.1\n",
              "38           4.4\n",
              "50           7.0\n",
              "24           4.8\n",
              "7            5.0"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gv72ZLsFBTAP",
        "outputId": "5c64143f-0dc5-41e3-d5c6-a75edf28f348"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([6.5       , 4.825     , 6.5       , 5.1       , 5.16666667])"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred[0:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jtETc32HBTAQ"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S5b_pxgCBTAQ",
        "outputId": "3200d791-4407-4cfc-fffd-3e4a348f66c4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.1370866183315431"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mean_squared_error(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dCj7oTaVBTAQ"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": "Decision Tree.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}