decision_tree.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOpVFd89OvbM344t+w/98iX",
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    },
    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Saani765/machine_learning-basics/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": "vsdcZ9JSgm9J"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv(\"Position_Salaries.csv\")\n",
        "X=dataset.iloc[:,1:-1].values\n",
        "y=dataset.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "dBBhIQKMkrfx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "regressor=DecisionTreeRegressor(random_state=2)\n",
        "regressor.fit(X,y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "7ALKPtTskrnB",
        "outputId": "94cb0e6a-4de4-4c11-f4da-2115bd7132dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(random_state=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(random_state=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=2)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.predict([[6.5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRhMGWwxkr4Q",
        "outputId": "d4aa994d-c6dc-4aff-9a2d-8361dc5bc89c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([150000.])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_grid=np.arange(min(X),max(X),0.1)\n",
        "X_grid=X_grid.reshape(len(X_grid),1)\n",
        "plt.scatter(X,y,color='red')\n",
        "plt.plot(X_grid,regressor.predict(X_grid),color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
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        "outputId": "2ec378bd-5d7a-4a47-fc41-ba6f226b6031"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f50085d3d00>]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}