Week5_decision_tree.ipynb

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    "colab": {
      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3cas2_1T98w"
      },
      "source": [
        "Adrien Montaigne 2248806"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IODliia6U1xO"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y98nA5UdU6Hf"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpjZ43YlU8eI"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLVaXoYVU_Uy"
      },
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "X = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X, X_test, y, Y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)"
      ],
      "metadata": {
        "id": "g9qp1NGb1WwI"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g16qFkFQVC35"
      },
      "source": [
        "## Training the Decision Tree Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLDKyv1SVUqS",
        "outputId": "ae0a735a-b176-46f9-d86e-28e44cf9364e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "regressor = DecisionTreeRegressor(random_state = 0)\n",
        "regressor.fit(X, y)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(random_state=0)"
            ],
            "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=0)</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=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQRGPTH3VcOn"
      },
      "source": [
        "## Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FpGZf7vVgrK",
        "outputId": "4e89005c-399c-4d8b-97eb-a4010077eb70",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "regressor.predict([[6.5]])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([150000.])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ph8ExBj0VkIT"
      },
      "source": [
        "## Visualising the Decision Tree Regression results (higher resolution)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzH1Vv1oVrqe",
        "outputId": "9ce4d41f-29e6-467f-8b23-01706bda6c72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "X_grid = np.arange(min(X), max(X), 0.01)\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')\n",
        "plt.title('Truth or Bluff (Decision Tree Regression)')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predict X_test"
      ],
      "metadata": {
        "id": "A_pCEAMx0-x9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "metadata": {
        "id": "JuhqElV203ed"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(Y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Woq3GeN1Gtn",
        "outputId": "96e9513b-03a4-4f3e-e2b3-b91b3c531ac0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.43390674513354455"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    }
  ]
}