9.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "code",
      "source": "# Decision Tree Regressor\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "cell_type": "code",
      "source": "dataset = pd.read_csv('Position_Salaries.csv')\nX = dataset.iloc[:, 1:2].values\ny = dataset.iloc[:, 2].values\n\nprint(X)\nprint(y)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "[[ 1]\n [ 2]\n [ 3]\n [ 4]\n [ 5]\n [ 6]\n [ 7]\n [ 8]\n [ 9]\n [10]]\n[  45000   50000   60000   80000  110000  150000  200000  300000  500000\n 1000000]\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 2
    },
    {
      "cell_type": "code",
      "source": "from sklearn.tree import DecisionTreeRegressor\nregressor = DecisionTreeRegressor(random_state=0)\nregressor.fit(X, y)\n\nlevel = 8.8\ny_pred = regressor.predict([[level]])\nprint(y_pred)\nprint(\"Predicted Salary at Level: {}% is -> {}%\".format(level, y_pred))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "[500000.]\nPredicted Salary at Level: 8.8% is -> [500000.]%\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 3
    },
    {
      "cell_type": "code",
      "source": "X_grid = np.arange(min(X), max(X), 0.01)\nX_grid = X_grid.reshape((len(X_grid), 1))\nplt.scatter(X, y, color = 'red')\nplt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\nplt.title('Decision Tree Regression')\nplt.xlabel('Years of Experience')\nplt.ylabel('Salary')\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-4-edd4e3c1fe63>:1: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n  X_grid = np.arange(min(X), max(X), 0.01)\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4
    },
    {
      "cell_type": "code",
      "source": "from sklearn.tree import export_graphviz\nexport_graphviz(regressor, out_file = \"9.dot\", feature_names = [\"Salary\"])",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 5
    },
    {
      "cell_type": "code",
      "source": "from sklearn import tree\ntree.plot_tree(regressor)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "data": {
            "text/plain": "[Text(0.703125, 0.9285714285714286, 'x[0] <= 8.5\\nsquared_error = 80662250000.0\\nsamples = 10\\nvalue = 249500.0'),\n Text(0.53125, 0.7857142857142857, 'x[0] <= 6.5\\nsquared_error = 6921484375.0\\nsamples = 8\\nvalue = 124375.0'),\n Text(0.375, 0.6428571428571429, 'x[0] <= 4.5\\nsquared_error = 1381250000.0\\nsamples = 6\\nvalue = 82500.0'),\n Text(0.25, 0.5, 'x[0] <= 3.5\\nsquared_error = 179687500.0\\nsamples = 4\\nvalue = 58750.0'),\n Text(0.1875, 0.35714285714285715, 'x[0] <= 2.5\\nsquared_error = 38888888.889\\nsamples = 3\\nvalue = 51666.667'),\n Text(0.125, 0.21428571428571427, 'x[0] <= 1.5\\nsquared_error = 6250000.0\\nsamples = 2\\nvalue = 47500.0'),\n Text(0.0625, 0.07142857142857142, 'squared_error = 0.0\\nsamples = 1\\nvalue = 45000.0'),\n Text(0.1875, 0.07142857142857142, 'squared_error = 0.0\\nsamples = 1\\nvalue = 50000.0'),\n Text(0.25, 0.21428571428571427, 'squared_error = 0.0\\nsamples = 1\\nvalue = 60000.0'),\n Text(0.3125, 0.35714285714285715, 'squared_error = 0.0\\nsamples = 1\\nvalue = 80000.0'),\n Text(0.5, 0.5, 'x[0] <= 5.5\\nsquared_error = 400000000.0\\nsamples = 2\\nvalue = 130000.0'),\n Text(0.4375, 0.35714285714285715, 'squared_error = 0.0\\nsamples = 1\\nvalue = 110000.0'),\n Text(0.5625, 0.35714285714285715, 'squared_error = 0.0\\nsamples = 1\\nvalue = 150000.0'),\n Text(0.6875, 0.6428571428571429, 'x[0] <= 7.5\\nsquared_error = 2500000000.0\\nsamples = 2\\nvalue = 250000.0'),\n Text(0.625, 0.5, 'squared_error = 0.0\\nsamples = 1\\nvalue = 200000.0'),\n Text(0.75, 0.5, 'squared_error = 0.0\\nsamples = 1\\nvalue = 300000.0'),\n Text(0.875, 0.7857142857142857, 'x[0] <= 9.5\\nsquared_error = 62500000000.0\\nsamples = 2\\nvalue = 750000.0'),\n Text(0.8125, 0.6428571428571429, 'squared_error = 0.0\\nsamples = 1\\nvalue = 500000.0'),\n Text(0.9375, 0.6428571428571429, 'squared_error = 0.0\\nsamples = 1\\nvalue = 1000000.0')]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    }
  ]
}