random_forest_regression_GITHUB.ipynb

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  "metadata": {
    "colab": {
      "name": "random forest_regression GITHUB.ipynb",
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      "toc_visible": true,
      "include_colab_link": true
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    "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/mahdiskj/DecisionTree_RandomForest/blob/main/random_forest_regression_GITHUB.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3cas2_1T98w"
      },
      "source": [
        "# Decision Tree Regression"
      ]
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpjZ43YlU8eI"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLVaXoYVU_Uy"
      },
      "source": [
        "dataset = pd.read_csv('loan_data.csv')\n",
        "X = dataset.iloc[:, 2:].values\n",
        "y = dataset.iloc[:, 0:1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NMYVjcZgGJFz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6535ef34-d092-425c-b1e8-9345babbf64f"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1]\n",
            " [1]\n",
            " [1]\n",
            " ...\n",
            " [0]\n",
            " [0]\n",
            " [0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Hwl5AWcGcf0"
      },
      "source": [
        "# SimpleImputer is a class of the sklearn.impute module\r\n",
        "from sklearn.impute import SimpleImputer\r\n",
        "# replacinf missing values (nan or empty values) with the mean of all the data :\r\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\r\n",
        "# fit method connects the Imputer object with the matrix of X \r\n",
        "imputer.fit(X)\r\n",
        "#trasnform will apply the method 'fit' on X (non number values should be excluded)\r\n",
        "X = imputer.transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07ejemAXGuND"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\r\n",
        "# random_state = 1 : to have the same result of the split every time the code is executed\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Vk2Sj4cGmRJ"
      },
      "source": [
        "# StandardScaler : the class for standadization\r\n",
        "from sklearn.preprocessing import StandardScaler\r\n",
        "# creating the standardization object:\r\n",
        "sc = StandardScaler()\r\n",
        "X_train = sc.fit_transform(X_train)\r\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fI_lqNCGyEB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd953682-93c2-47ea-8132-05cfa09d03b2"
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.62  0.52  1.44 ... -0.3  -0.23 -0.43]\n",
            " [ 1.04  1.81  0.57 ... -0.3  -0.23 -0.43]\n",
            " [-1.36 -0.01  0.49 ... -0.3  -0.23 -0.43]\n",
            " ...\n",
            " [ 0.13  0.4  -0.17 ... -0.3   3.51 -0.43]\n",
            " [ 1.26  1.5   0.25 ... -0.3  -0.23 -0.43]\n",
            " [-0.85 -1.08 -1.31 ... -0.3  -0.23 -0.43]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g16qFkFQVC35"
      },
      "source": [
        "## Training the Decision Tree Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLDKyv1SVUqS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b706a9d4-12c2-46b0-8364-bca124c47fe2"
      },
      "source": [
        "from sklearn import tree\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "# 'n_estimators' : the number of decision trees in the random forest; it can be tuned by trial and error\n",
        "regressor = RandomForestRegressor(n_estimators = 100, random_state = 0)\n",
        "regressor.fit(X_train, y_train)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=0, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cGTT9r0kMvx",
        "outputId": "9f202056-aa51-4ad1-9ffa-021a1e1295b6"
      },
      "source": [
        "y_pred=regressor.predict(X_test)\r\n",
        "np.set_printoptions(precision=2)\r\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\r\n",
        "from sklearn.metrics import r2_score\r\n",
        "r2_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.   0.  ]\n",
            " [1.   1.  ]\n",
            " [0.95 1.  ]\n",
            " ...\n",
            " [0.01 0.  ]\n",
            " [0.   0.  ]\n",
            " [1.   1.  ]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9724546047018212"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    }
  ]
}