Supervised_Learning.ipynb

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  "metadata": {
    "colab": {
      "name": "Supervised Learning",
      "provenance": [],
      "collapsed_sections": [
        "5DbvaZtEa1OO",
        "0SdZGGnTfRuL",
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      "authorship_tag": "ABX9TyM9x4OFGnJ+Y16jnmqxEUcn"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5DbvaZtEa1OO"
      },
      "source": [
        "# **Working with IRIS Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Siih6-ava4uS"
      },
      "source": [
        "### **Step 1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwkUCmW9qkuu"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1125,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hProakrGa_YJ"
      },
      "source": [
        "### **Step2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6K3wr5Casrb"
      },
      "source": [
        "from sklearn.datasets import load_iris #Loading the iris dataset"
      ],
      "execution_count": 1126,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcAt9bF5bM6y"
      },
      "source": [
        "**Load iris dataset into the IRIS value**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7PkKbyIbMJi"
      },
      "source": [
        "iris=load_iris()"
      ],
      "execution_count": 1127,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GTjT4my6b9i6"
      },
      "source": [
        "### **Step 3**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_PSb_yz0b5QS"
      },
      "source": [
        "Exploring the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "35O-ttGPbecZ",
        "outputId": "7e0ddcb0-9f6a-430d-d7ac-c6f6e9b3a910"
      },
      "source": [
        "iris.feature_names"
      ],
      "execution_count": 1128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sepal length (cm)',\n",
              " 'sepal width (cm)',\n",
              " 'petal length (cm)',\n",
              " 'petal width (cm)']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cLrlu4LOcCeF"
      },
      "source": [
        "Working with the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "hy80PH6BbgqP",
        "outputId": "9bff7b1c-48de-4dc1-9d4a-840e98909d51"
      },
      "source": [
        "dta_iris=pd.DataFrame(iris.data,columns=iris.feature_names)\n",
        "dta_iris"
      ],
      "execution_count": 1129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                  5.1               3.5                1.4               0.2\n",
              "1                  4.9               3.0                1.4               0.2\n",
              "2                  4.7               3.2                1.3               0.2\n",
              "3                  4.6               3.1                1.5               0.2\n",
              "4                  5.0               3.6                1.4               0.2\n",
              "..                 ...               ...                ...               ...\n",
              "145                6.7               3.0                5.2               2.3\n",
              "146                6.3               2.5                5.0               1.9\n",
              "147                6.5               3.0                5.2               2.0\n",
              "148                6.2               3.4                5.4               2.3\n",
              "149                5.9               3.0                5.1               1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hi9U54Fndf6H"
      },
      "source": [
        "### **Step4**\n",
        "\n",
        "**Knowing the labels**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqBg_2VzcIpS"
      },
      "source": [
        "dta_iris['label']=iris.target"
      ],
      "execution_count": 1130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "LXw5wPZYfE5A",
        "outputId": "d51ccd4d-cd8d-47be-b142-a0bbe6cf8581"
      },
      "source": [
        "dta_iris"
      ],
      "execution_count": 1131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>label</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>0</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>0</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>0</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>0</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>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  ...  petal width (cm)  label\n",
              "0                  5.1               3.5  ...               0.2      0\n",
              "1                  4.9               3.0  ...               0.2      0\n",
              "2                  4.7               3.2  ...               0.2      0\n",
              "3                  4.6               3.1  ...               0.2      0\n",
              "4                  5.0               3.6  ...               0.2      0\n",
              "..                 ...               ...  ...               ...    ...\n",
              "145                6.7               3.0  ...               2.3      2\n",
              "146                6.3               2.5  ...               1.9      2\n",
              "147                6.5               3.0  ...               2.0      2\n",
              "148                6.2               3.4  ...               2.3      2\n",
              "149                5.9               3.0  ...               1.8      2\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0SdZGGnTfRuL"
      },
      "source": [
        "### **Step 5**\n",
        "\n",
        "Vizualizing the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "6PR3mBCEfK1U",
        "outputId": "6bc54e68-c1fe-48ca-f415-74cc18a1c577"
      },
      "source": [
        "plt.scatter(dta_iris.iloc[:,2],dta_iris.iloc[:,3],c=iris.target)\n",
        "plt.xlabel('Petal Length (cm)')\n",
        "plt.ylabel('Petal Width (cm)')"
      ],
      "execution_count": 1132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Petal Width (cm)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1132
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRhKR2u6gZAd",
        "outputId": "e3a5abd1-5331-4b40-adc8-ae4325243cbe"
      },
      "source": [
        "x=dta_iris.iloc[:,0:4]\n",
        "print(x)\n",
        "y=dta_iris.iloc[:,4]\n",
        "print(y)"
      ],
      "execution_count": 1133,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
            "0                  5.1               3.5                1.4               0.2\n",
            "1                  4.9               3.0                1.4               0.2\n",
            "2                  4.7               3.2                1.3               0.2\n",
            "3                  4.6               3.1                1.5               0.2\n",
            "4                  5.0               3.6                1.4               0.2\n",
            "..                 ...               ...                ...               ...\n",
            "145                6.7               3.0                5.2               2.3\n",
            "146                6.3               2.5                5.0               1.9\n",
            "147                6.5               3.0                5.2               2.0\n",
            "148                6.2               3.4                5.4               2.3\n",
            "149                5.9               3.0                5.1               1.8\n",
            "\n",
            "[150 rows x 4 columns]\n",
            "0      0\n",
            "1      0\n",
            "2      0\n",
            "3      0\n",
            "4      0\n",
            "      ..\n",
            "145    2\n",
            "146    2\n",
            "147    2\n",
            "148    2\n",
            "149    2\n",
            "Name: label, Length: 150, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRTLNbBXoJuW"
      },
      "source": [
        "# **Classification**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2x4OD4wHwuuB"
      },
      "source": [
        "### ***KNN Algorithm***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCSCybdJjr8y"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": 1134,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DEs5RBAmYGHX"
      },
      "source": [
        "**Step 1- Train the model (FITTING)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRzflWZMYFTq"
      },
      "source": [
        "knn=KNeighborsClassifier(n_neighbors=6 , metric='minkowski', p=1)\n",
        "#p= 2 for euclidean and 1 for manhattan"
      ],
      "execution_count": 1135,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LdXImKB6acAx"
      },
      "source": [
        "**Step 2 - Define X and Y for preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwHEGB6daaoZ",
        "outputId": "b752e8ae-61b3-403b-dc52-40a1ef509a08"
      },
      "source": [
        "knn.fit(x,y)"
      ],
      "execution_count": 1136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=6, p=1,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fAfPvuQSi7wt"
      },
      "source": [
        "**Step 3- Start Prediction problem solving**\n",
        "\n",
        "for example, someone imagine that someone gives you a sample that contains Sepal and Petals width and length, find the type of flower."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KK7lk85Qjl7O"
      },
      "source": [
        "x_new=np.array([[5.6,3.4,1.4,0.1]]) #Sample datasets\n",
        "x_new2=np.array([[4.5,1.4,7.4,2.1]])"
      ],
      "execution_count": 1137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kV_VtsvTmoWx",
        "outputId": "ed6cd164-40eb-4a82-906a-f0c60e0c272e"
      },
      "source": [
        "knn.predict(x_new)"
      ],
      "execution_count": 1138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hRi6g0HPm6sj"
      },
      "source": [
        "Here it predicts the sample to be of Setosa"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2rSifufdmzZd",
        "outputId": "bdb73196-12a9-4ae2-bbd6-d7783a59e2d3"
      },
      "source": [
        "knn.predict(x_new2) #"
      ],
      "execution_count": 1139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVHJjp0Rpwll"
      },
      "source": [
        "Here it predicts the sample to be of Virginica"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-IKy3dDqHF5"
      },
      "source": [
        "### ***KNN Algorithm With training and test set creation***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_FWePzGVyyul"
      },
      "source": [
        "Step 1-Split dataset into training and test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wy9jLqPqpZTo"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 1140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wI1OJF0zUrB"
      },
      "source": [
        "x_train , x_test ,y_train, y_test =train_test_split(x,y,test_size=0.2, \n",
        "                                                    train_size=0.8, \n",
        "                                                    random_state=88, \n",
        "                                                    shuffle=True, stratify=y )\n",
        "#random_state is just a code for your implementation, can beany no\n"
      ],
      "execution_count": 1141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zUnwvYAIAVRp",
        "outputId": "d433b80b-37a3-43ba-c915-bbb1172974a9"
      },
      "source": [
        "y_train"
      ],
      "execution_count": 1142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10     0\n",
              "130    2\n",
              "46     0\n",
              "58     1\n",
              "12     0\n",
              "      ..\n",
              "64     1\n",
              "97     1\n",
              "134    2\n",
              "30     0\n",
              "94     1\n",
              "Name: label, Length: 120, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-6fDoszEQCM"
      },
      "source": [
        "**Step 2- Continue processing the data and train**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XImVmyFmAcwE",
        "outputId": "c90b5c89-f63a-472c-be1c-c34ee43d45db"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn1=KNeighborsClassifier(n_neighbors=6 , metric='minkowski', p=1)\n",
        "knn1.fit(x_train,y_train)"
      ],
      "execution_count": 1143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=6, p=1,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CKiSUoyUFWCD"
      },
      "source": [
        "**Step 3- Predict using test dataset and check with the existing output of the other test data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "voJDUcTyErK4",
        "outputId": "17b08492-dab1-4efc-d78c-09cd786c50b3"
      },
      "source": [
        "predicted_values=knn1.predict(x_test)\n",
        "predicted_values"
      ],
      "execution_count": 1144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 2, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 2, 2, 0, 1, 0, 0, 0, 2, 0,\n",
              "       2, 0, 0, 1, 1, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OXbxE2fNFP7T"
      },
      "source": [
        "Now Compare the both data as to get output as same as Y_test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTYRW1XHE9ZV",
        "outputId": "0bf51951-410f-4297-d4e4-f6055dd4ebec"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 1145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "92     1\n",
              "66     1\n",
              "128    2\n",
              "126    2\n",
              "50     1\n",
              "106    2\n",
              "74     1\n",
              "111    2\n",
              "84     1\n",
              "20     0\n",
              "78     1\n",
              "107    2\n",
              "143    2\n",
              "102    2\n",
              "109    2\n",
              "24     0\n",
              "71     1\n",
              "43     0\n",
              "42     0\n",
              "32     0\n",
              "113    2\n",
              "5      0\n",
              "120    2\n",
              "37     0\n",
              "34     0\n",
              "61     1\n",
              "88     1\n",
              "15     0\n",
              "86     1\n",
              "33     0\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1145
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIXfs-WQFk_L"
      },
      "source": [
        "**Step 4 - Find accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wy439jYHFKMv",
        "outputId": "7b500263-8413-4439-c93e-23cee3d6a9ee"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,predicted_values)"
      ],
      "execution_count": 1146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TV4cuHvvm-FC"
      },
      "source": [
        "# **Naive Bayes**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "upahl95wMyIc"
      },
      "source": [
        "### **Decision Trees**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlnVcxMtU3CP"
      },
      "source": [
        "**Step 1 - Import the descision tree classifier package from scikit learn**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B14gY_ePM9Nz"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 1147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBFyWOJdVACr"
      },
      "source": [
        "des_tree=DecisionTreeClassifier()"
      ],
      "execution_count": 1148,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U7_uihhgVLNh"
      },
      "source": [
        "**Step 2- Fit the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "athCAWAPVJGC",
        "outputId": "139c6e52-8651-4b41-c7cc-7a43de69c24a"
      },
      "source": [
        "des_tree.fit(x_train,y_train)"
      ],
      "execution_count": 1149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQcrf41BVUUn",
        "outputId": "197108c1-803a-43e9-8d81-f7dbb6c4a08b"
      },
      "source": [
        "predicted_values_destree=des_tree.predict(x_test)\n",
        "predicted_values_destree"
      ],
      "execution_count": 1150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 2, 2, 1, 2, 1, 2, 1, 0, 1, 2, 2, 2, 2, 0, 1, 0, 0, 0, 2, 0,\n",
              "       2, 0, 0, 1, 1, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKyQcekYVmmo",
        "outputId": "92f59e9d-8483-4b51-8dcc-e82fd34a7408"
      },
      "source": [
        "accuracy_score(y_test,predicted_values_destree)"
      ],
      "execution_count": 1151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGgG8zbimEr8"
      },
      "source": [
        "Step 1 import the package"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJ4i4Bz9mDuO"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB"
      ],
      "execution_count": 1152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WilrXFL4mxSZ"
      },
      "source": [
        "nb= GaussianNB()"
      ],
      "execution_count": 1153,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JiN9gxAO_oDf"
      },
      "source": [
        "Step 2 - Fit the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DeYP6nqj_qRA",
        "outputId": "a119b3cb-bcb5-4714-972f-59807fa466a7"
      },
      "source": [
        "nb.fit(x_train,y_train)"
      ],
      "execution_count": 1154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iejOxEt1_2Da",
        "outputId": "ba1bcb66-ddd5-4bb9-8ae4-b91b17882e9a"
      },
      "source": [
        "predicted_values_nb=nb.predict(x_test)\n",
        "predicted_values_nb"
      ],
      "execution_count": 1155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 2, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 2, 2, 0, 1, 0, 0, 0, 2, 0,\n",
              "       2, 0, 0, 1, 1, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yGe0xV9_7cl",
        "outputId": "f8caf444-4995-479d-ec81-239765664868"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 1156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "92     1\n",
              "66     1\n",
              "128    2\n",
              "126    2\n",
              "50     1\n",
              "106    2\n",
              "74     1\n",
              "111    2\n",
              "84     1\n",
              "20     0\n",
              "78     1\n",
              "107    2\n",
              "143    2\n",
              "102    2\n",
              "109    2\n",
              "24     0\n",
              "71     1\n",
              "43     0\n",
              "42     0\n",
              "32     0\n",
              "113    2\n",
              "5      0\n",
              "120    2\n",
              "37     0\n",
              "34     0\n",
              "61     1\n",
              "88     1\n",
              "15     0\n",
              "86     1\n",
              "33     0\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEBYbZyw__22",
        "outputId": "05896385-c504-48bd-dfe3-0950466e8d97"
      },
      "source": [
        "accuracy_score(y_test,predicted_values_nb)"
      ],
      "execution_count": 1157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "levOdH8mAG9m",
        "outputId": "9214a28a-91e2-42bc-e9ea-0195a1351392"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "score_nb=cross_val_score(nb, x, y,cv=10)\n",
        "print(score_nb)"
      ],
      "execution_count": 1158,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.93333333 0.93333333 1.         0.93333333 0.93333333 0.93333333\n",
            " 0.86666667 1.         1.         1.        ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TvYiPbhA-Ez"
      },
      "source": [
        "**LOGISTIC REGRESSION**\n",
        "\n",
        "Used mainly for binary classification eg. Mail spam or not spam."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rE08iJtWBwMr"
      },
      "source": [
        "**Step 1 - We import a different dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rF0btuGAvjW"
      },
      "source": [
        "from sklearn.datasets import load_breast_cancer"
      ],
      "execution_count": 1159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4cl2EYJB7OQ",
        "outputId": "153cedfb-7e48-4a14-c255-3be581155092"
      },
      "source": [
        "data_c= load_breast_cancer()\n",
        "print(data_c)"
      ],
      "execution_count": 1160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
            "        1.189e-01],\n",
            "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
            "        8.902e-02],\n",
            "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
            "        8.758e-02],\n",
            "       ...,\n",
            "       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
            "        7.820e-02],\n",
            "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
            "        1.240e-01],\n",
            "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
            "        7.039e-02]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
            "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
            "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
            "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
            "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
            "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
            "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
            "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
            "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
            "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
            "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
            "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
            "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
            "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
            "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
            "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]), 'target_names': array(['malignant', 'benign'], dtype='<U9'), 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry \\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 3 is Mean Radius, field\\n        13 is Radius SE, field 23 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.', 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
            "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
            "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
            "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
            "       'smoothness error', 'compactness error', 'concavity error',\n",
            "       'concave points error', 'symmetry error',\n",
            "       'fractal dimension error', 'worst radius', 'worst texture',\n",
            "       'worst perimeter', 'worst area', 'worst smoothness',\n",
            "       'worst compactness', 'worst concavity', 'worst concave points',\n",
            "       'worst symmetry', 'worst fractal dimension'], dtype='<U23'), 'filename': '/usr/local/lib/python3.7/dist-packages/sklearn/datasets/data/breast_cancer.csv'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uyqA0VysChNo",
        "outputId": "5b61daf2-eb2f-41e3-f26b-3ec13d136a3f"
      },
      "source": [
        "data_c.feature_names"
      ],
      "execution_count": 1161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "       'smoothness error', 'compactness error', 'concavity error',\n",
              "       'concave points error', 'symmetry error',\n",
              "       'fractal dimension error', 'worst radius', 'worst texture',\n",
              "       'worst perimeter', 'worst area', 'worst smoothness',\n",
              "       'worst compactness', 'worst concavity', 'worst concave points',\n",
              "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2oBPAKsCKwf"
      },
      "source": [
        "data_c_viz=pd.DataFrame(data_c.data,columns=data_c.feature_names)"
      ],
      "execution_count": 1162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "-xGF4x7GCdQO",
        "outputId": "e3db02b6-5828-4e73-bd8b-b21fed52ce0b"
      },
      "source": [
        "data_c_viz"
      ],
      "execution_count": 1163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 30 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
              "0          17.99         10.38  ...          0.4601                  0.11890\n",
              "1          20.57         17.77  ...          0.2750                  0.08902\n",
              "2          19.69         21.25  ...          0.3613                  0.08758\n",
              "3          11.42         20.38  ...          0.6638                  0.17300\n",
              "4          20.29         14.34  ...          0.2364                  0.07678\n",
              "..           ...           ...  ...             ...                      ...\n",
              "564        21.56         22.39  ...          0.2060                  0.07115\n",
              "565        20.13         28.25  ...          0.2572                  0.06637\n",
              "566        16.60         28.08  ...          0.2218                  0.07820\n",
              "567        20.60         29.33  ...          0.4087                  0.12400\n",
              "568         7.76         24.54  ...          0.2871                  0.07039\n",
              "\n",
              "[569 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuWuMUz5EAYE"
      },
      "source": [
        "x=data_c.data\n",
        "y=data_c.target"
      ],
      "execution_count": 1164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5p9soTwyeyXv"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 1165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2wX0mL7enPz"
      },
      "source": [
        "x_lr_train, x_lr_test, y_lr_train, y_lr_test = train_test_split(x,y,test_size=0.3, train_size=0.7, random_state=88)"
      ],
      "execution_count": 1166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "viiEaMiKfqmF"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 1167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VWhLk71f461"
      },
      "source": [
        "lr=LogisticRegression()"
      ],
      "execution_count": 1168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSq3URh2f-lU",
        "outputId": "e14fb85d-c5f5-47c9-fe46-e40857b42e2f"
      },
      "source": [
        "lr.fit(x_lr_train, y_lr_train)"
      ],
      "execution_count": 1169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WcxJxT4WgGpc"
      },
      "source": [
        "predicted_values_lr=lr.predict(x_lr_test)"
      ],
      "execution_count": 1170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFRG1Hq0gXZo",
        "outputId": "e23d57b7-9b17-40ea-cf22-a3e1f2f8a13a"
      },
      "source": [
        "predicted_values_lr"
      ],
      "execution_count": 1171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
              "       0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0,\n",
              "       1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1,\n",
              "       1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n",
              "       1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,\n",
              "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,\n",
              "       1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OO6vjkTigZqC",
        "outputId": "37777b74-929d-4c60-ceea-b23ba5c6cf50"
      },
      "source": [
        "y_lr_test"
      ],
      "execution_count": 1172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1,\n",
              "       1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n",
              "       1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,\n",
              "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1,\n",
              "       1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9f6rp42gegT",
        "outputId": "c5965df1-a315-4b59-94f2-c123ef5d94cb"
      },
      "source": [
        "accuracy_score(y_lr_test,predicted_values_lr)"
      ],
      "execution_count": 1173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9473684210526315"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V7sp6xBlWPHH"
      },
      "source": [
        "### **Decision tree CROSS VALIDATION**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usmR6Dkml-Np"
      },
      "source": [
        "**Step1  -  Import the cross validation module from sklearn**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TchHvbFMV4hW"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score"
      ],
      "execution_count": 1174,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9p39rvfmIXK"
      },
      "source": [
        "**Step 2 - import the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6x19pkAamHHv"
      },
      "source": [
        "score_dt= cross_val_score(des_tree,x,y,cv = 10) #name of model, x and y as features and label, no of folds as CV"
      ],
      "execution_count": 1175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zttn9mymh97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f440117-4b59-46b6-a88f-b969c177ec79"
      },
      "source": [
        "score_dt"
      ],
      "execution_count": 1176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.94736842, 0.89473684, 0.92982456, 0.87719298, 0.94736842,\n",
              "       0.89473684, 0.89473684, 0.94736842, 0.92982456, 0.91071429])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jHVhJ43iWxK"
      },
      "source": [
        "### **Classification - Evaluation Metrics**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uVjUwHZNsA1l"
      },
      "source": [
        "### **Confusion Matrix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDr7mDJVibid"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "execution_count": 1177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xABn2ZpesKTL"
      },
      "source": [
        "conf_mat=confusion_matrix(y_lr_test,predicted_values_lr) #(y_true, y_pred)\n",
        "class_rep=classification_report(y_lr_test, predicted_values_lr)"
      ],
      "execution_count": 1178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "toNgIDe5tX2a",
        "outputId": "fd468e46-28c6-40ac-f7f2-2b787d2131f9"
      },
      "source": [
        "conf_mat"
      ],
      "execution_count": 1179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 53,   4],\n",
              "       [  5, 109]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "yyPB7cjNtcx3",
        "outputId": "5e60cde8-4905-4ba9-fe33-1dac0c2591b2"
      },
      "source": [
        "class_rep"
      ],
      "execution_count": 1180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n           0       0.91      0.93      0.92        57\\n           1       0.96      0.96      0.96       114\\n\\n    accuracy                           0.95       171\\n   macro avg       0.94      0.94      0.94       171\\nweighted avg       0.95      0.95      0.95       171\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4x9n0pfBtgt_"
      },
      "source": [
        "from sklearn.metrics import roc_curve"
      ],
      "execution_count": 1181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rOd-BZOt1t8"
      },
      "source": [
        "y_lr_prob=lr.predict_proba(x_lr_test)"
      ],
      "execution_count": 1182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8dLJC1aWuMBv",
        "outputId": "0d7e79de-3ed5-4fb7-c46e-5170e4ba22ad"
      },
      "source": [
        "y_lr_prob"
      ],
      "execution_count": 1183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3.86996119e-04, 9.99613004e-01],\n",
              "       [9.99999991e-01, 8.63786983e-09],\n",
              "       [1.70434410e-03, 9.98295656e-01],\n",
              "       [1.00000000e+00, 4.21499992e-11],\n",
              "       [1.25649197e-03, 9.98743508e-01],\n",
              "       [5.17992742e-03, 9.94820073e-01],\n",
              "       [3.72327162e-01, 6.27672838e-01],\n",
              "       [4.64315921e-03, 9.95356841e-01],\n",
              "       [9.99999999e-01, 1.26126771e-09],\n",
              "       [9.99999759e-01, 2.41375974e-07],\n",
              "       [3.22136026e-03, 9.96778640e-01],\n",
              "       [5.71783774e-02, 9.42821623e-01],\n",
              "       [1.00000000e+00, 2.35410863e-11],\n",
              "       [9.99999760e-01, 2.39991679e-07],\n",
              "       [9.99925119e-01, 7.48813298e-05],\n",
              "       [9.99869900e-01, 1.30099667e-04],\n",
              "       [9.87678347e-01, 1.23216532e-02],\n",
              "       [1.11923874e-03, 9.98880761e-01],\n",
              "       [1.06202473e-03, 9.98937975e-01],\n",
              "       [1.70557003e-04, 9.99829443e-01],\n",
              "       [3.94875710e-04, 9.99605124e-01],\n",
              "       [9.99467019e-01, 5.32981261e-04],\n",
              "       [9.09748166e-01, 9.02518338e-02],\n",
              "       [4.35946152e-02, 9.56405385e-01],\n",
              "       [3.01425583e-02, 9.69857442e-01],\n",
              "       [2.08451085e-03, 9.97915489e-01],\n",
              "       [8.94708440e-05, 9.99910529e-01],\n",
              "       [1.13665348e-03, 9.98863347e-01],\n",
              "       [8.38896207e-04, 9.99161104e-01],\n",
              "       [2.67145738e-02, 9.73285426e-01],\n",
              "       [9.99997237e-01, 2.76265683e-06],\n",
              "       [1.00000000e+00, 3.28093563e-24],\n",
              "       [3.00793412e-01, 6.99206588e-01],\n",
              "       [1.79411972e-03, 9.98205880e-01],\n",
              "       [1.64887565e-01, 8.35112435e-01],\n",
              "       [6.96597079e-03, 9.93034029e-01],\n",
              "       [4.31022431e-02, 9.56897757e-01],\n",
              "       [1.96825964e-03, 9.98031740e-01],\n",
              "       [3.11256068e-03, 9.96887439e-01],\n",
              "       [1.78763965e-04, 9.99821236e-01],\n",
              "       [9.99999994e-01, 5.55511375e-09],\n",
              "       [1.56678673e-01, 8.43321327e-01],\n",
              "       [1.00000000e+00, 9.32013736e-16],\n",
              "       [4.33363493e-02, 9.56663651e-01],\n",
              "       [9.62904729e-01, 3.70952712e-02],\n",
              "       [3.87562760e-01, 6.12437240e-01],\n",
              "       [1.64272612e-03, 9.98357274e-01],\n",
              "       [7.96847259e-02, 9.20315274e-01],\n",
              "       [9.98119304e-01, 1.88069617e-03],\n",
              "       [9.15695043e-04, 9.99084305e-01],\n",
              "       [2.02700537e-03, 9.97972995e-01],\n",
              "       [8.85709085e-01, 1.14290915e-01],\n",
              "       [3.64404076e-03, 9.96355959e-01],\n",
              "       [2.65186431e-01, 7.34813569e-01],\n",
              "       [1.29502383e-02, 9.87049762e-01],\n",
              "       [1.68949283e-04, 9.99831051e-01],\n",
              "       [9.99960191e-01, 3.98092562e-05],\n",
              "       [2.51020911e-03, 9.97489791e-01],\n",
              "       [1.01903504e-03, 9.98980965e-01],\n",
              "       [3.69410321e-03, 9.96305897e-01],\n",
              "       [1.13261266e-02, 9.88673873e-01],\n",
              "       [9.69590208e-01, 3.04097918e-02],\n",
              "       [5.65381478e-04, 9.99434619e-01],\n",
              "       [3.59887641e-02, 9.64011236e-01],\n",
              "       [9.99991582e-01, 8.41765348e-06],\n",
              "       [9.99999988e-01, 1.16490201e-08],\n",
              "       [2.91192588e-01, 7.08807412e-01],\n",
              "       [2.56717855e-02, 9.74328215e-01],\n",
              "       [1.44414365e-03, 9.98555856e-01],\n",
              "       [9.93433654e-01, 6.56634563e-03],\n",
              "       [1.61203462e-04, 9.99838797e-01],\n",
              "       [1.00000000e+00, 2.62840538e-11],\n",
              "       [3.98754327e-03, 9.96012457e-01],\n",
              "       [8.01287492e-03, 9.91987125e-01],\n",
              "       [1.10427901e-01, 8.89572099e-01],\n",
              "       [1.56701717e-03, 9.98432983e-01],\n",
              "       [4.27474866e-03, 9.95725251e-01],\n",
              "       [2.59522666e-02, 9.74047733e-01],\n",
              "       [1.05771862e-03, 9.98942281e-01],\n",
              "       [2.34715251e-04, 9.99765285e-01],\n",
              "       [9.99999966e-01, 3.41897796e-08],\n",
              "       [2.44075468e-03, 9.97559245e-01],\n",
              "       [4.64534550e-02, 9.53546545e-01],\n",
              "       [9.99990846e-01, 9.15370127e-06],\n",
              "       [1.17635762e-03, 9.98823642e-01],\n",
              "       [9.99997813e-01, 2.18677700e-06],\n",
              "       [6.39389675e-02, 9.36061033e-01],\n",
              "       [5.84407978e-04, 9.99415592e-01],\n",
              "       [2.15036912e-02, 9.78496309e-01],\n",
              "       [9.99897348e-01, 1.02651584e-04],\n",
              "       [9.90440771e-03, 9.90095592e-01],\n",
              "       [9.68728731e-03, 9.90312713e-01],\n",
              "       [1.81136096e-02, 9.81886390e-01],\n",
              "       [9.99964135e-01, 3.58648690e-05],\n",
              "       [1.00000000e+00, 1.47022596e-25],\n",
              "       [1.51379678e-01, 8.48620322e-01],\n",
              "       [9.99998841e-01, 1.15852363e-06],\n",
              "       [9.74610454e-01, 2.53895458e-02],\n",
              "       [1.98869992e-02, 9.80113001e-01],\n",
              "       [3.26481468e-02, 9.67351853e-01],\n",
              "       [2.11803609e-02, 9.78819639e-01],\n",
              "       [3.47223507e-02, 9.65277649e-01],\n",
              "       [9.73610110e-01, 2.63898899e-02],\n",
              "       [8.38260612e-04, 9.99161739e-01],\n",
              "       [1.00000000e+00, 8.27183667e-18],\n",
              "       [3.14526013e-01, 6.85473987e-01],\n",
              "       [6.11635274e-03, 9.93883647e-01],\n",
              "       [9.99923050e-01, 7.69495353e-05],\n",
              "       [9.99907476e-01, 9.25238544e-05],\n",
              "       [3.74446705e-01, 6.25553295e-01],\n",
              "       [3.62211809e-03, 9.96377882e-01],\n",
              "       [8.34255808e-01, 1.65744192e-01],\n",
              "       [1.20469089e-01, 8.79530911e-01],\n",
              "       [8.84315715e-01, 1.15684285e-01],\n",
              "       [2.35544776e-02, 9.76445522e-01],\n",
              "       [4.73073887e-02, 9.52692611e-01],\n",
              "       [1.28188484e-01, 8.71811516e-01],\n",
              "       [5.14353054e-02, 9.48564695e-01],\n",
              "       [1.74202659e-03, 9.98257973e-01],\n",
              "       [1.17854273e-02, 9.88214573e-01],\n",
              "       [1.14879284e-03, 9.98851207e-01],\n",
              "       [4.69685450e-02, 9.53031455e-01],\n",
              "       [9.98155206e-01, 1.84479404e-03],\n",
              "       [9.96118366e-01, 3.88163417e-03],\n",
              "       [3.02521555e-04, 9.99697478e-01],\n",
              "       [3.09559234e-02, 9.69044077e-01],\n",
              "       [2.64434260e-03, 9.97355657e-01],\n",
              "       [9.75130355e-03, 9.90248696e-01],\n",
              "       [1.16320067e-02, 9.88367993e-01],\n",
              "       [1.00000000e+00, 7.45704284e-15],\n",
              "       [6.27592299e-03, 9.93724077e-01],\n",
              "       [1.00000000e+00, 2.32140644e-16],\n",
              "       [1.00000000e+00, 2.63796169e-24],\n",
              "       [1.00000000e+00, 1.51109746e-23],\n",
              "       [2.99674079e-02, 9.70032592e-01],\n",
              "       [9.99994525e-01, 5.47534061e-06],\n",
              "       [9.99993192e-01, 6.80824584e-06],\n",
              "       [2.35709407e-01, 7.64290593e-01],\n",
              "       [1.47475230e-01, 8.52524770e-01],\n",
              "       [1.04125540e-03, 9.98958745e-01],\n",
              "       [1.88215147e-04, 9.99811785e-01],\n",
              "       [7.24161424e-03, 9.92758386e-01],\n",
              "       [4.70267982e-02, 9.52973202e-01],\n",
              "       [1.87091038e-04, 9.99812909e-01],\n",
              "       [7.02035578e-01, 2.97964422e-01],\n",
              "       [5.28019560e-04, 9.99471980e-01],\n",
              "       [1.13261150e-02, 9.88673885e-01],\n",
              "       [4.23708977e-02, 9.57629102e-01],\n",
              "       [1.00000000e+00, 1.05525505e-34],\n",
              "       [1.00000000e+00, 3.38377545e-14],\n",
              "       [2.61569018e-02, 9.73843098e-01],\n",
              "       [9.99998461e-01, 1.53887212e-06],\n",
              "       [9.99999998e-01, 1.53369316e-09],\n",
              "       [2.25392831e-04, 9.99774607e-01],\n",
              "       [1.51534667e-01, 8.48465333e-01],\n",
              "       [3.21979322e-01, 6.78020678e-01],\n",
              "       [9.99340629e-01, 6.59370555e-04],\n",
              "       [3.51905829e-01, 6.48094171e-01],\n",
              "       [6.96757589e-03, 9.93032424e-01],\n",
              "       [1.00000000e+00, 8.06044328e-14],\n",
              "       [7.43559280e-02, 9.25644072e-01],\n",
              "       [3.20673571e-02, 9.67932643e-01],\n",
              "       [1.00000000e+00, 1.05189727e-12],\n",
              "       [6.01378152e-01, 3.98621848e-01],\n",
              "       [9.71230724e-03, 9.90287693e-01],\n",
              "       [1.61456404e-03, 9.98385436e-01],\n",
              "       [9.90742670e-01, 9.25733000e-03],\n",
              "       [3.55381940e-03, 9.96446181e-01],\n",
              "       [5.96530635e-01, 4.03469365e-01],\n",
              "       [1.00000000e+00, 3.43097662e-12],\n",
              "       [5.87093770e-02, 9.41290623e-01]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1183
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7i0lpWAauOJf"
      },
      "source": [
        "y_lr_prob=y_lr_prob[:,1]"
      ],
      "execution_count": 1184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhuuHzG4vcWB"
      },
      "source": [
        "FPR, TPR, thresholds,= roc_curve(y_lr_test, y_lr_prob)"
      ],
      "execution_count": 1185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "T7pKZjwuvsMA",
        "outputId": "97467924-5aad-473b-b860-ba27dc7eddcd"
      },
      "source": [
        "plt.plot(FPR,TPR)\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')"
      ],
      "execution_count": 1186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'TPR')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1186
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAsgmSAdv1ra"
      },
      "source": [
        "from sklearn.metrics import roc_auc_score"
      ],
      "execution_count": 1187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-p7V1qux8M1",
        "outputId": "707f1433-f2d9-4ab3-ec7e-bb55f2c4875a"
      },
      "source": [
        "roc_auc_score(y_lr_test,y_lr_prob)"
      ],
      "execution_count": 1188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.984610649430594"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1188
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3c3iqtWeyqLn"
      },
      "source": [
        "# **Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtL58eVpqNfD"
      },
      "source": [
        "## **Preprocessing data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9SRy3jb0sDWM"
      },
      "source": [
        "Step1 - Import dataset, numpy, pandas and matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pTVvrNhyySC"
      },
      "source": [
        "from sklearn.datasets import load_boston\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1189,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KlS1rtYXsJNV"
      },
      "source": [
        "Step 2- Load dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bu36hvTe3TmD",
        "outputId": "5d7d7134-8b60-4a92-d2a3-3f5994848500"
      },
      "source": [
        "dat_bos=load_boston()\n",
        "dat_bos_viz=pd.DataFrame(dat_bos.data,columns=dat_bos.feature_names)\n",
        "print(dat_bos_viz.head(10))\n",
        "dat_bos_viz.info()"
      ],
      "execution_count": 1190,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      CRIM    ZN  INDUS  CHAS    NOX  ...  RAD    TAX  PTRATIO       B  LSTAT\n",
            "0  0.00632  18.0   2.31   0.0  0.538  ...  1.0  296.0     15.3  396.90   4.98\n",
            "1  0.02731   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  396.90   9.14\n",
            "2  0.02729   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  392.83   4.03\n",
            "3  0.03237   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  394.63   2.94\n",
            "4  0.06905   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  396.90   5.33\n",
            "5  0.02985   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  394.12   5.21\n",
            "6  0.08829  12.5   7.87   0.0  0.524  ...  5.0  311.0     15.2  395.60  12.43\n",
            "7  0.14455  12.5   7.87   0.0  0.524  ...  5.0  311.0     15.2  396.90  19.15\n",
            "8  0.21124  12.5   7.87   0.0  0.524  ...  5.0  311.0     15.2  386.63  29.93\n",
            "9  0.17004  12.5   7.87   0.0  0.524  ...  5.0  311.0     15.2  386.71  17.10\n",
            "\n",
            "[10 rows x 13 columns]\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 506 entries, 0 to 505\n",
            "Data columns (total 13 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   CRIM     506 non-null    float64\n",
            " 1   ZN       506 non-null    float64\n",
            " 2   INDUS    506 non-null    float64\n",
            " 3   CHAS     506 non-null    float64\n",
            " 4   NOX      506 non-null    float64\n",
            " 5   RM       506 non-null    float64\n",
            " 6   AGE      506 non-null    float64\n",
            " 7   DIS      506 non-null    float64\n",
            " 8   RAD      506 non-null    float64\n",
            " 9   TAX      506 non-null    float64\n",
            " 10  PTRATIO  506 non-null    float64\n",
            " 11  B        506 non-null    float64\n",
            " 12  LSTAT    506 non-null    float64\n",
            "dtypes: float64(13)\n",
            "memory usage: 51.5 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQlFqpqrsPiH"
      },
      "source": [
        "Step 3- Load dataset data and target values into x and y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXviqLFh3md3"
      },
      "source": [
        "x=dat_bos.data\n",
        "y=dat_bos.target"
      ],
      "execution_count": 1191,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2LPWs2fTsVHj"
      },
      "source": [
        "Step4- Split dataset into Training and Test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bsWsh6J4dC0"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 1192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvOFeRkY4oeE"
      },
      "source": [
        "x_bos_train, x_bos_test, y_bos_train, y_bos_test = train_test_split(x,y, test_size=0.25, train_size=0.75, random_state=76) "
      ],
      "execution_count": 1193,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oivt0Vi15DnX"
      },
      "source": [
        "Step 5a -Normalize data for prediction calculation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rbunIx049Bn"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 1194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "88wpWqMOn4V5"
      },
      "source": [
        "mmsc=MinMaxScaler(feature_range=(0,1))"
      ],
      "execution_count": 1195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbIU3QgRoNHt"
      },
      "source": [
        "x_bos_train=mmsc.fit_transform(x_bos_train)\n",
        "x_bos_test=mmsc.fit_transform(x_bos_test)"
      ],
      "execution_count": 1196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFzy8MMSohV-",
        "outputId": "58b9f9cd-93b3-42f7-fc94-cef0fb6278a4"
      },
      "source": [
        "x_bos_train"
      ],
      "execution_count": 1197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.14089134e-01, 0.00000000e+00, 6.46627566e-01, ...,\n",
              "        8.08510638e-01, 2.17429890e-01, 1.00000000e+00],\n",
              "       [2.91478404e-03, 0.00000000e+00, 3.38343109e-01, ...,\n",
              "        7.02127660e-01, 1.00000000e+00, 3.50921986e-01],\n",
              "       [6.77193849e-03, 2.00000000e-01, 1.28665689e-01, ...,\n",
              "        4.25531915e-02, 9.81743496e-01, 9.61702128e-02],\n",
              "       ...,\n",
              "       [5.93926027e-02, 0.00000000e+00, 6.46627566e-01, ...,\n",
              "        8.08510638e-01, 9.53040215e-01, 4.83120567e-01],\n",
              "       [5.81113431e-04, 0.00000000e+00, 4.20454545e-01, ...,\n",
              "        8.93617021e-01, 1.00000000e+00, 1.10921986e-01],\n",
              "       [1.63464848e-02, 0.00000000e+00, 7.00879765e-01, ...,\n",
              "        2.23404255e-01, 9.43024494e-01, 0.00000000e+00]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1197
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ZU2M-68o3f2"
      },
      "source": [
        "Step 5b - Reshape y before training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_jSCr63oqLW"
      },
      "source": [
        "y_bos_train=y_bos_train.reshape(-1,1)"
      ],
      "execution_count": 1198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZS6gl8UpFDm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02c4ba7b-4fa1-409f-fb54-a5ecce088a94"
      },
      "source": [
        "y_bos_train=mmsc.fit_transform(y_bos_train)\n",
        "y_bos_train"
      ],
      "execution_count": 1199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [0.44444444],\n",
              "       [0.25555556],\n",
              "       [0.35777778],\n",
              "       [0.23555556],\n",
              "       [0.56222222],\n",
              "       [0.43333333],\n",
              "       [0.84      ],\n",
              "       [0.21333333],\n",
              "       [0.26888889],\n",
              "       [0.27777778],\n",
              "       [0.36888889],\n",
              "       [0.5       ],\n",
              "       [0.13111111],\n",
              "       [0.44444444],\n",
              "       [0.43777778],\n",
              "       [0.23555556],\n",
              "       [0.41333333],\n",
              "       [1.        ],\n",
              "       [0.42222222],\n",
              "       [0.58888889],\n",
              "       [0.41777778],\n",
              "       [0.28      ],\n",
              "       [0.71777778],\n",
              "       [0.56666667],\n",
              "       [0.62888889],\n",
              "       [0.37555556],\n",
              "       [0.29777778],\n",
              "       [0.42666667],\n",
              "       [0.62666667],\n",
              "       [0.51111111],\n",
              "       [0.55111111],\n",
              "       [0.06888889],\n",
              "       [0.33111111],\n",
              "       [0.        ],\n",
              "       [0.16666667],\n",
              "       [0.71111111],\n",
              "       [0.24666667],\n",
              "       [0.31555556],\n",
              "       [0.31333333],\n",
              "       [0.39111111],\n",
              "       [0.3       ],\n",
              "       [0.25777778],\n",
              "       [0.31777778],\n",
              "       [0.3       ],\n",
              "       [0.67111111],\n",
              "       [0.41777778],\n",
              "       [0.28444444],\n",
              "       [0.40222222],\n",
              "       [0.37333333],\n",
              "       [0.30666667],\n",
              "       [0.43555556],\n",
              "       [0.77333333],\n",
              "       [0.40222222],\n",
              "       [0.17111111],\n",
              "       [0.48222222],\n",
              "       [0.31111111],\n",
              "       [0.44888889],\n",
              "       [0.29111111],\n",
              "       [0.44666667],\n",
              "       [0.66444444],\n",
              "       [0.86222222],\n",
              "       [0.35777778],\n",
              "       [0.04888889],\n",
              "       [0.45111111],\n",
              "       [0.91111111],\n",
              "       [0.08444444],\n",
              "       [0.40444444],\n",
              "       [0.42      ],\n",
              "       [1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1199
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-mOKaotqZP1"
      },
      "source": [
        "## **Linear Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQEvR49h4WjF"
      },
      "source": [
        "### **Simple Linear Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHKBnfhBs0xc"
      },
      "source": [
        "Step 1 - Import model LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3n1QmfYqATS"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 1200,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3DacBxms6u6"
      },
      "source": [
        "Step 2 -Load and fit data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkCZwPFOqsZF"
      },
      "source": [
        "lin_r=LinearRegression()"
      ],
      "execution_count": 1201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bnq5kot1qwxC",
        "outputId": "b7c7a0e1-89a1-48a2-ea3a-a83039c0b11d"
      },
      "source": [
        "lin_r.fit(x_bos_train,y_bos_train)"
      ],
      "execution_count": 1202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1202
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T_mUd51Ds-VH"
      },
      "source": [
        "Step 3- Start analyzing data for prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQ7y3WzFq3Xh",
        "outputId": "7aa1289d-0163-421c-b6ab-b3aea2354963"
      },
      "source": [
        "predicted_values_mlr=lin_r.predict(x_bos_test)\n",
        "predicted_values_mlr"
      ],
      "execution_count": 1203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.14327016],\n",
              "       [ 0.30728044],\n",
              "       [ 0.41978008],\n",
              "       [ 0.59472732],\n",
              "       [ 0.55995583],\n",
              "       [ 0.50190144],\n",
              "       [ 0.36589015],\n",
              "       [ 0.44105588],\n",
              "       [ 0.51042542],\n",
              "       [ 0.28706374],\n",
              "       [ 0.30793721],\n",
              "       [ 0.21462711],\n",
              "       [ 0.15673253],\n",
              "       [ 0.41721547],\n",
              "       [ 0.28842011],\n",
              "       [ 0.4105379 ],\n",
              "       [ 0.29845638],\n",
              "       [ 0.00992905],\n",
              "       [ 0.35233715],\n",
              "       [ 0.32190852],\n",
              "       [ 0.38272493],\n",
              "       [ 0.56909258],\n",
              "       [ 0.34583186],\n",
              "       [ 0.31709011],\n",
              "       [ 0.11586898],\n",
              "       [ 0.55055255],\n",
              "       [ 0.49389675],\n",
              "       [ 0.6678577 ],\n",
              "       [ 0.34401572],\n",
              "       [ 0.42020137],\n",
              "       [ 0.28962115],\n",
              "       [ 0.31216023],\n",
              "       [ 0.41909893],\n",
              "       [ 0.40040973],\n",
              "       [ 0.53239415],\n",
              "       [ 0.39378046],\n",
              "       [ 0.36557888],\n",
              "       [ 0.32975378],\n",
              "       [ 0.40594942],\n",
              "       [ 0.35354142],\n",
              "       [ 0.1851786 ],\n",
              "       [ 0.32794278],\n",
              "       [ 0.75514357],\n",
              "       [ 0.44754725],\n",
              "       [ 0.32994569],\n",
              "       [ 0.39090704],\n",
              "       [ 0.23563843],\n",
              "       [ 0.58132042],\n",
              "       [ 0.51879347],\n",
              "       [ 0.23462943],\n",
              "       [ 0.43862882],\n",
              "       [ 0.57665559],\n",
              "       [ 0.18433729],\n",
              "       [ 0.33731423],\n",
              "       [ 0.29390956],\n",
              "       [ 0.36601169],\n",
              "       [ 0.53494292],\n",
              "       [ 0.41664735],\n",
              "       [ 0.35774851],\n",
              "       [ 0.08359368],\n",
              "       [ 0.35227116],\n",
              "       [ 0.24879138],\n",
              "       [ 0.41676849],\n",
              "       [ 0.25454181],\n",
              "       [ 0.26721567],\n",
              "       [ 0.38690781],\n",
              "       [ 0.42099251],\n",
              "       [ 0.39522103],\n",
              "       [ 0.06354719],\n",
              "       [ 0.45027024],\n",
              "       [ 0.46880929],\n",
              "       [ 0.2984855 ],\n",
              "       [ 0.44337828],\n",
              "       [ 0.16455306],\n",
              "       [ 0.61674485],\n",
              "       [ 0.31009159],\n",
              "       [ 0.2035197 ],\n",
              "       [ 0.36882004],\n",
              "       [ 0.45340549],\n",
              "       [ 0.60603397],\n",
              "       [ 0.43445752],\n",
              "       [ 0.49788784],\n",
              "       [-0.12693009],\n",
              "       [ 0.75337608],\n",
              "       [ 0.2517449 ],\n",
              "       [ 0.43546915],\n",
              "       [-0.04110964],\n",
              "       [ 0.70932668],\n",
              "       [ 0.35224545],\n",
              "       [ 0.53344087],\n",
              "       [ 0.50861772],\n",
              "       [-0.14246022],\n",
              "       [ 0.80260259],\n",
              "       [ 0.22532082],\n",
              "       [ 0.46268051],\n",
              "       [ 0.32742183],\n",
              "       [ 0.53338593],\n",
              "       [ 0.27294597],\n",
              "       [ 0.1823174 ],\n",
              "       [ 0.40862114],\n",
              "       [ 0.64412812],\n",
              "       [ 0.64306931],\n",
              "       [ 0.160113  ],\n",
              "       [ 0.25455386],\n",
              "       [ 0.4204449 ],\n",
              "       [ 0.2470853 ],\n",
              "       [ 0.35098649],\n",
              "       [ 0.26873376],\n",
              "       [ 0.19674269],\n",
              "       [ 0.3035757 ],\n",
              "       [ 0.35159638],\n",
              "       [ 0.50558737],\n",
              "       [ 0.34936636],\n",
              "       [ 0.27158247],\n",
              "       [ 0.09033683],\n",
              "       [ 0.42386617],\n",
              "       [ 0.4344074 ],\n",
              "       [ 0.24116145],\n",
              "       [ 0.20784026],\n",
              "       [ 0.07278575],\n",
              "       [ 0.34295291],\n",
              "       [ 0.32345159],\n",
              "       [ 0.46721603],\n",
              "       [ 0.55391127],\n",
              "       [ 0.65376199],\n",
              "       [ 0.75501753],\n",
              "       [ 0.86212048]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1203
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fag-Z_5mtCr4"
      },
      "source": [
        "Step 4 - Unonormalize or reverse the data back to its normal form to use it later on"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwaYNjohrIRS"
      },
      "source": [
        "predicted_values_mlr=mmsc.inverse_transform(predicted_values_mlr)"
      ],
      "execution_count": 1204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIO4oeMPrMk_",
        "outputId": "e7d59198-8474-4e38-988e-3b5bdb23db53"
      },
      "source": [
        "predicted_values_mlr"
      ],
      "execution_count": 1205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11.44715732],\n",
              "       [18.82761987],\n",
              "       [23.89010363],\n",
              "       [31.76272961],\n",
              "       [30.19801252],\n",
              "       [27.58556465],\n",
              "       [21.46505689],\n",
              "       [24.84751479],\n",
              "       [27.96914381],\n",
              "       [17.9178682 ],\n",
              "       [18.85717423],\n",
              "       [14.65822009],\n",
              "       [12.05296391],\n",
              "       [23.77469627],\n",
              "       [17.97890485],\n",
              "       [23.47420572],\n",
              "       [18.43053729],\n",
              "       [ 5.44680739],\n",
              "       [20.85517162],\n",
              "       [19.48588347],\n",
              "       [22.22262166],\n",
              "       [30.6091661 ],\n",
              "       [20.5624339 ],\n",
              "       [19.26905486],\n",
              "       [10.21410416],\n",
              "       [29.77486467],\n",
              "       [27.2253539 ],\n",
              "       [35.05359649],\n",
              "       [20.48070721],\n",
              "       [23.90906187],\n",
              "       [18.03295166],\n",
              "       [19.04721018],\n",
              "       [23.85945188],\n",
              "       [23.01843796],\n",
              "       [28.95773689],\n",
              "       [22.72012069],\n",
              "       [21.45104939],\n",
              "       [19.83891999],\n",
              "       [23.26772399],\n",
              "       [20.90936398],\n",
              "       [13.33303691],\n",
              "       [19.75742509],\n",
              "       [38.98146084],\n",
              "       [25.13962604],\n",
              "       [19.84755589],\n",
              "       [22.59081674],\n",
              "       [15.60372955],\n",
              "       [31.15941905],\n",
              "       [28.34570593],\n",
              "       [15.55832418],\n",
              "       [24.73829691],\n",
              "       [30.9495016 ],\n",
              "       [13.29517809],\n",
              "       [20.17914032],\n",
              "       [18.22593023],\n",
              "       [21.47052603],\n",
              "       [29.07243127],\n",
              "       [23.74913069],\n",
              "       [21.09868306],\n",
              "       [ 8.76171553],\n",
              "       [20.85220212],\n",
              "       [16.19561231],\n",
              "       [23.7545821 ],\n",
              "       [16.45438154],\n",
              "       [17.02470504],\n",
              "       [22.41085154],\n",
              "       [23.94466308],\n",
              "       [22.78494631],\n",
              "       [ 7.85962334],\n",
              "       [25.26216075],\n",
              "       [26.09641814],\n",
              "       [18.43184729],\n",
              "       [24.95202273],\n",
              "       [12.40488786],\n",
              "       [32.75351826],\n",
              "       [18.95412144],\n",
              "       [14.15838636],\n",
              "       [21.59690162],\n",
              "       [25.40324683],\n",
              "       [32.27152854],\n",
              "       [24.55058858],\n",
              "       [27.4049528 ],\n",
              "       [-0.71185398],\n",
              "       [38.90192376],\n",
              "       [16.32852062],\n",
              "       [24.5961117 ],\n",
              "       [ 3.15006633],\n",
              "       [36.91970075],\n",
              "       [20.85104536],\n",
              "       [29.00483928],\n",
              "       [27.88779731],\n",
              "       [-1.41071006],\n",
              "       [41.11711636],\n",
              "       [15.13943682],\n",
              "       [25.82062279],\n",
              "       [19.73398224],\n",
              "       [29.00236701],\n",
              "       [17.28256853],\n",
              "       [13.20428287],\n",
              "       [23.38795128],\n",
              "       [33.98576541],\n",
              "       [33.93811911],\n",
              "       [12.20508518],\n",
              "       [16.45492365],\n",
              "       [23.9200203 ],\n",
              "       [16.11883865],\n",
              "       [20.7943919 ],\n",
              "       [17.09301922],\n",
              "       [13.85342102],\n",
              "       [18.6609066 ],\n",
              "       [20.8218369 ],\n",
              "       [27.75143169],\n",
              "       [20.72148603],\n",
              "       [17.22121132],\n",
              "       [ 9.06515729],\n",
              "       [24.07397755],\n",
              "       [24.5483328 ],\n",
              "       [15.85226535],\n",
              "       [14.35281168],\n",
              "       [ 8.27535874],\n",
              "       [20.4328811 ],\n",
              "       [19.5553214 ],\n",
              "       [26.02472149],\n",
              "       [29.92600724],\n",
              "       [34.41928954],\n",
              "       [38.97578886],\n",
              "       [43.79542167]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6r36zEArmdh",
        "outputId": "762236d0-72ef-487b-e636-2e15f19722e0"
      },
      "source": [
        "y_bos_test"
      ],
      "execution_count": 1206,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([13.6, 27.1, 21.4, 28.4, 23. , 23.7, 21.5, 21.5, 23.7, 16. , 20. ,\n",
              "       15.4, 10.5, 23. , 14.9, 21.2, 20.1,  8.5, 20.5, 18.6, 20.6, 24. ,\n",
              "       21.7, 17.1, 12.7, 22.9, 23.2, 37.9, 19.3, 23. , 12.7, 18.5, 23.4,\n",
              "       20.3, 28.4, 11.9, 20.1, 17.8, 20.2, 19.8, 17.2, 19.1, 44. , 22.9,\n",
              "       20. , 17.4, 20.2, 24.8, 26.6, 20.1, 23.9, 23.5, 13.1, 20.9, 17.5,\n",
              "       20.7, 28.7, 20.5, 20.9,  8.3, 19.6, 19.4, 25. , 17.2, 17.8, 25. ,\n",
              "       23.4, 22.9, 11.8, 28.7, 26.5, 14.2, 27.5, 13.9, 31.1, 18.5, 15.7,\n",
              "       23.3, 23.1, 29.9, 22.8, 22.3, 17.9, 43.1, 17.8, 24.3,  8.8, 38.7,\n",
              "       16.7, 22.8, 24.5, 13.8, 43.5, 15.2, 23.9, 27.5, 33.4, 14.9, 19.7,\n",
              "       21.7, 37.2, 28.2,  8.3, 13.4, 22.6, 17.3, 21. , 16.6, 14.8, 19.6,\n",
              "       21.2, 22. , 19.7, 18.7,  9.7, 22.2, 23.1, 20.6, 18.9,  7.2, 21.7,\n",
              "       19.9, 15. , 24.3, 31.7, 50. , 50. ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1206
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iekw5rgNr7Eg"
      },
      "source": [
        "**Evaluation Metrics**\n",
        "\n",
        "\n",
        "1.   Mean Absolute Error(MAE)\n",
        "2.   Mean Squared Method(MSM)\n",
        "3.   Root Mean Square Error Method(RMSE)\n",
        "4.   Mean Absolute Percentage Error(MAPE)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSI2TSYZroMf"
      },
      "source": [
        "from sklearn.metrics import  mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score\n",
        "import math"
      ],
      "execution_count": 1207,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MC0WFReH1M4T",
        "outputId": "a42497a9-c6a1-4e98-ad26-e50caba86e04"
      },
      "source": [
        "mae=mean_absolute_error(y_bos_test, predicted_values_mlr)\n",
        "mae"
      ],
      "execution_count": 1208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.939433362698197"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1208
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UM6kakTe1YdI",
        "outputId": "936dce31-c94b-4fb8-8648-85b6f280ae5e"
      },
      "source": [
        "mse=mean_squared_error(y_bos_test, predicted_values_mlr)\n",
        "mse"
      ],
      "execution_count": 1209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17.309236065228887"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9c7M1GK1gL0",
        "outputId": "f4512198-92d6-489b-a93c-6a7a26f43e73"
      },
      "source": [
        "rmse=math.sqrt(mse) #calculating root mean squared error using mse\n",
        "rmse"
      ],
      "execution_count": 1210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.160437004117343"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41KjFlwx1rPW",
        "outputId": "31f3112d-c1b0-46db-f559-d92f0195ca14"
      },
      "source": [
        "r2=r2_score(y_bos_test, predicted_values_mlr)\n",
        "r2"
      ],
      "execution_count": 1211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6988343407769659"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-8Hv82t1zG7"
      },
      "source": [
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred=np.array(y_true), np.array(y_pred)\n",
        "    return (np.mean(np.abs((y_true-y_pred)/y_true))*100)"
      ],
      "execution_count": 1212,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_M26qtgP2nMB",
        "outputId": "77370efb-3b8d-4c22-9c25-525b475a0462"
      },
      "source": [
        "mape=mean_absolute_percentage_error(y_bos_test, predicted_values_mlr)\n",
        "mape"
      ],
      "execution_count": 1213,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43.713152116475015"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1213
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ygU9iLM4iA3"
      },
      "source": [
        "### **Polynomial Linear Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PwEsBxhi2xCJ"
      },
      "source": [
        "from sklearn.datasets import load_boston\n",
        "new_bos=load_boston()"
      ],
      "execution_count": 1214,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8uR38fy8UY-"
      },
      "source": [
        "x=new_bos.data[:,5]\n",
        "y=new_bos.target"
      ],
      "execution_count": 1215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvnkjSjK8z9V"
      },
      "source": [
        "x_bos_train, x_bos_test, y_bos_train, y_bos_test= train_test_split(x,y, test_size=0.2, train_size=0.8, random_state=76)"
      ],
      "execution_count": 1216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qnPSto59MJl"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures"
      ],
      "execution_count": 1217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7GMyNeSz9VPb"
      },
      "source": [
        "poly_p=PolynomialFeatures(degree=2)"
      ],
      "execution_count": 1218,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckgRZmPNBFwT"
      },
      "source": [
        "x_bos_train=x_bos_train.reshape(-1,1)"
      ],
      "execution_count": 1219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNxGNpK6Cwo-",
        "outputId": "22e17359-cf83-4db9-ab29-a37f59788a1e"
      },
      "source": [
        "poly_x=poly_p.fit_transform(x_bos_train)\n",
        "poly_x"
      ],
      "execution_count": 1220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.      ,  5.349   , 28.611801],\n",
              "       [ 1.      ,  6.749   , 45.549001],\n",
              "       [ 1.      ,  6.333   , 40.106889],\n",
              "       ...,\n",
              "       [ 1.      ,  6.051   , 36.614601],\n",
              "       [ 1.      ,  6.976   , 48.664576],\n",
              "       [ 1.      ,  7.489   , 56.085121]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1220
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81cLl7VtDUTs"
      },
      "source": [
        "lin_r=LinearRegression()"
      ],
      "execution_count": 1221,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hTC18B1DruA"
      },
      "source": [
        "poly_lr=lin_r.fit(poly_x,y_bos_train)"
      ],
      "execution_count": 1222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fyk1l0jPD6yY",
        "outputId": "a24c3eee-070a-412c-e890-b5ab05f986d9"
      },
      "source": [
        "poly_lr"
      ],
      "execution_count": 1223,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1223
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RN-2pD8qEAz3"
      },
      "source": [
        "x_bos_test=x_bos_test.reshape(-1,1)\n",
        "poly_xt=poly_p.fit_transform(x_bos_test)"
      ],
      "execution_count": 1224,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfDS0UE-EYwm"
      },
      "source": [
        "predicted_values_poly=poly_lr.predict(poly_xt)"
      ],
      "execution_count": 1225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mbvr0M7bEvV4",
        "outputId": "3e0da35e-6b52-48b8-d52a-d6ba6f119aa6"
      },
      "source": [
        "r2=r2_score(y_bos_test,predicted_values_poly)\n",
        "r2"
      ],
      "execution_count": 1226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.657015708640466"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1226
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfPn0oHh0FtB"
      },
      "source": [
        "# **Random Forest Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEcS583xkPLs"
      },
      "source": [
        "## **Random Forest Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ieubR6kekkKe"
      },
      "source": [
        "uses **Ensemble Learning** - Combination of several decision tree.\n",
        "\n",
        "Depends on the type of data,\n",
        "if\n",
        "1. Continous Data - Regression\n",
        "2. Discrete Data - Classification\n",
        "\n",
        "\n",
        "Important parameters used in Random Forest- \n",
        "\n",
        "*   n_estimator = number of trees\n",
        "*   List item\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TIxjhv6NkW63"
      },
      "source": [
        "from sklearn.datasets import load_boston\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": 1227,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5P_DkOIrpiE"
      },
      "source": [
        "dat_bos=load_boston()\n",
        "x=dat_bos.data\n",
        "y=dat_bos.target"
      ],
      "execution_count": 1228,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aD3xO02Gr65g"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x,y, train_size=0.75, test_size=0.25, random_state=76)"
      ],
      "execution_count": 1229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldcVAffVse6f"
      },
      "source": [
        "sc=MinMaxScaler(feature_range=(0,1))\n",
        "x_train=sc.fit_transform(x_train)\n",
        "x_test=sc.fit_transform(x_test)\n",
        "y_train=y_train.reshape(-1,1)\n",
        "y_train=sc.fit_transform(y_train)"
      ],
      "execution_count": 1230,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JxeP33Xmr41"
      },
      "source": [
        "random_f = RandomForestRegressor(n_estimators=10 ,random_state=33) #n_estimator is 100 by default if not entered "
      ],
      "execution_count": 1231,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1MhG6SOrnWIC",
        "outputId": "5fa6f0fa-f1a3-453e-c115-f59b4c0c6863"
      },
      "source": [
        "random_f.fit(x_train,y_train)"
      ],
      "execution_count": 1232,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: 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",
            "  \"\"\"Entry point for launching an IPython kernel.\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=10, n_jobs=None, oob_score=False,\n",
              "                      random_state=33, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ztn25XUntliF",
        "outputId": "ba2568f5-1c7d-481d-a9d5-009da7a8db76"
      },
      "source": [
        "predicted_values_rf=random_f.predict(x_test)\n",
        "predicted_values_rf"
      ],
      "execution_count": 1233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.20933333, 0.28266667, 0.37466667, 0.44977778, 0.404     ,\n",
              "       0.444     , 0.31133333, 0.31244444, 0.664     , 0.33333333,\n",
              "       0.356     , 0.23288889, 0.13355556, 0.45333333, 0.204     ,\n",
              "       0.33488889, 0.38644444, 0.03444444, 0.30466667, 0.37088889,\n",
              "       0.39711111, 0.54622222, 0.336     , 0.28311111, 0.21177778,\n",
              "       0.43111111, 0.41333333, 0.63022222, 0.31777778, 0.40977778,\n",
              "       0.17577778, 0.34333333, 0.39311111, 0.34444444, 0.47222222,\n",
              "       0.39      , 0.32555556, 0.20888889, 0.424     , 0.31844444,\n",
              "       0.29888889, 0.34866667, 0.86488889, 0.39888889, 0.36444444,\n",
              "       0.372     , 0.32822222, 0.61466667, 0.586     , 0.33333333,\n",
              "       0.39466667, 0.44644444, 0.25533333, 0.35822222, 0.338     ,\n",
              "       0.42288889, 0.44733333, 0.35066667, 0.33222222, 0.13311111,\n",
              "       0.34933333, 0.35266667, 0.632     , 0.176     , 0.31577778,\n",
              "       0.46688889, 0.42533333, 0.32911111, 0.216     , 0.44577778,\n",
              "       0.45066667, 0.28466667, 0.46888889, 0.27333333, 0.616     ,\n",
              "       0.34      , 0.224     , 0.42733333, 0.44422222, 0.48      ,\n",
              "       0.43266667, 0.41533333, 0.11911111, 0.91066667, 0.25466667,\n",
              "       0.352     , 0.14533333, 0.87111111, 0.23177778, 0.36466667,\n",
              "       0.51      , 0.13511111, 0.95066667, 0.252     , 0.41288889,\n",
              "       0.30111111, 0.63555556, 0.236     , 0.33155556, 0.30888889,\n",
              "       0.61155556, 0.56022222, 0.13755556, 0.20088889, 0.40022222,\n",
              "       0.23133333, 0.39266667, 0.31244444, 0.25      , 0.28466667,\n",
              "       0.35311111, 0.41755556, 0.36066667, 0.34888889, 0.13444444,\n",
              "       0.44488889, 0.40133333, 0.43244444, 0.36333333, 0.08177778,\n",
              "       0.33666667, 0.34288889, 0.44355556, 0.50444444, 0.87555556,\n",
              "       0.99355556, 0.98844444])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1ZNTnSYtsAy",
        "outputId": "2409a9a0-9ad5-4502-a9c0-db6b165d3c2a"
      },
      "source": [
        "predicted_values_rf=predicted_values_rf.reshape(-1,1)\n",
        "predicted_values_rf=sc.inverse_transform(predicted_values_rf)\n",
        "predicted_values_rf"
      ],
      "execution_count": 1234,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14.42],\n",
              "       [17.72],\n",
              "       [21.86],\n",
              "       [25.24],\n",
              "       [23.18],\n",
              "       [24.98],\n",
              "       [19.01],\n",
              "       [19.06],\n",
              "       [34.88],\n",
              "       [20.  ],\n",
              "       [21.02],\n",
              "       [15.48],\n",
              "       [11.01],\n",
              "       [25.4 ],\n",
              "       [14.18],\n",
              "       [20.07],\n",
              "       [22.39],\n",
              "       [ 6.55],\n",
              "       [18.71],\n",
              "       [21.69],\n",
              "       [22.87],\n",
              "       [29.58],\n",
              "       [20.12],\n",
              "       [17.74],\n",
              "       [14.53],\n",
              "       [24.4 ],\n",
              "       [23.6 ],\n",
              "       [33.36],\n",
              "       [19.3 ],\n",
              "       [23.44],\n",
              "       [12.91],\n",
              "       [20.45],\n",
              "       [22.69],\n",
              "       [20.5 ],\n",
              "       [26.25],\n",
              "       [22.55],\n",
              "       [19.65],\n",
              "       [14.4 ],\n",
              "       [24.08],\n",
              "       [19.33],\n",
              "       [18.45],\n",
              "       [20.69],\n",
              "       [43.92],\n",
              "       [22.95],\n",
              "       [21.4 ],\n",
              "       [21.74],\n",
              "       [19.77],\n",
              "       [32.66],\n",
              "       [31.37],\n",
              "       [20.  ],\n",
              "       [22.76],\n",
              "       [25.09],\n",
              "       [16.49],\n",
              "       [21.12],\n",
              "       [20.21],\n",
              "       [24.03],\n",
              "       [25.13],\n",
              "       [20.78],\n",
              "       [19.95],\n",
              "       [10.99],\n",
              "       [20.72],\n",
              "       [20.87],\n",
              "       [33.44],\n",
              "       [12.92],\n",
              "       [19.21],\n",
              "       [26.01],\n",
              "       [24.14],\n",
              "       [19.81],\n",
              "       [14.72],\n",
              "       [25.06],\n",
              "       [25.28],\n",
              "       [17.81],\n",
              "       [26.1 ],\n",
              "       [17.3 ],\n",
              "       [32.72],\n",
              "       [20.3 ],\n",
              "       [15.08],\n",
              "       [24.23],\n",
              "       [24.99],\n",
              "       [26.6 ],\n",
              "       [24.47],\n",
              "       [23.69],\n",
              "       [10.36],\n",
              "       [45.98],\n",
              "       [16.46],\n",
              "       [20.84],\n",
              "       [11.54],\n",
              "       [44.2 ],\n",
              "       [15.43],\n",
              "       [21.41],\n",
              "       [27.95],\n",
              "       [11.08],\n",
              "       [47.78],\n",
              "       [16.34],\n",
              "       [23.58],\n",
              "       [18.55],\n",
              "       [33.6 ],\n",
              "       [15.62],\n",
              "       [19.92],\n",
              "       [18.9 ],\n",
              "       [32.52],\n",
              "       [30.21],\n",
              "       [11.19],\n",
              "       [14.04],\n",
              "       [23.01],\n",
              "       [15.41],\n",
              "       [22.67],\n",
              "       [19.06],\n",
              "       [16.25],\n",
              "       [17.81],\n",
              "       [20.89],\n",
              "       [23.79],\n",
              "       [21.23],\n",
              "       [20.7 ],\n",
              "       [11.05],\n",
              "       [25.02],\n",
              "       [23.06],\n",
              "       [24.46],\n",
              "       [21.35],\n",
              "       [ 8.68],\n",
              "       [20.15],\n",
              "       [20.43],\n",
              "       [24.96],\n",
              "       [27.7 ],\n",
              "       [44.4 ],\n",
              "       [49.71],\n",
              "       [49.48]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1234
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rA5so5skztXq"
      },
      "source": [
        "**Evaluation Metrics**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmawvRTRuMy8",
        "outputId": "65b39295-b808-46b2-df3a-c469b2a97a21"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "mae_rf=mean_absolute_error(y_test, predicted_values_rf)\n",
        "mse_rf=mean_squared_error(y_test, predicted_values_rf)\n",
        "r2_rf=r2_score(y_test, predicted_values_rf)\n",
        "mape_rf = mean_absolute_percentage_error(y_test, predicted_values_rf)\n",
        "\n",
        "print( mae_rf, mse_rf, r2_rf, mape_rf)"
      ],
      "execution_count": 1235,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.388346456692913 11.528444094488187 0.7994150953601592 42.05365138529826\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YRgs3BHNFqTz"
      },
      "source": [
        "# **Support Vector Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8RItU_FOFte7"
      },
      "source": [
        "from sklearn.svm import SVR"
      ],
      "execution_count": 1236,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1OQLV1uKRoT"
      },
      "source": [
        "from sklearn.datasets import load_boston\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 1237,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JcQ6GNIKcwx"
      },
      "source": [
        "dat_bos=load_boston()\n",
        "x=dat_bos.data\n",
        "y=dat_bos.target"
      ],
      "execution_count": 1238,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99HFR5cOKnWC"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x,y, train_size=0.75, test_size=0.25, random_state=76)"
      ],
      "execution_count": 1239,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5zcX__YKsFj"
      },
      "source": [
        "sc=MinMaxScaler(feature_range=(0,1))\n",
        "x_train=sc.fit_transform(x_train)\n",
        "x_test=sc.fit_transform(x_test)\n",
        "y_train=y_train.reshape(-1,1)\n",
        "y_train=sc.fit_transform(y_train)"
      ],
      "execution_count": 1240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lA88IBKtKy-h"
      },
      "source": [
        "reg_svr=SVR(kernel='rbf') #Default is rbf"
      ],
      "execution_count": 1241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "as1MZHtYLAuQ",
        "outputId": "03fbba99-7c38-43dc-c935-c6449380733f"
      },
      "source": [
        "reg_svr.fit(x_train,y_train)"
      ],
      "execution_count": 1242,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: 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",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1242
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szi5iyHGLKw-"
      },
      "source": [
        "predicted_values_svr=reg_svr.predict(x_test)"
      ],
      "execution_count": 1243,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bzRhWZYlLbXx",
        "outputId": "38911676-e066-4063-d06c-75f9bd7ef8bc"
      },
      "source": [
        "predicted_values_svr=predicted_values_svr.reshape(-1,1)\n",
        "predicted_values_svr=sc.inverse_transform(predicted_values_svr)\n",
        "predicted_values_svr"
      ],
      "execution_count": 1244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14.24662499],\n",
              "       [17.45458279],\n",
              "       [22.15166285],\n",
              "       [30.71124346],\n",
              "       [27.90932297],\n",
              "       [27.14906328],\n",
              "       [22.20528399],\n",
              "       [22.17381442],\n",
              "       [29.31529709],\n",
              "       [17.4288741 ],\n",
              "       [18.50516684],\n",
              "       [16.00789931],\n",
              "       [10.14142804],\n",
              "       [25.33238834],\n",
              "       [15.16159783],\n",
              "       [19.96197706],\n",
              "       [21.09100787],\n",
              "       [ 7.40282748],\n",
              "       [23.25452187],\n",
              "       [20.55578811],\n",
              "       [24.12802719],\n",
              "       [29.52324452],\n",
              "       [19.946244  ],\n",
              "       [17.12608595],\n",
              "       [14.25606608],\n",
              "       [26.90120474],\n",
              "       [23.57813645],\n",
              "       [36.17147376],\n",
              "       [18.94423599],\n",
              "       [25.76845812],\n",
              "       [17.8769714 ],\n",
              "       [17.08708114],\n",
              "       [23.01227331],\n",
              "       [24.78276881],\n",
              "       [29.52411586],\n",
              "       [21.09594967],\n",
              "       [19.07741974],\n",
              "       [23.74707661],\n",
              "       [23.2836816 ],\n",
              "       [19.84092652],\n",
              "       [15.74149524],\n",
              "       [20.30990938],\n",
              "       [39.9526245 ],\n",
              "       [22.28321765],\n",
              "       [21.30408826],\n",
              "       [21.18632832],\n",
              "       [17.07310372],\n",
              "       [29.42079895],\n",
              "       [33.52030747],\n",
              "       [18.77235393],\n",
              "       [26.04766554],\n",
              "       [28.24570144],\n",
              "       [17.95058735],\n",
              "       [18.30820253],\n",
              "       [14.84103855],\n",
              "       [24.88424693],\n",
              "       [29.57585537],\n",
              "       [21.12069643],\n",
              "       [22.8172859 ],\n",
              "       [11.93469354],\n",
              "       [19.04474799],\n",
              "       [17.30591588],\n",
              "       [25.31152851],\n",
              "       [14.84533858],\n",
              "       [15.85989485],\n",
              "       [24.95595388],\n",
              "       [26.07920057],\n",
              "       [19.18231236],\n",
              "       [13.54829663],\n",
              "       [26.44423361],\n",
              "       [25.37531628],\n",
              "       [18.23042647],\n",
              "       [23.70386334],\n",
              "       [14.06168215],\n",
              "       [34.05349434],\n",
              "       [20.17854188],\n",
              "       [20.17213327],\n",
              "       [22.0608962 ],\n",
              "       [26.49615918],\n",
              "       [32.89805285],\n",
              "       [25.33448951],\n",
              "       [23.50754612],\n",
              "       [13.72125837],\n",
              "       [41.43205618],\n",
              "       [15.70987694],\n",
              "       [21.80029706],\n",
              "       [16.05801339],\n",
              "       [42.34030579],\n",
              "       [17.87861939],\n",
              "       [26.09693735],\n",
              "       [29.32495024],\n",
              "       [12.0289406 ],\n",
              "       [46.09238328],\n",
              "       [16.54072542],\n",
              "       [25.77110825],\n",
              "       [16.03403154],\n",
              "       [33.23144651],\n",
              "       [15.56685405],\n",
              "       [11.66168561],\n",
              "       [22.13115467],\n",
              "       [37.49282925],\n",
              "       [32.87121463],\n",
              "       [13.97467118],\n",
              "       [15.11356723],\n",
              "       [24.42598525],\n",
              "       [21.05486767],\n",
              "       [21.22852123],\n",
              "       [19.32935265],\n",
              "       [15.7971197 ],\n",
              "       [19.637294  ],\n",
              "       [19.04055974],\n",
              "       [26.59950552],\n",
              "       [19.56753468],\n",
              "       [16.2205947 ],\n",
              "       [11.77374913],\n",
              "       [22.71956334],\n",
              "       [23.57639031],\n",
              "       [24.66599716],\n",
              "       [18.0911897 ],\n",
              "       [10.4588089 ],\n",
              "       [18.6330254 ],\n",
              "       [18.45387122],\n",
              "       [21.33163401],\n",
              "       [30.5302902 ],\n",
              "       [37.48358252],\n",
              "       [46.28162461],\n",
              "       [45.84821551]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1244
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_yjMRRoLwWf",
        "outputId": "7484c51d-5610-4708-fc97-2f8879d91b0e"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "mae_svr=mean_absolute_error(y_test, predicted_values_svr)\n",
        "mse_svr=mean_squared_error(y_test, predicted_values_svr)\n",
        "r2_svr=r2_score(y_test, predicted_values_svr)\n",
        "mape_svr = mean_absolute_percentage_error(y_test, predicted_values_svr)\n",
        "\n",
        "print( mae_svr, mse_svr, r2_svr, mape_svr)"
      ],
      "execution_count": 1245,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.6210345549212413 11.41716611522538 0.8013512354564292 42.40440288679086\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}