Pipeline_nd_ColumnTransformer.ipynb

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  "metadata": {
    "colab": {
      "name": "Pipeline nd ColumnTransformer",
      "provenance": [],
      "authorship_tag": "ABX9TyOhHWYLAldvkYWCg31A9QdJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sudeshmu/Machine-Learning-practice/blob/master/Pipeline_nd_ColumnTransformer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oFOe5n7h7A_O",
        "colab_type": "text"
      },
      "source": [
        "This notebook is for the trying and learning Columntransformer and Pipeline classes in the sklearn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccyKKe407Pu9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GURkIkcV7je_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.compose import ColumnTransformer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgVE5zSe786F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqcAdua9M56_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAVm5ZUywHpg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Nt6dXHX7rlK",
        "colab_type": "text"
      },
      "source": [
        "# Example 1: Iris dataset classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOxG5Jh97oMM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv (\"https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/639388c2cbc2120a14dcf466e85730eb8be498bb/iris.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bmqkny4x8FTy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "2547f032-6680-4ad2-af9c-2e5b314c7757"
      },
      "source": [
        "dataset.head ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</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>setosa</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>setosa</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>setosa</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>setosa</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>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5hx7jXD8Jro",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "e318e6b1-960f-42ed-86ef-4c86b621c064"
      },
      "source": [
        "dataset.isnull ().sum ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal_length    0\n",
              "sepal_width     0\n",
              "petal_length    0\n",
              "petal_width     0\n",
              "species         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JlKGV6x9jrv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "ab1a3296-de03-4a24-93be-63009ea02552"
      },
      "source": [
        "dataset.values[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.1, 3.5, 1.4, 0.2, 'setosa'],\n",
              "       [4.9, 3.0, 1.4, 0.2, 'setosa'],\n",
              "       [4.7, 3.2, 1.3, 0.2, 'setosa'],\n",
              "       [4.6, 3.1, 1.5, 0.2, 'setosa'],\n",
              "       [5.0, 3.6, 1.4, 0.2, 'setosa']], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbYgnlGZ8RRS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNfw5cWQ86JX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ct = ColumnTransformer (transformers=[('label_encoding', OneHotEncoder (), [4])]) #, remainder=\"passthrough\"\n",
        "y = ct.fit_transform (dataset.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UK-948JV9rTv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = dataset.values [:, 0:4]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tPgUqZ2sHXYP",
        "colab_type": "text"
      },
      "source": [
        "Train test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFn58GP1HcQX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split (x,y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oM03bAwJGHm0",
        "colab_type": "text"
      },
      "source": [
        "Pipeline example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMkANeh_Gur2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.multioutput import MultiOutputClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoQLoqahBVHX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pipe = Pipeline (steps=[('scalar', StandardScaler ()), ('svm', MultiOutputClassifier (SVC (decision_function_shape=\"ovr\")))])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5U1Ro66BzpM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "0bb564db-c58f-4512-f8da-8251afd0a250"
      },
      "source": [
        "pipe.fit (X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('scalar',\n",
              "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
              "                ('svm',\n",
              "                 MultiOutputClassifier(estimator=SVC(C=1.0, break_ties=False,\n",
              "                                                     cache_size=200,\n",
              "                                                     class_weight=None,\n",
              "                                                     coef0=0.0,\n",
              "                                                     decision_function_shape='ovr',\n",
              "                                                     degree=3, gamma='scale',\n",
              "                                                     kernel='rbf', max_iter=-1,\n",
              "                                                     probability=False,\n",
              "                                                     random_state=None,\n",
              "                                                     shrinking=True, tol=0.001,\n",
              "                                                     verbose=False),\n",
              "                                       n_jobs=None))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__6v5id-VMrZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "137529cd-549b-43da-c8a9-6cfb4df31618"
      },
      "source": [
        "pipe.score (X_test, Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9473684210526315"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSU_rAtWNxad",
        "colab_type": "text"
      },
      "source": [
        "#*Example* 2: Wine Quality dataset from UCI\n",
        "\n",
        "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQIhRUpknfXl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv (\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\", delimiter=\";\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Afo5nRMZOsDd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c54a34e-54e5-4774-9159-89fb59a16147"
      },
      "source": [
        "dataset.head ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
              "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
              "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
              "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2ARPlu7Othw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2815e0dc-dbbf-48e8-965d-7e90a3270541"
      },
      "source": [
        "dataset.isnull ().sum ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           0\n",
              "volatile acidity        0\n",
              "citric acid             0\n",
              "residual sugar          0\n",
              "chlorides               0\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "density                 0\n",
              "pH                      0\n",
              "sulphates               0\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2R22y0qYQF8q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IH9QNCL-O2dM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pipeline = Pipeline (steps=[('scalar', MinMaxScaler ()), ('svc', SVC ())])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NaElCGD1PPGI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split (dataset.values [:,0:-1], dataset.values [:,-1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8fZAQnqPsyp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b190f8d-9fc1-44f4-e724-1bb7badb65d4"
      },
      "source": [
        "pipeline.fit (X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('scalar', MinMaxScaler(copy=True, feature_range=(0, 1))),\n",
              "                ('svc',\n",
              "                 SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None,\n",
              "                     coef0=0.0, decision_function_shape='ovr', degree=3,\n",
              "                     gamma='scale', kernel='rbf', max_iter=-1,\n",
              "                     probability=False, random_state=None, shrinking=True,\n",
              "                     tol=0.001, verbose=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JpwMraCP1Lv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08914c4c-401d-4c94-b63a-7e6b2191bca1"
      },
      "source": [
        "pipeline.score (X_test, Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5875"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkWOa522XO7U",
        "colab_type": "text"
      },
      "source": [
        "# Example 3 Adult Dataset\n",
        "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45lfb8a4Xa1m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv (\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\", names =['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'class'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8E2zkxWFXek_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d94bfe6-18af-47ab-e9be-8db82006e673"
      },
      "source": [
        "dataset.head ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education-num</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age          workclass  fnlwgt  ... hours-per-week  native-country   class\n",
              "0   39          State-gov   77516  ...             40   United-States   <=50K\n",
              "1   50   Self-emp-not-inc   83311  ...             13   United-States   <=50K\n",
              "2   38            Private  215646  ...             40   United-States   <=50K\n",
              "3   53            Private  234721  ...             40   United-States   <=50K\n",
              "4   28            Private  338409  ...             40            Cuba   <=50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esBXdNfGXfZ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e05555e7-6aaa-4f28-8a0f-6e76d2fe18bd"
      },
      "source": [
        "dataset.isnull ().sum ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "workclass         0\n",
              "fnlwgt            0\n",
              "education         0\n",
              "education-num     0\n",
              "marital-status    0\n",
              "occupation        0\n",
              "relationship      0\n",
              "race              0\n",
              "sex               0\n",
              "capital-gain      0\n",
              "capital-loss      0\n",
              "hours-per-week    0\n",
              "native-country    0\n",
              "class             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sah92svhad9Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ee3ab06-5c07-4080-8b9f-6eb360f4aabe"
      },
      "source": [
        "len (dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32561"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxyHeuPzZJuz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmwYjo73ZYCf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "859e2312-11e3-42d8-d1b2-e636c1801d8c"
      },
      "source": [
        "plt.plot (dataset['age'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1910eb84a8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "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": "dICC2RAxZeAo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc73f08a-2f32-4286-c4be-0b8673f41aba"
      },
      "source": [
        "dataset ['workclass'].unique ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' State-gov', ' Self-emp-not-inc', ' Private', ' Federal-gov',\n",
              "       ' Local-gov', ' ?', ' Self-emp-inc', ' Without-pay',\n",
              "       ' Never-worked'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GdDafRVYcmCg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3be384bd-b109-4704-9199-1f67d2324ec3"
      },
      "source": [
        "plt.plot (dataset ['fnlwgt'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1910990668>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "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": "WeL5lPrCc1Ew",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acb3f012-0164-4714-f6ab-9cf621a14fb2"
      },
      "source": [
        "#Printing education and it's values associated \n",
        "\n",
        "print (\" Education Name  Edu Score\")\n",
        "print (\" --------------  ---------\")\n",
        "\n",
        "for education_nm in dataset['education'].unique ():\n",
        "  print (\"{:10} \\t {} \".format (education_nm, dataset[dataset ['education'] == education_nm]['education-num'].values[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Education Name  Edu Score\n",
            " --------------  ---------\n",
            " Bachelors \t 13 \n",
            " HS-grad   \t 9 \n",
            " 11th      \t 7 \n",
            " Masters   \t 14 \n",
            " 9th       \t 5 \n",
            " Some-college \t 10 \n",
            " Assoc-acdm \t 12 \n",
            " Assoc-voc \t 11 \n",
            " 7th-8th   \t 4 \n",
            " Doctorate \t 16 \n",
            " Prof-school \t 15 \n",
            " 5th-6th   \t 3 \n",
            " 10th      \t 6 \n",
            " 1st-4th   \t 2 \n",
            " Preschool \t 1 \n",
            " 12th      \t 8 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gSQTVY-dDaR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1dd41466-9ae2-4d41-d660-ed50c4f1f812"
      },
      "source": [
        "dataset ['marital-status'].unique ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' Never-married', ' Married-civ-spouse', ' Divorced',\n",
              "       ' Married-spouse-absent', ' Separated', ' Married-AF-spouse',\n",
              "       ' Widowed'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-YsmRM6hbFx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8839ece-938c-42f2-c1ac-49b719f343fc"
      },
      "source": [
        "dataset ['occupation'].unique ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' Adm-clerical', ' Exec-managerial', ' Handlers-cleaners',\n",
              "       ' Prof-specialty', ' Other-service', ' Sales', ' Craft-repair',\n",
              "       ' Transport-moving', ' Farming-fishing', ' Machine-op-inspct',\n",
              "       ' Tech-support', ' ?', ' Protective-serv', ' Armed-Forces',\n",
              "       ' Priv-house-serv'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1J7IOn0hqce",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41b70647-3cf5-4930-e130-cb2063e7c99b"
      },
      "source": [
        "dataset ['relationship'].unique ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' Not-in-family', ' Husband', ' Wife', ' Own-child', ' Unmarried',\n",
              "       ' Other-relative'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDk1fZPYhyv1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "999055f3-2b1c-4ef5-aa5c-3d126f67e0a7"
      },
      "source": [
        "dataset ['race'].unique ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' White', ' Black', ' Asian-Pac-Islander', ' Amer-Indian-Eskimo',\n",
              "       ' Other'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "my9q9TvIh7qv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1eb8ca2f-9516-4fa4-a90e-c40e81f7e4d5"
      },
      "source": [
        "dataset ['sex'].unique ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' Male', ' Female'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhBkVYxGiDgP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3cbb3130-2d17-4ff3-d623-6f11ce6cf2b8"
      },
      "source": [
        "dataset ['capital-gain'].unique ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2174,     0, 14084,  5178,  5013,  2407, 14344, 15024,  7688,\n",
              "       34095,  4064,  4386,  7298,  1409,  3674,  1055,  3464,  2050,\n",
              "        2176,   594, 20051,  6849,  4101,  1111,  8614,  3411,  2597,\n",
              "       25236,  4650,  9386,  2463,  3103, 10605,  2964,  3325,  2580,\n",
              "        3471,  4865, 99999,  6514,  1471,  2329,  2105,  2885, 25124,\n",
              "       10520,  2202,  2961, 27828,  6767,  2228,  1506, 13550,  2635,\n",
              "        5556,  4787,  3781,  3137,  3818,  3942,   914,   401,  2829,\n",
              "        2977,  4934,  2062,  2354,  5455, 15020,  1424,  3273, 22040,\n",
              "        4416,  3908, 10566,   991,  4931,  1086,  7430,  6497,   114,\n",
              "        7896,  2346,  3418,  3432,  2907,  1151,  2414,  2290, 15831,\n",
              "       41310,  4508,  2538,  3456,  6418,  1848,  3887,  5721,  9562,\n",
              "        1455,  2036,  1831, 11678,  2936,  2993,  7443,  6360,  1797,\n",
              "        1173,  4687,  6723,  2009,  6097,  2653,  1639, 18481,  7978,\n",
              "        2387,  5060])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwxOxNhAiNa5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe73b91f-8f4b-4143-fb97-e7eb5c5a3b3e"
      },
      "source": [
        "dataset ['native-country'].unique ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' United-States', ' Cuba', ' Jamaica', ' India', ' ?', ' Mexico',\n",
              "       ' South', ' Puerto-Rico', ' Honduras', ' England', ' Canada',\n",
              "       ' Germany', ' Iran', ' Philippines', ' Italy', ' Poland',\n",
              "       ' Columbia', ' Cambodia', ' Thailand', ' Ecuador', ' Laos',\n",
              "       ' Taiwan', ' Haiti', ' Portugal', ' Dominican-Republic',\n",
              "       ' El-Salvador', ' France', ' Guatemala', ' China', ' Japan',\n",
              "       ' Yugoslavia', ' Peru', ' Outlying-US(Guam-USVI-etc)', ' Scotland',\n",
              "       ' Trinadad&Tobago', ' Greece', ' Nicaragua', ' Vietnam', ' Hong',\n",
              "       ' Ireland', ' Hungary', ' Holand-Netherlands'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmpLrauhimKW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "224535ed-2569-4c50-f8f2-b676e5f2555c"
      },
      "source": [
        "dataset.describe ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education-num</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>32561.000000</td>\n",
              "      <td>3.256100e+04</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.581647</td>\n",
              "      <td>1.897784e+05</td>\n",
              "      <td>10.080679</td>\n",
              "      <td>1077.648844</td>\n",
              "      <td>87.303830</td>\n",
              "      <td>40.437456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.640433</td>\n",
              "      <td>1.055500e+05</td>\n",
              "      <td>2.572720</td>\n",
              "      <td>7385.292085</td>\n",
              "      <td>402.960219</td>\n",
              "      <td>12.347429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>1.228500e+04</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>1.178270e+05</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>1.783560e+05</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>2.370510e+05</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>1.484705e+06</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>99999.000000</td>\n",
              "      <td>4356.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age        fnlwgt  ...  capital-loss  hours-per-week\n",
              "count  32561.000000  3.256100e+04  ...  32561.000000    32561.000000\n",
              "mean      38.581647  1.897784e+05  ...     87.303830       40.437456\n",
              "std       13.640433  1.055500e+05  ...    402.960219       12.347429\n",
              "min       17.000000  1.228500e+04  ...      0.000000        1.000000\n",
              "25%       28.000000  1.178270e+05  ...      0.000000       40.000000\n",
              "50%       37.000000  1.783560e+05  ...      0.000000       40.000000\n",
              "75%       48.000000  2.370510e+05  ...      0.000000       45.000000\n",
              "max       90.000000  1.484705e+06  ...   4356.000000       99.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jl-gN2ari7E1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3dd968e-cbb5-4881-b66f-0b9c5a9dd9b6"
      },
      "source": [
        "dataset.info ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32561 entries, 0 to 32560\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   age             32561 non-null  int64 \n",
            " 1   workclass       32561 non-null  object\n",
            " 2   fnlwgt          32561 non-null  int64 \n",
            " 3   education       32561 non-null  object\n",
            " 4   education-num   32561 non-null  int64 \n",
            " 5   marital-status  32561 non-null  object\n",
            " 6   occupation      32561 non-null  object\n",
            " 7   relationship    32561 non-null  object\n",
            " 8   race            32561 non-null  object\n",
            " 9   sex             32561 non-null  object\n",
            " 10  capital-gain    32561 non-null  int64 \n",
            " 11  capital-loss    32561 non-null  int64 \n",
            " 12  hours-per-week  32561 non-null  int64 \n",
            " 13  native-country  32561 non-null  object\n",
            " 14  class           32561 non-null  object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 3.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ut85C6i4jVyl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0df630b-fe99-40bc-c871-cde8d79beeae"
      },
      "source": [
        "dataset.head (5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education-num</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age          workclass  fnlwgt  ... hours-per-week  native-country   class\n",
              "0   39          State-gov   77516  ...             40   United-States   <=50K\n",
              "1   50   Self-emp-not-inc   83311  ...             13   United-States   <=50K\n",
              "2   38            Private  215646  ...             40   United-States   <=50K\n",
              "3   53            Private  234721  ...             40   United-States   <=50K\n",
              "4   28            Private  338409  ...             40            Cuba   <=50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8auVWKl6LKY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset.drop([\"education\"], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdaGVyM2119y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for colmn in ['workclass', 'education-num', 'marital-status', 'occupation', 'relationship', 'hours-per-week']:\n",
        "  dataset[colmn] = dataset[colmn].astype('category')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uq8Rjhb7jmFM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = dataset.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "momp02OtnA_q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b885f96-d838-4caa-8ae6-03fc102d354a"
      },
      "source": [
        "data[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([39, ' State-gov', 77516, 13, ' Never-married', ' Adm-clerical',\n",
              "       ' Not-in-family', ' White', ' Male', 2174, 0, 40, ' United-States',\n",
              "       ' <=50K'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjakiIrp0ZSn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc0cfefc-e7c0-471e-f7f2-c54e62d1cfdc"
      },
      "source": [
        "dataset.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                  int64\n",
              "workclass         category\n",
              "fnlwgt               int64\n",
              "education-num     category\n",
              "marital-status    category\n",
              "occupation        category\n",
              "relationship      category\n",
              "race                object\n",
              "sex                 object\n",
              "capital-gain         int64\n",
              "capital-loss         int64\n",
              "hours-per-week    category\n",
              "native-country      object\n",
              "class               object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpCCGhpBkZYw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3abef7bc-af22-4a40-a014-eb3662443200"
      },
      "source": [
        "dataset.columns.values[[1,4,5,6,7,12]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['workclass', 'marital-status', 'occupation', 'relationship',\n",
              "       'race', 'native-country'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6A3SF4pjtB1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ct = ColumnTransformer (transformers=[('encoding', OneHotEncoder (),[1,4,5,7])], remainder=\"passthrough\")\n",
        "data = ct.fit_transform (data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GYTqrVLFls6m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0ba3dce-0161-4ec8-a7d7-f0dabb6a6cb9"
      },
      "source": [
        "ind = 0\n",
        "for value in data [0]:\n",
        "  print (ind, \" \", value)\n",
        "  ind = ind + 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0   0.0\n",
            "1   0.0\n",
            "2   0.0\n",
            "3   0.0\n",
            "4   0.0\n",
            "5   0.0\n",
            "6   0.0\n",
            "7   1.0\n",
            "8   0.0\n",
            "9   0.0\n",
            "10   0.0\n",
            "11   0.0\n",
            "12   0.0\n",
            "13   1.0\n",
            "14   0.0\n",
            "15   0.0\n",
            "16   0.0\n",
            "17   1.0\n",
            "18   0.0\n",
            "19   0.0\n",
            "20   0.0\n",
            "21   0.0\n",
            "22   0.0\n",
            "23   0.0\n",
            "24   0.0\n",
            "25   0.0\n",
            "26   0.0\n",
            "27   0.0\n",
            "28   0.0\n",
            "29   0.0\n",
            "30   0.0\n",
            "31   0.0\n",
            "32   0.0\n",
            "33   0.0\n",
            "34   0.0\n",
            "35   1.0\n",
            "36   39\n",
            "37   77516\n",
            "38   13\n",
            "39    Not-in-family\n",
            "40    Male\n",
            "41   2174\n",
            "42   0\n",
            "43   40\n",
            "44    United-States\n",
            "45    <=50K\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IlTQWSbdmIKX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lb = LabelEncoder ()\n",
        "data[:,40] = lb.fit_transform (data[:,40])\n",
        "\n",
        "lb = LabelEncoder ()\n",
        "data[:,45] = lb.fit_transform (data[:,45])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1tfmPJ-vkQ6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lb = LabelEncoder ()\n",
        "data[:,39] = lb.fit_transform (data[:,39])\n",
        "\n",
        "lb = LabelEncoder ()\n",
        "data[:,44] = lb.fit_transform (data[:,44])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y899pbSI_WBx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = data [:, :-1]\n",
        "Y = data[:, -1]\n",
        "scalar = StandardScaler ()\n",
        "#data = scalar.fit_transform (data)\n",
        "X = scalar.fit_transform (X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WAEC3MFTGX3G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "mm_scalar = MinMaxScaler ()\n",
        "#Y = mm_scalar.fit_transform ([Y])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGViGw3GGqKH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y = Y.astype ('int')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0zq9nB691Sy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split (X, Y, test_size=0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5-oYAh6-Gtw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "svc = SVC ()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nT-OyEhp-M_T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ed382fd-7a3d-49e0-ada1-c0276ac50705"
      },
      "source": [
        "svc.fit (X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jy4jcOrDvcf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88c327d3-5a79-4164-c2c7-d948869c9ea8"
      },
      "source": [
        "svc.score (X_test, Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8481932644078206"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxUmbFCGpkN0",
        "colab_type": "text"
      },
      "source": [
        "# Example 3 UCI auto-mpg\n",
        "Dataset  https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zIjKAgR4CyJe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_fwf (\"https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\", names=['mpg','cylinders','displacement','horsepower','weight','acceleration','model year','origin','car name'], sep=\"\\t\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueF0SCFmp5m3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8fc7f62b-1105-48f8-d735-ecfcf35525f7"
      },
      "source": [
        "dataset.head ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>displacement</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
              "      <th>model year</th>\n",
              "      <th>origin</th>\n",
              "      <th>car name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18.0</td>\n",
              "      <td>8</td>\n",
              "      <td>307.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>3504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>\"chevrolet chevelle malibu\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>350.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>3693.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>\"buick skylark 320\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.0</td>\n",
              "      <td>8</td>\n",
              "      <td>318.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3436.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>\"plymouth satellite\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16.0</td>\n",
              "      <td>8</td>\n",
              "      <td>304.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3433.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>\"amc rebel sst\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17.0</td>\n",
              "      <td>8</td>\n",
              "      <td>302.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>3449.0</td>\n",
              "      <td>10.5</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>\"ford torino\"</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    mpg  cylinders  ...  origin                     car name\n",
              "0  18.0          8  ...       1  \"chevrolet chevelle malibu\"\n",
              "1  15.0          8  ...       1          \"buick skylark 320\"\n",
              "2  18.0          8  ...       1         \"plymouth satellite\"\n",
              "3  16.0          8  ...       1              \"amc rebel sst\"\n",
              "4  17.0          8  ...       1                \"ford torino\"\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LKplqHvrp6h6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "c76896cc-8225-4c04-cc3e-88aa33f6f007"
      },
      "source": [
        "dataset.info ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 398 entries, 0 to 397\n",
            "Data columns (total 9 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   mpg           398 non-null    float64\n",
            " 1   cylinders     398 non-null    int64  \n",
            " 2   displacement  398 non-null    float64\n",
            " 3   horsepower    398 non-null    object \n",
            " 4   weight        398 non-null    float64\n",
            " 5   acceleration  398 non-null    float64\n",
            " 6   model year    398 non-null    int64  \n",
            " 7   origin        398 non-null    int64  \n",
            " 8   car name      398 non-null    object \n",
            "dtypes: float64(4), int64(3), object(2)\n",
            "memory usage: 28.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cLn6qC2tmQj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset['car name'] = dataset['car name'].astype ('category')\n",
        "#dataset['horsepower'] = dataset['horsepower'] .astype ('float')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzGP3VbvruWn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "6e4183c2-2671-4a76-858d-515b8a3013d3"
      },
      "source": [
        "dataset.isnull ().sum ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mpg             0\n",
              "cylinders       0\n",
              "displacement    0\n",
              "horsepower      0\n",
              "weight          0\n",
              "acceleration    0\n",
              "model year      0\n",
              "origin          0\n",
              "car name        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VASlIgaltRjL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "417e4af3-e708-47cb-aa3d-d2701a2db43a"
      },
      "source": [
        "dataset['car name'].unique ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"chevrolet chevelle malibu\", \"buick skylark 320\", \"plymouth satellite\", \"amc rebel sst\", \"ford torino\", ..., \"ford mustang gl\", \"vw pickup\", \"dodge rampage\", \"ford ranger\", \"chevy s-10\"]\n",
              "Length: 305\n",
              "Categories (305, object): [\"chevrolet chevelle malibu\", \"buick skylark 320\", \"plymouth satellite\",\n",
              "                           \"amc rebel sst\", ..., \"vw pickup\", \"dodge rampage\", \"ford ranger\",\n",
              "                           \"chevy s-10\"]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxa7ZzOYr3pj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ct = ColumnTransformer (transformers=[('encoding', OneHotEncoder (), [8])], remainder='drop')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-JuZJJutLLG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "cca0044e-854e-4aac-b994-8308e4884cd9"
      },
      "source": [
        "ct.fit_transform (dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<398x305 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 398 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbxNGiMZtNvG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset.loc [dataset['horsepower'] == '?', 'horsepower']= None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lm7G_j8WuUtj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "1602420b-4ffb-44fc-e54d-39e96013c30d"
      },
      "source": [
        "dataset.isnull ().sum ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mpg             0\n",
              "cylinders       0\n",
              "displacement    0\n",
              "horsepower      6\n",
              "weight          0\n",
              "acceleration    0\n",
              "model year      0\n",
              "origin          0\n",
              "car name        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ujz7vtV5yoZd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "74e19293-3ba5-4839-a245-2979fbd9a267"
      },
      "source": [
        "dataset[dataset['horsepower'].notna ()].isnull ().sum ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mpg             0\n",
              "cylinders       0\n",
              "displacement    0\n",
              "horsepower      0\n",
              "weight          0\n",
              "acceleration    0\n",
              "model year      0\n",
              "origin          0\n",
              "car name        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNTOtSagCyTb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "b60a98e3-6689-47bf-c241-00ac7c7ea50c"
      },
      "source": [
        "dataset ['horsepower'].fillna (method=\"ffill\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      130.0\n",
              "1      165.0\n",
              "2      150.0\n",
              "3      150.0\n",
              "4      140.0\n",
              "       ...  \n",
              "393    86.00\n",
              "394    52.00\n",
              "395    84.00\n",
              "396    79.00\n",
              "397    82.00\n",
              "Name: horsepower, Length: 398, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xW8cwhWrUIh9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "420b0966-e41d-4b33-d914-079a967e40fe"
      },
      "source": [
        "dataset ['horsepower']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      130.0\n",
              "1      165.0\n",
              "2      150.0\n",
              "3      150.0\n",
              "4      140.0\n",
              "       ...  \n",
              "393    86.00\n",
              "394    52.00\n",
              "395    84.00\n",
              "396    79.00\n",
              "397    82.00\n",
              "Name: horsepower, Length: 398, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecwMTPx-v_rP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}