Census_DecisionTreeClassifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Census_DecisionTreeClassifier.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyM8oL7q7LwueYVxwNVZ4lTc",
      "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/alexandreleitetrevisani/DATA_SCIENCE_MACHINE_LEARNING/blob/master/Census_DecisionTreeClassifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dp_YrOtuaDaZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "1d1b12fc-e636-4a77-e7d5-083d9362ae62",
        "id": "DV5NWovOQ-Zl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        }
      },
      "source": [
        "file = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'\n",
        "colunas = ['age','workclass','final-weight','education','education-num','marital-status','occupation','relationship','race','sex','capital-gain','capital-loss','hours-per-week','native-country','income']\n",
        "train = pd.read_csv(file,names=colunas,usecols=colunas)\n",
        "train.head()"
      ],
      "execution_count": 0,
      "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>final-weight</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>income</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  final-weight  ... hours-per-week  native-country  income\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": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nIZY81Ladto",
        "colab_type": "code",
        "outputId": "7d6a1bf4-6991-4be0-8fab-056893448bbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "source": [
        "train.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'workclass', 'final-weight', 'education', 'education-num',\n",
              "       'marital-status', 'occupation', 'relationship', 'race', 'sex',\n",
              "       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
              "       'income'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qilGGqHL_it5",
        "colab_type": "code",
        "outputId": "07a77e9b-d1d3-4666-9edc-e72a0af70805",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        }
      },
      "source": [
        "# já não tem a coluna income\n",
        "previsores = train.iloc[:,0:14].values\n",
        "previsores"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[39, ' State-gov', 77516, ..., 0, 40, ' United-States'],\n",
              "       [50, ' Self-emp-not-inc', 83311, ..., 0, 13, ' United-States'],\n",
              "       [38, ' Private', 215646, ..., 0, 40, ' United-States'],\n",
              "       ...,\n",
              "       [58, ' Private', 151910, ..., 0, 40, ' United-States'],\n",
              "       [22, ' Private', 201490, ..., 0, 20, ' United-States'],\n",
              "       [52, ' Self-emp-inc', 287927, ..., 0, 40, ' United-States']],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsXKBsYTB_7Z",
        "colab_type": "code",
        "outputId": "7c90f77b-c2cc-4be8-ae10-d033f3cca146",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "classe = train.iloc[:,14].values\n",
        "classe"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' <=50K', ' <=50K', ' <=50K', ..., ' <=50K', ' <=50K', ' >50K'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szaUE5V0Chnm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JOwpPCVvDEOm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labelencoder_previsores = LabelEncoder()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NVjN-7kE8g2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "previsores[:,1] = labelencoder_previsores.fit_transform(previsores[:,1])\n",
        "previsores[:,3] = labelencoder_previsores.fit_transform(previsores[:,3])\n",
        "previsores[:,5] = labelencoder_previsores.fit_transform(previsores[:,5])\n",
        "previsores[:,6] = labelencoder_previsores.fit_transform(previsores[:,6])\n",
        "previsores[:,7] = labelencoder_previsores.fit_transform(previsores[:,7])\n",
        "previsores[:,8] = labelencoder_previsores.fit_transform(previsores[:,8])\n",
        "previsores[:,9] = labelencoder_previsores.fit_transform(previsores[:,9])\n",
        "previsores[:,13] = labelencoder_previsores.fit_transform(previsores[:,13])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMsK3UuKFwou",
        "colab_type": "code",
        "outputId": "018eedb2-9f44-439b-e23b-bca0767f8551",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "previsores[:,3]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 9, 11, ..., 11, 11, 11], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgh56uYWGIy9",
        "colab_type": "code",
        "outputId": "da6e94da-d8b9-4a7f-df7f-ac7dbfafc460",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "previsores[:,9]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 1, 0], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGkgqoRDIDfk",
        "colab_type": "code",
        "outputId": "b0061c64-d2e9-4122-f42d-d46b7df85262",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "train['race'].unique()"
      ],
      "execution_count": 0,
      "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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLlaAecpKcWM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "onehotencoder = OneHotEncoder()\n",
        "previsores = onehotencoder.fit_transform(previsores).toarray()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rx1nudD7MjUy",
        "colab_type": "code",
        "outputId": "38e7d30f-60e4-4316-d7e7-c10e3c664145",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "previsores"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 1., 0., 0.],\n",
              "       [0., 0., 0., ..., 1., 0., 0.],\n",
              "       [0., 0., 0., ..., 1., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 1., 0., 0.],\n",
              "       [0., 0., 0., ..., 1., 0., 0.],\n",
              "       [0., 0., 0., ..., 1., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPeIrPeTRyxb",
        "colab_type": "code",
        "outputId": "fe2c16ad-9a62-47bb-eaeb-dcfe74b80679",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "previsores.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32561, 22144)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Lv7zPWvR3kM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labelencoder_classe = LabelEncoder()\n",
        "classe = labelencoder_classe.fit_transform(classe)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFhwUFM8Sx9m",
        "colab_type": "code",
        "outputId": "c771f0a6-5cf0-4ed9-c77b-a273c6889fc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "classe"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4Nd_06vS1QH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIRQwkDOT4w8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = StandardScaler()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWEpjb33UA0E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "previsores = scaler.fit_transform(previsores)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3YQH0U8gULDM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6U8deYw10Pp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "previsores_train, previsores_test, classe_train, classe_test = train_test_split(previsores,classe, test_size=0.20, random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21fY7OyA20jd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5l-75yw3Hnb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier = DecisionTreeClassifier(criterion='entropy',random_state=0)\n",
        "classifier.fit(previsores_train,classe_train)\n",
        "previsoes = classifier.predict(previsores_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Korh4LDg5K-6",
        "colab_type": "code",
        "outputId": "f602d985-4b3b-4b7d-e729-c1484f6ed334",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "previsoes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbReHglb5Rbk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFw2BkpN5gyv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "precisao = accuracy_score(classe_test,previsoes)\n",
        "matriz = confusion_matrix(classe_test,previsoes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52fpR-ia56UL",
        "colab_type": "code",
        "outputId": "09921c76-9179-4b19-f75b-13a6cf4c4a2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "precisao"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8063872255489022"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHkRN6tj58CC",
        "colab_type": "code",
        "outputId": "7189fb9f-7837-4ba4-d4e4-1de6771b39e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "matriz"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4251,  667],\n",
              "       [ 594, 1001]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOpLh9Rj5_yq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}