hw02_Williams_Geiger.ipynb

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      "source": [
        "# Homework 2: Classification \n",
        "\n",
        "### Kobie Williams and Nyles Geiger \n",
        "\n",
        "##### 4/17/2022 "
      ],
      "metadata": {
        "id": "x6Td-WlJTp8P"
      }
    },
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.naive_bayes import GaussianNB\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://drive.google.com/uc?export=download&id=1oQMcpoR3QC7w_sWIbtyid2N2j0o49_pk') \n",
        "df.head(5) "
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              "        id  age   income  children  gender      region married  car  \\\n",
              "0  ID12101   48  17546.0         1  FEMALE  INNER_CITY      NO   NO   \n",
              "1  ID12102   40  30085.1         3    MALE        TOWN     YES  YES   \n",
              "2  ID12103   51  16575.4         0  FEMALE  INNER_CITY     YES  YES   \n",
              "3  ID12104   23  20375.4         3  FEMALE        TOWN     YES   NO   \n",
              "4  ID12105   57  50576.3         0  FEMALE       RURAL     YES   NO   \n",
              "\n",
              "  savings_acct current_acct mortgage  pep  \n",
              "0           NO           NO       NO  YES  \n",
              "1           NO          YES      YES   NO  \n",
              "2          YES          YES       NO   NO  \n",
              "3           NO          YES       NO   NO  \n",
              "4          YES           NO       NO   NO  "
            ],
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              "      <th>id</th>\n",
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              "      <th>children</th>\n",
              "      <th>gender</th>\n",
              "      <th>region</th>\n",
              "      <th>married</th>\n",
              "      <th>car</th>\n",
              "      <th>savings_acct</th>\n",
              "      <th>current_acct</th>\n",
              "      <th>mortgage</th>\n",
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              "      <th>0</th>\n",
              "      <td>ID12101</td>\n",
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              "      <td>17546.0</td>\n",
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              "      <td>FEMALE</td>\n",
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              "      <td>3</td>\n",
              "      <td>MALE</td>\n",
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              "      <td>51</td>\n",
              "      <td>16575.4</td>\n",
              "      <td>0</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>INNER_CITY</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID12104</td>\n",
              "      <td>23</td>\n",
              "      <td>20375.4</td>\n",
              "      <td>3</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>TOWN</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
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              "      <td>NO</td>\n",
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              "      <th>4</th>\n",
              "      <td>ID12105</td>\n",
              "      <td>57</td>\n",
              "      <td>50576.3</td>\n",
              "      <td>0</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>RURAL</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import normalize "
      ],
      "metadata": {
        "id": "EE8PDOtxQhsn"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2a: \n",
        "from sklearn import preprocessing\n",
        "#data \n",
        "X= df[['age', 'income', 'children', 'gender', 'region', 'married', 'car',\n",
        "       'savings_acct', 'current_acct', 'mortgage']] \n",
        "\n",
        "#converting data into numeric format \n",
        "X = pd.get_dummies(X) \n",
        "#normalize the data \n",
        "Xnorm = preprocessing.normalize(X, norm='l2')\n",
        "\n",
        "#target variable \n",
        "y = df['pep']\n"
      ],
      "metadata": {
        "id": "pKY7mPpJlv_8"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting data into training and test sets \n",
        "Xtrain, Xtest, ytrain, ytest = train_test_split(Xnorm, y, test_size=0.2, random_state=0) "
      ],
      "metadata": {
        "id": "x1aC2L0-nCOS"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2b: \n",
        "testNorm = preprocessing.normalize(Xtest,norm='l1')"
      ],
      "metadata": {
        "id": "8Qt3T-raSSWp"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#KNN model and classifier \n",
        "model = KNeighborsClassifier(n_neighbors=2, metric='euclidean')\n",
        "model.fit(Xtrain, ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9kF--PgwTRMw",
        "outputId": "bbc51652-7ef7-4fc8-86d9-543435580fb6"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean', n_neighbors=2)"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred = model.predict(Xtest)\n",
        "ypred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KaYiNirdTTpw",
        "outputId": "365df7a3-fc74-4a7d-f75c-bc786f10ef94"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['YES', 'YES', 'YES', 'NO', 'YES', 'NO', 'NO', 'YES', 'NO', 'YES',\n",
              "       'NO', 'NO', 'NO', 'NO', 'YES', 'YES', 'NO', 'NO', 'YES', 'NO',\n",
              "       'NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'NO', 'NO', 'NO', 'NO',\n",
              "       'NO', 'YES', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'NO', 'NO', 'NO',\n",
              "       'NO', 'YES', 'NO', 'NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'YES',\n",
              "       'NO', 'NO', 'YES', 'NO', 'NO', 'YES', 'YES', 'NO', 'YES', 'YES',\n",
              "       'YES', 'NO', 'NO', 'NO', 'NO', 'NO', 'NO', 'NO', 'NO', 'YES', 'NO',\n",
              "       'NO', 'NO', 'NO', 'YES', 'NO', 'YES', 'YES', 'NO', 'NO', 'NO',\n",
              "       'NO', 'YES', 'NO', 'NO', 'NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO',\n",
              "       'YES', 'YES', 'NO', 'NO', 'NO', 'YES', 'YES', 'YES', 'YES', 'NO',\n",
              "       'NO', 'NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'NO', 'NO', 'NO',\n",
              "       'YES', 'NO', 'NO', 'NO', 'NO'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix \n",
        "cm = confusion_matrix(ytest,ypred) \n",
        "\n",
        "#Creating a list of the labels from the classes \n",
        "label_list = model.classes_ \n",
        "\n",
        "#plot the confusion matrix \n",
        "plt.matshow(cm, cmap='BuPu') \n",
        "plt.colorbar() \n",
        "plt.grid(False) \n",
        "plt.ylabel('True label') \n",
        "plt.xlabel('Predicted label') \n",
        "plt.xticks(np.arange(len(label_list)), label_list)\n",
        "plt.yticks(np.arange(len(label_list)), label_list) \n",
        "plt.title('Confusion Matrix', pad=15) \n",
        "plt.show()"
      ],
      "metadata": {
        "id": "H6JBtAEkU4UR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "d41a8520-9109-41bf-8e6d-f458a12b0f00"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Classification Report \n",
        "print(\"Classification Report: \")\n",
        "print(classification_report(ytest, ypred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XqHbCod6f9xO",
        "outputId": "861347b3-12b5-4d08-d8af-07dad874c488"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          NO       0.59      0.82      0.68        61\n",
            "         YES       0.69      0.41      0.51        59\n",
            "\n",
            "    accuracy                           0.62       120\n",
            "   macro avg       0.64      0.61      0.60       120\n",
            "weighted avg       0.64      0.62      0.60       120\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We can do an experimentation of the different values with a grid search \n",
        "from sklearn.model_selection import GridSearchCV\n",
        "param_grid = {'n_neighbors': np.arange(1, 28),\n",
        "              'metric': ['euclidean', 'manhattan'],\n",
        "              'weights': ['uniform', 'distance']}\n",
        "grid = GridSearchCV(model, param_grid, cv=5, verbose=1) \n",
        "grid.fit(Xtrain, ytrain) \n",
        "print('Best parameters found: ') \n",
        "print(grid.best_params_) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acwa5pqYlZ07",
        "outputId": "858a31aa-cf0d-47e3-b11b-509fe06efa53"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n",
            "Best parameters found: \n",
            "{'metric': 'manhattan', 'n_neighbors': 17, 'weights': 'distance'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bestModel = KNeighborsClassifier(n_neighbors=17, metric='manhattan') "
      ],
      "metadata": {
        "id": "CmPYABCynVy4"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bestModel.fit(Xtrain, ytrain) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E5LuGXQOnvXk",
        "outputId": "529bf325-8a95-445c-af2a-f2faa4b338dd"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='manhattan', n_neighbors=17)"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_ypred = bestModel.predict(Xtest)"
      ],
      "metadata": {
        "id": "b8bQtTyhoE81"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix \n",
        "best_cm = confusion_matrix(ytest,best_ypred) \n",
        "\n",
        "#Creating a list of the labels from the classes \n",
        "label_list = bestModel.classes_ \n",
        "\n",
        "#plot the confusion matrix \n",
        "plt.matshow(best_cm, cmap='BuPu') \n",
        "plt.colorbar() \n",
        "plt.grid(False) \n",
        "plt.ylabel('True label') \n",
        "plt.xlabel('Predicted label') \n",
        "plt.xticks(np.arange(len(label_list)), label_list)\n",
        "plt.yticks(np.arange(len(label_list)), label_list) \n",
        "plt.title('Confusion Matrix', pad=15) \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "WrYjTS-KoXpQ",
        "outputId": "bcbfbdc9-02f8-48e6-9992-0010da8a9988"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Classification Report \n",
        "print(\"Classification Report: \")\n",
        "print(classification_report(ytest, best_ypred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g38GEol-oh79",
        "outputId": "955eb329-6c45-4d58-f031-36a545754291"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          NO       0.65      0.67      0.66        61\n",
            "         YES       0.65      0.63      0.64        59\n",
            "\n",
            "    accuracy                           0.65       120\n",
            "   macro avg       0.65      0.65      0.65       120\n",
            "weighted avg       0.65      0.65      0.65       120\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier "
      ],
      "metadata": {
        "id": "b_ZoKyssoqKb"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2c: \n",
        "tree = DecisionTreeClassifier() "
      ],
      "metadata": {
        "id": "g4zKnTfypyQ-"
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fit a model with the training data \n",
        "tree = tree.fit(Xtrain, ytrain)\n",
        "\n",
        "treepred = tree.predict(Xtest) "
      ],
      "metadata": {
        "id": "KwQlsBUlp7N3"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix for the tree \n",
        "tree_cm = confusion_matrix(ytest, ypred) \n",
        "\n",
        "tree_label_list = tree.classes_\n",
        "plt.matshow(tree_cm, cmap='BuPu')\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.ylabel('True Label')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.xticks(np.arange(len(tree_label_list)), tree_label_list)\n",
        "plt.yticks(np.arange(len(tree_label_list)), tree_label_list)\n",
        "plt.title('Confusion Matrix', pad=15) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "OSFNhAdiqY0a",
        "outputId": "63a8a70c-ddb0-402b-984e-e81ccfed18d0"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Confusion Matrix')"
            ]
          },
          "metadata": {},
          "execution_count": 172
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###3\n"
      ],
      "metadata": {
        "id": "NceeuToLk9xu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "census_df = pd.read_csv('https://drive.google.com/uc?export=download&id=1_1ca-CLXqcXmaJpGYJjE1xVc8hoPIiQm')"
      ],
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        "id": "ke7zqwM9qu45"
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      "execution_count": 173,
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      "execution_count": 174,
      "outputs": [
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              "  age workclass  education marital-status   race   sex  hours-per-week income\n",
              "0  39    Public         13         Single  White  Male              40  <=50K"
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      "cell_type": "code",
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        "census_df.shape"
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          "data": {
            "text/plain": [
              "(10000, 8)"
            ]
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          "metadata": {},
          "execution_count": 175
        }
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      "cell_type": "code",
      "source": [
        "age = census_df['age'] \n",
        "\n",
        "#Create a mask to identify rows that need a mask \n",
        "clean_age = age.str.contains('[^\\?]') \n",
        "\n",
        "#Calculate the mean age \n",
        "mAge = age[clean_age].astype(int).mean() \n",
        "\n",
        "#Turning the age column back into itself with the cleaned data \n",
        "age[~clean_age] = mAge "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLWfd0LWQ5q8",
        "outputId": "274d244c-dd6f-43d1-9e58-8198d15e36f7"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "work = census_df['workclass'] \n",
        "\n",
        "#Apply the mask \n",
        "clean_work = work.str.contains('[^\\?]') \n",
        "census_df = census_df[clean_work]  "
      ],
      "metadata": {
        "id": "QRUDp3lQmXFj"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#3aii. \n",
        "census_df.describe(include='all')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "S3bPt_7AjKJr",
        "outputId": "3a339ba1-1fcd-4246-8bd2-5bd0acd97f21"
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      "execution_count": 178,
      "outputs": [
        {
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            "text/plain": [
              "         age workclass    education marital-status   race   sex  \\\n",
              "count   9412      9412  9412.000000           9412   9412  9412   \n",
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              "max      NaN       NaN    16.000000            NaN    NaN   NaN   \n",
              "\n",
              "        hours-per-week income  \n",
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              "unique             NaN      2  \n",
              "top                NaN  <=50K  \n",
              "freq               NaN   7093  \n",
              "mean         41.080217    NaN  \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "3aii: Histograms for Numeric values"
      ],
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        "id": "gWwcePdkovA5"
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      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  age workclass  education marital-status   race   sex  hours-per-week income\n",
              "0  39    Public         13         Single  White  Male              40  <=50K"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>Public</td>\n",
              "      <td>13</td>\n",
              "      <td>Single</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bfadf093-9035-4ab1-aaa8-db9a0c79d7d9')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bfadf093-9035-4ab1-aaa8-db9a0c79d7d9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bfadf093-9035-4ab1-aaa8-db9a0c79d7d9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.hist(census_df['age'].astype(int))\n",
        "plt.xlabel('age') \n",
        "plt.ylabel('count')\n",
        "plt.title('Age Histogram') \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "x5O9OQBNW7qw",
        "outputId": "41728842-5985-4b49-80ca-784dfbcb7ab2"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(census_df['education'].astype(int))\n",
        "plt.xlabel('Number of education years') \n",
        "plt.ylabel('count')\n",
        "plt.title('Education Histogram') \n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hN_LNT0NXA-D",
        "outputId": "2188a53d-f806-407f-8136-c908b322e732"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(census_df['hours-per-week'].astype(int))\n",
        "plt.xlabel('hours worked per week') \n",
        "plt.ylabel('count')\n",
        "plt.title('Hours-per-week Histogram') \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "0XRBPxtVpoQl",
        "outputId": "a912bfa6-ff83-4fb5-e900-79eb1e6bf43d"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3aii: Bar graphs for categorical attributes "
      ],
      "metadata": {
        "id": "Ki1Cz78bqswm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "census_df.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "ZMExDpPtp97i",
        "outputId": "6854e2db-24b9-4385-b234-4ca2cdf6aadc"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  age workclass  education marital-status   race   sex  hours-per-week income\n",
              "0  39    Public         13         Single  White  Male              40  <=50K"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8268380-011e-4edc-9973-1178f89b6c19\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>Public</td>\n",
              "      <td>13</td>\n",
              "      <td>Single</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8268380-011e-4edc-9973-1178f89b6c19')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b8268380-011e-4edc-9973-1178f89b6c19 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b8268380-011e-4edc-9973-1178f89b6c19');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "census_df['workclass'].value_counts().plot(kind='bar') \n",
        "plt.title('Workclass value counts')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "E2msEJqJq6xk",
        "outputId": "956ace34-3a42-4cbd-dede-c3ef9856c3aa"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "census_df['marital-status'].value_counts().plot(kind='bar') \n",
        "plt.title('Marital status value counts')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "cp8aKq3WrDhe",
        "outputId": "5de219e2-961a-4478-e9cf-bb742b8311bb"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "census_df['race'].value_counts().plot(kind='bar') \n",
        "plt.title('Race value counts')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "LyDkMZtur9qe",
        "outputId": "2655e0a2-2759-421e-8e33-d6c367398b39"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "census_df['sex'].value_counts().plot(kind='bar') \n",
        "plt.title('Sex identification value counts')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "4egHTKwbsCHh",
        "outputId": "79b7a84d-245e-4548-c237-16718a74b55b"
      },
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "census_df['income'].value_counts().plot(kind='bar') \n",
        "plt.title('Income value counts')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "rG9_05TOsGL7",
        "outputId": "b67e199d-566f-441a-f834-9e36aa85fa01"
      },
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3aiii: Cross-tabulations"
      ],
      "metadata": {
        "id": "Tvg0ty0kszfA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ed_race = pd.crosstab(census_df['education'], census_df['race'])\n",
        "ed_race.plot(kind='bar')\n",
        "plt.title('Education and Race crosstab')\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "coxn6wnWsK5V",
        "outputId": "5b979577-3e9e-4e26-8f73-f65787239d1c"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ed_race = pd.crosstab(census_df['workclass'], census_df['income'])\n",
        "ed_race.plot(kind='bar')\n",
        "plt.title('Work-class and income crosstab')\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "s9xDLExGttmG",
        "outputId": "ae1fb3a6-aed1-463a-c5cb-b2d0b0685f99"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ed_race = pd.crosstab(census_df['workclass'], census_df['race'])\n",
        "ed_race.plot(kind='bar')\n",
        "plt.title('Work-class and Race crosstab')\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "eHBm1_Gnt8yl",
        "outputId": "7488aac4-63bf-42a3-f023-d7a7b0cd7727"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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y0jezOmv58uU8/vjjzJ49G0ls2LABSRWJuFyDBg0q1hs0aFDRT//ZZ5/x/PPP07hx4y8cu3xa5nvuuaeipX3jjTd+oV7ueUpKSvK+B7B27Vp+/OMfM2PGDDp06MDo0aM3mu65/Lg1OWY+3L1jZnXWXXfdxdChQ3n33XeZN28e8+fPp1OnTnn3gQ8aNKiiqwegrKzsC3WOP/54ysrKKCsro3fvSucw+4L+/ftz7733smbNGlatWsU//vGPL9QpT/CtW7dm9erVFf38W5tb+mZWa/IdYllbJkyYwC9+8YuNyk444QSuvfZa9txzz2r3HzNmDGeddRbdu3dn/fr19O/fn+uuu+5Lx9WrVy9OOukkevToQdu2bSuezJWrRYsWnHHGGXTt2pWvfOUrldbZGjy1ch3nqZWtmDy1cvF5amUzM6uSk76ZWYY46ZuZZYiTvplZhjjpm5lliJO+mVmGeJy+mdWe0TvV8vFW5FUt36mVjzrqKG6//XZatGhRWxHWOXm19CXNkzRbUpmkGWlZS0mPSpqb/rtzWi5JYyS9JellSb1yjjM8rT9X0vCtc0lmljX5Tq384IMPZjrhQ826dw6LiJ45A/5HAY9FxN7AY+k6wDeBvdO/kcC1kHxIABcBBwF9gIvKPyjMzLZUZVMrL1q0iP79+9OzZ0+6du1aMS1Dx44dWbp0KQDHHXccBx54IF26dOH666+vOF6zZs341a9+RY8ePejbty+LFy8u/EVtRV+mT/9YYGy6PBY4Lqd8XCSeB1pIagccCTwaEcsj4gPgUWDwlzi/mVmlUyvffvvtHHnkkZSVlTFr1ix69uz5hf1uvvlmZs6cyYwZMxgzZgzLli0D4KOPPqJv377MmjWL/v37c8MNNxT6kraqfJN+AI9ImilpZFq2S0QsSpffA3ZJl3cD5ufsuyAtq6p8I5JGSpohacaSJUvyDM/MsmrChAmcfPLJwOdTK5eWlnLLLbcwevRoZs+eTfPmzb+w35gxYypa8/Pnz2fu3LkANGrUiGOOOQao/WmNtwX53sg9NCIWSmoLPCrp9dyNERGSamUSn4i4Hrgekrl3auOYZlY/VTW18h/+8AeefvppHnjgAUaMGMF5553HsGHDKvZ78sknmTx5MlOnTqVp06YMGDCgYtbLhg0bIgmo/WmNtwV5tfQjYmH67/vAPSR98ovTbhvSf99Pqy8EOuTs3j4tq6rczGyLVDW18tNPP80uu+zCGWecwemnn86LL7640X4rVqxg5513pmnTprz++us8//zzRbqCwqu2pS9pB6BBRKxKlwcBlwD3AcOBy9N/J6W73AecLekOkpu2KyJikaSHgctybt4OAn5Zq1djZsWV5xDL2lLV1MojRoxghx12oGHDhjRr1oxx48ZtVGfw4MFcd9117Lfffuy777707du3kGEXVbVTK0vag6R1D8mHxO0RcamkVsCdwFeBd4ETI2K5ku9FV5PcpP0YODUiyod5/hC4ID3WpRFxy+bO7amVq+epla2YPLVy8dV0auVqW/oR8TbQo5LyZcDASsoDOKuKY90M3FzdOc3MbOvwNAxmZhnipG9mliFO+mZmGeKkb2aWIU76ZmYZ4qmVzazWdBvbrVaPN3v47GrrlJSU0K1bNyKCkpISrr76avr168e8efM45phjeOWVV2p83gEDBnDllVfSu3elox7rNCd9M6vTmjRpQllZGQAPP/wwv/zlL3nqqaeKHNW2y907ZlZvrFy5kp13/uKM7fPmzeNrX/savXr1olevXkyZMqVi2xVXXEG3bt3o0aMHo0aN2mi/zz77jBEjRvDrX/96q8deKG7pm1mdtmbNGnr27MnatWtZtGgRjz/+xV+pt23blkcffZTGjRszd+5cTjnlFGbMmMFDDz3EpEmTeOGFF2jatCnLly+v2Gf9+vUMGTKErl278qtf/aqQl7RVOembWZ2W270zdepUhg0b9oV+/HXr1nH22WdTVlZGSUkJb775JgCTJ0/m1FNPpWnTpgC0bNmyYp8f/ehHnHjiifUq4YO7d8ysHjn44INZunQpmz6L46qrrmKXXXZh1qxZzJgxg08//bTaY/Xr148nnniiYsrl+sJJ38zqjddff50NGzbQqlWrjcpXrFhBu3btaNCgAbfddhsbNmwA4IgjjuCWW27h448/Btioe+e0007jqKOO4sQTT6xXc+q7e8fMak0+QyxrW3mfPkBEMHbsWEpKSjaq8+Mf/5gTTjiBcePGMXjwYHbYYQcgmWK5rKyM3r1706hRI4466iguu+yyiv3OO+88VqxYwdChQxk/fjwNGtT9dnK1UysXk6dWrp6nVrZi8tTKxVfTqZXr/seWmZnlzUnfzCxDnPTNzDLESd/MLEOc9M3MMsRJ38wsQzxO38xqzZzOtTt8c7/X51Rbp1mzZqxevbpi/dZbb2XGjBlcffXVXHfddTRt2pRhw4bValyVufDCC+nfvz/f+MY3tvq5vgwnfTOrt84888yCneuSSy4p2Lm+DHfvmFm9NXr0aK688koAxowZw/7770/37t05+eSTK7YPHTqUgw8+mL333psbbrgBgNWrVzNw4EB69epFt27dmDRpEpBM0bzffvtxxhln0KVLFwYNGsSaNWsAGDFiBHfddRcA06dPp1+/fvTo0YM+ffqwatWqQl96ldzSN7M6LXcaBkjmz/n2t7/9hXqXX34577zzDttvvz0ffvhhRfnLL7/M888/z0cffcQBBxzA0UcfTdu2bbnnnnvYcccdWbp0KX379q045ty5c5kwYQI33HADJ554InfffTc/+MEPKo736aefctJJJzFx4kRKS0tZuXIlTZo02YqvQM046ZtZnZY7tTJ83qe/qe7duzNkyBCOO+44jjvuuIryY489liZNmtCkSRMOO+wwpk2bxtFHH80FF1zA008/TYMGDVi4cCGLFy8GoFOnThUfMgceeCDz5s3b6DxvvPEG7dq1o7S0FIAdd9yxti/5S8m7e0dSiaSXJN2frneS9IKktyRNlNQoLd8+XX8r3d4x5xi/TMvfkHRkbV+MmVlVHnjgAc466yxefPFFSktLK2bOlLRRPUmMHz+eJUuWMHPmTMrKythll10qpljefvvtK+qWlJTUuRk4a9Knfy6Qeyv9CuCqiNgL+AA4LS0/DfggLb8qrYek/YGTgS7AYOB/JG08FZ6Z2Vbw2WefMX/+fA477DCuuOIKVqxYUTHiZ9KkSaxdu5Zly5bx5JNPUlpayooVK2jbti0NGzbkiSee4N133837XPvuuy+LFi1i+vTpAKxatWqb+mDIq3tHUnvgaOBS4DwlH42HA99Pq4wFRgPXAsemywB3AVen9Y8F7oiIT4B3JL0F9AGm1sqVmFnR5TPEshg2bNjAD37wA1asWEFE8JOf/IQWLVoASbfPYYcdxtKlS/mv//ovdt11V4YMGcK3vvUtunXrRu/evencuXPe52rUqBETJ07knHPOYc2aNTRp0oTJkyfTrFmzrXV5NZJvn/6fgP8HNE/XWwEfRkT5x9cCYLd0eTdgPkBErJe0Iq2/G/B8zjFz9zEz2yK5Y/QhGUUzYsQIIBmdU+7ZZ5+tdP/u3bszbty4jcpat27N1KmVt0dzH8V4/vnnVyzfeuutFculpaU8/3xuutt2VNu9I+kY4P2ImFmAeJA0UtIMSTM2feSZmZl9Ofm09A8Bvi3pKKAxsCPwZ6CFpO3S1n57YGFafyHQAVggaTtgJ2BZTnm53H0qRMT1wPWQPERlSy7KzCwfud8EsqLaln5E/DIi2kdER5IbsY9HxBDgCeC7abXhwKR0+b50nXT745E8nus+4OR0dE8nYG9gWq1diZmZVevLjNP/BXCHpN8CLwE3peU3AbelN2qXk3xQEBGvSroTeA1YD5wVERu+xPnNzKyGapT0I+JJ4Ml0+W2S0Teb1lkLfK+K/S8lGQFkZmZF4Ll3zMwyxNMwmFmtuebMx2v1eGddd3i1dX72s5+x++6789Of/hSAI488kg4dOnDjjTcC8POf/5zddtuNxx9/nPvvv/8L+59++umcd9557L///lx22WVccMEFtXoN2xq39M2sTjvkkEOYMmUKkPzydunSpbz66qsV26dMmcKnn35a5f433ngj+++/PwCXXXbZ1g12G+Ckb2Z1Wr9+/Sp+SPXqq6/StWtXmjdvzgcffMAnn3zCnDlz6NWrF6tXr+a73/0unTt3ZsiQISSDCmHAgAHMmDGDUaNGVczYOWTIEAD+9re/0adPH3r27MmPfvQjNmyo+2NPnPTNrE7bdddd2W677fjXv/7FlClTOPjggznooIOYOnUqM2bMoFu3bjRq1IiXXnqJP/3pT7z22mu8/fbbPPfccxsd5/LLL6+YsXP8+PHMmTOHiRMn8txzz1FWVkZJSQnjx48v0lXWHvfpm1md169fP6ZMmcKUKVM477zzWLhwIVOmTGGnnXbikEMOAaBPnz60b98egJ49ezJv3jwOPfTQKo/52GOPMXPmzIopktesWUPbtm23/sVsZU76Zlbnlffrz549m65du9KhQwf++Mc/suOOO3LqqacCNZ8SOSIYPnw4v/vd77Zq7IXm7h0zq/P69evH/fffT8uWLSkpKaFly5Z8+OGHTJ06lX79+uV9nIYNG7Ju3ToABg4cyF133cX7778PJE/kqskUy9sqt/TNrNbkM8Rya+jWrRtLly7l+9///kZlq1evpnXr1nkfZ+TIkXTv3p1evXoxfvx4fvvb3zJo0CA+++wzGjZsyDXXXMPuu+++NS6hYFR+B3tb1Lt376jssWf2uZqMiy7W/5BWf82ZM4f99tuv2GFkWmXvgaSZEdG7svru3jEzyxAnfTOzDHHSN7MvZVvuIq7vtuS1d9I3sy3WuHFjli1b5sRfBBHBsmXLaNy4cY328+gdM9ti7du3Z8GCBfjRpsXRuHHjih+c5ctJ38y2WMOGDenUqVOxw7AacPeOmVmGOOmbmWWIk76ZWYY46ZuZZYiTvplZhjjpm5lliJO+mVmGOOmbmWWIk76ZWYY46ZuZZUi1SV9SY0nTJM2S9Kqki9PyTpJekPSWpImSGqXl26frb6XbO+Yc65dp+RuSjtxaF2VmZpXLp6X/CXB4RPQAegKDJfUFrgCuioi9gA+A09L6pwEfpOVXpfWQtD9wMtAFGAz8j6SS2rwYMzPbvGqTfiRWp6sN078ADgfuSsvHAsely8em66TbB0pSWn5HRHwSEe8AbwF9auUqzMwsL3n16UsqkVQGvA88CvwT+DAi1qdVFgC7pcu7AfMB0u0rgFa55ZXsk3uukZJmSJrh6VrNzGpXXkk/IjZERE+gPUnrvPPWCigiro+I3hHRu02bNlvrNGZmmVSj0TsR8SHwBHAw0EJS+Xz87YGF6fJCoANAun0nYFlueSX7mJlZAeQzeqeNpBbpchPgCGAOSfL/blptODApXb4vXSfd/ngkz1K7Dzg5Hd3TCdgbmFZbF2JmZtXL58lZ7YCx6UibBsCdEXG/pNeAOyT9FngJuCmtfxNwm6S3gOUkI3aIiFcl3Qm8BqwHzoqIDbV7OWZmtjnVJv2IeBk4oJLyt6lk9E1ErAW+V8WxLgUurXmYZmZWG/yLXDOzDHHSNzPLECd9M7MMcdI3M8sQJ30zswxx0jczyxAnfTOzDHHSNzPLECd9M7MMcdI3M8sQJ30zswxx0jczyxAnfTOzDHHSNzPLECd9M7MMcdI3M8sQJ30zswxx0jczyxAnfTOzDHHSNzPLECd9M7MMcdI3M8sQJ30zswxx0jczyxAnfTOzDHHSNzPLkGqTvqQOkp6Q9JqkVyWdm5a3lPSopLnpvzun5ZI0RtJbkl6W1CvnWMPT+nMlDd96l2VmZpXJp6W/Hvh5ROwP9AXOkrQ/MAp4LCL2Bh5L1wG+Ceyd/o0EroXkQwK4CDgI6ANcVP5BYWZmhVFt0o+IRRHxYrq8CpgD7AYcC4xNq40FjkuXjwXGReJ5oIWkdsCRwKMRsTwiPgAeBQbX6tWYmdlm1ahPX1JH4ADgBWCXiFiUbnoP2CVd3g2Yn7PbgrSsqvJNzzFS0gxJM5YsWVKT8MzMrBp5J31JzYC7gZ9GxMrcbRERQNRGQBFxfUT0jojebdq0qeR/OFwAAA02SURBVI1DmplZKq+kL6khScIfHxH/mxYvTrttSP99Py1fCHTI2b19WlZVuZmZFUg+o3cE3ATMiYj/ztl0H1A+Amc4MCmnfFg6iqcvsCLtBnoYGCRp5/QG7qC0zMzMCmS7POocAgwFZksqS8suAC4H7pR0GvAucGK67UHgKOAt4GPgVICIWC7pN8D0tN4lEbG8Vq7CzMzyUm3Sj4hnAVWxeWAl9QM4q4pj3QzcXJMAzcys9vgXuWZmGeKkb2aWIU76ZmYZ4qRvZpYhTvpmZhnipG9mliFO+mZmGeKkb2aWIU76ZmYZ4qRvZpYhTvpmZhnipG9mliFO+mZmGeKkb2aWIU76ZmYZ4qRvZpYhTvpmZhnipG9mliFO+mZmGeKkb2aWIU76ZmYZ4qRvZpYhTvpmZhnipG9mliFO+mZmGeKkb2aWIdUmfUk3S3pf0is5ZS0lPSppbvrvzmm5JI2R9JaklyX1ytlneFp/rqThW+dyzMxsc7bLo86twNXAuJyyUcBjEXG5pFHp+i+AbwJ7p38HAdcCB0lqCVwE9AYCmCnpvoj4oLYuxKyuuebMx2tU/6zrDt9KkdiWqMn7ty29d9W29CPiaWD5JsXHAmPT5bHAcTnl4yLxPNBCUjvgSODRiFieJvpHgcG1cQFmZpa/Le3T3yUiFqXL7wG7pMu7AfNz6i1Iy6oq/wJJIyXNkDRjyZIlWxiemZlV5kvfyI2IIOmyqRURcX1E9I6I3m3atKmtw5qZGVue9Ben3Tak/76fli8EOuTUa5+WVVVuZmYFtKVJ/z6gfATOcGBSTvmwdBRPX2BF2g30MDBI0s7pSJ9BaZmZmRVQtaN3JE0ABgCtJS0gGYVzOXCnpNOAd4ET0+oPAkcBbwEfA6cCRMRySb8Bpqf1LomITW8Om5nZVlZt0o+IU6rYNLCSugGcVcVxbgZurlF0ZmZWq/yLXDOzDHHSNzPLECd9M7MMcdI3M8sQJ30zswxx0jczy5B8Ztm0WtBtbLe8684ePnsrRmJmWeaWvplZhjjpm5lliJO+mVmGOOmbmWWIk76ZWYY46ZuZZYiTvplZhjjpm5lliJO+mVmGOOmbmWWIk76ZWYY46ZuZZYiTvplZhjjpm5lliJO+mVmGeD79LTV6p5rV7/TVrROHbXV+FoLVJ27pm5lliJO+mVmGOOmbmWVIwZO+pMGS3pD0lqRRhT6/mVmWFfRGrqQS4BrgCGABMF3SfRHxWiHjqErHUQ/kXXde460Xx5zO++VfecA1Wy+Q+qwmN+J9E37bU5P3b/SKrRdHHVTo0Tt9gLci4m0ASXcAxwLbRNK3um1b+dC2bYtHX21MEVG4k0nfBQZHxOnp+lDgoIg4O6fOSGBkurov8EbBAiy81sDSYgdhW8zvX91V39+73SOiTWUbtrlx+hFxPXB9seMoBEkzIqJ3seOwLeP3r+7K8ntX6Bu5C4EOOevt0zIzMyuAQif96cDekjpJagScDNxX4BjMzDKroN07EbFe0tnAw0AJcHNEvFrIGLYxmejGqsf8/tVdmX3vCnoj18zMisu/yDUzyxAnfTOzDHHSNzPLECd9sxqQtIOkBjnrDSQ1LWZMZjXhpF8EknaX9I10uYmk5sWOyfL2GJCb5JsCk4sUi9WQpF6SfiLpHEm9ih1PMTjpF5ikM4C7gL+mRe2Be4sXkdVQ44hYXb6SLrulXwdIuhAYC7QimYbhFkm/Lm5UhbfNTcOQAWeRTDz3AkBEzJXUtrghWQ18JKlXRLwIIOlAYE2RY7L8DAF6RMRaAEmXA2XAb4saVYE56RfeJxHxqSQAJG0H+McSdcdPgb9L+jcg4CvAScUNyfL0b6AxsDZd354MTgPjpF94T0m6AGgi6Qjgx8A/ihyT5SkipkvqTDIDLMAbEbGumDFZ3lYAr0p6lKShdQQwTdIYgIj4STGDKxT/IrfA0pEfpwGDSFqKD0fEDcWNyqoj6fCIeFzSdyrbHhH/W+iYrGYkDd/c9ogYW6hYislJv8AknRsRf66uzLYtki6OiIsk3VLJ5oiIHxY8KLMt4KRfYJJejIhem5S9FBEHFCsmsyyQdAzwG2B3kq5tkXxg71jUwArMSb9AJJ0CfB84FHgmZ1Nz4LOIGFiUwCwvks7b3PaI+O9CxWJbRtJbwHeA2ZHhxOcbuYUzBVhEMj74jznlq4CXixKR1YR/QFf3zQdeyXLCB7f0zSwjJJWSdO88BXxSXp61b2n+RW6BSeorabqk1ZI+lbRB0spix2X5kbSHpH9IWiLpfUmTJO1R7LgsL5cCH5OM1W+e85cp7t4pvKtJHhP5d6A3MAzYp6gRWU3cDlwDHJ+unwxMAA4qWkSWr10jomuxgyg2t/SLICLeAkoiYkNE3AIMLnZMlremEXFbRKxP//5G0nK0bd+DkgYVO4hic0u/8D5OHwpfJun3JDd3/eG7jZPUMl18SNIo4A6SX3WeBDxYtMCsJv4DOF/Sp8CneMimFYKk3YHFQCPgZ8BOwP+krX/bRkl6hyTJq5LNERHu17c6wUm/wNKf8T8QEZ9UW9nMao2SWQ6HAJ0i4jeSOgDtImJakUMrKCf9Akt/xn848DQwEfi/iFhf3KgsX5KGVVYeEeMKHYvVjKRrgc+AwyNiP0k7A49ERGmRQyso9+kXWEScKqkh8E3gFOAaSY9GxOlFDs3yk5sgGgMDgRcBJ/1t30ER0UvSSwAR8UF6fy1TnPSLICLWSXqIpI+4CXAc4KRfB0TEObnrklqQ3NS1bd86SSWkz6+Q1Iak5Z8pHjVSYJK+KelWYC5wAnAjyYM4rG76COhU7CAsL2OAe4C2ki4FngUuK25Ihec+/QKTNIGkL/8h38yteyT9g8+fdNYA2B+4MyJGFS8qy1f6AJyBJKOwHouIOUUOqeCc9M1qQNLXc1bXA+9GxIJixWNbRtLIiLi+2HEUg5N+gUh6NiIOlbSKjZ+Jm8kfiNQ1khoDZwJ7AbOBmzzqqu6q7LkWWeEbuQUSEYem/2Zugqd6YiywjuRZCN8k6dY5t6gRWV4kbV9JV2plP7LLBLf0CygdOfBqRHQudixWM5JmR0S3dHk7YFpWW4p1TXmrXtJtETE0LWuf1W45t/QLKCI2SHpD0lcj4l/FjsdqZF35QkSsT37caXVEI0nfB/rlPtheUh/I3kPtnfQLb2fgVUnTSIb7ARAR3y5eSJaHHjnPPRDQJF33PZlt35kk0y+0AL61ybYAMpX03b1TYJuM/qgQEU8VOhazLJF0WkTcVOw4is1Jv0A8+sOsOHK7dCrj7h3bWjz6w6w4Nu3SyeXuHds6PPrDzLYFnnuncDYa/VHMQMyySNIukm5KJztE0v6STit2XIXmln6BSNrA56N1RDK75sd49IdZQaTJ/hbgVxHRI/3G/VL5N/CscEu/QCKiJCJ2TP+aR8R2OctO+GZbX+uIuJN0OuX0G/eG4oZUeE76ZpYVH0lqxefz6fcFVhQ3pMLz6B0zy4rzgPuAPSU9B7QBvlvckArPLX0zq9cklUr6SkS8CHwduAD4BHgEyNz8O076Zlbf/RX4NF3uB/wKuAb4AMjcnPru3jGz+q4kIpanyycB10fE3cDdksqKGFdRuKVvZvVdSTo8E5JHJT6esy1zDd/MXbCZZc4E4ClJS4E1JFOhIGkvMjh6xz/OMrN6Lx2e2Q54JCI+Ssv2AZqlN3gzw0nfzCxD3KdvZpYhTvpmZhnipG9WDUnzJLXOs+5oSedv7ZjMtpSTvtlmSCopdgxmtclJ3+otSf8p6Sfp8lWSHk+XD5c0XtIpkmZLekXSFTn7rZb0R0mzgINzyptIekjSGen6MEkvS5ol6bZKzn+GpOnp9rslNU3Lv5eec5akp9OyLpKmSSpLj7n3Vn1xLLOc9K0+ewb4WrrcG2gmqWFa9iZwBXA40BMolXRcWncH4IWI6BERz6ZlzYB/ABMi4gZJXYBfA4dHRA8qf/Tl/0ZEabp9DlD+wI4LgSPT8m+nZWcCf46InmmsmZsTxgrDSd/qs5nAgZJ2JJlgaypJQv0a8CHwZEQsSedVHw/0T/fbANy9ybEmAbdExLh0/XDg7xGxFCDnZ/65ukp6RtJsYAjQJS1/Drg1/cZQ3n00FbhA0i+A3SNizZe5cLOqOOlbvRUR64B3gBHAFJKW/2HAXsC8zey6NiI2fbjGc8BgSapBCLcCZ6dPZroYaJzGdSbJt4QOwExJrSLidpJW/xrgQUmH1+A8Znlz0rf67hngfODpdPlM4CVgGvB1Sa3Tm7WnAE9t5jgXkszKeE26/jjwvfShHEhqWck+zYFFaZfSkPJCSXtGxAsRcSGwBOggaQ/g7YgYQ/KtovuWXrDZ5jjpW333DMnP76dGxGJgLfBMRCwCRgFPALOAmRExqZpjnQs0kfT7iHgVuJRkTpdZwH9XUv+/gBdIviW8nlP+h/IbyCTfQGYBJwKvpLM+dgXGbXows9rgaRjMzDLELX0zswxx0jczyxAnfTOzDHHSNzPLECd9M7MMcdI3M8sQJ30zswz5/yxLbVLXCAvMAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ed_race = pd.crosstab(census_df['race'], census_df['income'])\n",
        "ed_race.plot(kind='bar')\n",
        "plt.title('Race and income crosstab')\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "OhnWrV48uC_I",
        "outputId": "8ad0b1db-c1ab-4717-d78f-605f5776c32b"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating something for the race+income \n",
        "race_income = census_df[['race', 'income']] "
      ],
      "metadata": {
        "id": "S4ystqyKuKfP"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting the total amount of white people, black, asian, hispanic, amer-indian\n",
        "tWhite = len(race_income[race_income==\"White\"])\n",
        "tBlack = len(race_income[race_income==\"Black\"])\n",
        "tAsain = len(race_income[race_income==\"Asian\"])\n",
        "tHispanic = len(race_income[race_income==\"Hispanic\"])\n",
        "tAmerIndian = len(race_income[race_income==\"Amer-Indian\"]) "
      ],
      "metadata": {
        "id": "G6A722hmYktC"
      },
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "race_income_cats = [\"White <=50k\", \"White >50k\", \\\n",
        "                    \"Black <=50k\", \"Black >50k\",\\\n",
        "                    \"Asian <=50k\", \"Asian >50k\",\\\n",
        "                    \"Hispanic <=50k\", \"Hispanic >50k\",\\\n",
        "                    \"Amer-Indian <=50k\", \"Amer-Indian >50k\"]\n",
        "race_income_percs = [5994/tWhite, 2118/tWhite,\\\n",
        "                    773/tBlack, 119/tBlack,\\\n",
        "                    224/tAsain, 67/tAsain,\\\n",
        "                    69/tHispanic, 6/tHispanic,\\\n",
        "                    83/tAmerIndian, 9/tAmerIndian]\n",
        "race_percent = {'race-income': race_income_cats, 'percent': race_income_percs}\n",
        "\n",
        "race_percent_df = df = pd.DataFrame(data=race_percent)\n",
        "\n",
        "race_percent_df.plot(kind='bar', x=\"race-income\",title=\"Income % by Race\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "djLqvKsJZH3O",
        "outputId": "49ec9f39-4765-4fb6-d5c8-1239908ad705"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efcccfe9750>"
            ]
          },
          "metadata": {},
          "execution_count": 195
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bar_df = pd.crosstab(census_df[\"workclass\"], census_df[\"income\"])\n",
        "bar_df.plot(kind=\"bar\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "8OpApp6XBO-G",
        "outputId": "bcbd7663-cdb8-44b9-ecc4-65f82342466d"
      },
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bar_df = pd.crosstab(census_df[\"education\"], census_df[\"income\"])\n",
        "bar_df.plot(kind=\"bar\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "D15GmiOKa_PR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "10bcbf5d-d609-457c-e7b1-0217bb4b13c2"
      },
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bar_df = pd.crosstab(census_df[\"marital-status\"], census_df[\"income\"])\n",
        "bar_df.plot(kind=\"bar\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "Et0hcl7ru6cx",
        "outputId": "1d79ec13-3f7d-454e-a687-2e0b861b85a4"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bar_df = pd.crosstab(census_df[\"sex\"], census_df[\"income\"])\n",
        "bar_df.plot(kind=\"bar\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "-Jw5FQ4iu8Ep",
        "outputId": "1b907f12-0710-4c21-85f7-a84e55cebc8b"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bar_df = pd.crosstab(census_df[\"hours-per-week\"], census_df[\"income\"])\n",
        "bar_df.plot(kind=\"line\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "OG_8LcDlu93T",
        "outputId": "18eebfab-42ba-41a9-9fde-e443bd95ff52"
      },
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predictive Modeling and Model Evaluation "
      ],
      "metadata": {
        "id": "CCHIa_advByB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "census_df[\"age\"] = pd.to_numeric(census_df[\"age\"])\n",
        "dum_census = pd.get_dummies(census_df)\n",
        "print(dum_census.head(5))\n",
        "\n",
        "X = dum_census.iloc[:, :-2]\n",
        "y = dum_census.iloc[:, -1]\n",
        "\n",
        "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJAfR9WrvDwl",
        "outputId": "f116f22e-cd3d-4b50-e351-e3d328efb5a8"
      },
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    age  education  hours-per-week  workclass_Private  workclass_Public  \\\n",
            "0  39.0         13              40                  0                 1   \n",
            "1  50.0         13              13                  0                 0   \n",
            "2  38.0          9              40                  1                 0   \n",
            "3  53.0          7              40                  1                 0   \n",
            "4  28.0         13              40                  1                 0   \n",
            "\n",
            "   workclass_Self-emp  marital-status_Married  marital-status_Single  \\\n",
            "0                   0                       0                      1   \n",
            "1                   1                       1                      0   \n",
            "2                   0                       0                      1   \n",
            "3                   0                       1                      0   \n",
            "4                   0                       1                      0   \n",
            "\n",
            "   race_Amer-Indian  race_Asian  race_Black  race_Hispanic  race_White  \\\n",
            "0                 0           0           0              0           1   \n",
            "1                 0           0           0              0           1   \n",
            "2                 0           0           0              0           1   \n",
            "3                 0           0           1              0           0   \n",
            "4                 0           0           1              0           0   \n",
            "\n",
            "   sex_Female  sex_Male  income_<=50K  income_>50K  \n",
            "0           0         1             1            0  \n",
            "1           0         1             1            0  \n",
            "2           0         1             1            0  \n",
            "3           0         1             1            0  \n",
            "4           1         0             1            0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a tree object using entropy to decide splits\n",
        "gnb = GaussianNB()\n",
        "\n",
        "# Fit a model to the training data\n",
        "# (or, learn the decision tree)\n",
        "gnb = gnb.fit(Xtrain, ytrain)\n",
        "\n",
        "ypred = gnb.predict(Xtest)\n",
        "\n",
        "### Classification Report \n",
        "print(classification_report(ytest, ypred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "su9hR0BQwJG8",
        "outputId": "717568bc-6c70-4e36-e2b2-684741e985b8"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.71      0.79      1414\n",
            "           1       0.46      0.76      0.58       469\n",
            "\n",
            "    accuracy                           0.72      1883\n",
            "   macro avg       0.68      0.73      0.68      1883\n",
            "weighted avg       0.79      0.72      0.74      1883\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "cKySvfzL0p5y"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest"
      ],
      "metadata": {
        "id": "Ty6ixY7f052C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest Classifier \n",
        "rModel = RandomForestClassifier(n_estimators=7, random_state=0, criterion='entropy') \n",
        "rModel.fit(Xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07MPm4_WwO9T",
        "outputId": "aad8b956-3a44-4b88-ba9e-eecc61d5de3b"
      },
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', n_estimators=7, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rypred = rModel.predict(Xtest)"
      ],
      "metadata": {
        "id": "syVYFUa12K4x"
      },
      "execution_count": 250,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Random Forest Classification report\")\n",
        "print(classification_report(ytest, rypred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdIiS6Ms2oQ-",
        "outputId": "6e522fb5-c842-42f7-cd95-a1b996715018"
      },
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Classification report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.88      0.86      1414\n",
            "           1       0.59      0.53      0.56       469\n",
            "\n",
            "    accuracy                           0.79      1883\n",
            "   macro avg       0.72      0.70      0.71      1883\n",
            "weighted avg       0.78      0.79      0.79      1883\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "KNN Classifier"
      ],
      "metadata": {
        "id": "tRCgROaE081D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#KNN Classifier \n",
        "kModel = KNeighborsClassifier(n_neighbors=1, metric='euclidean') \n",
        "kModel.fit(Xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FCPyDSr4yAzm",
        "outputId": "86674213-4411-423c-8609-261723f4342b"
      },
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean', n_neighbors=1)"
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kypred = kModel.predict(Xtest) "
      ],
      "metadata": {
        "id": "OxxHxLu32cva"
      },
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('KNN Classification report') \n",
        "print(classification_report(ytest, kypred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwlHOSZ-2i7P",
        "outputId": "782e4862-aa37-4445-c99f-9f9aafa6e975"
      },
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Classification report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.85      0.84      1414\n",
            "           1       0.53      0.51      0.52       469\n",
            "\n",
            "    accuracy                           0.76      1883\n",
            "   macro avg       0.68      0.68      0.68      1883\n",
            "weighted avg       0.76      0.76      0.76      1883\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I see that the random forest classifier is better at classifying the information. This could be because it's such a vast amount of information. "
      ],
      "metadata": {
        "id": "T5TL5E463B_A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "6MddN6cm42FL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}