Project_DecisionTree_RandomForest_Census_Data.ipynb

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      "source": [
        "# import the relevant packages\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn import tree\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmksaWSsaa3b"
      },
      "source": [
        "## Preprocessing the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-FVfhY_aa3e"
      },
      "source": [
        "### Training dataset"
      ]
    },
    {
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      "execution_count": 2,
      "metadata": {
        "id": "1hNzvhrFaa3f"
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      "source": [
        "# load the dataset\n",
        "train_data = pd.read_csv(\"Census_income_train.csv\")"
      ]
    },
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              "   Age          Workclass  fnlwgt   Education  Education-num  \\\n",
              "0   39          State-gov   77516   Bachelors             13   \n",
              "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
              "2   38            Private  215646     HS-grad              9   \n",
              "3   53            Private  234721        11th              7   \n",
              "4   28            Private  338409   Bachelors             13   \n",
              "\n",
              "        Marital status          Occupation    Relationship    Race      Sex  \\\n",
              "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
              "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
              "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
              "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
              "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
              "\n",
              "   Capital-gain  Capital-loss  Hours-per-week  Native-country  Income  \n",
              "0          2174             0              40   United-States   <=50K  \n",
              "1             0             0              13   United-States   <=50K  \n",
              "2             0             0              40   United-States   <=50K  \n",
              "3             0             0              40   United-States   <=50K  \n",
              "4             0             0              40            Cuba   <=50K  "
            ],
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              "      <td>38</td>\n",
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              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
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              "      <td>Not-in-family</td>\n",
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              "      <td>40</td>\n",
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              "      <td>28</td>\n",
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              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
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              "      <td>Prof-specialty</td>\n",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# inspect the dataset\n",
        "train_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LFNakQE-aa3n",
        "outputId": "4f0d3d53-3a54-4f24-f27e-2fe6b7f1927f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32560"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "len(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjaq-9H8aa3o",
        "outputId": "c6ecbffc-b7d5-42b2-e57f-939964c34429"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age               0\n",
              "Workclass         0\n",
              "fnlwgt            0\n",
              "Education         0\n",
              "Education-num     0\n",
              "Marital status    0\n",
              "Occupation        0\n",
              "Relationship      0\n",
              "Race              0\n",
              "Sex               0\n",
              "Capital-gain      0\n",
              "Capital-loss      0\n",
              "Hours-per-week    0\n",
              "Native-country    0\n",
              "Income            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# No null or NaN values\n",
        "train_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhZJ9zrXaa3p"
      },
      "source": [
        "#### Removing rows with unknown values ('?')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "scrolled": true,
        "colab": {
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        },
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        "outputId": "08e8d3cd-4787-416e-e963-6bdfd6743b67"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "32555    False\n",
              "32556    False\n",
              "32557    False\n",
              "32558    False\n",
              "32559    False\n",
              "Name: Workclass, Length: 32560, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Let's start with the Workclass column\n",
        "# We can obtain a list of boolean values indicating whether there is a '?' on the current row\n",
        "train_data[\"Workclass\"].str.contains(\"\\?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "scrolled": true,
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        },
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        True\n",
              "1        True\n",
              "2        True\n",
              "3        True\n",
              "4        True\n",
              "         ... \n",
              "32555    True\n",
              "32556    True\n",
              "32557    True\n",
              "32558    True\n",
              "32559    True\n",
              "Name: Workclass, Length: 32560, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Let's reverse all the boolean values\n",
        "train_data[\"Workclass\"].str.contains(\"\\?\") == False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "6BHIiH11aa3u"
      },
      "outputs": [],
      "source": [
        "# Take the subset of the dataframe rows which don't contain '?'\n",
        "clean_train_data = train_data[train_data[\"Workclass\"].str.contains(\"\\?\") == False]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
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        },
        "id": "dk5eiLRvaa3u",
        "outputId": "6aca9178-0455-4207-8578-bc9d4ec79023"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30724"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "len(clean_train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "BiMw80Zwaa3v"
      },
      "outputs": [],
      "source": [
        "# Let's do the same for 'Occupation'\n",
        "clean_train_data = clean_train_data[clean_train_data[\"Occupation\"].str.contains(\"\\?\") == False]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kLV1_RNIaa3v",
        "outputId": "16d3cdf6-e81e-41a0-d59c-5db81c28e4a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30717"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "len(clean_train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "VqBHsrk6aa3w"
      },
      "outputs": [],
      "source": [
        "# And for 'Native-country'\n",
        "clean_train_data = clean_train_data[clean_train_data[\"Native-country\"].str.contains(\"\\?\") == False]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAZ7V6Zdaa3w",
        "outputId": "33d251b3-8167-49c0-edd5-5aefcddd2e5d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30161"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "len(clean_train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "-bXtqtUnaa3x"
      },
      "outputs": [],
      "source": [
        "# Finally, let's reset the index\n",
        "clean_train_data = clean_train_data.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVXyFwmuaa3x"
      },
      "source": [
        "#### Creating dummy variables and separating inputs and targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "YT3C0C7waa3z"
      },
      "outputs": [],
      "source": [
        "# automatically do that with '.get_dummies'\n",
        "train_dummies = pd.get_dummies(clean_train_data, drop_first=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "6-Fty_vLaa3z",
        "outputId": "f8b6e5b9-9772-4d45-dbbe-19cce82ced83"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  fnlwgt  Education-num  Capital-gain  Capital-loss  Hours-per-week  \\\n",
              "0   39   77516             13          2174             0              40   \n",
              "1   50   83311             13             0             0              13   \n",
              "2   38  215646              9             0             0              40   \n",
              "3   53  234721              7             0             0              40   \n",
              "4   28  338409             13             0             0              40   \n",
              "\n",
              "   Workclass_ Federal-gov  Workclass_ Local-gov  Workclass_ Private  \\\n",
              "0                       0                     0                   0   \n",
              "1                       0                     0                   0   \n",
              "2                       0                     0                   1   \n",
              "3                       0                     0                   1   \n",
              "4                       0                     0                   1   \n",
              "\n",
              "   Workclass_ Self-emp-inc  ...  Native-country_ Scotland  \\\n",
              "0                        0  ...                         0   \n",
              "1                        0  ...                         0   \n",
              "2                        0  ...                         0   \n",
              "3                        0  ...                         0   \n",
              "4                        0  ...                         0   \n",
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              "   Native-country_ South  Native-country_ Taiwan  Native-country_ Thailand  \\\n",
              "0                      0                       0                         0   \n",
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              "2                      0                       0                         0   \n",
              "3                      0                       0                         0   \n",
              "4                      0                       0                         0   \n",
              "\n",
              "   Native-country_ Trinadad&Tobago  Native-country_ United-States  \\\n",
              "0                                0                              1   \n",
              "1                                0                              1   \n",
              "2                                0                              1   \n",
              "3                                0                              1   \n",
              "4                                0                              0   \n",
              "\n",
              "   Native-country_ Vietnam  Native-country_ Yugoslavia  Income_ <=50K  \\\n",
              "0                        0                           0              1   \n",
              "1                        0                           0              1   \n",
              "2                        0                           0              1   \n",
              "3                        0                           0              1   \n",
              "4                        0                           0              1   \n",
              "\n",
              "   Income_ >50K  \n",
              "0             0  \n",
              "1             0  \n",
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              "4             0  \n",
              "\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-87e406e7-bded-4fd7-b2a6-b48c5ca7fde4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "train_dummies.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "p1c8Sg77aa30"
      },
      "outputs": [],
      "source": [
        "# The last 2 columns are whether the income <= 50k and whether it is >50k\n",
        "# Both of these carry the same information, so we will remove one of them\n",
        "train_dummies = train_dummies.drop(['Income_ <=50K'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
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              "   Age  fnlwgt  Education-num  Capital-gain  Capital-loss  Hours-per-week  \\\n",
              "0   39   77516             13          2174             0              40   \n",
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              "\n",
              "   Workclass_ Federal-gov  Workclass_ Local-gov  Workclass_ Private  \\\n",
              "0                       0                     0                   0   \n",
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              "   Workclass_ Self-emp-inc  ...  Native-country_ Puerto-Rico  \\\n",
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              "4                        0  ...                            0   \n",
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              "4                              0                        0   \n",
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              "   Native-country_ Yugoslavia  Income_ >50K  \n",
              "0                           0             0  \n",
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              "[5 rows x 104 columns]"
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              "<p>5 rows × 104 columns</p>\n",
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              "\n",
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              "        document.querySelector('#df-e66acd4c-ff47-466e-aa98-5e91c24a47b2 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-e66acd4c-ff47-466e-aa98-5e91c24a47b2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "train_dummies.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "gmX0Slhuaa31"
      },
      "outputs": [],
      "source": [
        "# The input features are everything besides the last column\n",
        "train_input = train_dummies.iloc[:,:-1]\n",
        "\n",
        "# The target/output is just the last column\n",
        "train_target = train_dummies.iloc[:,-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "RIjOcpPRaa31",
        "outputId": "8a6ab72d-61b7-4da6-8f32-dc809037d6c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  fnlwgt  Education-num  Capital-gain  Capital-loss  Hours-per-week  \\\n",
              "0   39   77516             13          2174             0              40   \n",
              "1   50   83311             13             0             0              13   \n",
              "2   38  215646              9             0             0              40   \n",
              "3   53  234721              7             0             0              40   \n",
              "4   28  338409             13             0             0              40   \n",
              "\n",
              "   Workclass_ Federal-gov  Workclass_ Local-gov  Workclass_ Private  \\\n",
              "0                       0                     0                   0   \n",
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              "3                       0                     0                   1   \n",
              "4                       0                     0                   1   \n",
              "\n",
              "   Workclass_ Self-emp-inc  ...  Native-country_ Portugal  \\\n",
              "0                        0  ...                         0   \n",
              "1                        0  ...                         0   \n",
              "2                        0  ...                         0   \n",
              "3                        0  ...                         0   \n",
              "4                        0  ...                         0   \n",
              "\n",
              "   Native-country_ Puerto-Rico  Native-country_ Scotland  \\\n",
              "0                            0                         0   \n",
              "1                            0                         0   \n",
              "2                            0                         0   \n",
              "3                            0                         0   \n",
              "4                            0                         0   \n",
              "\n",
              "   Native-country_ South  Native-country_ Taiwan  Native-country_ Thailand  \\\n",
              "0                      0                       0                         0   \n",
              "1                      0                       0                         0   \n",
              "2                      0                       0                         0   \n",
              "3                      0                       0                         0   \n",
              "4                      0                       0                         0   \n",
              "\n",
              "   Native-country_ Trinadad&Tobago  Native-country_ United-States  \\\n",
              "0                                0                              1   \n",
              "1                                0                              1   \n",
              "2                                0                              1   \n",
              "3                                0                              1   \n",
              "4                                0                              0   \n",
              "\n",
              "   Native-country_ Vietnam  Native-country_ Yugoslavia  \n",
              "0                        0                           0  \n",
              "1                        0                           0  \n",
              "2                        0                           0  \n",
              "3                        0                           0  \n",
              "4                        0                           0  \n",
              "\n",
              "[5 rows x 103 columns]"
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>Education-num</th>\n",
              "      <th>Capital-gain</th>\n",
              "      <th>Capital-loss</th>\n",
              "      <th>Hours-per-week</th>\n",
              "      <th>Workclass_ Federal-gov</th>\n",
              "      <th>Workclass_ Local-gov</th>\n",
              "      <th>Workclass_ Private</th>\n",
              "      <th>Workclass_ Self-emp-inc</th>\n",
              "      <th>...</th>\n",
              "      <th>Native-country_ Portugal</th>\n",
              "      <th>Native-country_ Puerto-Rico</th>\n",
              "      <th>Native-country_ Scotland</th>\n",
              "      <th>Native-country_ South</th>\n",
              "      <th>Native-country_ Taiwan</th>\n",
              "      <th>Native-country_ Thailand</th>\n",
              "      <th>Native-country_ Trinadad&amp;Tobago</th>\n",
              "      <th>Native-country_ United-States</th>\n",
              "      <th>Native-country_ Vietnam</th>\n",
              "      <th>Native-country_ Yugoslavia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>77516</td>\n",
              "      <td>13</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>83311</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>215646</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>234721</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>338409</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 103 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "      background-color: #434B5C;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                    [key], {});\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "train_input.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpd02Sctaa32",
        "outputId": "ee73d020-ad66-40b6-dab2-cd6d1ba09961"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: Income_ >50K, dtype: uint8"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "train_target.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQK9JI2paa32"
      },
      "source": [
        "### Test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tL4_I4dqaa32"
      },
      "outputs": [],
      "source": [
        "# Let's do the same preprocessing on the test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "tcoo5T-Xaa33"
      },
      "outputs": [],
      "source": [
        "# Load test data\n",
        "test_data = pd.read_csv(\"Census_income_test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5MuhqjKQaa33",
        "outputId": "bd198d4e-0b4b-4f39-c4bb-ff68abafeabd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age   Workclass  fnlwgt      Education  Education-num       Marital status  \\\n",
              "0   25     Private  226802           11th              7        Never-married   \n",
              "1   38     Private   89814        HS-grad              9   Married-civ-spouse   \n",
              "2   28   Local-gov  336951     Assoc-acdm             12   Married-civ-spouse   \n",
              "3   44     Private  160323   Some-college             10   Married-civ-spouse   \n",
              "4   18           ?  103497   Some-college             10        Never-married   \n",
              "\n",
              "           Occupation Relationship    Race      Sex  Capital-gain  \\\n",
              "0   Machine-op-inspct    Own-child   Black     Male             0   \n",
              "1     Farming-fishing      Husband   White     Male             0   \n",
              "2     Protective-serv      Husband   White     Male             0   \n",
              "3   Machine-op-inspct      Husband   Black     Male          7688   \n",
              "4                   ?    Own-child   White   Female             0   \n",
              "\n",
              "   Capital-loss  Hours-per-week  Native-country   Income  \n",
              "0             0              40   United-States   <=50K.  \n",
              "1             0              50   United-States   <=50K.  \n",
              "2             0              40   United-States    >50K.  \n",
              "3             0              40   United-States    >50K.  \n",
              "4             0              30   United-States   <=50K.  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
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              "      <th>Sex</th>\n",
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              "      <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>25</td>\n",
              "      <td>Private</td>\n",
              "      <td>226802</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Own-child</td>\n",
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              "      <td>40</td>\n",
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              "      <td>&lt;=50K.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>89814</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Farming-fishing</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>Local-gov</td>\n",
              "      <td>336951</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Protective-serv</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>United-States</td>\n",
              "      <td>&gt;50K.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44</td>\n",
              "      <td>Private</td>\n",
              "      <td>160323</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>7688</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18</td>\n",
              "      <td>?</td>\n",
              "      <td>103497</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>?</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-961945f3-01ea-4ceb-ab8b-2fa9c7ef01fe')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-961945f3-01ea-4ceb-ab8b-2fa9c7ef01fe 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-961945f3-01ea-4ceb-ab8b-2fa9c7ef01fe');\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",
              "\n",
              "\n",
              "<div id=\"df-adc38625-754e-4917-a6e6-8c9a2fbbfa48\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-adc38625-754e-4917-a6e6-8c9a2fbbfa48')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-adc38625-754e-4917-a6e6-8c9a2fbbfa48 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "test_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrfWI9Ocaa33",
        "outputId": "0de9ae41-9c68-493e-ac44-5d7d498f9e34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16281"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "len(test_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XA8tTTLwaa34"
      },
      "source": [
        "#### Cleaning unknown ('?') values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "KyZqiTZ5aa34"
      },
      "outputs": [],
      "source": [
        "clean_test_data = test_data[test_data[\"Workclass\"].str.contains(\"\\?\") == False]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-SiLEqVtaa35",
        "outputId": "82311b6c-8745-42eb-9956-762d305590bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15318"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "len(clean_test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "f5FUeis7aa35"
      },
      "outputs": [],
      "source": [
        "clean_test_data = clean_test_data[clean_test_data[\"Occupation\"].str.contains(\"\\?\") == False]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKdeQuehaa36",
        "outputId": "0109cb08-9cca-4bc0-9d90-380027bbd909"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15315"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "len(clean_test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "WPux_R2Caa36"
      },
      "outputs": [],
      "source": [
        "clean_test_data = clean_test_data[clean_test_data[\"Native-country\"].str.contains(\"\\?\") == False]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOxgGkL7aa36",
        "outputId": "aa20cced-a372-406e-a0a1-d8932445f8d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15060"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "len(clean_test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "o7ZbgXspaa38"
      },
      "outputs": [],
      "source": [
        "clean_test_data = clean_test_data.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vM3EKxwcaa38"
      },
      "source": [
        "#### Creating dummy variables and sepratting inputs and targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "gVz_qdmAaa39"
      },
      "outputs": [],
      "source": [
        "test_dummies = pd.get_dummies(clean_test_data, drop_first=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "uXFlAicjaa39",
        "outputId": "77ce4193-b4f8-4776-ae0e-3c9ca877ae47"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  fnlwgt  Education-num  Capital-gain  Capital-loss  Hours-per-week  \\\n",
              "0   25  226802              7             0             0              40   \n",
              "1   38   89814              9             0             0              50   \n",
              "2   28  336951             12             0             0              40   \n",
              "3   44  160323             10          7688             0              40   \n",
              "4   34  198693              6             0             0              30   \n",
              "\n",
              "   Workclass_ Federal-gov  Workclass_ Local-gov  Workclass_ Private  \\\n",
              "0                       0                     0                   1   \n",
              "1                       0                     0                   1   \n",
              "2                       0                     1                   0   \n",
              "3                       0                     0                   1   \n",
              "4                       0                     0                   1   \n",
              "\n",
              "   Workclass_ Self-emp-inc  ...  Native-country_ Scotland  \\\n",
              "0                        0  ...                         0   \n",
              "1                        0  ...                         0   \n",
              "2                        0  ...                         0   \n",
              "3                        0  ...                         0   \n",
              "4                        0  ...                         0   \n",
              "\n",
              "   Native-country_ South  Native-country_ Taiwan  Native-country_ Thailand  \\\n",
              "0                      0                       0                         0   \n",
              "1                      0                       0                         0   \n",
              "2                      0                       0                         0   \n",
              "3                      0                       0                         0   \n",
              "4                      0                       0                         0   \n",
              "\n",
              "   Native-country_ Trinadad&Tobago  Native-country_ United-States  \\\n",
              "0                                0                              1   \n",
              "1                                0                              1   \n",
              "2                                0                              1   \n",
              "3                                0                              1   \n",
              "4                                0                              1   \n",
              "\n",
              "   Native-country_ Vietnam  Native-country_ Yugoslavia  Income_ <=50K.  \\\n",
              "0                        0                           0               1   \n",
              "1                        0                           0               1   \n",
              "2                        0                           0               0   \n",
              "3                        0                           0               0   \n",
              "4                        0                           0               1   \n",
              "\n",
              "   Income_ >50K.  \n",
              "0              0  \n",
              "1              0  \n",
              "2              1  \n",
              "3              1  \n",
              "4              0  \n",
              "\n",
              "[5 rows x 105 columns]"
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44</td>\n",
              "      <td>160323</td>\n",
              "      <td>10</td>\n",
              "      <td>7688</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34</td>\n",
              "      <td>198693</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 105 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c638d838-742f-4e14-8d7a-2bf86f28c9fd')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c638d838-742f-4e14-8d7a-2bf86f28c9fd 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-c638d838-742f-4e14-8d7a-2bf86f28c9fd');\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",
              "\n",
              "\n",
              "<div id=\"df-0e30f4c5-8afe-4269-8f88-aa454034ec44\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0e30f4c5-8afe-4269-8f88-aa454034ec44')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0e30f4c5-8afe-4269-8f88-aa454034ec44 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "test_dummies.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "JxyF2lhsaa3-"
      },
      "outputs": [],
      "source": [
        "test_dummies = test_dummies.drop(['Income_ <=50K.'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "h87Gf0gSaa3-",
        "outputId": "81572bdb-46ce-476f-8ba6-36c80f8c5ef6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  fnlwgt  Education-num  Capital-gain  Capital-loss  Hours-per-week  \\\n",
              "0   25  226802              7             0             0              40   \n",
              "1   38   89814              9             0             0              50   \n",
              "2   28  336951             12             0             0              40   \n",
              "3   44  160323             10          7688             0              40   \n",
              "4   34  198693              6             0             0              30   \n",
              "\n",
              "   Workclass_ Federal-gov  Workclass_ Local-gov  Workclass_ Private  \\\n",
              "0                       0                     0                   1   \n",
              "1                       0                     0                   1   \n",
              "2                       0                     1                   0   \n",
              "3                       0                     0                   1   \n",
              "4                       0                     0                   1   \n",
              "\n",
              "   Workclass_ Self-emp-inc  ...  Native-country_ Puerto-Rico  \\\n",
              "0                        0  ...                            0   \n",
              "1                        0  ...                            0   \n",
              "2                        0  ...                            0   \n",
              "3                        0  ...                            0   \n",
              "4                        0  ...                            0   \n",
              "\n",
              "   Native-country_ Scotland  Native-country_ South  Native-country_ Taiwan  \\\n",
              "0                         0                      0                       0   \n",
              "1                         0                      0                       0   \n",
              "2                         0                      0                       0   \n",
              "3                         0                      0                       0   \n",
              "4                         0                      0                       0   \n",
              "\n",
              "   Native-country_ Thailand  Native-country_ Trinadad&Tobago  \\\n",
              "0                         0                                0   \n",
              "1                         0                                0   \n",
              "2                         0                                0   \n",
              "3                         0                                0   \n",
              "4                         0                                0   \n",
              "\n",
              "   Native-country_ United-States  Native-country_ Vietnam  \\\n",
              "0                              1                        0   \n",
              "1                              1                        0   \n",
              "2                              1                        0   \n",
              "3                              1                        0   \n",
              "4                              1                        0   \n",
              "\n",
              "   Native-country_ Yugoslavia  Income_ >50K.  \n",
              "0                           0              0  \n",
              "1                           0              0  \n",
              "2                           0              1  \n",
              "3                           0              1  \n",
              "4                           0              0  \n",
              "\n",
              "[5 rows x 104 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-edd6134c-ec95-4332-9b68-670e7f65978b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>Education-num</th>\n",
              "      <th>Capital-gain</th>\n",
              "      <th>Capital-loss</th>\n",
              "      <th>Hours-per-week</th>\n",
              "      <th>Workclass_ Federal-gov</th>\n",
              "      <th>Workclass_ Local-gov</th>\n",
              "      <th>Workclass_ Private</th>\n",
              "      <th>Workclass_ Self-emp-inc</th>\n",
              "      <th>...</th>\n",
              "      <th>Native-country_ Puerto-Rico</th>\n",
              "      <th>Native-country_ Scotland</th>\n",
              "      <th>Native-country_ South</th>\n",
              "      <th>Native-country_ Taiwan</th>\n",
              "      <th>Native-country_ Thailand</th>\n",
              "      <th>Native-country_ Trinadad&amp;Tobago</th>\n",
              "      <th>Native-country_ United-States</th>\n",
              "      <th>Native-country_ Vietnam</th>\n",
              "      <th>Native-country_ Yugoslavia</th>\n",
              "      <th>Income_ &gt;50K.</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38</td>\n",
              "      <td>89814</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>336951</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44</td>\n",
              "      <td>160323</td>\n",
              "      <td>10</td>\n",
              "      <td>7688</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34</td>\n",
              "      <td>198693</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 104 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edd6134c-ec95-4332-9b68-670e7f65978b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-edd6134c-ec95-4332-9b68-670e7f65978b 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-edd6134c-ec95-4332-9b68-670e7f65978b');\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",
              "\n",
              "\n",
              "<div id=\"df-0c785553-5b46-4a6b-8d56-7cb9724b3306\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0c785553-5b46-4a6b-8d56-7cb9724b3306')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0c785553-5b46-4a6b-8d56-7cb9724b3306 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "test_dummies.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "GQVRScFOaa3-"
      },
      "outputs": [],
      "source": [
        "test_input = test_dummies.iloc[:,:-1]\n",
        "test_target = test_dummies.iloc[:,-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2HvfVBfaa3_",
        "outputId": "54ae5458-a954-4f67-df85-b67a779b9a88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    1\n",
              "3    1\n",
              "4    0\n",
              "Name: Income_ >50K., dtype: uint8"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "test_target.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating and Visualizing the tree"
      ],
      "metadata": {
        "id": "DRprCUAjdAsb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "M9JEQGUTaa3_"
      },
      "outputs": [],
      "source": [
        "clf = tree.DecisionTreeClassifier(random_state=365, ccp_alpha=0.001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "N0t5lTnNaa4A",
        "outputId": "3a6dfd6c-5768-4830-f252-4a1bb6de8df0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.001, random_state=365)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(ccp_alpha=0.001, random_state=365)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(ccp_alpha=0.001, random_state=365)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "clf.fit(train_input, train_target)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "tree.plot_tree(clf, filled=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "D8wGxnTAdZEr",
        "outputId": "bbdf26a6-6990-4136-8016-06b7a11f5650"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing the model"
      ],
      "metadata": {
        "id": "8hTMrM4Fee-E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_pred= clf.predict(test_input)"
      ],
      "metadata": {
        "id": "qKTg2p28eM4L"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(test_target, test_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVJHK71jeTNX",
        "outputId": "b8e62f92-8149-4ba5-970f-5e5d73ef560e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.96      0.90     11360\n",
            "           1       0.79      0.51      0.62      3700\n",
            "\n",
            "    accuracy                           0.85     15060\n",
            "   macro avg       0.82      0.73      0.76     15060\n",
            "weighted avg       0.84      0.85      0.83     15060\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating and training Random Forest model"
      ],
      "metadata": {
        "id": "Ie_5FQ59fo1x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf2 = RandomForestClassifier(random_state=365, ccp_alpha=0.0001)"
      ],
      "metadata": {
        "id": "xJHQVmmvfvJK"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf2.fit(train_input, train_target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "FMLdR8QWgFoR",
        "outputId": "9afcfcf7-145a-49b6-d840-5b53e17177af"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(ccp_alpha=0.0001, random_state=365)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(ccp_alpha=0.0001, random_state=365)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(ccp_alpha=0.0001, random_state=365)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing the model"
      ],
      "metadata": {
        "id": "IKCLfLSLgimP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_pred2 = clf2.predict(test_input)"
      ],
      "metadata": {
        "id": "s9ekgcW8gIZa"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(test_target, test_pred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gEpCVfHagLQn",
        "outputId": "82b5c3a4-85fb-403e-d9b0-94b573d365dc"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.94      0.91     11360\n",
            "           1       0.77      0.62      0.68      3700\n",
            "\n",
            "    accuracy                           0.86     15060\n",
            "   macro avg       0.83      0.78      0.80     15060\n",
            "weighted avg       0.86      0.86      0.86     15060\n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}