census_incomeds.ipynb

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      "metadata": {
        "id": "h-qCu2v5kw12"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('adult.csv')"
      ],
      "metadata": {
        "id": "7Cu8-jYB1Z43"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
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        "outputId": "e604a7bf-0f6a-4c3b-a790-806bfa363fb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "workclass         0\n",
              "fnlwgt            0\n",
              "education         0\n",
              "education.num     0\n",
              "marital.status    0\n",
              "occupation        0\n",
              "relationship      0\n",
              "race              0\n",
              "sex               0\n",
              "capital.gain      0\n",
              "capital.loss      0\n",
              "hours.per.week    0\n",
              "native.country    0\n",
              "income            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 340
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        "id": "MtbbBrqd2602",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
              "0   90         ?   77053       HS-grad              9        Widowed   \n",
              "1   82   Private  132870       HS-grad              9        Widowed   \n",
              "2   66         ?  186061  Some-college             10        Widowed   \n",
              "3   54   Private  140359       7th-8th              4       Divorced   \n",
              "4   41   Private  264663  Some-college             10      Separated   \n",
              "\n",
              "          occupation   relationship   race     sex  capital.gain  \\\n",
              "0                  ?  Not-in-family  White  Female             0   \n",
              "1    Exec-managerial  Not-in-family  White  Female             0   \n",
              "2                  ?      Unmarried  Black  Female             0   \n",
              "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
              "4     Prof-specialty      Own-child  White  Female             0   \n",
              "\n",
              "   capital.loss  hours.per.week native.country income  \n",
              "0          4356              40  United-States  <=50K  \n",
              "1          4356              18  United-States  <=50K  \n",
              "2          4356              40  United-States  <=50K  \n",
              "3          3900              40  United-States  <=50K  \n",
              "4          3900              40  United-States  <=50K  "
            ],
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              "      <th></th>\n",
              "      <th>age</th>\n",
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              "      <th>capital.gain</th>\n",
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              "      <th>0</th>\n",
              "      <td>90</td>\n",
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              "      <td>77053</td>\n",
              "      <td>HS-grad</td>\n",
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              "      <th>1</th>\n",
              "      <td>82</td>\n",
              "      <td>Private</td>\n",
              "      <td>132870</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</td>\n",
              "      <td>18</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>66</td>\n",
              "      <td>?</td>\n",
              "      <td>186061</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>?</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>54</td>\n",
              "      <td>Private</td>\n",
              "      <td>140359</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>4</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3900</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41</td>\n",
              "      <td>Private</td>\n",
              "      <td>264663</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Separated</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3900</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.workclass = df.workclass.replace(['?'],df.workclass.mode())\n",
        "df.occupation = df.occupation.replace(['?'],df.occupation.mode())\n",
        "df['native.country'] = df['native.country'].replace(['?'],df['native.country'].mode())"
      ],
      "metadata": {
        "id": "yqnLB5rE3FNm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(['income'],axis=1)\n",
        "y = df['income']"
      ],
      "metadata": {
        "id": "wwZf3vBU3eTm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)"
      ],
      "metadata": {
        "id": "OPObx0ER4USW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "categorical = ['workclass', 'education', 'marital.status', 'occupation', 'relationship', 'race', 'sex', 'native.country']\n",
        "for feature in categorical:\n",
        "    le = preprocessing.LabelEncoder()\n",
        "    X_train[feature] = le.fit_transform(X_train[feature])\n",
        "    X_test[feature] = le.transform(X_test[feature])"
      ],
      "metadata": {
        "id": "YFzNhgxK4aqm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n"
      ],
      "metadata": {
        "id": "g5ZSTxhn43tm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pd.DataFrame(scaler.fit_transform(X_train), columns = X.columns)"
      ],
      "metadata": {
        "id": "52YRiKCU48tF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = pd.DataFrame(scaler.transform(X_test), columns = X.columns)"
      ],
      "metadata": {
        "id": "K_ScyUtz_ZC5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "WuzlZJ7A5-qX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train, y_train)\n",
        "y_pred = log_reg.predict(X_test)\n"
      ],
      "metadata": {
        "id": "GEEQC3AP6b5G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "id": "EdYB3Z9M68d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e1fc19e-42c3-4cda-f76b-cb869913c9a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8216808271061521\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oVrpr67u7KjV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_yTjdoW7lB1",
        "outputId": "e84b3209-b97f-452c-fb80-abe0da0f89ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.24147814515303512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0lYtsuEv8HzF"
      },
      "execution_count": null,
      "outputs": []
    }
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}