fastai_tabular_beg.ipynb

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      "name": "fastai_tabular_beg.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/MUKESHRAJMAHENDRAN/Deep-Learning--fastai/blob/main/fastai_tabular_beg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Working on tabular data "
      ],
      "metadata": {
        "id": "S-82mmsmw6Ze"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "ubgzyCFdw4bO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fastai --upgrade"
      ],
      "metadata": {
        "id": "hdVm8HSewiPZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from fastai.tabular.all import *"
      ],
      "metadata": {
        "id": "QwejnqP-wuyF"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = untar_data(URLs.ADULT_SAMPLE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "BeA4QKDcw1SS",
        "outputId": "1417352b-47f3-4a0c-8fc3-84ca8204b555"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#3) [Path('/root/.fastai/data/adult_sample/export.pkl'),Path('/root/.fastai/data/adult_sample/models'),Path('/root/.fastai/data/adult_sample/adult.csv')]"
            ]
          },
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(path/\"adult.csv\")\n",
        "df.head()"
      ],
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      "execution_count": 9,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age          workclass  fnlwgt     education  education-num  \\\n",
              "0   49            Private  101320    Assoc-acdm           12.0   \n",
              "1   44            Private  236746       Masters           14.0   \n",
              "2   38            Private   96185       HS-grad            NaN   \n",
              "3   38       Self-emp-inc  112847   Prof-school           15.0   \n",
              "4   42   Self-emp-not-inc   82297       7th-8th            NaN   \n",
              "\n",
              "        marital-status        occupation    relationship                 race  \\\n",
              "0   Married-civ-spouse               NaN            Wife                White   \n",
              "1             Divorced   Exec-managerial   Not-in-family                White   \n",
              "2             Divorced               NaN       Unmarried                Black   \n",
              "3   Married-civ-spouse    Prof-specialty         Husband   Asian-Pac-Islander   \n",
              "4   Married-civ-spouse     Other-service            Wife                Black   \n",
              "\n",
              "       sex  capital-gain  capital-loss  hours-per-week  native-country salary  \n",
              "0   Female             0          1902              40   United-States  >=50k  \n",
              "1     Male         10520             0              45   United-States  >=50k  \n",
              "2   Female             0             0              32   United-States   <50k  \n",
              "3     Male             0             0              40   United-States  >=50k  \n",
              "4   Female             0             0              50   United-States   <50k  "
            ],
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
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              "      <td>96185</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Divorced</td>\n",
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              "      <td>United-States</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>Self-emp-inc</td>\n",
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              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Asian-Pac-Islander</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</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>42</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>82297</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Other-service</td>\n",
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          "metadata": {},
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      "source": [
        "df.info()"
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      "metadata": {
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        },
        "id": "liBUiAZAxdnc",
        "outputId": "407b54cb-193e-4f74-cec4-c0fd0bca08af"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32561 entries, 0 to 32560\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             32561 non-null  int64  \n",
            " 1   workclass       32561 non-null  object \n",
            " 2   fnlwgt          32561 non-null  int64  \n",
            " 3   education       32561 non-null  object \n",
            " 4   education-num   32074 non-null  float64\n",
            " 5   marital-status  32561 non-null  object \n",
            " 6   occupation      32049 non-null  object \n",
            " 7   relationship    32561 non-null  object \n",
            " 8   race            32561 non-null  object \n",
            " 9   sex             32561 non-null  object \n",
            " 10  capital-gain    32561 non-null  int64  \n",
            " 11  capital-loss    32561 non-null  int64  \n",
            " 12  hours-per-week  32561 non-null  int64  \n",
            " 13  native-country  32561 non-null  object \n",
            " 14  salary          32561 non-null  object \n",
            "dtypes: float64(1), int64(5), object(9)\n",
            "memory usage: 3.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGsbhl9pyM0O",
        "outputId": "c5dc90fa-e97b-4d65-c30e-8c4f40a4aa61"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                 0\n",
              "workclass           0\n",
              "fnlwgt              0\n",
              "education           0\n",
              "education-num     487\n",
              "marital-status      0\n",
              "occupation        512\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",
              "salary              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_col = [col for col in df if df[col].dtype == \"O\"]\n",
        "continous_col = [col for col in df if df[col].dtype != \"O\"]"
      ],
      "metadata": {
        "id": "_nS-E7E0yQSD"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_col.remove(\"salary\")"
      ],
      "metadata": {
        "id": "1mIOj5M325lX"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_col, continous_col"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvZTGY_Jy9YN",
        "outputId": "7db12f9f-4367-46d9-bbc4-0546fd350488"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['workclass',\n",
              "  'education',\n",
              "  'marital-status',\n",
              "  'occupation',\n",
              "  'relationship',\n",
              "  'race',\n",
              "  'sex',\n",
              "  'native-country'],\n",
              " ['age',\n",
              "  'fnlwgt',\n",
              "  'education-num',\n",
              "  'capital-gain',\n",
              "  'capital-loss',\n",
              "  'hours-per-week'])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "splits = RandomSplitter(valid_pct = 0.2)(range_of(df))"
      ],
      "metadata": {
        "id": "r2nuakD4y-wa"
      },
      "execution_count": 28,
      "outputs": []
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        "splits"
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      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((#26049) [9529,11951,27016,15732,9578,25914,21049,32276,12457,11599...],\n",
              " (#6512) [8465,6701,24118,2554,1275,19096,22829,18143,6212,23383...])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "to = TabularPandas(df, \n",
        "                   procs = [Categorify, FillMissing, Normalize],\n",
        "                   cat_names = categorical_col,\n",
        "                   cont_names = continous_col,\n",
        "                   y_names = \"salary\",\n",
        "                   splits = splits\n",
        "                   )"
      ],
      "metadata": {
        "id": "0i1paema1yJE"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "to.xs.iloc[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "-xczLjkc3kmn",
        "outputId": "b169b2e8-b847-42c3-8cd6-ea8122faa06b"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       workclass  education  marital-status  occupation  relationship  race  \\\n",
              "9529           8         12               3          12             1     5   \n",
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              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.318914</td>\n",
              "      <td>0.317539</td>\n",
              "      <td>0.849969</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn.show_results()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "0ckaZ2094uf7",
        "outputId": "aa0c77e6-f1d0-47ed-bfc0-74cb20814552"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              ""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>native-country</th>\n",
              "      <th>education-num_na</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>salary</th>\n",
              "      <th>salary_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.690263</td>\n",
              "      <td>-0.435772</td>\n",
              "      <td>-0.417679</td>\n",
              "      <td>-0.147037</td>\n",
              "      <td>-0.216957</td>\n",
              "      <td>-0.040407</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.250419</td>\n",
              "      <td>-1.518703</td>\n",
              "      <td>0.757069</td>\n",
              "      <td>-0.147037</td>\n",
              "      <td>-0.216957</td>\n",
              "      <td>-0.040407</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.215726</td>\n",
              "      <td>-0.595931</td>\n",
              "      <td>-0.026097</td>\n",
              "      <td>-0.147037</td>\n",
              "      <td>-0.216957</td>\n",
              "      <td>-1.666626</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.215726</td>\n",
              "      <td>1.155715</td>\n",
              "      <td>-1.200845</td>\n",
              "      <td>-0.147037</td>\n",
              "      <td>-0.216957</td>\n",
              "      <td>-0.446962</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.030498</td>\n",
              "      <td>0.912217</td>\n",
              "      <td>1.148652</td>\n",
              "      <td>-0.147037</td>\n",
              "      <td>-0.216957</td>\n",
              "      <td>-0.040407</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.336039</td>\n",
              "      <td>-0.159768</td>\n",
              "      <td>-0.809262</td>\n",
              "      <td>-0.147037</td>\n",
              "      <td>-0.216957</td>\n",
              "      <td>-0.040407</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.362341</td>\n",
              "      <td>-1.558239</td>\n",
              "      <td>-0.417679</td>\n",
              "      <td>-0.147037</td>\n",
              "      <td>-0.216957</td>\n",
              "      <td>-1.666626</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.470341</td>\n",
              "      <td>0.258544</td>\n",
              "      <td>1.540235</td>\n",
              "      <td>-0.147037</td>\n",
              "      <td>-0.216957</td>\n",
              "      <td>-0.040407</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.362341</td>\n",
              "      <td>2.142828</td>\n",
              "      <td>-0.026097</td>\n",
              "      <td>-0.147037</td>\n",
              "      <td>-0.216957</td>\n",
              "      <td>-0.040407</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predict on one row\n",
        "row, clas, probs = learn.predict(df.iloc[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "4EYWOwuD485i",
        "outputId": "4cb6b89d-4faf-4288-9b20-c04c391b351e"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              ""
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "row.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "Ombqrk2o5YBK",
        "outputId": "51df855a-989d-4d35-9e2d-8ec9fc970837"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>native-country</th>\n",
              "      <th>education-num_na</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>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Private</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>#na#</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>United-States</td>\n",
              "      <td>False</td>\n",
              "      <td>49.0</td>\n",
              "      <td>101320.001638</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>1901.999981</td>\n",
              "      <td>40.0</td>\n",
              "      <td>&gt;=50k</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "row1, clas, probs = learn.predict(df.iloc[4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "PjyqW0M55cL3",
        "outputId": "652c2762-1c37-44e6-ce59-4eea4e13e08f"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              ""
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "row1.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "lcXpyCbK5izB",
        "outputId": "f21e4243-9d1e-4738-9bb7-c50cc97f43ba"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>workclass</th>\n",
              "      <th>education</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>native-country</th>\n",
              "      <th>education-num_na</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>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Other-service</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>United-States</td>\n",
              "      <td>True</td>\n",
              "      <td>42.0</td>\n",
              "      <td>82296.999366</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>50.0</td>\n",
              "      <td>&lt;50k</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clas, probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2oGULEVV5j1I",
        "outputId": "129b6247-9af7-43bc-db75-05e3e58a68d4"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(0), tensor([0.7722, 0.2278]))"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = df.copy()\n",
        "test_df.drop(['salary'], axis=1, inplace=True)\n",
        "dl = learn.dls.test_dl(test_df)"
      ],
      "metadata": {
        "id": "h61uWowx53Uo"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learn.get_preds(dl=dl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "LZqk65Ot6JxH",
        "outputId": "019d075e-1fdc-415d-c76d-0cb56acbf36a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              ""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[0.1531, 0.8469],\n",
              "         [0.2313, 0.7687],\n",
              "         [0.9889, 0.0111],\n",
              "         ...,\n",
              "         [0.6030, 0.3970],\n",
              "         [0.7360, 0.2640],\n",
              "         [0.7714, 0.2286]]), None)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "to.xs[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "0I1yOGs-6QFO",
        "outputId": "2f3ecb2d-19ce-45b6-99d0-a6ff1e3a0eca"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       workclass  education  marital-status  occupation  relationship  race  \\\n",
              "9529           8         12               3          12             1     5   \n",
              "11951          5         12               5          11             2     5   \n",
              "27016          5         16               3           5             1     5   \n",
              "\n",
              "       sex  native-country  education-num_na       age    fnlwgt  \\\n",
              "9529     2              40                 1  2.376330  0.199330   \n",
              "11951    1              40                 1 -1.435648 -0.840233   \n",
              "27016    2              40                 1 -1.215726 -0.822807   \n",
              "\n",
              "       education-num  capital-gain  capital-loss  hours-per-week  \n",
              "9529       -0.417679     -0.147037     -0.216957       -1.016139  \n",
              "11951      -0.417679     -0.147037     -0.216957       -0.040407  \n",
              "27016      -0.026097     -0.147037     -0.216957        0.366147  "
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            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train = to.train.xs, to.train.ys.values.ravel()\n",
        "X_test, y_test = to.valid.xs, to.valid.ys.values.ravel()"
      ],
      "metadata": {
        "id": "5iJOwR3e6ZN7"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "rAQnKAkT6h9T"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "3xvFaCo96q5f"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gr4MlMDv64U9",
        "outputId": "ebe483db-9e3b-43fe-dc73-1b515b434ad6"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3koIKHo7AEs",
        "outputId": "2f3166b3-8226-43be-e64f-3e67c6b1bbcc"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8556511056511057"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    }
  ]
}