Ordinal_Encoding.ipynb

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      "metadata": {
        "id": "dfiXj9e9Kgpz"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXq4jH3RKq-J",
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      "execution_count": null,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/100-days-of-machine-learning/day26-ordinal-encoding/customer.csv')"
      ],
      "metadata": {
        "id": "4y_IT5RkKt95"
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      "outputs": []
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      "execution_count": null,
      "metadata": {
        "id": "dCwIeOqyKgp2"
      },
      "outputs": [],
      "source": [
        "# df = pd.read_csv('customer.csv')"
      ]
    },
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            "text/plain": [
              "    age  gender   review education purchased\n",
              "19   97    Male     Poor        PG       Yes\n",
              "22   18  Female     Poor        PG       Yes\n",
              "26   53  Female     Poor        PG        No\n",
              "32   92    Male  Average        UG       Yes\n",
              "11   74    Male     Good        UG       Yes"
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              "      <th>purchased</th>\n",
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              "      <td>Male</td>\n",
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              "      <td>Yes</td>\n",
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              "      <td>Yes</td>\n",
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              "      <td>Yes</td>\n",
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        "df.sample(5)"
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      "execution_count": null,
      "metadata": {
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        "df = df.iloc[:,2:]"
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          "output_type": "execute_result",
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            "text/plain": [
              "    review education purchased\n",
              "0  Average    School        No\n",
              "1     Poor        UG        No\n",
              "2     Good        PG        No\n",
              "3     Good        PG        No\n",
              "4  Average        UG        No"
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              "      <th>2</th>\n",
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              "      <th>3</th>\n",
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              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Average</td>\n",
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              "      <td>No</td>\n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "cxTeIw6NL4c9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(df.iloc[:,0:2],df.iloc[:,-1],test_size=0.2)"
      ],
      "metadata": {
        "id": "4fGkMmRtL_Sn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PQBSG5jLKgp6"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OrdinalEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pjgm1Jp3Kgp7"
      },
      "outputs": [],
      "source": [
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# OrdinalEncoding for Single column\n",
        "oe1 = OrdinalEncoder(categories=[['Poor','Average','Good']])\n",
        "oe1.fit_transform(df[[\"review\"]])"
      ],
      "metadata": {
        "id": "sOmcE3onEJnu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zRQQX4t4Kgp8"
      },
      "outputs": [],
      "source": [
        "oe = OrdinalEncoder(categories=[['Poor','Average','Good'],['School','UG','PG']])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "yCvfmJ-dzQXb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7W7oe8qQKgp8",
        "outputId": "8737fb74-7539-4ac3-fa09-7fc15fe0c370"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrdinalEncoder(categories=[['Poor', 'Average', 'Good'], ['School', 'UG', 'PG']])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "oe.fit(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jVdu9MKRKgp9"
      },
      "outputs": [],
      "source": [
        "x_train = oe.transform(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LmKpCr0JKgp9"
      },
      "outputs": [],
      "source": [
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUroeujkKgp-",
        "outputId": "8b5cfa38-f893-48e0-bd25-31da7f2338aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array(['Poor', 'Average', 'Good'], dtype=object),\n",
              " array(['School', 'UG', 'PG'], dtype=object)]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "oe.categories_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y7BcV4h_Kgp-"
      },
      "outputs": [],
      "source": [
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zlU_w0PeKgp_"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0jWKDqRyKgp_"
      },
      "outputs": [],
      "source": [
        "le = LabelEncoder()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "UWsGCwZKKgqA",
        "outputId": "be59b83d-35ce-4751-d9f5-62164d79f023"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelEncoder()"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "le.fit(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0lBtTtJKgqA",
        "outputId": "edb02593-f7d6-458f-fdc8-43a6c23c70fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'Yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "le.classes_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GnaHKu6XKgqB"
      },
      "outputs": [],
      "source": [
        "y_train = le.transform(y_train)\n",
        "y_test = le.transform(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7nNGlPCFKgqB",
        "outputId": "ba2963a5-878c-4425-e12b-9d2adcafdae7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
              "       1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xY-G46OaKgqC"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.3"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}