Feature_Engineering_2.ipynb

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      "name": "Feature_Engineering_2.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Chandradithya8/Feature_Engineering/blob/main/Feature_Engineering_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vZ0tiOuumNue"
      },
      "source": [
        "# HANDLE CATEGORICAL FEATURES"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxmuIYk06L4C"
      },
      "source": [
        "Nominal Encoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYlnwbPPmVPY"
      },
      "source": [
        "One Hot Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5IKtas6mTyz",
        "outputId": "49486032-5f41-452f-e635-db6ede44ce77",
        "colab": {
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        }
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/titanic.csv.txt',usecols=['Sex'])\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sex</th>\n",
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            "text/plain": [
              "      Sex\n",
              "0    male\n",
              "1  female\n",
              "2  female\n",
              "3  female\n",
              "4    male"
            ]
          },
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      },
      "source": [
        "a=pd.get_dummies(df.Sex)\n",
        "a.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <th>female</th>\n",
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            "text/plain": [
              "   female  male\n",
              "0       0     1\n",
              "1       1     0\n",
              "2       1     0\n",
              "3       1     0\n",
              "4       0     1"
            ]
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          "metadata": {
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        }
      },
      "source": [
        "b=pd.get_dummies(df.Sex,drop_first=True)\n",
        "b.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>male</th>\n",
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            "text/plain": [
              "   male\n",
              "0     1\n",
              "1     0\n",
              "2     0\n",
              "3     0\n",
              "4     1"
            ]
          },
          "metadata": {
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TnrxQUAgwvlZ"
      },
      "source": [
        "One hot encoding with many features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldVqxXpbvApP",
        "outputId": "3ea6958b-5e36-4f91-a7be-c4626416cbd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv(\"/content/mercedesbenz.csv.txt\",usecols=['X0','X1','X2','X3','X4','X5','X6'])\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
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              "      <td>y</td>\n",
              "      <td>l</td>\n",
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              "      <th>2</th>\n",
              "      <td>az</td>\n",
              "      <td>w</td>\n",
              "      <td>n</td>\n",
              "      <td>c</td>\n",
              "      <td>d</td>\n",
              "      <td>x</td>\n",
              "      <td>j</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>az</td>\n",
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            "text/plain": [
              "   X0 X1  X2 X3 X4 X5 X6\n",
              "0   k  v  at  a  d  u  j\n",
              "1   k  t  av  e  d  y  l\n",
              "2  az  w   n  c  d  x  j\n",
              "3  az  t   n  f  d  x  l\n",
              "4  az  v   n  f  d  h  d"
            ]
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          "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        }
      },
      "source": [
        "df['X0'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "z     360\n",
              "ak    349\n",
              "y     324\n",
              "ay    313\n",
              "t     306\n",
              "x     300\n",
              "o     269\n",
              "f     227\n",
              "n     195\n",
              "w     182\n",
              "j     181\n",
              "az    175\n",
              "aj    151\n",
              "s     106\n",
              "ap    103\n",
              "h      75\n",
              "d      73\n",
              "al     67\n",
              "v      36\n",
              "af     35\n",
              "m      34\n",
              "ai     34\n",
              "e      32\n",
              "ba     27\n",
              "at     25\n",
              "a      21\n",
              "ax     19\n",
              "i      18\n",
              "aq     18\n",
              "am     18\n",
              "u      17\n",
              "aw     16\n",
              "l      16\n",
              "ad     14\n",
              "b      11\n",
              "au     11\n",
              "k      11\n",
              "as     10\n",
              "r      10\n",
              "bc      6\n",
              "ao      4\n",
              "c       3\n",
              "q       2\n",
              "aa      2\n",
              "g       1\n",
              "ac      1\n",
              "ab      1\n",
              "Name: X0, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtLdRNfpx3L8",
        "outputId": "83add45d-d701-4795-f38f-a2ba9cb97d4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "for i in df.columns:\n",
        "  print(len(df[i].unique()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "47\n",
            "27\n",
            "44\n",
            "7\n",
            "4\n",
            "29\n",
            "12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pR4aixv57KPh",
        "outputId": "b012a525-db43-42b0-e39a-b5925a8b193a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "df['X0'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['k', 'az', 't', 'al', 'o', 'w', 'j', 'h', 's', 'n', 'ay', 'f', 'x',\n",
              "       'y', 'aj', 'ak', 'am', 'z', 'q', 'at', 'ap', 'v', 'af', 'a', 'e',\n",
              "       'ai', 'd', 'aq', 'c', 'aa', 'ba', 'as', 'i', 'r', 'b', 'ax', 'bc',\n",
              "       'u', 'ad', 'au', 'm', 'l', 'aw', 'ao', 'ac', 'g', 'ab'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
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      "metadata": {
        "id": "e9k0FjBu0tOV",
        "outputId": "372f0a6f-879c-4f53-889f-04f400e8720f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "labels=df['X0'].value_counts().head(10).index\n",
        "labels=list(labels)\n",
        "labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['z', 'ak', 'y', 'ay', 't', 'x', 'o', 'f', 'n', 'w']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "le6ga1H9yaEF"
      },
      "source": [
        "import numpy as np\n",
        "def ohe(df,categories,labels):\n",
        "  for i in labels:\n",
        "    df[categories+'_'+i]=np.where(df[categories]==i,1,0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5gSmyBD4hR_",
        "outputId": "051a38a6-6822-4c6a-9991-9fd062796f62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "ohe(df,'X0',labels)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>az</td>\n",
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              "      <td>f</td>\n",
              "      <td>d</td>\n",
              "      <td>x</td>\n",
              "      <td>l</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",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>az</td>\n",
              "      <td>v</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>d</td>\n",
              "      <td>h</td>\n",
              "      <td>d</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",
              "</div>"
            ],
            "text/plain": [
              "   X0 X1  X2 X3 X4 X5 X6  X0_z  ...  X0_y  X0_ay  X0_t  X0_x  X0_o  X0_f  X0_n  X0_w\n",
              "0   k  v  at  a  d  u  j     0  ...     0      0     0     0     0     0     0     0\n",
              "1   k  t  av  e  d  y  l     0  ...     0      0     0     0     0     0     0     0\n",
              "2  az  w   n  c  d  x  j     0  ...     0      0     0     0     0     0     0     0\n",
              "3  az  t   n  f  d  x  l     0  ...     0      0     0     0     0     0     0     0\n",
              "4  az  v   n  f  d  h  d     0  ...     0      0     0     0     0     0     0     0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0KTnP9MFbFB5"
      },
      "source": [
        "#def fn(df,categories):\n",
        " # for i in categories:\n",
        "  #  labels=df[i].value_counts().head(10).index\n",
        "   # for j in labels:\n",
        "    #  df[categories+'_'+j]=np.where(df[i]==j,1,0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpO9ZHkscslA"
      },
      "source": [
        "#for k in df.columns:\n",
        " # fn(df,k)\n",
        "#df.head()  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJ9rG9V16G8_"
      },
      "source": [
        "Ordinal Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZr12Pb04qql",
        "outputId": "f9ceb685-e2a6-4dd5-a423-1ec8c93b3c29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import datetime\n",
        "today_date=datetime.datetime.today()\n",
        "today_date"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2020, 10, 23, 3, 22, 7, 128900)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rgxs8YeR70fK",
        "outputId": "572bb3bc-477a-4718-b386-8ea1b6c7d774",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "today_date-datetime.timedelta(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2020, 10, 22, 3, 22, 7, 128900)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZzHAsbD47_2o",
        "outputId": "73ce1725-88a3-4665-925b-9686410be7da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "today_date-datetime.timedelta(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2020, 10, 20, 3, 22, 7, 128900)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AdfMwH_D8EUw",
        "outputId": "aca04c8d-81e2-4c53-85d1-ca3171e6fce9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "a=[today_date-datetime.timedelta(x) for x in range(0,15)] # date for 15 days\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[datetime.datetime(2020, 10, 23, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 22, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 21, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 20, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 19, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 18, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 17, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 16, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 15, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 14, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 13, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 12, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 11, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 10, 3, 22, 7, 128900),\n",
              " datetime.datetime(2020, 10, 9, 3, 22, 7, 128900)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Pk77BAE8ZIR",
        "outputId": "d375549c-f6e4-4047-aaf8-64aea536001d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.DataFrame(a)\n",
        "df.columns=['Date']\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-10-23 03:22:07.128900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-10-22 03:22:07.128900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-10-21 03:22:07.128900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-10-20 03:22:07.128900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-10-19 03:22:07.128900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Date\n",
              "0 2020-10-23 03:22:07.128900\n",
              "1 2020-10-22 03:22:07.128900\n",
              "2 2020-10-21 03:22:07.128900\n",
              "3 2020-10-20 03:22:07.128900\n",
              "4 2020-10-19 03:22:07.128900"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmpeFEm684-7",
        "outputId": "a8c2731d-ae7f-4d53-eb0f-0b993c8a7d42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df['weekday']=df['Date'].dt.strftime(\"%A\") #week_days\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>weekday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-10-23 03:22:07.128900</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-10-22 03:22:07.128900</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-10-21 03:22:07.128900</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-10-20 03:22:07.128900</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-10-19 03:22:07.128900</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Date    weekday\n",
              "0 2020-10-23 03:22:07.128900     Friday\n",
              "1 2020-10-22 03:22:07.128900   Thursday\n",
              "2 2020-10-21 03:22:07.128900  Wednesday\n",
              "3 2020-10-20 03:22:07.128900    Tuesday\n",
              "4 2020-10-19 03:22:07.128900     Monday"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBp_s9_ZByQ6",
        "outputId": "e824989f-5666-4f40-9b57-3bd8fd7fa5ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "dic={\n",
        "    'Monday':1,\n",
        "     'Tuesday':2,\n",
        "     'Wednesday':3,\n",
        "     'Thursday':4,\n",
        "     'Friday':5,\n",
        "     'Saturday':6,\n",
        "     'Sunday':7\n",
        "}\n",
        "dic"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Friday': 5,\n",
              " 'Monday': 1,\n",
              " 'Saturday': 6,\n",
              " 'Sunday': 7,\n",
              " 'Thursday': 4,\n",
              " 'Tuesday': 2,\n",
              " 'Wednesday': 3}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtZbYoQVDIUr",
        "outputId": "ae76e1a4-ed7b-41f4-ddc1-3f40f9c88e3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df['ordinal']=df['weekday'].map(dic)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>weekday</th>\n",
              "      <th>ordinal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-10-23 03:22:07.128900</td>\n",
              "      <td>Friday</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-10-22 03:22:07.128900</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-10-21 03:22:07.128900</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-10-20 03:22:07.128900</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-10-19 03:22:07.128900</td>\n",
              "      <td>Monday</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Date    weekday  ordinal\n",
              "0 2020-10-23 03:22:07.128900     Friday        5\n",
              "1 2020-10-22 03:22:07.128900   Thursday        4\n",
              "2 2020-10-21 03:22:07.128900  Wednesday        3\n",
              "3 2020-10-20 03:22:07.128900    Tuesday        2\n",
              "4 2020-10-19 03:22:07.128900     Monday        1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-gturPyEGOR"
      },
      "source": [
        "Count or Frequency Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kzNRhWhDTwz",
        "outputId": "ee2c74ea-d09e-42e4-97c6-5e3274419bd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv(\"/content/mercedesbenz.csv.txt\",usecols=['X1','X2'])\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>v</td>\n",
              "      <td>at</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>t</td>\n",
              "      <td>av</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>w</td>\n",
              "      <td>n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>t</td>\n",
              "      <td>n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>v</td>\n",
              "      <td>n</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  X1  X2\n",
              "0  v  at\n",
              "1  t  av\n",
              "2  w   n\n",
              "3  t   n\n",
              "4  v   n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jhhgaLWrPKg",
        "outputId": "81cdd451-9534-4b07-9dc3-aa57190426c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "for i in df.columns:\n",
        "  print(i,':',len(df[i].unique()),\"labels\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X1 : 27 labels\n",
            "X2 : 44 labels\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N82A0emLrkWD",
        "outputId": "0f51e30f-7e41-461a-8683-f5ce6b677e18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "source": [
        "freq_map=df.X2.value_counts()\n",
        "freq_map"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "as    1659\n",
              "ae     496\n",
              "ai     415\n",
              "m      367\n",
              "ak     265\n",
              "r      153\n",
              "n      137\n",
              "s       94\n",
              "f       87\n",
              "e       81\n",
              "aq      63\n",
              "ay      54\n",
              "a       47\n",
              "t       29\n",
              "k       25\n",
              "i       25\n",
              "b       21\n",
              "ao      20\n",
              "ag      19\n",
              "z       19\n",
              "d       18\n",
              "ac      13\n",
              "g       12\n",
              "y       11\n",
              "ap      11\n",
              "x       10\n",
              "aw       8\n",
              "at       6\n",
              "h        6\n",
              "q        5\n",
              "an       5\n",
              "al       5\n",
              "ah       4\n",
              "av       4\n",
              "p        4\n",
              "au       3\n",
              "ar       1\n",
              "am       1\n",
              "af       1\n",
              "c        1\n",
              "l        1\n",
              "o        1\n",
              "aa       1\n",
              "j        1\n",
              "Name: X2, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SR4FItyBr3gC",
        "outputId": "05728a24-86cd-4549-a2a1-0c9f4ce3df90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.X2=df.X2.map(freq_map) #Disadvantage- provides the same weight if the frequencies are same.\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "      <th>X2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>v</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>t</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>w</td>\n",
              "      <td>137</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
              "      <td>v</td>\n",
              "      <td>137</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  X1   X2\n",
              "0  v    6\n",
              "1  t    4\n",
              "2  w  137\n",
              "3  t  137\n",
              "4  v  137"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZxO914XtgYc"
      },
      "source": [
        "Target guided ordinal encoding\n",
        "1. ordering the labels according to the target.\n",
        "2.replace the labels by the joint probability of being 0 or 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHKjO2fGsMJC",
        "outputId": "75546bb9-2da3-4872-9578-9d94390682fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        " import pandas as pd\n",
        " df=pd.read_csv('/content/titanic.csv.txt',usecols=['Cabin','Survived'])\n",
        " df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Cabin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>C85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>C123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived Cabin\n",
              "0         0   NaN\n",
              "1         1   C85\n",
              "2         1   NaN\n",
              "3         1  C123\n",
              "4         0   NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLBnnQw_vebi",
        "outputId": "b408c2ee-5306-48ff-f46b-13b17618f5a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import numpy as np\n",
        "df['Cabin'].fillna('Missing',inplace=True)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Cabin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Missing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>C85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Missing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>C123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Missing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived    Cabin\n",
              "0         0  Missing\n",
              "1         1      C85\n",
              "2         1  Missing\n",
              "3         1     C123\n",
              "4         0  Missing"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-O91yXtXvoMS"
      },
      "source": [
        "df['Cabin']=df['Cabin'].astype(str).str[0] #taking the first letter"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rfpC_Msv5Gz",
        "outputId": "030e7ba4-670e-4548-bd0f-1eb4fec9f852",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Cabin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived Cabin\n",
              "0         0     M\n",
              "1         1     C\n",
              "2         1     M\n",
              "3         1     C\n",
              "4         0     M"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4d5xB9Pnv6lT",
        "outputId": "3547f16d-ca2f-490d-9c89-da1cb614436f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['Cabin'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6hLqm8Qzgvn",
        "outputId": "f55c46a7-2454-43f6-d39d-b9b9a8587ef4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.groupby('Cabin')['Survived'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin\n",
              "A    0.466667\n",
              "B    0.744681\n",
              "C    0.593220\n",
              "D    0.757576\n",
              "E    0.750000\n",
              "F    0.615385\n",
              "G    0.500000\n",
              "M    0.299854\n",
              "T    0.000000\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BdDt0zDz97W",
        "outputId": "19b20cb9-d0eb-4401-a87a-ab7654f5b84f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.groupby('Cabin')['Survived'].mean().sort_values(ascending=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin\n",
              "T    0.000000\n",
              "M    0.299854\n",
              "A    0.466667\n",
              "G    0.500000\n",
              "C    0.593220\n",
              "F    0.615385\n",
              "B    0.744681\n",
              "E    0.750000\n",
              "D    0.757576\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ip-EtA70HrK",
        "outputId": "268e36b6-eadc-4a45-969b-a6a0a9bddda7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ordinal_freq=df.groupby('Cabin')['Survived'].mean().sort_values(ascending=True).index\n",
        "ordinal_freq"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['T', 'M', 'A', 'G', 'C', 'F', 'B', 'E', 'D'], dtype='object', name='Cabin')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZRhkv6B0O-h",
        "outputId": "d0702eba-6031-466c-ffb6-f310ac337503",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "freq={k:i for i,k in enumerate(ordinal_freq,0)}\n",
        "freq"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A': 2, 'B': 6, 'C': 4, 'D': 8, 'E': 7, 'F': 5, 'G': 3, 'M': 1, 'T': 0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2FKEtPl3WkD",
        "outputId": "d639b518-ce6e-4df2-d2b2-852d32f21f9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df['Cabin_label']=df['Cabin'].map(freq)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Cabin_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived Cabin  Cabin_label\n",
              "0         0     M            1\n",
              "1         1     C            4\n",
              "2         1     M            1\n",
              "3         1     C            4\n",
              "4         0     M            1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtGby4Dq4Jyt"
      },
      "source": [
        "Mean Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVSAXL7U30S7",
        "outputId": "e7cb7448-1fd4-41b5-93fb-54f5a9805d80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "mean_ordinal=df.groupby('Cabin')['Survived'].mean().to_dict()\n",
        "mean_ordinal"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A': 0.4666666666666667,\n",
              " 'B': 0.7446808510638298,\n",
              " 'C': 0.5932203389830508,\n",
              " 'D': 0.7575757575757576,\n",
              " 'E': 0.75,\n",
              " 'F': 0.6153846153846154,\n",
              " 'G': 0.5,\n",
              " 'M': 0.29985443959243085,\n",
              " 'T': 0.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8xeztSH4Wor",
        "outputId": "9c931080-0a96-4c95-a5d2-603a3d5b3b18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df['mean_ordinal_encode']=df['Cabin'].map(mean_ordinal)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Cabin_label</th>\n",
              "      <th>mean_ordinal_encode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "      <td>4</td>\n",
              "      <td>0.593220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>0.299854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "      <td>4</td>\n",
              "      <td>0.593220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>0.299854</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived Cabin  Cabin_label  mean_ordinal_encode\n",
              "0         0     M            1             0.299854\n",
              "1         1     C            4             0.593220\n",
              "2         1     M            1             0.299854\n",
              "3         1     C            4             0.593220\n",
              "4         0     M            1             0.299854"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ELVUJkOo7yNl"
      },
      "source": [
        "Probability Ratio Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXOUkSjr7sIU",
        "outputId": "93025558-8bba-4965-f47b-bf5a3207bd64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv(\"/content/titanic.csv.txt\",usecols=['Cabin','Survived'])\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Cabin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>C85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>C123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived Cabin\n",
              "0         0   NaN\n",
              "1         1   C85\n",
              "2         1   NaN\n",
              "3         1  C123\n",
              "4         0   NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rh_Y2Vh-8P-g",
        "outputId": "c5dba015-28c6-49ce-e818-0b6641d9faf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df['Cabin'].fillna('Missing',inplace=True)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Cabin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Missing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>C85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Missing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>C123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Missing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived    Cabin\n",
              "0         0  Missing\n",
              "1         1      C85\n",
              "2         1  Missing\n",
              "3         1     C123\n",
              "4         0  Missing"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zhRsve18eKu",
        "outputId": "24038838-47ef-42fa-f91f-d7c6a8593c82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df['Cabin']=df['Cabin'].astype(str).str[0]\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Cabin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived Cabin\n",
              "0         0     M\n",
              "1         1     C\n",
              "2         1     M\n",
              "3         1     C\n",
              "4         0     M"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c11zKzTI8s7c",
        "outputId": "0201e0d2-7dbf-4675-b7d8-f8e774911063",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['Cabin'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNPp-uxc9PUb",
        "outputId": "b7cc2528-7b46-4861-ecd6-13dcbe6d6dc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "prob=df.groupby('Cabin')['Survived'].mean()\n",
        "prob"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin\n",
              "A    0.466667\n",
              "B    0.744681\n",
              "C    0.593220\n",
              "D    0.757576\n",
              "E    0.750000\n",
              "F    0.615385\n",
              "G    0.500000\n",
              "M    0.299854\n",
              "T    0.000000\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTNn3QIO9m4A",
        "outputId": "2c0f3852-ced1-4e27-b1a8-e39a2b783bfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "prob=pd.DataFrame(prob)\n",
        "prob"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>0.466667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.744681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.593220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.757576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>0.615385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G</th>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>0.299854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Survived\n",
              "Cabin          \n",
              "A      0.466667\n",
              "B      0.744681\n",
              "C      0.593220\n",
              "D      0.757576\n",
              "E      0.750000\n",
              "F      0.615385\n",
              "G      0.500000\n",
              "M      0.299854\n",
              "T      0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjyj6ySY95ug",
        "outputId": "91fef1c3-678a-4b07-b374-d782f52a1e6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "prob['Died']=1-prob['Survived']\n",
        "prob"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Died</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>0.466667</td>\n",
              "      <td>0.533333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.744681</td>\n",
              "      <td>0.255319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.593220</td>\n",
              "      <td>0.406780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.757576</td>\n",
              "      <td>0.242424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>0.615385</td>\n",
              "      <td>0.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>0.299854</td>\n",
              "      <td>0.700146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Survived      Died\n",
              "Cabin                    \n",
              "A      0.466667  0.533333\n",
              "B      0.744681  0.255319\n",
              "C      0.593220  0.406780\n",
              "D      0.757576  0.242424\n",
              "E      0.750000  0.250000\n",
              "F      0.615385  0.384615\n",
              "G      0.500000  0.500000\n",
              "M      0.299854  0.700146\n",
              "T      0.000000  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7tdXMKe-7ks",
        "outputId": "0af6fe76-015c-432b-d447-828ac43aa89f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "prob['Probability']=prob['Survived']/prob['Died']\n",
        "prob.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Died</th>\n",
              "      <th>Probability</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>0.466667</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.875000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.744681</td>\n",
              "      <td>0.255319</td>\n",
              "      <td>2.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.593220</td>\n",
              "      <td>0.406780</td>\n",
              "      <td>1.458333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.757576</td>\n",
              "      <td>0.242424</td>\n",
              "      <td>3.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Survived      Died  Probability\n",
              "Cabin                                 \n",
              "A      0.466667  0.533333     0.875000\n",
              "B      0.744681  0.255319     2.916667\n",
              "C      0.593220  0.406780     1.458333\n",
              "D      0.757576  0.242424     3.125000\n",
              "E      0.750000  0.250000     3.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ni_5hr8-_T5F",
        "outputId": "8ad3138c-ecff-44fc-f9fe-1fb94f0f6510",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "a=prob['Probability'].to_dict()\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A': 0.875,\n",
              " 'B': 2.916666666666666,\n",
              " 'C': 1.4583333333333333,\n",
              " 'D': 3.125,\n",
              " 'E': 3.0,\n",
              " 'F': 1.6000000000000003,\n",
              " 'G': 1.0,\n",
              " 'M': 0.42827442827442824,\n",
              " 'T': 0.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zW-VjXrm_fB9",
        "outputId": "95af2a4f-2add-41ad-c670-d00d470a3338",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df['Cabin_encoded']=df['Cabin'].map(a)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Cabin_encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "      <td>0.428274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "      <td>1.458333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>0.428274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "      <td>1.458333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "      <td>0.428274</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived Cabin  Cabin_encoded\n",
              "0         0     M       0.428274\n",
              "1         1     C       1.458333\n",
              "2         1     M       0.428274\n",
              "3         1     C       1.458333\n",
              "4         0     M       0.428274"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    }
  ]
}