Hold_Out_+_Stratify.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/kopiladevkota/Python/blob/main/Hold_Out_%2B_Stratify.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qMQu8Mgg-P9A"
      },
      "source": [
        "# Hold_out Validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "eWchGPfa9xW9"
      },
      "outputs": [],
      "source": [
        "#importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHKgifnpjyvh"
      },
      "source": [
        "### Importing the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "NB5xYyHg9xXA"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv('data_cleaned.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
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        "outputId": "03837000-cb46-4ae6-b789-d5bf38248e2b",
        "scrolled": true
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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>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>SibSp_0</th>\n",
              "      <th>SibSp_1</th>\n",
              "      <th>...</th>\n",
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              "      <th>Parch_1</th>\n",
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              "      <th>Parch_4</th>\n",
              "      <th>Parch_5</th>\n",
              "      <th>Parch_6</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>53.1000</td>\n",
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            "text/plain": [
              "   Survived   Age     Fare  Pclass_1  Pclass_2  Pclass_3  Sex_female  \\\n",
              "0         0  22.0   7.2500         0         0         1           0   \n",
              "1         1  38.0  71.2833         1         0         0           1   \n",
              "2         1  26.0   7.9250         0         0         1           1   \n",
              "3         1  35.0  53.1000         1         0         0           1   \n",
              "4         0  35.0   8.0500         0         0         1           0   \n",
              "\n",
              "   Sex_male  SibSp_0  SibSp_1     ...      Parch_0  Parch_1  Parch_2  Parch_3  \\\n",
              "0         1        0        1     ...            1        0        0        0   \n",
              "1         0        0        1     ...            1        0        0        0   \n",
              "2         0        1        0     ...            1        0        0        0   \n",
              "3         0        0        1     ...            1        0        0        0   \n",
              "4         1        1        0     ...            1        0        0        0   \n",
              "\n",
              "   Parch_4  Parch_5  Parch_6  Embarked_C  Embarked_Q  Embarked_S  \n",
              "0        0        0        0           0           0           1  \n",
              "1        0        0        0           1           0           0  \n",
              "2        0        0        0           0           0           1  \n",
              "3        0        0        0           0           0           1  \n",
              "4        0        0        0           0           0           1  \n",
              "\n",
              "[5 rows x 25 columns]"
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      "source": [
        "data.head()"
      ]
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "1UYXqY_P6YQz",
        "outputId": "1771157b-cce5-44d4-a91e-f389c9bb320a"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "Survived      0\n",
              "Age           0\n",
              "Fare          0\n",
              "Pclass_1      0\n",
              "Pclass_2      0\n",
              "Pclass_3      0\n",
              "Sex_female    0\n",
              "Sex_male      0\n",
              "SibSp_0       0\n",
              "SibSp_1       0\n",
              "SibSp_2       0\n",
              "SibSp_3       0\n",
              "SibSp_4       0\n",
              "SibSp_5       0\n",
              "SibSp_8       0\n",
              "Parch_0       0\n",
              "Parch_1       0\n",
              "Parch_2       0\n",
              "Parch_3       0\n",
              "Parch_4       0\n",
              "Parch_5       0\n",
              "Parch_6       0\n",
              "Embarked_C    0\n",
              "Embarked_Q    0\n",
              "Embarked_S    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjA78YQGQt_j"
      },
      "source": [
        "## Splitting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ntmyWlUIQt_r"
      },
      "source": [
        "### Separating Dependent and Independent Variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "rMIXkyRzQt_r"
      },
      "outputs": [],
      "source": [
        "# For train set\n",
        "data_x = data.drop(['Survived'], axis=1)\n",
        "data_y = data['Survived']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DSzs3pR9TLGf"
      },
      "source": [
        "### Creating Validation and test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "U7WjxkbMyKdi"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split as tts\n",
        "train1_x, test_x , train1_y, test_y = tts( data_x, data_y , test_size = 0.2 , random_state = 50, stratify = data_y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1dHVS25yKdj"
      },
      "source": [
        "<img src=\"Image 1.png\"  style=\"width:600px;\" align=\"left\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "3uqmQjw-WBOa",
        "outputId": "84f9e8e5-1664-414e-ee88-65c9cf04f3dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "training data     (569, 24) (569,)\n",
            "validation data   (143, 24) (143,)\n",
            "test data         (179, 24) (179,)\n"
          ]
        }
      ],
      "source": [
        "train_x, val_x, train_y, val_y = tts(train1_x, train1_y, test_size = 0.2 , random_state = 51, stratify = train1_y)\n",
        "\n",
        "print('training data    ',train_x.shape,train_y.shape)\n",
        "print('validation data  ',val_x.shape,val_y.shape)\n",
        "print('test data        ',test_x.shape,test_y.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chID0aP7yKdj"
      },
      "source": [
        "<img src=\"Image 1a.png\"  style=\"width:600px;\" align=\"left\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1yJmpVyeFLe"
      },
      "source": [
        "### Checking Distribution of target class in train, test and validation set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "GvwJdbuxeLGP",
        "outputId": "7e98843a-0fc9-4fa1-c59b-fdf1fb85cff3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    0.616872\n",
              "1    0.383128\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_y.value_counts()/len(train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "pBvBfSxBecrc",
        "outputId": "9ba7f28c-2cfd-477d-aca1-f8da4ec4ff52"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    0.615385\n",
              "1    0.384615\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "val_y.value_counts()/len(val_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "Yy8PdnDLeK9g",
        "outputId": "9690dbf6-c5ae-446b-e4e0-afbd6a61490e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    0.614525\n",
              "1    0.385475\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_y.value_counts()/len(test_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "Dv5PmFhSyKdk"
      },
      "outputs": [],
      "source": []
    }
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