Day_37_Imputing_missing_categorical_values.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOGIc51cW46zYPkzlN70xDU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Manish-k723/100-days-of-ML/blob/main/Day_37_Imputing_missing_categorical_values.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "KTNeT4wV55sV"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer"
      ],
      "metadata": {
        "id": "_XHNWZKR6EaW"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day37-handling-missing-categorical-data/train.csv', usecols =['GarageQual','FireplaceQu','SalePrice'])"
      ],
      "metadata": {
        "id": "4Vbl_bTX6Ng-"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().mean()*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LlX3Zpw8H4I",
        "outputId": "b6bf2abc-6a8a-428f-d5fe-ae65c3bdbebe"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FireplaceQu    47.260274\n",
              "GarageQual      5.547945\n",
              "SalePrice       0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhZMERUf8MyQ",
        "outputId": "84d10bc5-930e-413a-9406-e9e35ef22750"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1460 entries, 0 to 1459\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   FireplaceQu  770 non-null    object\n",
            " 1   GarageQual   1379 non-null   object\n",
            " 2   SalePrice    1460 non-null   int64 \n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 34.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['GarageQual'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "lx2Og2Db8Nv7",
        "outputId": "e86c1b5a-6ed0-4a79-f469-9d856181c4a6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['GarageQual'].mode()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYVSH3Ui8Q9Z",
        "outputId": "60f1810e-3a51-4fca-e9f1-3108d3aac6c8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    TA\n",
              "Name: GarageQual, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "62xXZ4YQ8f2D"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "\n",
        "df[df['GarageQual']=='TA']['SalePrice'].plot(kind='kde', ax=ax)\n",
        "\n",
        "df[df['GarageQual'].isnull()]['SalePrice'].plot(kind='kde', ax=ax, color='red')\n",
        "\n",
        "lines, labels = ax.get_legend_handles_labels()\n",
        "labels = ['Houses with TA', 'Houses with NA']\n",
        "ax.legend(lines, labels, loc='best')\n",
        "\n",
        "plt.title('GarageQual')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "3M8_T62z8TY-",
        "outputId": "0e13b32b-7074-4fc1-af9d-4aeb6f16a407"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GarageQual')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(df.drop(columns=['SalePrice']),df['SalePrice'],test_size=0.2)\n"
      ],
      "metadata": {
        "id": "duho2Udj8Wd5"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imputer = SimpleImputer(strategy='most_frequent')\n",
        "X_train = imputer.fit_transform(X_train)\n",
        "X_test = imputer.transform(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6PS-7WY8iv6",
        "outputId": "46548665-aded-4b9b-92c4-27acf103ff5a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but SimpleImputer was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imputer.statistics_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHrcvcM78prI",
        "outputId": "1be5603f-2ee0-46ae-a123-30466fd0fa58"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Gd', 'TA'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J0bjeV8u8q29"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}