data_preprocessing.ipynb

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      "name": "data preprocessing.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/wizardlk/data-preprocessing/blob/master/data_preprocessing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmapU52WN0jr",
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      },
      "source": [
        "# Wizard LK\n",
        "\n",
        "Data Preprocessing Template\n",
        "\n",
        "@author: [gayan](https://www.linkedin.com/in/kanushka-gayan/)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xX_PCRMPQZN3",
        "colab_type": "text"
      },
      "source": [
        "###Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qGIzRaSOHcW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEb5XKebOKDf",
        "colab_type": "text"
      },
      "source": [
        "###Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uwY7aTkfUbMB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/wizardlk/data-preprocessing.git dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRTVvG1EOMPG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('dataset/data.csv')\n",
        "X = dataset.iloc[:,:-1].values\n",
        "y = dataset.iloc[:,3].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bKcDH-BO1XO",
        "colab_type": "code",
        "outputId": "5c269ed3-df68-4ace-aa00-0b85215707e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "X"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['France', 44.0, 72000.0],\n",
              "       ['Spain', 27.0, 48000.0],\n",
              "       ['Germany', 30.0, 54000.0],\n",
              "       ['Spain', 38.0, 61000.0],\n",
              "       ['Germany', 40.0, nan],\n",
              "       ['France', 35.0, 58000.0],\n",
              "       ['Spain', nan, 52000.0],\n",
              "       ['France', 48.0, 79000.0],\n",
              "       ['Germany', 50.0, 83000.0],\n",
              "       ['France', 37.0, 67000.0]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYP2gJRXOOHH",
        "colab_type": "text"
      },
      "source": [
        "###Taking care of missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ss-ThogSOPt_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy=\"mean\")\n",
        "imputer = imputer.fit(X[:, 1:3])\n",
        "X[:, 1:3] = imputer.transform(X[:, 1:3])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4pzz9Yp5O4NO",
        "colab_type": "code",
        "outputId": "fd4f01d0-bdd0-413b-ed60-cfd1edc72b53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "X"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['France', 44.0, 72000.0],\n",
              "       ['Spain', 27.0, 48000.0],\n",
              "       ['Germany', 30.0, 54000.0],\n",
              "       ['Spain', 38.0, 61000.0],\n",
              "       ['Germany', 40.0, 63777.77777777778],\n",
              "       ['France', 35.0, 58000.0],\n",
              "       ['Spain', 38.77777777777778, 52000.0],\n",
              "       ['France', 48.0, 79000.0],\n",
              "       ['Germany', 50.0, 83000.0],\n",
              "       ['France', 37.0, 67000.0]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtTYm3F7OSbX",
        "colab_type": "text"
      },
      "source": [
        "###Encoding categorical data\n",
        "\n",
        "\n",
        "> Encoding the Independent Variable\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Idzv9AjROTHm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "labelencoder_X = LabelEncoder()\n",
        "X[:, 0] = labelencoder_X.fit_transform(X[:, 0])\n",
        "onehotencoder = OneHotEncoder(categorical_features = [0])\n",
        "X = onehotencoder.fit_transform(X).toarray()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJ24Hn0UPHSA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VK_FarqROh1m",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "> Encoding the Dependent Variable\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vuPhpVY0OlAQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labelencoder_y = LabelEncoder()\n",
        "y = labelencoder_y.fit_transform(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35DxR_u0PLR_",
        "colab_type": "code",
        "outputId": "68b62d0b-7ab4-4b48-ee1d-f8e80c2212c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 0, 1, 1, 0, 1, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLl1uFLOOsKP",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "###Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iG5o1evKOwef",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q22BVVwVPOZA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}