data_preprocessing_tools.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "data_preprocessing_tools.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37puETfgRzzg"
      },
      "source": [
        "# Data Preprocessing Tools"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EoRP98MpR-qj"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-qiINBQSK2g"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RopL7tUZSQkT"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwEPNDWySTKm"
      },
      "source": [
        "dataset = pd.read_csv('Data.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCsz2yCebe1R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        },
        "outputId": "1e4cc568-4e51-4b38-9d46-4aa3f15204be"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['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]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYrOQ43XcJR3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0873b2a-3b08-4bab-ef0d-15b88858ca44"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nhfKXNxlSabC"
      },
      "source": [
        "## Taking care of missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c93k7ipkSexq"
      },
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "imputer.fit(X[:, 1:3])\n",
        "X[:, 1:3] = imputer.transform(X[:, 1:3])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3UgLdMS_bjq_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        },
        "outputId": "254af4e0-681e-47f5-aaa7-b9c6f43258e9"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['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]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CriG6VzVSjcK"
      },
      "source": [
        "## Encoding categorical data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhSpdQWeSsFh"
      },
      "source": [
        "### Encoding the Independent Variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hwuVddlSwVi"
      },
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough')\n",
        "X = np.array(ct.fit_transform(X))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7QspewyeBfx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        },
        "outputId": "5b35feef-7fe2-46ef-ce70-80495f94f4ed"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.0 0.0 0.0 44.0 72000.0]\n",
            " [0.0 0.0 1.0 27.0 48000.0]\n",
            " [0.0 1.0 0.0 30.0 54000.0]\n",
            " [0.0 0.0 1.0 38.0 61000.0]\n",
            " [0.0 1.0 0.0 40.0 63777.77777777778]\n",
            " [1.0 0.0 0.0 35.0 58000.0]\n",
            " [0.0 0.0 1.0 38.77777777777778 52000.0]\n",
            " [1.0 0.0 0.0 48.0 79000.0]\n",
            " [0.0 1.0 0.0 50.0 83000.0]\n",
            " [1.0 0.0 0.0 37.0 67000.0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXh8oVSITIc6"
      },
      "source": [
        "### Encoding the Dependent Variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgHCShVyTOYY"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FyhY8-gPpFCa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7f76ef29-5423-4c3e-cf69-45fbc366a997"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 0 0 1 1 0 1 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qb_vcgm3qZKW"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXgA6CzlqbCl"
      },
      "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 = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuwQhFdKrYTM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "outputId": "de1e527f-c229-4daf-e7c5-ea9d2485148d"
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.0 0.0 1.0 38.77777777777778 52000.0]\n",
            " [0.0 1.0 0.0 40.0 63777.77777777778]\n",
            " [1.0 0.0 0.0 44.0 72000.0]\n",
            " [0.0 0.0 1.0 38.0 61000.0]\n",
            " [0.0 0.0 1.0 27.0 48000.0]\n",
            " [1.0 0.0 0.0 48.0 79000.0]\n",
            " [0.0 1.0 0.0 50.0 83000.0]\n",
            " [1.0 0.0 0.0 35.0 58000.0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUrX_Tvcrbi4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9a041a9b-2642-4828-fa2f-a431d7d77631"
      },
      "source": [
        "print(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.0 1.0 0.0 30.0 54000.0]\n",
            " [1.0 0.0 0.0 37.0 67000.0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSMHiIsWreQY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5afe91e0-9244-4bf5-ec1b-e3e092b85c08"
      },
      "source": [
        "print(y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 0 0 1 1 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_tW7H56rgtW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2a93f141-2a99-4a69-eec5-c82a3bb8d36b"
      },
      "source": [
        "print(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpGqbS4TqkIR"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AxjSUXFQqo-3"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train[:, 3:] = sc.fit_transform(X_train[:, 3:])\n",
        "X_test[:, 3:] = sc.transform(X_test[:, 3:])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWPET8ZdlMnu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "outputId": "dea86927-5124-4e2a-e974-2804df9a913c"
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.0 0.0 1.0 -0.19159184384578545 -1.0781259408412425]\n",
            " [0.0 1.0 0.0 -0.014117293757057777 -0.07013167641635372]\n",
            " [1.0 0.0 0.0 0.566708506533324 0.633562432710455]\n",
            " [0.0 0.0 1.0 -0.30453019390224867 -0.30786617274297867]\n",
            " [0.0 0.0 1.0 -1.9018011447007988 -1.420463615551582]\n",
            " [1.0 0.0 0.0 1.1475343068237058 1.232653363453549]\n",
            " [0.0 1.0 0.0 1.4379472069688968 1.5749910381638885]\n",
            " [1.0 0.0 0.0 -0.7401495441200351 -0.5646194287757332]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTXykB_QlRjE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b68f0cfc-d07c-48cb-80d0-6800028c41f9"
      },
      "source": [
        "print(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.0 1.0 0.0 -1.4661817944830124 -0.9069571034860727]\n",
            " [1.0 0.0 0.0 -0.44973664397484414 0.2056403393225306]]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}