data_preprocessing_tools.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jayavathsan/DeepLearning/blob/main/DL/data_preprocessing_tools.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "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": 2,
      "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": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCsz2yCebe1R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "613c2e66-8309-489a-c600-4e116d27c355"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYrOQ43XcJR3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46c7a075-eb5d-48b6-83bb-c98511f61a13"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']\n"
          ]
        }
      ]
    },
    {
      "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": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3UgLdMS_bjq_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca843929-0836-43ca-fab2-e56a349cf8bd"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "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": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7QspewyeBfx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3fccbc4e-56c7-4af2-ddac-3f0aa4b540e8"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "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": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FyhY8-gPpFCa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1882175f-937e-4bc1-fd8d-dd9bb4860155"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 0 1 1 0 1 0 1]\n"
          ]
        }
      ]
    },
    {
      "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": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuwQhFdKrYTM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9925598-79b0-4cf8-9843-54740d54637b"
      },
      "source": [
        "print(x_train)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUrX_Tvcrbi4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4742890b-958f-458a-87bd-00d3f2bd318d"
      },
      "source": [
        "print(x_test)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.0 1.0 0.0 30.0 54000.0]\n",
            " [1.0 0.0 0.0 37.0 67000.0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSMHiIsWreQY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "398ded96-3649-4683-bd28-a86f049f01de"
      },
      "source": [
        "print(y_train)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 0 1 1 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_tW7H56rgtW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b00d0bf4-087d-4600-d1eb-7af432f7743c"
      },
      "source": [
        "print(y_test)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1]\n"
          ]
        }
      ]
    },
    {
      "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 = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWPET8ZdlMnu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b12755d4-9079-4fb2-920c-ef81ba319471"
      },
      "source": [
        "print(x_train)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.77459667 -0.57735027  1.29099445 -0.19159184 -1.07812594]\n",
            " [-0.77459667  1.73205081 -0.77459667 -0.01411729 -0.07013168]\n",
            " [ 1.29099445 -0.57735027 -0.77459667  0.56670851  0.63356243]\n",
            " [-0.77459667 -0.57735027  1.29099445 -0.30453019 -0.30786617]\n",
            " [-0.77459667 -0.57735027  1.29099445 -1.90180114 -1.42046362]\n",
            " [ 1.29099445 -0.57735027 -0.77459667  1.14753431  1.23265336]\n",
            " [-0.77459667  1.73205081 -0.77459667  1.43794721  1.57499104]\n",
            " [ 1.29099445 -0.57735027 -0.77459667 -0.74014954 -0.56461943]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTXykB_QlRjE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "473bbd7b-9d37-4e9a-d4cf-22fb2090033e"
      },
      "source": [
        "print(x_test)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.77459667  1.73205081 -0.77459667 -1.46618179 -0.9069571 ]\n",
            " [ 1.29099445 -0.57735027 -0.77459667 -0.44973664  0.20564034]]\n"
          ]
        }
      ]
    }
  ]
}