data_preprocessing_tools.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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  "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",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "xuOh0EW9WVzg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RopL7tUZSQkT"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('/content/Data.csv')\n",
        "x = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "jl0cU3IMaAry"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGIrCDLBii1Z",
        "outputId": "ea4daf5c-4b50-42eb-bd9b-58cbdf71d79f"
      },
      "execution_count": null,
      "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",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSdoJbE2im7s",
        "outputId": "f9b34c2e-dd56-4e09-bf1f-dd54126e943e"
      },
      "execution_count": null,
      "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",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy='mean') #nesne oluşturduk\n",
        "imputer.fit(x[:, 1:3]) #fit metodu x matrisindeki ilgili sütunlarla bağlantı kurar ve sütunların ort. hesabını yapar\n",
        "x[:, 1:3] = imputer.transform(x[:, 1:3]) #transform metodu olmayan değerleri fit metodunun hesapladığı ort. ile degistirir"
      ],
      "metadata": {
        "id": "oVIitUxm0Xnk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMmCm3Bv4vJk",
        "outputId": "93e33c57-5b47-4e4b-d4a2-9b1383dfaaeb"
      },
      "execution_count": null,
      "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",
      "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))"
      ],
      "metadata": {
        "id": "Oa1Gflov772R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8FEI2yuAByq",
        "outputId": "600bd667-eb33-4c4b-cdec-905a65683a35"
      },
      "execution_count": null,
      "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",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "Gux_hUCQCbqi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gvzOQdBQCpKz",
        "outputId": "3cfebe9d-dde0-49c5-f3d9-7a7f202b49d2"
      },
      "execution_count": null,
      "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",
      "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)"
      ],
      "metadata": {
        "id": "Pqa4aBHOA6kR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03vPkBC5Cafz",
        "outputId": "e2a65902-d646-4cb1-d029-e7b255915b15"
      },
      "execution_count": null,
      "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",
      "source": [
        "print(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhmI0fqNCayp",
        "outputId": "df651475-a6cc-4943-8ea1-c9b0e96b7f34"
      },
      "execution_count": null,
      "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",
      "source": [
        "print(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MzTKu17dCbIX",
        "outputId": "31627b0d-6f23-4163-976a-673309945d57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 0 1 1 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqn_uvf2CbbZ",
        "outputId": "a75ec31d-1bff-41ba-fd26-5ece20b7b16a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpGqbS4TqkIR"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "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:])"
      ],
      "metadata": {
        "id": "4LFdgMkAAnCL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Kn7__swJ8om",
        "outputId": "48113602-d18d-4499-fdd7-cbcb038cfa4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-p7fNYuiJ9AD",
        "outputId": "673b9692-b923-4421-928c-e0c1e732404f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.0 1.0 0.0 -1.4661817944830124 -0.9069571034860727]\n",
            " [1.0 0.0 0.0 -0.44973664397484414 0.2056403393225306]]\n"
          ]
        }
      ]
    }
  ]
}