MachineLearning_customer_prediction_data_preprocessing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MachineLearning_customer_prediction_data_preprocessing.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "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": "hLAl9oPYqQCm"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RopL7tUZSQkT"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unH8YkpAqcj1"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vuCC3Ojq-Ya",
        "outputId": "bd37711f-0211-4c06-f8dc-f52803ff6a2c"
      },
      "source": [
        "print(x)\n"
      ],
      "execution_count": 6,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRRMnLfOq-qL",
        "outputId": "3c2cc77f-54cb-4a3d-a7cd-00a99c8a0385"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 5,
      "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": "E1qQlT-grSkM"
      },
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "sc = SimpleImputer(missing_values = np.nan, strategy = 'mean')\n",
        "x[:, 1:] = sc.fit_transform(x[:,1:])"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SB3T_TG8tAOH",
        "outputId": "a0b2de61-8b0e-4505-d497-a7112322848e"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 10,
      "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 (country column)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIITYfrktM9u"
      },
      "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": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3BOyiFC-vgIL",
        "outputId": "f7664429-71ea-4dde-c5bc-de5db747074f"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 16,
      "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 (purchase column)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cg-n17CRvwNJ"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0NAMCmjw7hT",
        "outputId": "0558b00b-cf98-418c-e7c8-94a2fecf0df2"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 20,
      "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": "J3xK0TKvw_xG"
      },
      "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)\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXK_dN7dyIQe",
        "outputId": "94f949b8-ae88-4531-bad0-87b2b59cf17d"
      },
      "source": [
        "print(x_train)"
      ],
      "execution_count": 22,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CcuJ4u51yLrV",
        "outputId": "762a3f4e-c8ef-4e74-c608-fc8f7c5304c0"
      },
      "source": [
        "print(y_train)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 0 0 1 1 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3-_EACEySIz",
        "outputId": "533e8927-2771-42e2-a862-d87e7df3d56b"
      },
      "source": [
        "print(x_test)"
      ],
      "execution_count": 24,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXRnL6buyV6U",
        "outputId": "cadc91ea-b68c-4ce6-c804-44c59c4cfbbf"
      },
      "source": [
        "print(y_test)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpGqbS4TqkIR"
      },
      "source": [
        "## Feature Scaling (leave the dummy variables)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSsjjg_Eye3N"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "ss = StandardScaler()\n",
        "x_train[:, 3:] = ss.fit_transform(x_train[:,3:])\n",
        "x_test[:, 3:] = ss.transform(x_test[:,3:])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kLCR3jkl0D24",
        "outputId": "b658c206-6fc3-4459-b916-f7c2452d7869"
      },
      "source": [
        "print(x_train)"
      ],
      "execution_count": 29,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdEj4y-S0HZ4",
        "outputId": "24e421f7-145d-4310-dfb2-3a6ebe1b1069"
      },
      "source": [
        "print(x_test)"
      ],
      "execution_count": 30,
      "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"
        }
      ]
    }
  ]
}