Scaling.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNZJq9Ms97sGnHR478NY/CN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/arssite/MachineLearning/blob/main/Scaling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "U1kPkEsiozcQ"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=np.array([[1,2],[7,8]])"
      ],
      "metadata": {
        "id": "4movE3JzpFno"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FeT86CVcpMxW",
        "outputId": "4a2353ff-60a3-40cd-8cd5-b6696242f1b7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2],\n",
              "       [7, 8]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c=np.array([[5,6],[15,63],[55,67]])\n",
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M654n_XzpNKl",
        "outputId": "a13071fb-ef0f-4fc9-ecff-7950f7149bd7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5,  6],\n",
              "       [15, 63],\n",
              "       [55, 67]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "zbj5PVJQplBn"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=MinMaxScaler(feature_range=(0,1))\n",
        "n=x.fit(c)\n",
        "m=x.transform(c)\n",
        "print(type(n))\n",
        "print(type(m))\n",
        "print(m,n)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MRqNQbsppyi3",
        "outputId": "9dcc1618-b6cf-462e-9968-c753cea19ff2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'sklearn.preprocessing._data.MinMaxScaler'>\n",
            "<class 'numpy.ndarray'>\n",
            "[[0.         0.        ]\n",
            " [0.2        0.93442623]\n",
            " [1.         1.        ]] MinMaxScaler()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "col=MinMaxScaler(feature_range=(0,1))\n",
        "col.fit_transform(c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-dZe0Lbqn64",
        "outputId": "90588595-9788-4867-b452-6bbe5349fd16"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        ],\n",
              "       [0.2       , 0.93442623],\n",
              "       [1.        , 1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ThresholdValue\n",
        "from sklearn.preprocessing import Binarizer\n",
        "x=np.array([[1,2,3],[4,5,6]])\n",
        "sc=Binarizer(threshold=3)\n",
        "sc.fit_transform(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mfRA-_XIrSB2",
        "outputId": "7975ca0f-2ecb-41f9-a8b8-49a3f52ba74b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0],\n",
              "       [1, 1, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#x=(x-x^)/@\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "sc.fit_transform(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4UThe8zspxH",
        "outputId": "eee4899a-970e-42f9-cef1-928f1dc7d88c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1., -1., -1.],\n",
              "       [ 1.,  1.,  1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#x/(underroot(xsquare))\n",
        "from sklearn.preprocessing import Normalizer\n",
        "n=Normalizer()\n",
        "n.fit_transform(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-K_LByKs7pM",
        "outputId": "07eb3cb7-6cf3-45f5-fd09-3f55ec13e0a3"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.26726124, 0.53452248, 0.80178373],\n",
              "       [0.45584231, 0.56980288, 0.68376346]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zWpsuYgEtnW1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}