02_Data_Statistics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNPYp0k2R9/UzYPiJWguKAM",
      "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/satwiksps/Machine_Learning/blob/main/2_Data_Statistics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "XLguls92m_yA",
        "outputId": "1a29e2ac-422b-463e-c331-6c9826cb7d8f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     preg  plas  pres  skin  test  mass   pedi  age  class\n",
            "0       6   148    72    35     0  33.6  0.627   50      1\n",
            "1       1    85    66    29     0  26.6  0.351   31      0\n",
            "2       8   183    64     0     0  23.3  0.672   32      1\n",
            "3       1    89    66    23    94  28.1  0.167   21      0\n",
            "4       0   137    40    35   168  43.1  2.288   33      1\n",
            "..    ...   ...   ...   ...   ...   ...    ...  ...    ...\n",
            "763    10   101    76    48   180  32.9  0.171   63      0\n",
            "764     2   122    70    27     0  36.8  0.340   27      0\n",
            "765     5   121    72    23   112  26.2  0.245   30      0\n",
            "766     1   126    60     0     0  30.1  0.349   47      1\n",
            "767     1    93    70    31     0  30.4  0.315   23      0\n",
            "\n",
            "[768 rows x 9 columns]\n"
          ]
        }
      ],
      "source": [
        "from pandas import read_csv\n",
        "\n",
        "url = 'https://raw.githubusercontent.com/erojaso/MLMasteryEndToEnd/master/data/pima-indians-diabetes.data.csv'\n",
        "column_names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
        "data = read_csv(url, names=column_names) #Load CSV using Pandas\n",
        "print(data)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "peek = data.head(10) #Peek into n number of rows\n",
        "print(peek)"
      ],
      "metadata": {
        "id": "rR9SedSWQN2D",
        "outputId": "6904f347-1ebd-49fc-f465-32cd973d3b76",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   preg  plas  pres  skin  test  mass   pedi  age  class\n",
            "0     6   148    72    35     0  33.6  0.627   50      1\n",
            "1     1    85    66    29     0  26.6  0.351   31      0\n",
            "2     8   183    64     0     0  23.3  0.672   32      1\n",
            "3     1    89    66    23    94  28.1  0.167   21      0\n",
            "4     0   137    40    35   168  43.1  2.288   33      1\n",
            "5     5   116    74     0     0  25.6  0.201   30      0\n",
            "6     3    78    50    32    88  31.0  0.248   26      1\n",
            "7    10   115     0     0     0  35.3  0.134   29      0\n",
            "8     2   197    70    45   543  30.5  0.158   53      1\n",
            "9     8   125    96     0     0   0.0  0.232   54      1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.shape) #Number of rows and columns"
      ],
      "metadata": {
        "id": "Kx7DQCw-QpHh",
        "outputId": "7084a5ab-75ec-4a7c-c095-7d5c095fc315",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(768, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "types = data.dtypes #Data types of each column\n",
        "print(types)"
      ],
      "metadata": {
        "id": "Y-UafptlRBBB",
        "outputId": "a99860d9-960d-437c-9476-d7ef4681727d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "preg       int64\n",
            "plas       int64\n",
            "pres       int64\n",
            "skin       int64\n",
            "test       int64\n",
            "mass     float64\n",
            "pedi     float64\n",
            "age        int64\n",
            "class      int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas import set_option\n",
        "set_option('display.width', 100)\n",
        "set_option('display.precision', 3) # decimals\n",
        "\n",
        "description = data.describe() #Summary statistics\n",
        "print(description)"
      ],
      "metadata": {
        "id": "6dvx9hwZRKfE",
        "outputId": "5c2b45a1-d1ab-47d3-97e7-fb0bfd1a4522",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          preg     plas     pres     skin     test     mass     pedi      age    class\n",
            "count  768.000  768.000  768.000  768.000  768.000  768.000  768.000  768.000  768.000\n",
            "mean     3.845  120.895   69.105   20.536   79.799   31.993    0.472   33.241    0.349\n",
            "std      3.370   31.973   19.356   15.952  115.244    7.884    0.331   11.760    0.477\n",
            "min      0.000    0.000    0.000    0.000    0.000    0.000    0.078   21.000    0.000\n",
            "25%      1.000   99.000   62.000    0.000    0.000   27.300    0.244   24.000    0.000\n",
            "50%      3.000  117.000   72.000   23.000   30.500   32.000    0.372   29.000    0.000\n",
            "75%      6.000  140.250   80.000   32.000  127.250   36.600    0.626   41.000    1.000\n",
            "max     17.000  199.000  122.000   99.000  846.000   67.100    2.420   81.000    1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_counts = data.groupby('class').size()\n",
        "print(class_counts)\n",
        "print()\n",
        "#class_counts1 = data.groupby('preg').size()\n",
        "#print(class_counts1)"
      ],
      "metadata": {
        "id": "LuCE_K53RyZ1",
        "outputId": "b4dd5f8e-5775-4e6c-9af6-7fe36d66fd19",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class\n",
            "0    500\n",
            "1    268\n",
            "dtype: int64\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pearson's Correlation Coeficient**\n",
        "\n",
        "Lies b/w -1 to 1.\n",
        "\n",
        "Poor performance if there are highly correlated attributes."
      ],
      "metadata": {
        "id": "C-PV3g4Avsp2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "set_option('display.width', 100)\n",
        "set_option('display.precision', 3)\n",
        "correlations = data.corr(method='pearson')\n",
        "print(correlations)"
      ],
      "metadata": {
        "id": "ZeOVLRZMS3FV",
        "outputId": "f72e845e-69f1-42b3-e150-f3a6e5322645",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        preg   plas   pres   skin   test   mass   pedi    age  class\n",
            "preg   1.000  0.129  0.141 -0.082 -0.074  0.018 -0.034  0.544  0.222\n",
            "plas   0.129  1.000  0.153  0.057  0.331  0.221  0.137  0.264  0.467\n",
            "pres   0.141  0.153  1.000  0.207  0.089  0.282  0.041  0.240  0.065\n",
            "skin  -0.082  0.057  0.207  1.000  0.437  0.393  0.184 -0.114  0.075\n",
            "test  -0.074  0.331  0.089  0.437  1.000  0.198  0.185 -0.042  0.131\n",
            "mass   0.018  0.221  0.282  0.393  0.198  1.000  0.141  0.036  0.293\n",
            "pedi  -0.034  0.137  0.041  0.184  0.185  0.141  1.000  0.034  0.174\n",
            "age    0.544  0.264  0.240 -0.114 -0.042  0.036  0.034  1.000  0.238\n",
            "class  0.222  0.467  0.065  0.075  0.131  0.293  0.174  0.238  1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.matshow(correlations) #Heatmap\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "zMDBZtxowCrb",
        "outputId": "8ad49249-160b-40ab-9b4f-bd6b463c00c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 480x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "skew = data.skew()\n",
        "print(skew)"
      ],
      "metadata": {
        "id": "fRECQioXwPF5",
        "outputId": "c37fb3af-bef3-45fb-9033-2969ea30bed8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "preg     0.902\n",
            "plas     0.174\n",
            "pres    -1.844\n",
            "skin     0.109\n",
            "test     2.272\n",
            "mass    -0.429\n",
            "pedi     1.920\n",
            "age      1.130\n",
            "class    0.635\n",
            "dtype: float64\n"
          ]
        }
      ]
    }
  ]
}