Untitled3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM4g1NT+KKayzm7phXzNKcj",
      "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/dewhitee/ml/blob/main/Untitled3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HypEHkbB-VYt"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXIEGZ7x_mvs"
      },
      "source": [
        "rwines = np.genfromtxt('winequality-red.csv', delimiter=';', skip_header=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_Lsb--RAJNe",
        "outputId": "ca67e8b7-137e-4533-97fc-0a17fa2d3717"
      },
      "source": [
        "print(rwines[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[7.400e+00 7.000e-01 0.000e+00 1.900e+00 7.600e-02 1.100e+01 3.400e+01\n",
            "  9.978e-01 3.510e+00 5.600e-01 9.400e+00 5.000e+00]\n",
            " [7.800e+00 8.800e-01 0.000e+00 2.600e+00 9.800e-02 2.500e+01 6.700e+01\n",
            "  9.968e-01 3.200e+00 6.800e-01 9.800e+00 5.000e+00]\n",
            " [7.800e+00 7.600e-01 4.000e-02 2.300e+00 9.200e-02 1.500e+01 5.400e+01\n",
            "  9.970e-01 3.260e+00 6.500e-01 9.800e+00 5.000e+00]\n",
            " [1.120e+01 2.800e-01 5.600e-01 1.900e+00 7.500e-02 1.700e+01 6.000e+01\n",
            "  9.980e-01 3.160e+00 5.800e-01 9.800e+00 6.000e+00]\n",
            " [7.400e+00 7.000e-01 0.000e+00 1.900e+00 7.600e-02 1.100e+01 3.400e+01\n",
            "  9.978e-01 3.510e+00 5.600e-01 9.400e+00 5.000e+00]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKKVqajWAVpu",
        "outputId": "7a159336-6345-4382-b3cd-fc1379dd1449"
      },
      "source": [
        "n, m = rwines.shape\n",
        "print('Number of observations: ', n)\n",
        "print('Number of variables: ', m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of observations:  1599\n",
            "Number of variables:  12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7hYiFjFBGyB",
        "outputId": "509051d4-12b4-46b6-e19e-263c46a2d8cc"
      },
      "source": [
        "print('Average score: ', np.mean(rwines[:, -1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average score:  5.6360225140712945\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "lXAe1LECBXOD",
        "outputId": "cc058aa4-39b5-488f-ff5c-5644081828be"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(rwines[:, -1])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "iHeQMn-wBp2T",
        "outputId": "a2d74b19-7a4b-4216-d661-1a3003e0f0f7"
      },
      "source": [
        "plt.title('Correlation coefficient {}'.format(\n",
        "    np.round(np.corrcoef(rwines[:, -1], rwines[:, -2])[0, 1], 2)))\n",
        "plt.scatter(rwines[:, -1], rwines[:, -2]) #checking scores and alcohol values\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}