Google_colaboratory_Basics.ipynb

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      "provenance": [],
      "authorship_tag": "ABX9TyNlAPsyH9fjKllC+ygb2tfa",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Vigneswarsiddu/Machine-Learning-Basics/blob/main/Google_colaboratory_Basics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Author: K. VIGNESWAR SIDDU**\n",
        "**Email id: vigneswarsiddu07@gmail.com**"
      ],
      "metadata": {
        "id": "2SDPeKLUuVaY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "System Specifications"
      ],
      "metadata": {
        "id": "Q3k4x9HykTYB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "HZjZoDnskDZW",
        "outputId": "ea255704-7d00-45cc-8151-a366611fce78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "processor\t: 0\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 79\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0xffffffff\n",
            "cpu MHz\t\t: 2200.214\n",
            "cache size\t: 56320 KB\n",
            "physical id\t: 0\n",
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            "cpu cores\t: 1\n",
            "apicid\t\t: 0\n",
            "initial apicid\t: 0\n",
            "fpu\t\t: yes\n",
            "fpu_exception\t: yes\n",
            "cpuid level\t: 13\n",
            "wp\t\t: yes\n",
            "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n",
            "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa mmio_stale_data retbleed\n",
            "bogomips\t: 4400.42\n",
            "clflush size\t: 64\n",
            "cache_alignment\t: 64\n",
            "address sizes\t: 46 bits physical, 48 bits virtual\n",
            "power management:\n",
            "\n",
            "processor\t: 1\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 79\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0xffffffff\n",
            "cpu MHz\t\t: 2200.214\n",
            "cache size\t: 56320 KB\n",
            "physical id\t: 0\n",
            "siblings\t: 2\n",
            "core id\t\t: 0\n",
            "cpu cores\t: 1\n",
            "apicid\t\t: 1\n",
            "initial apicid\t: 1\n",
            "fpu\t\t: yes\n",
            "fpu_exception\t: yes\n",
            "cpuid level\t: 13\n",
            "wp\t\t: yes\n",
            "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n",
            "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa mmio_stale_data retbleed\n",
            "bogomips\t: 4400.42\n",
            "clflush size\t: 64\n",
            "cache_alignment\t: 64\n",
            "address sizes\t: 46 bits physical, 48 bits virtual\n",
            "power management:\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!cat /proc/cpuinfo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cat /proc/meminfo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "bV9gVe0Gkt4Q",
        "outputId": "4a31c551-fd69-4ece-de20-7184d7602a0b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Mlocked:               0 kB\n",
            "SwapTotal:             0 kB\n",
            "SwapFree:              0 kB\n",
            "Dirty:               512 kB\n",
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            "Slab:             158392 kB\n",
            "SReclaimable:     122024 kB\n",
            "SUnreclaim:        36368 kB\n",
            "KernelStack:        4432 kB\n",
            "PageTables:         7832 kB\n",
            "NFS_Unstable:          0 kB\n",
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            "VmallocChunk:          0 kB\n",
            "Percpu:             1312 kB\n",
            "HardwareCorrupted:     0 kB\n",
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    {
      "cell_type": "markdown",
      "source": [
        "Installing Libraries"
      ],
      "metadata": {
        "id": "QsytDSdAmH28"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Po9jOZIwmMdT",
        "outputId": "fed57ddf-6ea2-4f63-d9ad-a5a30f75a97e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2022.7.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.22.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "wAF3-BiOmefv"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/BostonHousing.csv')"
      ],
      "metadata": {
        "id": "b4Un0bqxmiC4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tsKeAuLbmv7u",
        "outputId": "5ced6235-5c1c-410a-9f2d-07f9725bd475"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
              "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
              "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
              "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
              "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
              "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
              "\n",
              "        b  lstat  medv  \n",
              "0  396.90   4.98  24.0  \n",
              "1  396.90   9.14  21.6  \n",
              "2  392.83   4.03  34.7  \n",
              "3  394.63   2.94  33.4  \n",
              "4  396.90   5.33  36.2  "
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              "      <th></th>\n",
              "      <th>crim</th>\n",
              "      <th>zn</th>\n",
              "      <th>indus</th>\n",
              "      <th>chas</th>\n",
              "      <th>nox</th>\n",
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              "      <th>age</th>\n",
              "      <th>dis</th>\n",
              "      <th>rad</th>\n",
              "      <th>tax</th>\n",
              "      <th>ptratio</th>\n",
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              "      <th>lstat</th>\n",
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              "      <td>4.9671</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
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              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
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              "      <td>18.7</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d41c24af-dd2e-43c0-bb92-bb50b13a5564 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d41c24af-dd2e-43c0-bb92-bb50b13a5564');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "jFN7jVX2nAeX",
        "outputId": "b077e14d-e292-40e5-d56a-e94c108d5a08"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BostonHousing.csv  sample_data\n"
          ]
        }
      ]
    }
  ]
}