Week_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Week 1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "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/anubhawbhalotia/Competitive-Programming/blob/master/Week_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1nqnQ3AjxGaC",
        "colab_type": "text"
      },
      "source": [
        "# **WEEK 1**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZzCUU2fv3GM",
        "colab_type": "text"
      },
      "source": [
        "## **Linear Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HrTuztXow230",
        "colab_type": "text"
      },
      "source": [
        "## Visualisation of Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHL8r6cc-aSt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "7f0571e1-941f-4f7d-aab6-a43d731d8391"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "url = 'Salary_Data.csv'\n",
        "dataset = pd.read_csv(url)\n",
        "x = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, 1].values\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 1/3, shuffle = False)\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(x_train, y_train)\n",
        "y_pred = lr.predict(x_test)\n",
        "\n",
        "plt.scatter(x_train, y_train, color = \"red\")\n",
        "plt.plot(x_train, lr.predict(x_train), color = \"green\")\n",
        "plt.title(\"Salary vs Experience (Training set)\")\n",
        "plt.xlabel(\"Years of Experience\")\n",
        "plt.ylabel(\"Salary\")\n",
        "plt.show()\n",
        "\n",
        "plt.scatter(x_test, y_test, color = \"red\")\n",
        "plt.plot(x_train, lr.predict(x_train), color = \"green\")\n",
        "plt.title(\"Salary vs Experience (Testing set)\")\n",
        "plt.xlabel(\"Years of Experience\")\n",
        "plt.ylabel(\"Salary\")\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V1ap6BPjA1TXGmKixdf9WnjzlR+o+lMqw5tBlOfw2qTqJN4yhzl2Phn37YRudpaqbROQ1YD1wEPgWWATsVtVUV20jUMNN1wA2uHVTRWQPcLIrnxvQdOA6GzKVt8gqFhHpDnQHqB2lNwQZYyLLnwf+ZMDsAQybP4zDaYe5s9Gd9Gzdk9MrnZ6vcYQtiYhIRbyewWnAbuBjvNNR+U5VE4FEgLi4uNAPTzDGmHyyI2UHr81+jTfmv8HB1IPc3vB2erbuSYOTG/gSTzjvE7kMWKOqfwKIyCTgQqCCiMS43khNYJOrvwmoBWx0p7/KAzsCyjMErpNduTHGRJSdB3fy+pzXGTJvCAeOHODWc2/lxTYvcuYpZ/oaVziTyHqgpYiUxjuddSmwEJgG3Ih3DaMb8LmrP9nNz3HL/6uqKiKTgfEi8jpQHagPzAcEqC8ip+Elj1uB28O4P8YYk+92H9rNoDmDGDxvMHsP7+Xmc24mvk08Z1cuGOOIwnlNZJ6ITAR+BFKBxXinlP4DfCAifV3Zu26Vd4H3RSQZ2ImXFFDVn0TkI+Bn186DqpoGICIPAVPwRn6NUtWfwrU/xhiTn/Yc2sOQeUN4fc7r7Dm8hxvOuoH4NvE0rNrQ79COIeG4g7Egi4uL04ULF/odhjHGZGnf4X0MnTeUgXMGsuvQLq4981ri28TT5NQmvsUkIotUNS6rZfbsLGOMKQD2H9nPsPnDGDB7ADsP7uTqBleT0DaBptWa+h1ajiyJGGOMjw4cOcCbC97k1dmvsj1lOx3rdyShTQLn1zjf79ByxZKIMcb4IOVoCm8tfIv+s/qz7cA2rjz9ShLaJtCyZku/Q8sTSyLGGJOPDh49SOKiRF6Z9Qpb9m/hsrqX0attL1rVauV3aEGxJGKMMfngUOohRv44kpe+f4nN+zdzSewlfHTjR1xc52K/QzshlkSMMSaMDqceZtTiUbz0w0ts3LuRi2tfTNL1SVxy2iV+hxYSlkSMMSYMjqQdYfSS0fT7vh/r96ynVa1WjO48mnantcN7tmxksCRijDEhdDTtKGOXjqXv931Zu3stLWq04J2r3+HyupdHVPLIYEnEGGNCIDU9lXHLxtFnZh9W71pNXPU4hnccTod6HSIyeWSwJGKMMScgLT2N8cvH03tmb5J3JnPeqecx+dbJdGrQKaKTRwZLIsYYE4S09DQ+/OlDes/oza87fqVx1cZ8esundD6jc1QkjwyWRIwxJg/SNZ2Pf/qYXjN6sXL7Ss6tci4Tb5rIdWddRxEJ94/FFjyWRIwxJhfSNZ1JKyfRa0YvVmxbwVmnnMUHN3zATefcFJXJI4MlEWOMyYGq8tkvn5EwI4FlW5dxxslnMP768dx8zs0ULVLU7/B8Z0nEGGOyoKp88dsXJExPYPGWxdSvVJ/3r3uf2869zZJHAEsixhgTQFX5atVXJMxIYOEfC6lbsS6jO4+mS6MuxBSxj8zM7IgYYwxe8pjy+xTip8czf9N8YivE8u4173JnozspVrSY3+EVWJZEjDFRTVX5bs13vDjtReZsnEPt8rVJ7JRItybdKF60uN/hFXiWRIwxUWvammm8OP1Fflj/AzXL1WTEVSO457x7LHnkgSURY0zUmbluJvHT45m+djrVy1ZnWIdh/KPpPygRU8Lv0AodSyLGmKgxa/0s4qfH892a7zj1pFMZ0n4I3Zt1p2RMSb9DK7QsiRhjIt7cjXOJnx7Pt79/S5UyVXj9ite5L+4+ShUr5XdohZ4lEWNMxFqwaQHx0+P5OvlrTil9CgMuH8D9cfdTpngZv0OLGJZEjDER58fNP5IwPYEvfvuCSqUq8cqlr/Bg8wc5qfhJfocWcSyJGGMixtItS0mYkcBnv3xGxZIV6deuHw83f5iyJcr6HVrEsiRijCn0lm9dTq8Zvfhk5SeUL1Ge3m1780iLRyhfsrzfoUW8sD16UkTOEJElAa+9IvKYiFQSkakissq9V3T1RUSGikiyiCwTkaYBbXVz9VeJSLeA8mYistytM1Si6SH+xhh+2vYTN398M43easTU1VOJbxPP2sfW0rNNT0sg+SRsPRFV/RVoAiAiRYFNwKfAc8B3qvqKiDzn5p8FOgD13asFMAJoISKVgHggDlBgkYhMVtVdrs4/gXnAV0B74Otw7ZMxpmD4Zfsv9JrRiw9XfEiZ4mV44eIXePyCx6lUqpLfoUWd/DqddSnwu6quE5HOQFtXPgaYjpdEOgNjVVWBuSJSQUSqubpTVXUngIhMBdqLyHSgnKrOdeVjgWuxJGJMxPptx2/0ntGbCSsmUCqmFM9d9BxPXvAkJ5c+2e/QolZ+JZFbgQluuqqqbnbTW4CqbroGsCFgnY2uLKfyjVmU/42IdAe6A9SuXTvonTDG+CN5ZzJ9ZvZh3LJxlIwpyVMXPMVTrZ6icpnKfocW9cKeRESkOHAN8HzmZaqqIqLhjkFVE4FEgLi4uLBvzxgTGqt3rabvzL6MXTqWYkWL8ViLx3jmwmeoelLV469s8kV+9EQ6AD+q6lY3v1VEqqnqZne6apsr3wTUClivpivbxF+nvzLKp7vymlnUN8YUcut2r6PvzL6MXjqaolKUh5o/xLMXPku1stX8Ds1kkqvRWe7CeLBu469TWQCTgYwRVt2AzwPKu7pRWi2BPe601xTgChGp6EZyXQFMccv2ikhLNyqra0BbxphCaMOeDdz35X3Uf6M+Y5eN5b5m9/H7I78zuP1gSyAFVG6H+K4SkQEicnZeGheRMsDlwKSA4leAy0VkFXCZmwdvdNVqIBl4B3gAwF1Q7wMscK/eGRfZXZ2Rbp3fsYvqxhRKm/Zu4qGvHqLeG/UYtXgU/2j6D5IfTuaNjm9Qo1yWlzqzl5QEsbFQpIj3npQUjpCNI95gqONUEimLd3H8brzEMwr4QFX3hje80IuLi9OFCxf6HYYxBti8bzOv/PAKby96mzRN454m9/Dvi/9NnQp1gmswKQm6d4eUlL/KSpeGxETo0iU0QUchEVmkqnFZLstNEsnUWBtgPFABmAj0UdXkE44yn1gSMcZ/W/dvpf+s/oxYOIKjaUfp1rgbL7R+gdMqnnZiDcfGwrp1fy+vUwfWrj2xtqNYTkkkVxfW3TWRq/B6IrHAQCAJuBjvNFSDkERqjIlo2w5sY8CsAQxfMJzDaYe5s9Gd9Gzdk9MrnR6aDaxfn7dyc8JyOzprFTANGKCqswPKJ4pI69CHZYyJJNtTtvPa7Nd4Y/4bHEo9xO0Nb6dn6540ODnE3z9r1866J2L3h4XNcZOI64WMVtXeWS1X1UdCHpUxJiLsPLiTgbMHMnT+UA4cOcCt597Ki21e5MxTzgzPBvv1y/qaSL9+4dmeOf7oLFVNAzrlQyzGmAix6+AuXpz2IrGDY3nph5foWL8jKx5YwfgbxocvgYB38Twx0bsGIuK9H++iuo3mOiG5HZ01CCgGfAgcyChX1R/DF1p42IV1Y8Jnz6E9DJ47mEFzB7Hn8B5uOOsG4tvE07BqQ79Dy5qN5sqVEx6dJSLTsihWVW13osHlN0sixoTe3sN7GTpvKAPnDGT3od1ce+a1JLRJoPGpjf0OLWc2mitXTnh0lqpeEtqQjDGRYN/hfQybP4zX5rzGzoM7ubrB1SS0TaBptabHX7kgsNFcJyzXz84SkauAc4CSGWXZXWw3xkS2A0cOMHzBcAbMHsD2lO10rN+RhDYJnF/jfL9DyxsbzXXCcnufyFtAaeASvMeM3AjMD2NcxpgCKOVoCiMWjODV2a+y7cA2rjz9Snq17UWLmi38Di04NprrhOX22VmtVLUrsEtVewEXYDcYmmgTxaN4Dh49yOC5g6k7pC5PTX2KRlUbMeueWXxzxzeFN4FAcKO5zDFyezrroHtPEZHqwA7AHqlpokfmUTzr1nnzENEfOIdSD/HOond4+YeX2bx/M5fEXsLHN33MxXUu9ju00OnSJaL/DcMtt0nkSxGpAAwAfsT7rfORYYvKmIKmR49jT3mAN9+jR0R+AB1OPcyoxaPo930/Nu3bROs6rRl/w3jaxrb1OzRTwOR2dFYfN/mJiHwJlFTVPeELy5gCJkpG8RxJO8LoJaPpO7MvG/Zu4MJaFzLm2jG0O60d3s/2GHOsHJOIiFyfwzJUdVJ2y42JKIVxFE9SktdTWr/ei7Nfv2x7TUfTjjJ26Vj6zOzDuj3raFmzJSOvGcnldS+35GFydLyeyNU5LFOO/bEpYyJXYRvFk8trOKnpqYxbNo4+M/uwetdqzq9+PiOuGkH7eu0teZhcyfPviRR2dse6CVoevtn77jh3YqelpzF++Xh6z+xN8s5kmlZrSq+2vbiq/lWWPMzfnPAd664Ru9nQRLfCNIonm2s1aRvW8eHy8fSe0Ztfd/xK46qN+eyWz7jmjGsseZig2M2GxkSiTNdw0gU+Pht6XV6MlZO6cG6Vc5l400SuO+s6ikhubxcz5u/sZkNjIlG/flC6NOkCE8+GxvfBrTeBVKnCRzd+xNL7lnLD2TdYAjEnLNibDXdiNxsaU2Dp7bfz2f4FJPz8JssqHeXM3TFMqPovbuo+hKJFivodnokguf0aknGz4avAImANMCFsURljgqKqTP51Ms0Sm3H9liEcrBfL+9e9z4qBh7j1vmH+J5AofnRMpDrefSLnAxsybjYUkZOA5cAvwKDwh2eMyQ1V5atVX5EwI4GFfyzk9IqnM+baMdze8HZiiuR6/Ex4RemjYyLd8XoibwNHAESkNfCKK9sDJIY3NGPM8agq3yR/Q8t3W9JpQid2pOxg1DWj+OWhX+jauGvBSSCQ86NjTKF1vL+woqq6003fAiSq6id4jz9ZEt7QjDHZUVX+b/X/ET89njkb51CnfB3eufodujXuRrGixfwOL2tR8uiYaHO8nkhREclINJcC/w1YdtyvOCJSQUQmisgvIrJSRC4QkUoiMlVEVrn3iq6uiMhQEUkWkWUi0jSgnW6u/ioR6RZQ3kxElrt1hooNdDd5UUjPz09bM43Wo1tzxbgr2Lh3I29d9Ra/Pfwb/2j6j4KbQCD7R8QU5EfHmOM6XhKZAMwQkc/xRmh9DyAi9fBOaR3PEOAbVT0TaAysBJ4DvlPV+sB3bh6gA1DfvboDI9y2KgHxQAugORCfkXhcnX8GrNc+FzEZ89f5+XXrQPWv8/MFOJHMXDeTtqPb0m5sO1bvWs3wjsNZ9fAq/hX3L4oXLe53eMfnhh0foyA/Osbkjqrm+AJaAtcBZQLKGgBNj7NeebxRXJKp/FegmpuuBvzqpt8GbstcD7gNeDug/G1XVg34JaD8mHrZvZo1a6bGaJ06ql76OPZVp47fkf3N9+u+13Zj2ikJ6KmvnapD5w7Vg0cP+h1WcMaN846xiPc+bpzfEZlcABZqNp+pxz0lpapzsyj7LRf56TTgT+A9EWmMNzT4UaCqqm52dbYAVd10DWBDwPobXVlO5RuzKP8bEemO17uhtnWdDRSK8/NzNswhfno8U1dPpWqZqgy6chD/avYvShUr5XdowStMj44xuRLO21VjgKbACFU9DzjAX6euAHAZLuxPgFTVRFWNU9W4ypUrh3tzpjAowOfn52+aT4ekDrQa1YolW5bw2uWvsfrR1TzW8rHCnUBMRApnEtkIbFTVeW5+Il5S2Soi1QDc+za3fBNQK2D9mq4sp/KaWZQbc3wF8Pz8oj8W0Wl8J1qMbMGCTQvof1l/1jy6hidbPUnpYqWP34AxPghbElHVLcAGETnDFV0K/AxMBjJGWHUDPnfTk4GubpRWS2CPO+01BbhCRCq6C+pXAFPcsr0i0tKNyuoa0JYxOevSBRITvUeji3jviYm+nGpZsmUJ135wLXHvxDF7w2xeavcSax5dwzMXPkOZ4mXyPR5j8iLcdyI9DCSJSHFgNXA3XuL6SETuBdYBN7u6XwEdgWQgxdVFVXeKSB9ggavXW/+6d+UBYDRQCvjavYzJHZ/Pzy/fupyEGQlMWjmJCiUr0OeSPjzS4hHKlSjnW0zG5JX9KJUx+eynbT/Ra0YvPv75Y8qVKMfjLR/nsZaPUaFkBb9DMyZLIflRKmPMiVn550p6z+zNhys+5KTiJ/HCxS/wxAVPULFUxeOvbEwBZT8mYEyY/bbjN+6YdAfnvHkOX+r+d9IAABZSSURBVPz6Bc9d9BxrHl1Dn3Z9cp9A8uPu+kJ6B7/xl/VEjAmT5J3J9JnZh3HLxlEypiRPt3qap1o9ReUyeRxmHuqn32b1W/FgT9g1QbFrIibyZfWhGcYPxtW7VtN3Zl/GLh1L8aLFeeD8B3jmwmeoUqZKcA3Gxh7zU7f/U6cOrF2bt7YyJyTwhjaXKgU7doRmGybi2DURE73y8Tcs1u5eS7+Z/Ri9dDQxRWJ4uPnDPHvRs5x60qkn1nAo767P7nHsmctOZBsmqlgSMZEtp9+wCFESWb9nPS99/xKjFo9CRLg/7n6eu+g5qpetHpL2qV07655IMHfX5zUpFIA7+E3BZknERLYwPiNr496NvPz9y4xcPBJV5Z9N/8nzFz9PzXI1j79yXvTrl/UpqGDurs8uIZ18Mhw8GJptmKhio7NMZAvDM7L+2PcHj3z9CPWG1iPxx0TubnI3yY8kM/yq4aFPIBDau+s7dsy6/OabC8wd/KZwsQvrJrJldyE5iA/ILfu30P+H/ry16C2Oph3l7iZ306N1D2IrxIY25nAK5UV6EzXswrqJXhmJ4gRGZ207sI1XZ73Kmwve5EjaEe5sfCc9W/ekbsW6YQo6jArBI/BN4WI9EWOysT1lOwNmDWDYgmEcSj3EHSe1oud7q6n30+Z8GSocFtYTMUGwnogxebAjZQcD5wzkjflvcODIAW5veDs99zTmjIcSCv/NeKG8SG8MlkSM+Z9dB3fx+pzXGTJvCPuP7OeWc2/hxdYvclbls7xv8GEeKpwvQnB6z5hAdjrLRL3dh3YzeO5gBs0dxN7De7nx7BuJbxPPuVXO/atSkSLer7BnJgLp6fkXrDE+sNNZxmRh7+G9DJ03lIFzBrL70G6uO/M6Etom0Khqo79XDuUNf8ZEEEsiJursO7yPYfOH8dqc19h5cCdXN7iaXm17cV6187Jfya4lGJMlSyImahw4coDhC4bz6qxX2XFwB1fVv4qEtgnEVc+yl34su5ZgTJYsiZiIl3I0hRELRtB/Vn/+TPmT9vXa06ttL5rXaJ63hnz+OV1jCiJLIiZiHTx6kLcXvc0rP7zC1gNbubzu5fRq24sLal3gd2jGRAxLIibiHEo9xDuL3uHlH15m8/7NtDutHRPbTuSi2hf5HZoxEceSiIkYh1MPM2rxKPp9349N+zbRuk5rJtwwgTaxbfwOzZiIZUnEFHpH0o4weslo+s7sy4a9G7io9kWMvW4sl8Regoj4HZ4xEc2SiCm0jqYdZezSsfSZ2Yd1e9ZxQc0LePead7ms7mWWPIzJJ5ZETKGTmp7KuGXj6DOzD6t3raZ5jea81ektrjz9SksexuQzSyKm0EhNT2XC8gn0ntmb5J3JNKvWjC9v+5KO9Tta8jDGJ5ZETIGXlp7Ghz99SK8Zvfhtx280ObUJn9/6OVc3uNqShzE+C+vP44rIWhFZLiJLRGShK6skIlNFZJV7r+jKRUSGikiyiCwTkaYB7XRz9VeJSLeA8mau/WS3rn2iRJB0TefDFR/ScERDukzqQsmYkky6eRI/dv+Ra864xhKIMQVAfvzG+iWq2iTgCZDPAd+pan3gOzcP0AGo717dgRHgJR0gHmgBNAfiMxKPq/PPgPXah393TLilazoTf55IoxGNuPWTWykiRfj4po9Z/K/FXHfWdZY8jClA8iOJZNYZGOOmxwDXBpSPVc9coIKIVAOuBKaq6k5V3QVMBdq7ZeVUda56z7MfG9CWKYRUlU9Xfsp5b5/HTR/fRLqm88ENH7Ds/mXcePaNFBE//lyNMTkJ9zURBb4VEQXeVtVEoKqqbnbLtwBV3XQNYEPAuhtdWU7lG7Mo/xsR6Y7Xu6G2Pbq7wFFVvvjtC+Knx7NkyxIanNyApOuTuOWcWyhapKjf4RljchDuJHKRqm4SkSrAVBH5JXChqqpLMGHlklcieD9KFe7tmdxRVb5a9RXx0+NZtHkR9SrVY+y1Y7mt4W3EFLExH8YUBmH9n6qqm9z7NhH5FO+axlYRqaaqm90pqW2u+iagVsDqNV3ZJqBtpvLprrxmFvVNAaeqTPl9CvHT45m/aT51K9blvc7vcUejOyx5GFPIhO0ks4iUEZGyGdPAFcAKYDKQMcKqG/C5m54MdHWjtFoCe9xprynAFSJS0V1QvwKY4pbtFZGWblRW14C2TAGkqkz9fSqtRrWiQ1IHth3YxsirR/LLg79wV5O7LIEYUwiF839tVeBTN5ImBhivqt+IyALgIxG5F1gH3OzqfwV0BJKBFOBuAFXdKSJ9gAWuXm9V3emmHwBGA6WAr93LFDCqyrS103hx2ovM2jCLWuVq8Xant7mryV0UL1rc7/CMMSdAvIFN0SMuLk4XLlzodxhRY8baGcRPj2fGuhnUKFuDHhf34J7z7qFETAm/QzPG5JKILAq4TeMYdv7AhMUP638gfno8/13zX6qdVI03OrzBP5r+g5IxJf0OzRgTQpZETEjN2TCH+OnxTF09laplqjL4ysF0b9adUsVK+R2aMSYMLImYkJi/aT7x0+P5JvkbKpeuzMArBnJf3H2ULlba79CMMWFkScSckEV/LCJ+ejz/WfUfTil9Cq9e9ioPnP8AZYqX8Ts0Y0w+sOdIhENSEsTGQpEi3ntSkt8RhdzizYvp/EFn4t6JY87GObx86cuseXQNT1/4tCUQY6KI9URCLSkJuneHlBRvft06bx6gSxf/4gqRZVuXkTA9gU9/+ZQKJSvQ95K+PNziYcqVKOd3aMYYH9gQ31CLjfUSR2Z16sDateHbbpit2LaCXjN6MfHniZQvUZ7HWz7OYy0fo3zJ8n6HZowJMxvim5/Wr89beQG38s+V9JrRi49++oiTip9Ez9Y9ebzl41QsVfH4KxtjIp4lkVCrXTvrnkghe3rwr9t/pffM3kxYPoHSxUrz/EXP82SrJ6lUqpLfoRljChBLIqHWr9+x10QASpf2yguB5J3J9J7Rm6TlSZSMKckzFz7DU62e4pTSp/gdmjGmALIkEmoZF8979PBOYdWu7SWQAn5RffWu1fSZ2Yf3l75P8aLFeaLlEzx94dNUKVPF79CMMQWYJZFw6NKlwCeNDGt3r6XvzL6MWTqGmCIxPNz8YZ696FlOPelUv0MzxhQClkSi1Po96+k3sx+jloyiiBTh/rj7ee6i56hetrrfoRljChG72TDKbNy7kQf/8yD1htbjvSXv0b1pd36vNYCh90+mevmaEXtzpDEmPKwnEiX+2PcHr/zwCm8vept0Tefe8+7l3xf/m9pffg8PRO7NkcaY8LKbDSPclv1b6P9Df95a9BZH045yd5O76dG6B7EVYr0KEXpzpDEmdOxmwyi07cA2Xp31Km8ueJMjaUfo2rgrL7R+gboV6x5bMcJujjTG5C9LIhFme8p2BswawLAFwziUeog7Gt1Bz9Y9qVepXtYrRMjNkcYYf1gSiRA7UnYwcM5Ahs4bSsrRFG5veDs9W/fkjFPOyHnFQn5zpDHGX5ZECrldB3fx+pzXGTJvCPuP7OeWc2/hxdYvclbls3LXQCG9OdIYUzBYEimkdh/azeC5gxk0dxB7D+/lprNvIr5NPOdUOSfvjRWimyONMQWLJZFCZu/hvQyZO4TX577O7kO7uf6s64lvE0+jqo38Ds0YE4UsiRQS+w7v4435b/Da7NfYdWgXnc/oTELbBJqc2sTv0IwxUcySSAG3/8h+hs8fzoDZA9hxcAedGnQioU0Czao38zs0Y4yxJFJQpRxN4c0Fb9J/Vn+2p2ynQ70OJLRNoHmN5n6HZowx/xP2Z2eJSFERWSwiX7r500Rknogki8iHIlLclZdw88lueWxAG8+78l9F5MqA8vauLFlEngv3vuSHg0cPMmjOIE4bchpPT32aptWaMvue2XzV5StLIMaYAic/HsD4KLAyYL4/MEhV6wG7gHtd+b3ALlc+yNVDRM4GbgXOAdoDb7rEVBQYDnQAzgZuc3ULpUOphxg6byh1h9bliW+foGGVhvxw9w9MuWMKF9S6wO/wjpWU5D0upUgRe2CjMVEurElERGoCVwEj3bwA7YCJrsoY4Fo33dnN45Zf6up3Bj5Q1cOqugZIBpq7V7KqrlbVI8AHrm7ohfFD83DqYYbPH87pQ0/n0W8e5YyTz2DGXTP4v67/x4W1LwzZdkImKcm7OXHdOlD964GNlkiMiUrhviYyGHgGKOvmTwZ2q2qqm98I1HDTNYANAKqaKiJ7XP0awNyANgPX2ZCpvEVWQYhId6A7QO28Ps4j40MzxE+5PZJ2hPcWv0e/7/uxYe8GLqp9Ee9f9z7tTmsXdJv5okePY+9uB2++Rw+718SYKBS2noiIdAK2qeqicG0jt1Q1UVXjVDWucuXKeVs5pw/NIBxNO8rIH0fS4I0G3Pef+6hZriZT75zKzLtmFvwEAvbARmPMMcLZE7kQuEZEOgIlgXLAEKCCiMS43khNYJOrvwmoBWwUkRigPLAjoDxD4DrZlYdOiD40U9NTeX/p+/SZ2Yc1u9fQvEZz3u70NlecfgXeWbtCwh7YaIwJELaeiKo+r6o1VTUW78L4f1W1CzANuNFV6wZ87qYnu3nc8v+q92Mnk4Fb3eit04D6wHxgAVDfjfYq7rYxOeQ7kt2HYy4/NFPTUxm7dCxnDjuTeybfQ6VSlfjyti+Ze+9crqx3ZeFKIOA9V6t06WPL7IGNxkQtP34e91ngCRFJxrvm8a4rfxc42ZU/ATwHoKo/AR8BPwPfAA+qaprryTwETMEb/fWRqxtaQX5opqWnkbQsibOHn023z7pRtkRZPr/1cxb8cwFXNbiq8CWPDF26QGKi96NVIt57YqJdDzEmWqlqVL2aNWumeTZunGqdOqoi3vu4cdlWTU1L1QnLJ+iZw85UEtBGIxrppJ8naXp6esi2YYwx+QlYqNl8ptod67mRi6fcpms6n/z8CQkzEvj5z585p/I5fHzTx1x/1vUUkeN0+MI0AswYY8LNj9NZESVd05m0chJN3mrCzRNvRlX54IYPWHb/Mm48+8bjJxAI+QgwY4zJL9YTCZKqMvnXycRPj2fp1qU0OLkBSdcnccs5t1C0SNG8NWbDZo0xhZQlkTxSVf6z6j/ET4/nx80/Uq9SPcZeO5bbGt5GTJEgD6cNmzXGFFJ2OiuXVJWvV31Ni5EtuHrC1ew+tJv3Or/HygdXcmfjO4NPIGDDZo0xhZb1RHJhz6E9tE9qz9yNc4mtEMvIq0fStXFXihUtFpoN2O+cG2MKKUsiuVCuRDnqVqzL3U3u5q4md1G8aPHQb8R+59wYUwhZEskFESHpentKrTHGZGbXRIwxxgTNkogxxpigWRIxxhgTNEsixhhjgmZJxBhjTNAsiRhjjAmaJRFjjDFBsyRijDEmaOL93kj0EJE/gSyedniMU4Dt+RBOQWP7HV1sv6PLiex3HVWtnNWCqEsiuSEiC1U1zu848pvtd3Sx/Y4u4dpvO51ljDEmaJZEjDHGBM2SSNYS/Q7AJ7bf0cX2O7qEZb/tmogxxpigWU/EGGNM0CyJGGOMCZolkQAiMkpEtonICr9jyS8iUktEponIzyLyk4g86ndM+UFESorIfBFZ6va7l98x5ScRKSoii0XkS79jyU8islZElovIEhFZ6Hc8+UVEKojIRBH5RURWisgFIWvbron8RURaA/uBsap6rt/x5AcRqQZUU9UfRaQssAi4VlV/9jm0sBIRAcqo6n4RKQb8ADyqqnN9Di1fiMgTQBxQTlU7+R1PfhGRtUCcqkbVzYYiMgb4XlVHikhxoLSq7g5F29YTCaCqM4GdfseRn1R1s6r+6Kb3ASuBGv5GFX7q2e9mi7lXVHyjEpGawFXASL9jMeEnIuWB1sC7AKp6JFQJBCyJmAAiEgucB8zzN5L84U7pLAG2AVNVNSr2GxgMPAOk+x2IDxT4VkQWiUh3v4PJJ6cBfwLvuVOYI0WkTKgatyRiABCRk4BPgMdUda/f8eQHVU1T1SZATaC5iET8KUwR6QRsU9VFfsfik4tUtSnQAXjQncKOdDFAU2CEqp4HHACeC1XjlkQM7prAJ0CSqk7yO5785rr204D2fseSDy4ErnHXBj4A2onIOH9Dyj+qusm9bwM+BZr7G1G+2AhsDOhpT8RLKiFhSSTKuQvM7wIrVfV1v+PJLyJSWUQquOlSwOXAL/5GFX6q+ryq1lTVWOBW4L+qeofPYeULESnjBo/gTudcAUT8SExV3QJsEJEzXNGlQMgGzsSEqqFIICITgLbAKSKyEYhX1Xf9jSrsLgTuBJa76wMA/1bVr3yMKT9UA8aISFG8L1MfqWpUDXeNQlWBT73vTcQA41X1G39DyjcPA0luZNZq4O5QNWxDfI0xxgTNTmcZY4wJmiURY4wxQbMkYowxJmiWRIwxxgTNkogxxpigWRIxEUE8P4hIh4Cym0TElyGcInKme1LsYhE5PdOywCfJLhGRoWGOJS7c2zDRy4b4mojhHlvyMd7zv2KAxUB7Vf09iLZiVDX1BGJ5DohR1b5ZLFtLPj1J9kT3w5jjsZ6IiRiqugL4AngWeBEYB/RwvxuyWEQ6g/egSRH5XkR+dK9WrrytK58M/OzucP6P+82RFSJyS+ZtikgTEZkrIstE5FMRqSgiHYHHgPtFZFpuYheRGBFZICJt3fzLItLPTa8VkVdd72W+iNRz5ZVF5BO33gIRudCVJ4jI+yIyC3jf7deXblkZ8X43J/MxuUtEJonINyKySkReDYitvTtOS0Xku5zaMVFIVe1lr4h5AWWAX4HlwMvAHa68AvCbW14aKOnK6wML3XRbvIfTnebmbwDeCWi7fBbbWwa0cdO9gcFuOgF4KpsY17r4lrjX4678HLxH8V+G14sqHlC/h5vuCnzppsfjPVAQoDbeo2sytr0IKBWwXxnrvJTNMbkL707m8kBJYB1QC6gMbAg4JpVyasfvf3975f/LHntiIoqqHhCRD/F+XOxm4GoRecotLon3YfsHMExEmgBpQIOAJuar6ho3vRwYKCL98T6Evw/clvudhgqqOsMVjcE7nZYbl2im01mq+pOIvA98CVygqkcCFk8IeB/kpi8DznaP8QAo557GDDBZVQ9msd0r8B7AmPmYAHynqnvcvv0M1AEqAjMzjomq7jxOOytztfcmYlgSMZEo3b0EuEFVfw1cKCIJwFagMd4p3UMBiw9kTKjqbyLSFOgI9BWR71S1d5hjbwjsBqpkKtcsposALVU1MH5cUjlA1rI7Ji2AwwFFaeT8+ZBlOyb62DURE8mmAA+7JxUjIue58vLAZlVNx3v4ZNGsVhaR6kCKqo4DBpDp8dnuW/suEbnYFd0JzCBIInI9UAnvV+jeyHjKsHNLwPscN/0t3oP1MtZvkovNZHdMsjMXaC0ip7n6lYJsx0Qo64mYSNYH71f8lolIEWAN0Al4E/hERLoC35D9t/aGwAARSQeOAvdnUacb8JaIlCZvT0edJiJpbnoZ8ATwCnCpqm4QkWHAENc+QEURWYbXW7jNlT0CDHflMcBM4L7jbDe7Y5IlVf1TvF8AnOTqb8N7bH6e2jGRy4b4GlPA5eeQYGPyyk5nGWOMCZr1RIwxxgTNeiLGGGOCZknEGGNM0CyJGGOMCZolEWOMMUGzJGKMMSZo/w8qf5xAgIwUegAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "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": {
        "id": "tt7aegSo4zda",
        "colab_type": "code",
        "outputId": "51cade8e-962e-41fd-a8de-fb2c050fe47e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Finding inverse of a matrix\n",
        "import numpy as np\n",
        "x = np.array([[3,4], [2,16]])\n",
        "y = np.linalg.inv(x)\n",
        "print(x)\n",
        "print(y)\n",
        "# Multiplication of 2 matrices\n",
        "print(np.dot(x,y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 3  4]\n",
            " [ 2 16]]\n",
            "[[ 0.4   -0.1  ]\n",
            " [-0.05   0.075]]\n",
            "[[1. 0.]\n",
            " [0. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0EAuXdi5Xso",
        "colab_type": "code",
        "outputId": "3b9cf096-5a7c-4974-900a-d0e0e871acbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Transpose of a matrix\n",
        "print(np.transpose(x))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 3  2]\n",
            " [ 4 16]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfQnKl9Ut-5F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}