Parul_Task_1.ipynb

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    "colab": {
      "name": "Parul Task 1.ipynb",
      "provenance": []
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**GRIP : THE SPARKS FOUNDATION**"
      ],
      "metadata": {
        "id": "zGd_lQ6RJRJn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATA SCIENCE AND BUSINESS ANALYTICS INTERN**"
      ],
      "metadata": {
        "id": "QXQnl84HJYBI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**AUTHOR : PARUL AGARWAL**"
      ],
      "metadata": {
        "id": "7n55xon-Jd97"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TASK 1: Prediction Using Supervised ML**"
      ],
      "metadata": {
        "id": "VtvGrX4KJnjL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this task we have to predict the percentage score of a student based on the number of hours studied. The task has two variables wher ethe feature is the number of hours studied and the target value is the percentage score. This can be solved using simple linear regression."
      ],
      "metadata": {
        "id": "9HK-fF9TJ0ia"
      }
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    {
      "cell_type": "code",
      "source": [
        "# Importing all libraries required in this notebook\n",
        "import pandas as pd\n",
        "import numpy as np  \n",
        "import matplotlib.pyplot as plt  \n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import scipy.stats as stats"
      ],
      "metadata": {
        "id": "awK4iA5iKPB0"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading data from remote link\n",
        "url = \"http://bit.ly/w-data\"\n",
        "data = pd.read_csv(url)"
      ],
      "metadata": {
        "id": "6Y9iccb7LKcw"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Exploring data\n",
        "print(data.shape)\n",
        "data.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "LV5laLsNLbq_",
        "outputId": "c5be69eb-4fd2-4d96-c434-5e4677ed25a5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25, 2)\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.5</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.1</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.5</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>30</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "   Hours  Scores\n",
              "0    2.5      21\n",
              "1    5.1      47\n",
              "2    3.2      27\n",
              "3    8.5      75\n",
              "4    3.5      30"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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        "data.describe()"
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      "execution_count": 7,
      "outputs": [
        {
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          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.012000</td>\n",
              "      <td>51.480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.525094</td>\n",
              "      <td>25.286887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.100000</td>\n",
              "      <td>17.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.700000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.800000</td>\n",
              "      <td>47.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>95.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "           Hours     Scores\n",
              "count  25.000000  25.000000\n",
              "mean    5.012000  51.480000\n",
              "std     2.525094  25.286887\n",
              "min     1.100000  17.000000\n",
              "25%     2.700000  30.000000\n",
              "50%     4.800000  47.000000\n",
              "75%     7.400000  75.000000\n",
              "max     9.200000  95.000000"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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      "cell_type": "code",
      "source": [
        "data.info()"
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        "id": "LN5qQYgILyEH",
        "outputId": "50392d08-43d8-416b-a83e-393d916e49f0"
      },
      "execution_count": 9,
      "outputs": [
        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 25 entries, 0 to 24\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Hours   25 non-null     float64\n",
            " 1   Scores  25 non-null     int64  \n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 528.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.plot(kind='scatter',x='Hours',y='Scores');\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "aJ3oLwsUL_-h",
        "outputId": "b0b0cfdc-1ec0-4325-cece-78f4f72308bb"
      },
      "execution_count": 10,
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.corr(method='pearson')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "JQCugAirMW9i",
        "outputId": "b717e019-b7bb-4619-dbb6-401fbc02089d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Hours</th>\n",
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              "      <th>Hours</th>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>Scores</th>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-16a16a88-a353-4384-b193-159718977ae5');\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",
              "  "
            ],
            "text/plain": [
              "           Hours    Scores\n",
              "Hours   1.000000  0.976191\n",
              "Scores  0.976191  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
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          "data": {
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              "          document.querySelector('#df-fcf7a7ac-cf18-4815-80b3-5a3ab7c5f749 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fcf7a7ac-cf18-4815-80b3-5a3ab7c5f749');\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",
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              "  "
            ],
            "text/plain": [
              "           Hours    Scores\n",
              "Hours   1.000000  0.976191\n",
              "Scores  0.976191  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.corr(method='spearman')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "2XbihaPQMsX0",
        "outputId": "27964b34-43ed-44c1-d927-23d12d642b87"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th>Hours</th>\n",
              "      <th>Scores</th>\n",
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              "  </thead>\n",
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              "      <th>Hours</th>\n",
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              "      <td>0.971891</td>\n",
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              "    <tr>\n",
              "      <th>Scores</th>\n",
              "      <td>0.971891</td>\n",
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              "</table>\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4a1e47a9-63a1-4720-87f8-0ddf3e8d6562 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4a1e47a9-63a1-4720-87f8-0ddf3e8d6562');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           Hours    Scores\n",
              "Hours   1.000000  0.971891\n",
              "Scores  0.971891  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hours=data['Hours']\n",
        "scores=data['Scores']"
      ],
      "metadata": {
        "id": "arDX5HIcM9dw"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(hours)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "VVK8aRXeNINV",
        "outputId": "cbf38442-9efa-48fc-d5e1-fd487bd17288"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb975cc6750>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "Afb6BFpiNScT",
        "outputId": "43fe1255-6aac-4a65-888c-33d9e237c353"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9724dd750>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+p9IvKZYnb53OrX9fw/efXk/+nkP811WjifV4RoP9R+pZuqGMJQVlrNl7CFWI8gkDU+OZkptKZnIs6YkxxEX5iImKIDJCqDveTF1jM0fqmyirrmNXVS3vbK/krW2VZKXEcu6wdM7K6dNj/y/Np3fKJKOqjwGPici1qvrM6VYuIrPwj0LzAX9V1V+22h4D/B2YBBwArlPV3c5ItkXAFOBRVb0zYJ9JwKNAHLAU+LaqqoikAk8CufiHWX9BVW1MaYhZt+8wzapMGhz+Hf6nkhwbxePzp/LrV7fxl7d2sH7fYe6/YSJ56QndGkfV0QZe3ljOkvWlrNp9EFUY1T+Jb88YzrnD0tlSdoTIiPavmgf2mZ3pPK30mDMV0Ac7D7BoTTFvFu5n1pj+nJGVbC0b8wkdXS77kqr+A8gVke+13q6qv21jtxP7+oD7gUuAYmC1iCxW1c0BxeYDh1R1mIjMA+4FrgPqgZ8AY51XoD8DXwdW4k8ys4CXgQXAclX9pYgscJb/z6mOz3S/tXsPkd0njv4p4XWHf2dE+iJYcNkopuT25XtPrWfm797mGxcM4baLhhEX7V6r5lBtI8s2lbOkoIz3dlTRojA0I4FvzxjOleOyGNYv6WTZ7RVHT7v++JhIpg9JY1peKlvKanh1czn/WLmXM/onMXtCNsk9ZDCH6RodXS478bWrM8OUpwJFqroTQEQWArOBwCQzG7jHeb8I+JOIiKrWAu+KyLDACkUkC0hW1Q+c5b8D1+BPMrOBi5yijwFvYkkmpJQerqOsup6rzhrgdSjdasYZmSz7zgX8z8tb+MOKIp5ZW8IPZ43kijOziOyiRxvsr6nntc0VLNtUwXtFVTS1KLlp8dx+0TCuPCuLkZlJXd7KEBFGD0hmZP8k3ttRxetbKvjj8u3MnTyQEZlJHVdgeoWOLpf9r/PvTztRdzYQOKlTMTCtvTKq2iQi1UAa0N4cHdlOPYF1ZjvvM08MrwbKgcxOxGxctG7fYXwinJWT4nUo3a5/Siy/nzeBG6YN5r8Wb+LbC9fx61cLuX7qID43IZuslLjTqq+lRdlaXsO/i6pYtqn8ZB/L4LR45p+fx1XjBjBmQPdcvvJFCOcPz2BkZhILV+/jsfd2c+mY/lwwPN0un5kOWzIAiMiv8A9jrgNeAcYB33UupYUcp4+mzaEyInILcAvAoEGDujWu3qylRdlQUs3wzETio4P6teuRpual8tI3z+PVzRU8/O5OfvVKIb96pZAxA5KZmpfK6KxkslLiyEyOISUuioamFhqamqmuO87Oylp2VdWytbyG/N0HOVLfBMAZWcl8Z8YIZo7NdKXFEqx+ybF848KhPLO2mGWbyjl8rJGrzhpAhCWaXi3Yv/ZLVfWHIvI5/J3qnwfeBk6VZEqAgQHLOc66tsoUi0gkkIJ/AMCp6gwcDxpYZ4WIZKlqmXNZbX9bFajqg8CDAJMnT7aHgXSTtXsPUV13nJljrIEZESHMGtufWWP7s6uqllc2lvNG4X7+tWov9cdPfS9KZISQm57AFeOymJKbytS8VHL6hs7dBNGREcybMpA+8VG8s72K5hblmgnZlmh6sWCTzIlyVwBPq2p1EN+WVgPDRSQPfyKYh38m50CLgZvw39g5B1ihp3gKlJNAjojIdPwd/zcCf2xV1y+df18I8thMN1hSUEZkhHBGD7w35tPIS0/gtouGcttFQzne3ELxoToqjtRTcaSeI/VNxERGEBvlIykmktz0BHL6xoXsI6pPEBFmjelPZEQEbxTup7lFuXZSjiWaXirYJLNERLbiv1x2m4hk4B8B1i6nj+VOYBn+IcyPqOomEfkZkK+qi4GHgcdFpAg4iD8RASAiu/Hf9BktItfgb01tBm7noyHMLzsv8CeXp0RkPrAH+EKQx2Zc1tyivLShjJH9k8Li6ZdeifJFkJee0O3DnN0gIlwyOhNfhPD6lgoifRFcM36A9dH0QkElGVVd4PTLVKtqs4jU4h/N1dF+S/EPMw5cd3fA+3pgbjv75razPp9PDmtGVQ8AMzqKyXS/lbsOUFnTwIxR/bwOxXSzz4zqx/HmFt7aVkl6YjTnD8/wOiTTzU6nB3YU/vtlAvf5exfHY3qgJQVlxEX5GGWXynqlS0ZncqC2kVc2lpOaEM2YAb1vdGFvFuzosseBocA6oNlZrViSMR1oam7hlY3lzDijH9GRod2XYNwRIcLcSTlUH2vkqfx9fP38qJAarGDcFWxLZjIw+lSd8sa05b0dBzhY28iV4wZwsLbR63CMR6J8EXz57FweeKOIf63ay50XD3d11gMTOoL9arkR6O9mIKZnWlJQSmJMJBeNtGvxvV1iTCTzpg6iuu44z60rwb6z9g7BtmTSgc0isgpoOLFSVa92JSrTIzQ2+S+VXTI60/MZiE1oGJQazyWj+7NsUzmrMxKZmtezJ0o1wSeZe9wMwvRM7xZVcqS+iavOyvI6FBNCzh+ezs7KoywpKGVQanyvmCy1NwvqcpmqvoX/Tv8o5/1qYK2LcZkeYMn6MlLiojhvmF0qMx+JEGHu5IHERvl4es0+mlvssllPFlSSEZGv458l+X+dVdnA824FZcJf/fFmXt1cwcwxmTaqzHxCYkwks8cPoKy6nne2h89jqs3pC/av/w7gXOAIgKpuB+zOOtOut7ZVcrShiSvH9a5p/U3wxgxIYWx2Csu37qfiyCknEDFhLNgk06CqJ8efOjdkWhvXtGtJQRmpCdGcMzTN61BMCLtqXBbRvgieXVtMi40265GCTTJviciPgDgRuQR4GnjRvbBMOKtrbGb5lgpmje3fZQ/lMj1TUmwUV47LYt+hOt7fcaoJ2E24CvYTYAFQCWwAbsU/H9l/uhWUCW8rtu7nWGMzV46zUWWmY+MH9mFEZiKvb6mgpv641+GYLhbs6LIW/B39t6vqHFV9yO7+N+1ZUlBKRlIM0/LsUpnpmIhw5ZkDaGpWXtlY7nU4poudMsmI3z0iUgUUAoUiUikid59qP9N7HW1oYsXW/Vw+tj++CJvW3QQnPSmG84an8+G+w+w5UOt1OKYLddSS+S7+UWVTVDVVVVOBacC5IvJd16MzYWf5lgoamlq48iwbVWZOz0UjM0iOjeTFglIbBNCDdJRkvgxcr6q7TqxQ1Z3Al/A/ldKYj3lxfRn9k2OZNKiv16GYMBMT6eOyM7MoPVxP/u5DXodjukhHSSZKVatar1TVSiDKnZBMuKquO87b2yq5YlwWEXapzHTCuOwUctMSeG1zOQ3HmzvewYS8jpLMqeZmt3nbzce8trmCxuYWG1VmOk1EuPzM/tQ2NvP29k98vzVhqKMJMs8SkSNtrBfAZrUzH7OkoJScvnGMH9jH61BMGMvpG8+4nBTeLapkWl4qyXF20SScnbIlo6o+VU1u45WkqvY/b046VNvIu9uruGJcFiJ2qcx8OpeO7k9LC7y+pcLrUMyn5Ort2CIyS0QKRaRIRBa0sT1GRJ50tq8UkdyAbXc56wtFZKazbqSIrAt4HRGR7zjb7hGRkoBtl7t5bObjlm0qp6lFucrmKjNdIDUhmulDUlmz55DNaxbmgn2ezGkTER9wP3AJUAysFpHFqro5oNh84JCqDhORecC9wHUiMhqYB4wBBgCvi8gIVS0ExgfUXwI8F1Dffar6a7eOybRvSUEZuWnxjBmQ7HUorvvnyr1eh9ArXDyyH2v2HmLZpnJuPDvX63BMJ7nZkpkKFKnqTmdyzYXA7FZlZgOPOe8XATPEf61lNrBQVRuc4dNFTn2BZgA7VHWPa0dgglJ1tIH3dlRx5bgBdqnMdJn4mEguGJ7B1vIa9h485nU4ppPcTDLZwL6A5WJnXZtlVLUJqAbSgtx3HvCvVuvuFJECEXlERNq8UUNEbhGRfBHJr6y051h0hZc3ltOicKU9AdN0sbOHppEQ7bO+mTAWllPkikg0cDX+2aBP+DMwFP/ltDLgN23tq6oPqupkVZ2ckWFPbOwKS9aXMqxfIiMzk7wOxfQwMZE+LhiRQdH+o+yqsulmwpGbSaYEGBiwnOOsa7OM84yaFOBAEPteBqxV1ZNfb1S1QlWbnck8H+KTl9eMCyqO1LNq90GutFFlxiXT8tJIio3ktc0V2Ly84cfNJLMaGC4ieU7LYx6wuFWZxcBNzvs5wApndufFwDxn9FkeMBxYFbDf9bS6VCYigddqPgds7LIjMe16qaAMVewJmMY10ZERXDQig90HatlRaa2ZcONaknH6WO4ElgFbgKdUdZOI/ExErnaKPQykiUgR8D38z61BVTcBTwGbgVeAO1S1GUBEEvCPWHu21Y/8lYhsEJEC4GL8k3saly0pKGVU/ySG9Uv0OhTTg03JTSUlLorXNpdbaybMuDaEGUBVl+J/wFngursD3tcDc9vZ9xfAL9pYX4t/cEDr9V/+tPGa01NyuI61ew/zHzNHeh2K6eEifRFcPLIfz68robCihlH9e/5Q+Z4iLDv+TWh4qaAUwOYqM91i0uC+pCZE8/oW65sJJ5ZkTKctKSjjzOwUBqcleB2K6QV8EcJnRvaj9HA9m8vamlLRhCJLMqZT9hyopaC42loxpludNbAPaQnRrNi631ozYcKSjOmUJQVlAFxhScZ0I1+EcPGofpRV17PFWjNhwZKM6ZQlBWVMGNSHnL7xXodiepmzcvytmeXWmgkLlmTMadtReZQtZUfs3hjjCV+EcPHIE62ZGq/DMR2wJGNO2+J1pYjYqDLjnbMG9iE1IZoVW22kWaizJGNOi6qyeH0pZw9JIzPZHo5qvHFypFl1PVvLrTUTyizJmNNSUFzNrqpaZo+3S2XGWydaM8vtvpmQZknGnJYX1pUS7Ytg1li7VGa8daJvprS6nuVb9nsdjmmHJRkTtOYW5cWCUi4elUFKXJTX4RjDeKc187vl26w1E6IsyZigfbDzAJU1Dcwe3/r5ccZ4w9+ayWBjyRFrzYQoSzImaC+sKyExJpLPjOrndSjGnDR+YF8Gpsbx++XbrTUTgizJmKDUH2/m5Q3lzBzTn9gon9fhGHOSL0L45sXD2VBSzYqt1poJNZZkTFDeLNxPTUOTjSozIelzE7OtNROiLMmYoLywrpT0xGjOGfqJR/kY47koXwR3XjyMguJq3ii01kwosSRjOnSk/jjLt+7nynEDiPTZr4wJTZ+fmENO3zh+/7q1ZkKJfWKYDi3bWE5jU4tdKjMh7URrZn1xNW8WVnodjnFYkjEdemFdKYNS4xk/sI/XoRhzSidaM7973e6bCRWuJhkRmSUihSJSJCIL2tgeIyJPOttXikhuwLa7nPWFIjIzYP1uEdkgIutEJD9gfaqIvCYi251/+7p5bL1FyeE6/r2jimsmZCMiXodjzClFRwa0ZrZZayYUuJZkRMQH3A9cBowGrheR0a2KzQcOqeow4D7gXmff0cA8YAwwC3jAqe+Ei1V1vKpODli3AFiuqsOB5c6y+ZSeXVOMKsydlON1KMYE5fMTc8juE8fvrG8mJLjZkpkKFKnqTlVtBBYCs1uVmQ085rxfBMwQ/9fl2cBCVW1Q1V1AkVPfqQTW9RhwTRccQ6+mqixaW8z0IakMTLWHk5nwEB0ZwZ2fGcb6fYd5y1oznnMzyWQD+wKWi511bZZR1SagGkjrYF8FXhWRNSJyS0CZTFUtc96XA5ltBSUit4hIvojkV1baL+CprN59iD0HjjF30kCvQzHmtFxrrZmQEY4d/+ep6kT8l+HuEJELWhdQ/29Vm79Zqvqgqk5W1ckZGRkuhxrens7fR0K0j8vO7O91KMaclujICO64eBjrrDXjOTeTTAkQ+BU4x1nXZhkRiQRSgAOn2ldVT/y7H3iOjy6jVYhIllNXFmB3ZH0KtQ1NvLShjCvGZREfHel1OMactjmT/K0ZmwXAW24mmdXAcBHJE5Fo/B35i1uVWQzc5LyfA6xwWiGLgXnO6LM8YDiwSkQSRCQJQEQSgEuBjW3UdRPwgkvH1Sss3VDGscZm5k62S2UmPEVHRnD7xUP5cO9h3t5e5XU4vZZrScbpY7kTWAZsAZ5S1U0i8jMRudop9jCQJiJFwPdwRoSp6ibgKWAz8Apwh6o24+9neVdE1gOrgJdU9RWnrl8Cl4jIduCzzrLppKfXFJOXng/SJVoAABQeSURBVMDkwTYS3ISvuZMGMiAllt/bfTOecfU6iKouBZa2Wnd3wPt6YG47+/4C+EWrdTuBs9opfwCY8SlDNkDR/hpW7TrIf8wcaffGmLDmb80M4z+f38g726u4YIT1w3a3cOz4Ny57YuVeonzCdVPsUpkJf3Mn5/hbM9Y34wlLMuZj6hqbeWZNMbPGZpGeGON1OMZ8ajGRPm67eBhr9hzi3SLrm+lulmTMx7xYUMqR+iZumDbI61CM6TJfmJxDVkqs3TfjAUsy5mOeWLmXYf0SmZaX6nUoxnSZmEgftzutmX8XHfA6nF7Fkow5aWNJNev3HeaGaYOsw9/0OB+1ZmykWXeyJGNOemLlHmKjIvj8RJsM0/Q8MZE+br9oKPl7DvHeDmvNdBdLMgaAw8caef7DUq4+awApcVFeh2OMK74wZSD9k601050syRjA3xdTd7yZr5yb53UoxrjG3zczlNW7D9nzZrqJJRlDY1MLj723m/OHp3NGVrLX4RjjqnlTBjE4LZ7/WbqFpuYWr8Pp8SzJGF5cX8r+mgbmn2etGNPzRUdGsGDWKLZVHOWp/GKvw+nxLMn0cqrKQ+/sZERmIhfalBuml5g1tj+TB/flt69t42hDk9fh9GiWZHq5fxcdYGt5DV87b4gNWza9hojw4yvOoOpoA//71g6vw+nRLMn0cg+9s5P0xGiuHj/A61CM6VYTBvXlqrMG8NA7Oyk9XOd1OD2WJZlebGNJNW9tq+Sms3OJjfJ5HY4x3e6HM0eiCr94aYvXofRYlmR6sd8v305ybCQ3npPrdSjGeGJgajy3XzSMlzaU8a492MwVlmR6qY0l1by2uYL55w2xmy9Nr3brhUMYlBrPfy3eSGOTDWnuapZkeqkTrZibz831OhRjPBUb5eOeq0ezo7KWv/17l9fh9DiWZHoha8UY83GfGZXJZ8/ox++Xb6es2gYBdCVLMr2QtWKM+aT/umoMLar85PmNNq9ZF3I1yYjILBEpFJEiEVnQxvYYEXnS2b5SRHIDtt3lrC8UkZnOuoEi8oaIbBaRTSLy7YDy94hIiYisc16Xu3ls4WrNnkPWijGmDQNT4/nBpSN5fct+Fq8v9TqcHsO1JCMiPuB+4DJgNHC9iIxuVWw+cEhVhwH3Afc6+44G5gFjgFnAA059TcD3VXU0MB24o1Wd96nqeOe11K1jC1ctLcp/L9lMv6QYvna+TSFjTGtfOTeP8QP78NMXN3PgaIPX4fQIbrZkpgJFqrpTVRuBhcDsVmVmA4857xcBM8R/2/lsYKGqNqjqLqAImKqqZaq6FkBVa4AtQLaLx9CjvFhQyrp9h/mPmSNJiIn0OhxjQo4vQvjVnHHU1B/nnhc3ex1Oj+BmkskG9gUsF/PJhHCyjKo2AdVAWjD7OpfWJgArA1bfKSIFIvKIiPRtKygRuUVE8kUkv7Ky90z1XdfYzL0vb2VsdjLX2kPJjGnXiMwkvvmZ4by4vpRXNpZ5HU7YC8uOfxFJBJ4BvqOqR5zVfwaGAuOBMuA3be2rqg+q6mRVnZyR0XsmhPzrOzspra7nP68YTUSEzVFmzKl848KhnJmdwoJnN9hos0/JzSRTAgwMWM5x1rVZRkQigRTgwKn2FZEo/AnmCVV99kQBVa1Q1WZVbQEewn+5zgClh+v481s7mDkmk+lD0rwOx5iQFx0ZwR+un0BjUwvffXIdzS022qyz3Ewyq4HhIpInItH4O/IXtyqzGLjJeT8HWKH+sYOLgXnO6LM8YDiwyumveRjYoqq/DaxIRLICFj8HbOzyIwpDqsqPntuAKvznFa3HXRhj2pOXnsBPrx7DBzsP8hebqbnTXOv9VdUmEbkTWAb4gEdUdZOI/AzIV9XF+BPG4yJSBBzEn4hwyj0FbMY/ouwOVW0WkfOALwMbRGSd86N+5Iwk+5WIjAcU2A3c6taxhZPn15XwZmEld185moGp8V6HY0xYmTMph7e2VfLb17YxfUgakwa32dVrTkF6801HkydP1vz8fK/DcE1lTQOX3PcWQ9ITePob5+DzuC/mnyv3evrzTc/1xWmDXKu7uu44V/3xXeqPN7Pkm+fRLznWtZ8VLkRkjapODqZsWHb8m+Dcs3gTxxqa+dWccZ4nGGPCVUpcFA/eOImjDU3c+o81NDQ1ex1SWLEk00M9u7aYlzaU8e3PDmdYvySvwzEmrI3qn8xv5p7Fh3sPc/fzm2zamdNgSaYH2lZRw4+f28i0vFRuvWCI1+EY0yNcdmYWd148jCfz9/HwuzZbc7Dstu8e5mhDE9/4xxoSYiL54/UTiPTZ9whjusr3LhnBjsqj/PylLaQmRPN5u7G5Q/YJ1IOoKnc9u4HdVbX88foJ1kFpTBeLiBDuu248Zw9J4z8WFbBia4XXIYU8SzI9yP1vFPHi+lK+f+lIzh5qN10a44bYKB8P3jiJ0VnJ3P7EWlbuPOB1SCHNkkwPsXDVXn796jY+PyGb2y4c6nU4xvRoSbFRPPqVKWT3ieOmv63ine29Zx7E02VJpgd4bXMFP3puAxeOyODeOeNsbjJjukFaYgxP3no2eemJzH80n1c2lnsdUkiyJBPm3tleyZ3/XMuZOX144IaJRFlHvzHdJj0xhoVfn86Y7GTu+Odanlq9r+Odehn7RApjSwpK+eqjq8lLT+CRmybbM2KM8UBKfBT/mD+Nc4am8cNnCrhn8SaON7d4HVbIsCQTph5/fzff/NeHTBjYlydvPZu0xBivQzKm10qIieRvN09h/nl5PPrebm58eBUHaxu9DiskWJIJM41NLfz0xU385IVNzBiVyd/nTyUlLsrrsIzp9SJ9EfzkytH8Zu5ZrNl7iJm/e5s3tu73OizPWZIJI/sOHmPuX97jb//ezVfOzeUvX5pIbJTP67CMMQGunZTDc7efQ2p8NF95dDULningaEOT12F5xi7ih4GWFmXR2mL+e4n/meN/+dIkZo3t73FUxpj2jBmQwuJvnst9r23nwbd38Ebhfn44cxSfm5Dd60Z/WksmxG0uPcLc/32fHy4qYERmEku/db4lGGPCQEykjwWXjWLRbefQPyWO7z+9nmse+Dfv7ajqVRNsWksmRO2sPMqf39zBM2uL6RMfza/mjGPOxJxe9y3ImHA3cVBfnrvtHF5YX8K9LxfyxYdWMmFQH267cCifPSOzx/9NW5IJIarKh/sO88i7u1i6oYwoXwQ3nZPLt2cMp098tNfhGWM6KSJC+NyEHC4bm8XTa4p58O0d3PL4GnLT4pk7eSCfn5hNVkqc12G6wpJMCNh38BgvFpSyaE0xOytrSYyJ5JYLhjL/vDwykmxosjE9RWyUjy9PH8z1UwaydGM5T3ywh/+3rJDfvFrI2UPTuOSMTGackdmjHpVuj1/24PHLh2obWbfvMP8uquKNwv3sqKwFYGpuKnMm5XD5uCwSe+CNlfb4ZeMWNx+/7LY9B2p5Zo3/IYMnPguG9UtkWl4qU/NSmTS4L9l94hAJnctqp/P4ZVc/yURkFvB7wAf8VVV/2Wp7DPB3YBJwALhOVXc72+4C5gPNwLdUddmp6hSRPGAhkAasAb6sqp7dDaWqHKlvoqy6jt1VtWyrOMr2/UfZWFLNrir/L1K0L4JpQ1K5YdpgPntGJoPSes63F2NMcAanJfC9S0fyvUtHsquqluVbKnhnexUvrCvlCeeLWVJMJCP6JzGyfxKj+icxOC2B7D6xZKXEhfxMH661ZETEB2wDLgGKgdXA9aq6OaDM7cA4Vf2GiMwDPqeq14nIaOBfwFRgAPA6MMLZrc06ReQp4FlVXSgifwHWq+qfTxVjZ1syu6tq2VJ2hOq64xypP+7/t66J6rrjHDrWSFl1PWWH66ht/PizwHP6xjGqfzITB/dh4qC+jMtJIT46tH9BupK1ZIxbwrkl057mFmVL2RHW7TtMYXkNhRU1FJbXUF13/GPlUuKiyEqJpW98NClxUfSJjyIlPoo+cf7l+GgfsVE+YqMiiIvyEecsZybHdvpG7lBpyUwFilR1pxPUQmA2sDmgzGzgHuf9IuBP4m8TzgYWqmoDsEtEipz6aKtOEdkCfAb4olPmMafeUyaZzlq2qZz/eXnryWVfhJAcG0lKXBQp8dEMy0jk/OHpDEiJI6tPLINS4xnWL7FXJRRjzKfjixDGZqcwNjvl5DpVpeJIA/sOHaP0cB2lh+spPVxHWXU91XWN7Kg8yuG641QfO05jB/On/fc1Y/ny9MFuH4arSSYbCJyStBiY1l4ZVW0SkWr8l7uygQ9a7ZvtvG+rzjTgsKo2tVH+Y0TkFuAWZ/GoiBSexjEFIx2o6uI6u0KoxgUWW2eEalzgQWw3BFfMzlmAG++FG4Mr2lZsQWenXvfVWlUfBB50q34RyQ+2GdmdQjUusNg6I1TjgtCNLVTjgp4dm5t3/JcAAwOWc5x1bZYRkUggBf8AgPb2bW/9AaCPU0d7P8sYY0w3czPJrAaGi0ieiEQD84DFrcosBm5y3s8BVqh/JMJiYJ6IxDijxoYDq9qr09nnDacOnDpfcPHYjDHGBMG1y2VOH8udwDL8w40fUdVNIvIzIF9VFwMPA487HfsH8ScNnHJP4R8k0ATcoarNAG3V6fzI/wMsFJGfAx86dXvBtUtxn1KoxgUWW2eEalwQurGFalzQg2Pr1TdjGmOMcZfNwmyMMcY1lmSMMca4xpJMFxCR/yciW0WkQESeE5E+AdvuEpEiESkUkZkexTfL+flFIrLAixicOAaKyBsisllENonIt531qSLymohsd/7t62GMPhH5UESWOMt5IrLSOXdPOgNOvIirj4gscn7PtojI2aFw3kTku87/5UYR+ZeIxHp1zkTkERHZLyIbA9a1eY7E7w9OjAUiMtGD2Dz/3GgrroBt3xcRFZF0Z7lT58ySTNd4DRirquPwT3tzF4D4p8eZB4wBZgEPiH+6nW7j/Lz7gcuA0cD1TlxeaAK+r6qjgenAHU4sC4DlqjocWO4se+XbwJaA5XuB+1R1GHAI/3x6Xvg98IqqjgLOwh+jp+dNRLKBbwGTVXUs/sE48/DunD2K/+8sUHvn6DL8o1aH478525XZQTqILRQ+N9qKCxEZCFwKBM4F1alzZkmmC6jqqwGzDXyA/z4dCJgeR1V3AYHT43SXk9P7OBOGnpjep9upapmqrnXe1+D/oMx24nnMKfYYcI0X8YlIDnAF8FdnWfBPV7TIy9hEJAW4AGfEpKo2quphQuO8RQJxzj1q8UAZHp0zVX0b/yjVQO2do9nA39XvA/z32WV1Z2yh8LnRzjkDuA/4IRA4MqxT58ySTNf7KvCy876tqXXanO7GRaEQwyeISC4wAVgJZKpqmbOpHMj0KKzf4f/DOjHpU9DTFbksD6gE/uZcyvuriCTg8XlT1RLg1/i/7ZYB1fhnQA+Fc3ZCe+co1P4uQuZzQ0RmAyWqur7Vpk7FZUkmSCLyunPdufVrdkCZH+O/JPSEd5GGPhFJBJ4BvqOqRwK3OTfWdvu4ehG5Etivqmu6+2cHIRKYCPxZVScAtbS6NObFeXP6N2bjT4IDgATauPQSKrz63epIKH1uiEg88CPg7q6qs9fNXdZZqvrZU20XkZuBK4EZ+tHNR8FMreO2UIjhJBGJwp9gnlDVZ53VFSKSpaplTvN7vwehnQtcLSKXA7FAMv5+kD4iEul8M/fq3BUDxaq60llehD/JeH3ePgvsUtVKABF5Fv95DIVzdkJ75ygk/i5C8HNjKP4vDev9V4vJAdaKyNTOxmUtmS4g/gep/RC4WlWPBWxqb3qc7hTM9D7dwunjeBjYoqq/DdgUOL2QJ1MCqepdqpqjqrn4z9EKVb2BEJiuSFXLgX0iMtJZNQP/bBhen7e9wHQRiXf+b0/E5fk5C9DeOVoM3OiMmJoOVAdcVusWofi5oaobVLWfquY6fwvFwETnd7Bz50xV7fUpX/g75vYB65zXXwK2/RjYARQCl3kU3+X4R6/sAH7s4Xk6D//lioKAc3U5/r6P5cB2/A+oS/X4//MiYInzfgj+P/Ai4GkgxqOYxgP5zrl7HugbCucN+CmwFdgIPA7EeHXO8D/osAw47nw4zm/vHAGCf9TlDmAD/hFy3R2b558bbcXVavtuIP3TnDObVsYYY4xr7HKZMcYY11iSMcYY4xpLMsYYY1xjScYYY4xrLMkYY4xxjSUZY1wkIj92ZikuEJF1IjLN65iM6U52x78xLhGRs/HfzT1RVRucKdM7Pe19wF30xoQNa8kY454soEpVGwBUtUpVS0Vkioi8JyLrRWSViCSJ/zksfxORDc4kmBeDf9oREVksIiuA5SKS4DwDZJVTbrZTboyzbp3Tahru3WEb8xG7GdMYlzgTgb6Lfwr814Engffx3yF/naquFpFk4Bj+59iMUdWvisgo4FVgBP4pbn4OjFPVgyLyf4HNqvoP5yFXq/DPZv1L4ANVfcKZPsinqnXdesDGtMEulxnjElU9KiKTgPOBi/EnmV8AZaq62ilzBEBEzgP+6KzbKiJ78CcZgNdU9cQzPy7FP5HnD5zlWGAQ/uT1Y+eZOM+q6nbXD9CYIFiSMcZFqtoMvAm8KSIbgDs6UU1twHsBrlXVwlZltojISvwPXVsqIreq6orOxGxMV7I+GWNcIiIjW/WNjMf/NNAsEZnilElynir5DnCDs24E/tZJ60QCsAz4pjPrMSIywfl3CLBTVf+Af6bhce4clTGnx1oyxrgnEfij03fShH/W3VuAvznr44A6/M9leQD4s9PaaQJudkakta7zv/E/wbNARCKAXfhHsH0B+LKIHMf/BMj/6/bBGRMM6/g3xhjjGrtcZowxxjWWZIwxxrjGkowxxhjXWJIxxhjjGksyxhhjXGNJxhhjjGssyRhjjHHN/wdJdxczkpqRNQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outlier Removal**"
      ],
      "metadata": {
        "id": "W596ihcbNxD5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def null_detection(data):\n",
        "  num_cols = []\n",
        "\n",
        "  count = 0\n",
        "  t = []\n",
        "  for i in num_cols:\n",
        "    z = np.abs(stats.zscore(df[i]))\n",
        "    for j in range(len(z)):\n",
        "      if z[j]>3 or z[j]<-3:\n",
        "        t.append(j)\n",
        "        count+=1\n",
        "  data = data.drop(list(set(t)))\n",
        "  data = data.reset_index()\n",
        "  data = data.drop('index', axis=1)\n",
        "  print(count)\n",
        "  return data"
      ],
      "metadata": {
        "id": "ZFh2VIiJN8eL"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = null_detection(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XueLW8RqOy9g",
        "outputId": "9e13859a-91e0-46f4-ca0b-656eafcb228f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LINEAR REGRESSION**"
      ],
      "metadata": {
        "id": "ZU0Tv8PQP3J0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.iloc[:, :-1].values  \n",
        "y = data.iloc[:, 1].values "
      ],
      "metadata": {
        "id": "jS9GxVR_P6TE"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                            test_size=0.2, random_state=50)"
      ],
      "metadata": {
        "id": "4oCmob3TQC3f"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression  \n",
        "reg = LinearRegression()  \n",
        "reg.fit(X_train, y_train) \n",
        "\n",
        "print(\"Training complete.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fc55I8zIQK1o",
        "outputId": "09bb40af-940d-4015-b9a9-ad13d49dafaf"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m=reg.coef_\n",
        "c=reg.intercept_\n",
        "line=m*X+c\n",
        "plt.scatter(X,y)\n",
        "plt.plot(X,line);\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "8anTDHlyQVPG",
        "outputId": "2b76597b-170a-49e4-c2c3-e007a1c56c90"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=reg.predict(X_test)"
      ],
      "metadata": {
        "id": "lmYA4G_hRCw3"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Comparing actual vs predicted\n",
        "actual_predicted=pd.DataFrame({'Target':y_test,'Predicted':y_pred})\n",
        "actual_predicted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NOrLrvtRRSzP",
        "outputId": "4b742ac0-b7b0-4df9-cb37-9f022bcd1854"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-48f4c169-a547-46e9-b74a-7b1517804c3b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Target</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>95</td>\n",
              "      <td>88.211394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30</td>\n",
              "      <td>28.718453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>76</td>\n",
              "      <td>69.020122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35</td>\n",
              "      <td>39.273652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>13.365436</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48f4c169-a547-46e9-b74a-7b1517804c3b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-48f4c169-a547-46e9-b74a-7b1517804c3b 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-48f4c169-a547-46e9-b74a-7b1517804c3b');\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",
              "  "
            ],
            "text/plain": [
              "   Target  Predicted\n",
              "0      95  88.211394\n",
              "1      30  28.718453\n",
              "2      76  69.020122\n",
              "3      35  39.273652\n",
              "4      17  13.365436"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating difference between target value and predicted value\n",
        "sns.set_style('whitegrid')\n",
        "sns.distplot(np.array(y_test-y_pred))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "Z5HIE4sZSWMQ",
        "outputId": "c5fc5a0a-948d-4bbb-ce72-079c5621069c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The difference is close to 0 (It is between -.5 to .5) which implies that it is fitting our data well."
      ],
      "metadata": {
        "id": "2i3CGZ4VSzIu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What would be predicted score if a student studies for 9.25 hours/day?**"
      ],
      "metadata": {
        "id": "eweoSEGoTHyo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "h = 9.25\n",
        "s=reg.predict([[h]])\n",
        "print(\"If a student studies for {} hours per day he/she will score {} % in exam.\".format(h,s))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "um5eRhN4TWit",
        "outputId": "ee6fa20e-6077-4d62-9e66-fe8dc9710ff3"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "If a student studies for 9.25 hours per day he/she will score [91.56986604] % in exam.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Evaluation**"
      ],
      "metadata": {
        "id": "EFWbB_dBTwqY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "from sklearn.metrics import r2_score\n",
        "print('Mean Absolute Error:',metrics.mean_absolute_error(y_test,y_pred))\n",
        "print('R2 Score:',r2_score(y_test,y_pred)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-opbDgATT1rE",
        "outputId": "0b44ab5d-6b6f-4123-9200-3f2bf149de57"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 4.5916495300630285\n",
            "R2 Score: 0.971014141329942\n"
          ]
        }
      ]
    }
  ]
}