Student_Marks_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Student_Marks_Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "1eX4r205KofP"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEuAnZhVxbAk"
      },
      "source": [
        "## **Student Mark Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/"
        },
        "id": "9le6P1nGxjqo",
        "outputId": "a4fc9bca-202a-457a-baf1-a7c51c70263d"
      },
      "source": [
        "from google.colab import files\n",
        "upload=files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ebc2e60b-5067-4b5a-9fd7-207e2121cc70\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ebc2e60b-5067-4b5a-9fd7-207e2121cc70\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving student_info.csv to student_info.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mCipfyEqyFcn"
      },
      "source": [
        "#Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4A251jNyWeX"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "n1-GBlIJyjmk",
        "outputId": "c7fc79e0-9c3d-4bc6-93c8-f41fb72a1b93"
      },
      "source": [
        "#Loading the Dataset\n",
        "df=pd.read_csv(\"student_info.csv\")\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>study_hours</th>\n",
              "      <th>student_marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.83</td>\n",
              "      <td>78.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.56</td>\n",
              "      <td>76.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>78.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.67</td>\n",
              "      <td>71.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.67</td>\n",
              "      <td>84.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>7.53</td>\n",
              "      <td>81.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>8.56</td>\n",
              "      <td>84.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>8.94</td>\n",
              "      <td>86.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>6.60</td>\n",
              "      <td>78.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>8.35</td>\n",
              "      <td>83.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     study_hours  student_marks\n",
              "0           6.83          78.50\n",
              "1           6.56          76.74\n",
              "2            NaN          78.68\n",
              "3           5.67          71.82\n",
              "4           8.67          84.19\n",
              "..           ...            ...\n",
              "195         7.53          81.67\n",
              "196         8.56          84.68\n",
              "197         8.94          86.75\n",
              "198         6.60          78.05\n",
              "199         8.35          83.50\n",
              "\n",
              "[200 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "96W2zS-syy6p",
        "outputId": "35b7aa4a-a3c7-4390-d6c3-effe35b1b41a"
      },
      "source": [
        "#showing first five rows\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>study_hours</th>\n",
              "      <th>student_marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.83</td>\n",
              "      <td>78.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.56</td>\n",
              "      <td>76.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>78.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.67</td>\n",
              "      <td>71.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.67</td>\n",
              "      <td>84.19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   study_hours  student_marks\n",
              "0         6.83          78.50\n",
              "1         6.56          76.74\n",
              "2          NaN          78.68\n",
              "3         5.67          71.82\n",
              "4         8.67          84.19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "BKmk_P_Qy4RR",
        "outputId": "bc26d335-1c0d-4820-e972-4c49b4489911"
      },
      "source": [
        "#Showing Last five rows\n",
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>study_hours</th>\n",
              "      <th>student_marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>7.53</td>\n",
              "      <td>81.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>8.56</td>\n",
              "      <td>84.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>8.94</td>\n",
              "      <td>86.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>6.60</td>\n",
              "      <td>78.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>8.35</td>\n",
              "      <td>83.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     study_hours  student_marks\n",
              "195         7.53          81.67\n",
              "196         8.56          84.68\n",
              "197         8.94          86.75\n",
              "198         6.60          78.05\n",
              "199         8.35          83.50"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5MhPO4HAzJuF",
        "outputId": "3e221885-9778-4cde-8ec4-8a9e8b041491"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74kZvurtzVbO"
      },
      "source": [
        "Discover and Visualize the data to gain insights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3Puk5mvzNIM",
        "outputId": "32faf4c6-2e42-414c-b4f4-fd0fe73f9e16"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   study_hours    195 non-null    float64\n",
            " 1   student_marks  200 non-null    float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 3.2 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "JVHAtVWeziZ1",
        "outputId": "3d8611a2-bdc4-4c28-e192-a6708bbc5b84"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>study_hours</th>\n",
              "      <th>student_marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>195.000000</td>\n",
              "      <td>200.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.995949</td>\n",
              "      <td>77.93375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.253060</td>\n",
              "      <td>4.92570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.010000</td>\n",
              "      <td>68.57000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.775000</td>\n",
              "      <td>73.38500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.120000</td>\n",
              "      <td>77.71000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.085000</td>\n",
              "      <td>82.32000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.990000</td>\n",
              "      <td>86.99000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       study_hours  student_marks\n",
              "count   195.000000      200.00000\n",
              "mean      6.995949       77.93375\n",
              "std       1.253060        4.92570\n",
              "min       5.010000       68.57000\n",
              "25%       5.775000       73.38500\n",
              "50%       7.120000       77.71000\n",
              "75%       8.085000       82.32000\n",
              "max       8.990000       86.99000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "4aatOtTD0bhQ",
        "outputId": "72ec299c-df68-497a-856e-ba4e268d7527"
      },
      "source": [
        "plt.figure(figsize=(7,5))\n",
        "plt.scatter(x=df.study_hours,y=df.student_marks,color=\"red\",marker=\"*\",s=100,alpha=0.5)\n",
        "plt.xlabel(\"study_hours\")\n",
        "plt.ylabel(\"student_marks\")\n",
        "plt.title(\"study_hours Vs student_marks\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A1rujMcG2t-H"
      },
      "source": [
        "Preparing the Data for Machine Learning Algo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "A3LJHJ2B25Iw",
        "outputId": "1685d15d-7b54-41ab-e213-8d786dc22404"
      },
      "source": [
        "#Data Cleaning\n",
        "df.isnull()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>study_hours</th>\n",
              "      <th>student_marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     study_hours  student_marks\n",
              "0          False          False\n",
              "1          False          False\n",
              "2           True          False\n",
              "3          False          False\n",
              "4          False          False\n",
              "..           ...            ...\n",
              "195        False          False\n",
              "196        False          False\n",
              "197        False          False\n",
              "198        False          False\n",
              "199        False          False\n",
              "\n",
              "[200 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDp2AKE73Rs5",
        "outputId": "275c709e-f957-4b5b-8296-f305f9d51ea0"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "study_hours      5\n",
              "student_marks    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oKk5o2P3siQ",
        "outputId": "9d6b02c8-8c86-4729-ea1a-df4c8bcaf324"
      },
      "source": [
        "df.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "study_hours       6.995949\n",
              "student_marks    77.933750\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQSw8ZbG32K6"
      },
      "source": [
        "df2=df.fillna(df.mean())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DsEKPdln4GWb",
        "outputId": "4d88b5f3-349c-4d18-a22c-effc63f3ba70"
      },
      "source": [
        "df2.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "study_hours      0\n",
              "student_marks    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "Dpqy3F_w4Kpw",
        "outputId": "693b5f99-9f35-4ead-e308-cf2dfed724bf"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(df.corr(),annot=True,linewidths=3)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "usrf6dtQ7zJ8",
        "outputId": "41b5ba91-d4f0-4b31-c7cc-5ddc54b87bc3"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.barplot(x=\"study_hours\",y=\"student_marks\",data=df2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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blVoH+F7AR4DLQwi7Aqsp6Y4PIQQglJvZzI43s+lmNn3xqhU1TqqIiEhx1DrAvwm8GUJ4In6ejAf8+WY2CiD+X1Bu5hDCVSGE3UIIu209cHCNkyoiIlIcNQ3wIYR5wBtm9t44aj9gJnA3cEwcdwxwVy3TISIisrnp1Qnr+AZwo5n1BmYBX8ErFrea2bHAa8BhnZAOERGRzUbNA3wI4RlgtzJf7VfrdYuIiGyu9CQ7ERGRAlKAFxERKSAFeBERkQJqc4A3s73iQ2ows6PN7EIze0ftkiYiIiLtVUkL/nJgjZntApwBvAz8tiapEhERkQ6pJMDXx6fOjQN+GUK4DBhUm2SJiIhIR1TyM7mVZnYucDSwt5n1ALaoTbJERESkIyppwX8BWA8cG59QNxr4WU1SJSIiIh1SSQt+1xDChelDCOF1M+tfgzSJiIhIB1XSgv8vM9s3fTCzs/Hr8SIiItLNVNKCHwtMNbOzgP2BnVCAFxER6ZbaHOBDCIvMbCzwAPAkMDHeVS8iIiLdTKsB3sxWAvlA3ht4JzDRzEIIQS9qFxER6WZaDfAhhEFmZsDbQwivd0KaREREpIPadJNd7IqfVuO0iIiISJVUchf9U2b20ZqlRERERKqmkrvodweOMrPXgNWA4Y37D9UkZSIiItJulQT4z9UsFSIiIlJVlfxM7jUAM9sG6FuzFImIiEiHVfI++LFm9iLwCvAw8Cpwb43SJSIiIh1QyU12PwT2AF4IIewA7Af8rSapEhERkQ6pJMBvCCEsBnqYWY8QwkPAbjVKl4iIiHRAJTfZLTOzgcBfgRvNbAF+N72IiIh0M5W04McBa4FvAvcBLwOfr0WiREREpGMquYt+NYCZDQb+ULMUiYiISIe1OcCb2QnAfwPrgEbig27wF8+IiIhIN1LJNfgzgQ+EEBbVKjEiIiJSHZVcg38ZWFOrhIiIiEj1VNKCPxd4zMyeANankSGEU6ueKhEREemQSgL8lcCfgRn4NXgRERHppioJ8FuEEL5Vs5SIiIhI1VRyDf5eMzvezEaZ2dD0V7OUiYiISLtV0oI/Iv4/NzdOP5MTERHphip50M0OG/vezD4TQvhTx5MkIiIiHVVJF31rLqjiskRERKQDqhngrYrLEhERkQ6oZoAPVVyWiIiIdEA1A7yIiIh0E20O8GbWp5Vxr1YjQSIiItJxlbTgH9/YuBDCIR1PjoiIiFRDqz+TM7ORwLZAPzPblexmusFA/xqmTURERNqpLb+D/xzwZWA0cGFu/ErgvBqkSURERDqo1QAfQrgeuN7MJoQQpnRCmkRERKSDKnlU7VQzOxLYPj9fCOEH1U6UiIiIdEwlAf4uYDnwJLn3wYuIiEj3U0mAHx1C2L9mKREREZGqqeRnco+Z2QdrlhIRERGpmkpa8J8Avmxmr+Bd9AaEEMKHapIyERERabdKAvwBNUuFiIiIVFWbu+hDCK8Bbwf2jcNrKplfREREOk8lz6L/PvBt4Nw4agvghlokSkRERDqmkhb4eGAssBoghDAHGNSWGc2sp5k9bWZT4+cdzOwJM3vJzG4xs96VJlxERERaVkmArwshBOJ7381sQAXzngY8l/t8AfCLEMK7gaXAsRUsS0RERFpRSYC/1cyuBLYys68BDwC/bm0mMxsNjAGujp8N2BeYHCe5Hji4kkSLiIjIxrX5LvoQws/N7DPACuC9wPdCCH9qw6wXAWeTdedvDSwLIdTHz2/ib6sTERGRKqnkZ3LEgN6WoA6AmR0ELAghPGlmn6owbZjZ8cDxAKOHbl3p7CIiIputtrwPfiXxuns5IYTBG5l9L2CsmR0I9MXfIX8x3s3fK7biRwOzW1j2VcBVAB9+xztbTIOIiIg01+o1+BDCoBjELwbOwbvTR+M/mbuolXnPDSGMDiFsDxwO/DmEcBTwEDAxTnYM/iIbERERqZJKbrIbG0L4VQhhZQhhRQjhcmBcO9f7beBbZvYSfk3+mnYuR0RERMqo5Br8ajM7CrgZ77I/gvib+LYIIfwF+EscngV8rIJ1i4iISAUqacEfCRwGzI9/h8ZxIiIi0s1U8jO5V2l/l7yIiIh0ojYHeDO7ljJ304cQvlrVFImIiEiHVXINfmpuuC/+bPo51U2OiIiIVEMlXfRT8p/N7CbgkaqnSERERDqsI+9z3xHYploJERERkeqp5Bp86RPt5uG/ZxcREZFuppIu+ja9+11ERES6Xpu76M3swbaMExERka7XlpfN9AX6A8PMbAhg8avB6DWvIiIi3VJbuuhPAE4H3gY8iQf4AKwELq1d0kRERKS92vI2uYtDCDsAPwI+HIevBWYBj9c4fSIiItIOlfxMbmIIYYWZfQLYF7gauLw2yRIREZGOqCTAN8T/Y4BfhxCmAb2rnyQRERHpqEoC/GwzuxL4AnCPmfWpcH4RERHpJJUE6MOAPwKfCyEsA4YCZ9UkVSIiItIhlTzoZg1we+7zXGBuLRIlIiIiHaMudhERkQJSgBcRESkgBXgREZECUoAXEREpIAV4ERGRAlKAFxERKSAFeBERkQJSgBcRESkgBXgREZECUoAXEREpIAV4ERGRAlKAFxERKSAFeBERkQJSgBcRESkgBXgREZECUoAXEREpIAV4ERGRAlKAFxERKSAFeBERkQJSgBcRESkgBXgREZECUoAXEREpIAV4ERGRAlKAFxERKSAFeBERkQJSgBcRESkgBXgREZECUoAXEREpIAV4ERGRAlKAFxERKSAFeBERkQJSgBcRESkgBXgREZECqmmAN7O3m9lDZjbTzJ41s9Pi+KFm9iczezH+H1LLdIiIiGxuat2CrwfOCCHsDOwBnGxmOwPnAA+GEHYEHoyfRUREpEpqGuBDCHNDCE/F4ZXAc8C2wDjg+jjZ9cDBtUyHiIjI5qbTrsGb2fbArsATwIgQwtz41TxgRGelQ0REZHPQKQHezAYCU4DTQwgr8t+FEAIQWpjveDObbmbTF69aUW4SERERKaPmAd7MtsCD+40hhNvj6PlmNip+PwpYUG7eEMJVIYTdQgi7bT1wcK2TKiIiUhi1vovegGuA50IIF+a+uhs4Jg4fA9xVy3SIiIhsbnrVePl7AV8EZpjZM3HcecBPgVvN7FjgNeCwGqdDRERks1LTAB9CeASwFr7er5brFhER2ZzpSXYiIiIFpAAvIiJSQArwIiIiBaQALyIiUkAK8CIiIgWkAC8iIlJACvAiIiIFpAAvIiJSQArwIiIiBaQALyIiUkAK8CIiIgWkAC8iIlJACvAiIiIFpAAvIiJSQArwIiIiBaQALyIiUkAK8CIiIgWkAC8iIlJACvAiIiIFpAAvIiJSQArwIiIiBaQALyIiUkAK8CIiIgWkAC8iIlJACvAiIiIFpAAvIiJSQArwIiIiBaQALyIiUkAK8CIiIgWkAC8iIlJACvAiIiIFpAAvIiJSQArwIiIiBaQALyIiUkAK8CIiIgWkAC8iIlJACvAiIiIFpAAvIiJSQArwIiIiBaQALyIiUkAK8CIiIgWkAC8iIlJACvAiIiIFpAAvIiJSQArwIiIiBaQALyIiUkAK8CIiIgWkAC8iIlJACvAiIiIFpAAvIiJSQArwIiIiBdRlAd7M9jezf5vZS2Z2TlelQ0REpIi6JMCbWU/gMuAAYGfgCDPbuSvSIiIiUkRd1YL/GPBSCGFWCKEOuBkY10VpERERKZyuCvDbAm/kPr8Zx4mIiEgVWAih81dqNhHYP4RwXPz8RWD3EMIpJdMdDxwfP74XWAwsip+HtTC8se+6w3B3SYfSV+w0KX3FS5PSV+z0lftuQAhhOO0VQuj0P+DjwB9zn88Fzm3DfNNbG27rdF013F3SofQVO01KX/HSpPQVO32tfdeev67qov8HsKOZ7WBmvYHDgbu7KC0iIiKF06srVhpCqDezU4A/Aj2B34QQnu2KtIiIiBRRlwR4gBDCPcA9Fc52VRuG2zpdVw13l3QofcVOk9JXvDQpfcVOX2vfVaxLbrITERGR2tKjakVERAqoy7roS5nZq8BKoAF4H/BCmeEPAvVAI9A7N9w3LqYRr7SkbgkrM7wQ2KZMEuqBZ+P6GoEt8PsDiPMZsD7+712y3PVxnMV5rcy618W0LQdKf/aQlk9cVtqmBvwYNQKvAO+K6XwD2CFOXxfXnba/3Lrz3TTrgT4bmaZcuvPTG+XlvysdTssql756YC0wuGQbSpeTT38gO+Yh/tWTHZf8/HUx/Wl9DfhxLV1HPn0pHT3KbHd+OC0rqY+fA80rzyFO+ziwO9nxyn+/BujHWyvdjWXGpfEp3XkNZPk3rz5+17vMPPltqqd5ubAEGFoyXQBeBHaM4/PLy0/TlrxSblwazuftvA28dfvS+F5xG2hhmrTsDfix6lEyvjRt6XN9/Oub+64hfrcAz5fv2Mi2bMCPS5+S9NTH7/rE73uWfJ8/f9L/QMv5Ii+fT4nLnoefa/1563FfHMctx8uXLWh+PmyI0/XHj00vsnMkpXVD/Jz2bf6cAi9PVgAj4ud0jDfEv/7Av4F3t7DseXGed+LlxoCS70NMV8+47nX4MSs9zqXDefn8ld/P+XOuXDnV0jkJby2Hk7RfAzAnpnfHuE/W5dIzKw5/AJiNx8ofhhBuKbPMJt2tBb9PCOHDwNzSYeA8/CTaNg5bbjgAR+E7IeAB/Adxme8GxuDB+yfAw3iGD8DlwKvAamBxXN9q4H/xnToHGBn/B2A08OP43TbAd+L4q4BH4vrOAMbG4Q8A/4kfjBOBzwFD4jyPAMuA14Cz4vSvA1/FM/CCmJb6+H8ucC+wFC8M5sR5/g48EZd5WNwfjfgJelQc/05gu7jPbgKmpP2NV2gagF3jNAAT4vYF4PY4fYhpGI6fnK/hP28MeIY7FT9xZ8d1N8Tt2Aa4IU53BvDruI5dgZ/iJ+OzeMBI+29a3IZvAuk9Bb+M29YHeBR4Ok7zSNxvL+HBKKXh9/gTEvvE9J8OPBSX9TTwWDwupwP7xvHfx3/C2QhcAUzFC5GXgefjNGcD+8XtmQVch/8q5Li4zQuA/8MLS4BLgb/GbeqDH68GYFXc/jV4nrwnbsff4nbOisv6Jp5PVgBP4oUvMX2v5vZTup/lo8ADMX1vj+sGOBrYEz8mE/HjF+KyJ+B5eiFeAfnfmMYlMT3rgWuB38Xxv8aP657A9bk07Uv2RMpv4/kp5Y8vxvEfAj4Sx6+O6wTPA4/G7Xk+rvusON1rMX3LgJlx+nr82M+K370/7r9G4JPA2+K8X8TPQ4BD8POvAX9M9n/k0v3juMz3AF+J4/cCjojDhwFP4flhPvAbPIgsBQ7Ey4k78OOcHuL1Sbz8CfgjuV+L43fGzyvw43I3Xhb/PG6D4efBLXH8DcDVcfxZeDnQM46fFsd/N04PcALwpzj8t5gu8PxzIX68AvBc3A8BPx+2wvPUPXggbcTLvJUxHTcB58f1fQPPKwG4CC9LDfgWWXl2I36MGmOaTsTPgSExLevx839RTO+auP2D47IW4Q9BC8CdeJ7YOqb/73hwvwYvY4n74NMxrb/GK9T98XNlNH7svpHbT6eRxYkr8TJoblz+/4vj1+HlyAL83HkgjqvHH7deH9f3jdx+OiGu2+I2p/xwAjAjjj8vbkM61k/GNP4Nz+chhNAPj113xdj0k7gvPhD3/ZlmlhpGZXW3AL8x4/ACAWAnPNOMyA1vgdd60vhRcdr3hhDujd/3idOnO/ZXAH8m7gczG423op7BC54NeGFwC1mt7BKy/XZFHP8p4Fdx3A25aevIanRzaF57/z+yGt3HyGrZO8ft3BIPSGvwGugf8Ew2MKbtlTj9H2I6wQPFO+I+SJWCRjwgjcUz4254gUv8fj882MzOTdODrHKwc/yuIaUxhLAgzn9MnLceryw8hJ9Q6Titj/93ze2bkXF4TpzW4v/GOP4SvBLRE7gLz8wG3Io/7AiySlc9fjxnAoPwguE1vHDaC/hRnP5TeAE0FC9Qzo7L74dXHIbE6X6DB4oeeOXto3ieuois5XZZLk1z8UJ/AfBgXOZgPLg8Fad/GC/k9saDYv+4zxrwE/8pYBe8UBkYt+2ncbgPHuwa4nxfyqXjLrIWziUx3Y3AP/FC0PBz4APEIBtCeCoua0TcF/V4gcWWB5cAABE6SURBVLo6TvMqXvnZCS9kGoHt4zSv48dzVVzv7DjdeLyHjRDCQ2Qtn13wCgD4+fYfPkmYgQe+gBeCxOEr4voCcD+e78fghWlKyzI8uPaIaVkZj8/aEMLMuM1LQwiP4/m3MR6bHwF1IYQ78IJxeVzGWcCGmO6jgX/hlYDTgXUhhMfwQjn1yAyI++/luO9C3B/L4j7sEY/blnG7HsHz4Xq8IpLOzVfxyhdxO98Thz9FVjFswPMRwF/w3kvisraPw6+Q9QbuhDd4DA+EqQxaS3asV8R53ozfN+CtZch6PVbh59K0+LlP3C/geeCfcXgJXiFI29kvDs8jK8uWxv8pTf1y611K1tvSI6bxFXxfLonjZpD1ms7A837vmP53xWXfix/renwfp+HUyxnwMj712s0DPkvWYh6DH5Mh+INl5sbv0vieeJ5J2/IevFzvQVZZDHG5qdxbQnaMFpP1gC0l63GYi+/nHngeGB63bVhMfzp+D5FVmg8FZoYQVoYQVuMxZH82pqM/pK/WH35wn8JrMovKDC+Lf2l4A174peHXybp5nsczasAz+Cr8gF+FF0wzyVqYjXF4QzwAy/ATsCEenKfJLgU8hWeudXE4ZYYX4kFNXbH5/6kFs47sEkHITZOmW1mSlkaySkBad1rHDLJCeXVuGSmdabvTNHXxL3UF19M8jfVx/aXj0/LTclP6FsW/1L20hKynYS1eoKflrsnN25hb7oaS9afxi8m6rDbkxt+T256/xeG6eKzeiMtYh59oqTX6z9w60/81cd5UCcpva3778+teUmY5abrGON+ruXH57WnMLXtRC9815vbTWmINPh6Tl+P4FWQtr9J0pOHleN6oz82fpvkPvKAI+LmW8uIpeMVmOTAJr4gsivs4VSSfwls6/4zpnIqfKy/hrY9VMQ1bA9NL9mtjbnguHlDWx3Fj4/JCnG89WWVxTlzH+WSXZdL+XVryOQXdRvx8TenZkNsHK/BAuSSmb3A8ZivxVnY6xy6Py7sTr1SltD4T0156TFOlZ2JcdzrP6sjy+BWxjFsft3dZbhlH4eVHwPNvXW4d+fWEMn+ry3xXR1aBzOftxXhvy0lkZWm59aTesNl4JTK/n1fh+TMd0/qS9a/OfTc7t+x8mbcOz1/5eVI6fk92bq/KTZMvP/Lf5bcvHafGeIzTd3W5aZbk1pfKx9SNn8733+Ot9zRPyp8byM6/VD7nt6ncsVpash35v1W5dT6bS+8HyPLZ07nhrfE8OBPPl8PwCs0Z3fFBN+V8IoTwEbzLaD5ei84P/x3fYWm4Hvif3PCt8ft6vHsmXcM7FS8wtsW7o7fEu5qW4id4qqluwE/q2Xg31vP4jhwel7UcP0HWxPluwnf6arxVl1rRJwFHxnluwVsE2wA/w7vZBuE1yJ/grYsNeGarww/mgWS13FTgrY/bDZ4xTsYz1bq4j1Kt+lvA18gKtFfwjHdNLn0Hk3VVHwdMxmupV5Kd9MeRXXL4SZw+dSW9n6zmWY8X/APifrgtbssQPCP+G28hp1r4iXi3PjF9K+PwI3gLLnk6/l9L1oW7JVnPyXy88gPeDVxH1kOyBu96HIb3PqSa94N4wdIPb1XPwI/vdWRdirfiJ3gPPF8lL5L1MPwO77JL6X44Tt8H38cpjSty2zAH38dD8YpKOhbppDa8m9/wVsoG/NgOjNu3LA4/G7cVPK+mffAAvv8H4XntIZoXuPVx+t/ghfdp8ft0nMbGffEAHnD64nmcuLx/4I+Mfj9+OWhPPCj1jPMPwI/xY3hF4g3gvrgPDD8eq+P6Un5bSXbfwSVxman1PwpvHfbGW0Kponh8TP9WeL5/e9yOZfj53YC3ZEfgvUU9yR51PRDPn4PjuneI+3YSfmmgPm7bDmQ9OF+IaTordpH+OS5rFt5T1DMu88N4GfAqWWt3Qy5NE+PbMreI6U33hAS8W36HeJzexAMceL5/LA6vJstPT8T9CZ5HUuXnf/HzfQs8j6au/ieBr+N5YyLwZbwiMzCmdds4f7oENQs/p1cCB+EV5hS0nsTz4FK8Ynhu3AcX4XmrH94tfxJeNv4Or1QNiuP+GqdfiFc4nsePayrLxuB5rD5ux400v6T1RNx3Z8Xt7hHTOh3PK3fj5+RWeGNuZVzOT/BW+FZxH6WAvIGsN/N1/PhtiZevy/FenUb8/O8Z1/EEfk5vwMvklWQ9bS/GNJ0c07hVybGbjed/4vh0fg6KxyTgZVDqBemJ54F0CXVbPO48Fv8/TtZbUla3CfAhhNnx/wL8pPlYfhg/ME/mhuvxaxZpeBi+4+vxQPQoXhiODiH8Iw6/G/hXCOGHeOBJmWApfvA+g+/E8/GTbj5e2NyKB4T/wjP9w3i33/SY/AvJbkw6nqxmu2vsFqzDr7/3j+v5fZxuBNlJanh3+wfJblL5SFxuX/yhQEPwE/MveLDoi1+72gnPHAeHEK6J++HVEMIHyFpK6Zr9LcAecfi/4j5dhVcM/ha3eR+yk/q7uem/EUJ4Hj/J+wCL4r6chbfqUsa0uH9uwwviFXEfDMKvly2O49bG5e6OX+MkjrsvDs+KwwEvRFOw/izeBdyLrMAcFYe3w7vvAh6E0vHdIXeM9sNvZAl4kEj3CLwfL1TAT8508uxG1mX2frzgAhgVQtgnbksK1Kn1f2EcnknWimjAT87Ug5SuGTfgx3Y1HqzS9e/UbZpuVtodL7Aa8WOeuuV3icsNeCFzf0zDKvxYrcbz2Y/i/O+I8y3Cr1G/EdM4Ew9qT8bpewNvhhBOwCsNs+JxmIkXhNuRXfvvi59fj+JB+Q28orYM79Z8LO7/dH/KILLC7hS8spvy/VV4sOiPVzh6xb+FZN2uo/DKxGt40E6tz/Vx3pl4fjwPDyLz8WDwMl6p/x6wJObfhrjOpXH7VuF55xfAvBDChTGdR8R9dgVZK7QxhPBSTPfLeDmUAuaX8EruOvwa/wr88svheIUzBebUpb4FWVf8CrxBAR7QUy/S28hakHPxCk+qLKTeiv3IeicH4ufb4/FYTMIvddXFffWJON+QuH9n4Of7K3GaoXhFcime/y7HKwYfjOmtwxtO98dtSOdpL7x8TtfZP4jn4154PhmK5+Gt8LyTuu/7xeOZ8squcXhFnLYncAFZZXCfuL5G/Dxdh5ch6d6iRvxcf0cc/96Y/ulxP16BB/MReBn5Jl45mUR23s4mu89kANm9F+8la7U/gceOevzS3mtx3avwvF2PV0DuJruJ+pS43KlkN9ENI7tMeA9+bX893mi8I4TwwxDCh0MIn4n76wU2olsEeDMbYGaD4vBw/LrCv/LDeFDbLw6/iWeEx3PDM8nuqrwbb3X0Ax43s7F4YdUHeMbM3oNn/FF4Bhoc590VPzHPx0+q7fCTbHe8YvEjvNA5Nq77JPxgn4nX+OvxEyDV1p81sxPwgzkdL1ga8GB2I17wDMIL5f549+i/8UD7Ip4B6vATeTzecnwRb+VPx0/YcXHfAEw1s8Pj+v5hZmfFdPwDPylSl+D5cfqf4a3/gXgGuwZvsT0ep7c47ntx+kvMbBh+YvYHJpvZR/ET9iG8hR7i8p7Ba+Tbx3l7kN0sNhQvSNI12P/GW3Ehbsu74/iXyVqBC/HWToj77lqybrf5eDBJN9jV4yfVAvwYr4rLTbXkB/AToxEPCgfFdbyEn9xp3ekSyWq8kEzXuNOdzvPN7CCy+w5SRS1feelDdk18ZVxf+hVHupM74AVCult7KFnlIuABfH78LnUTvkF23Reyno5n8HzcA8+/z+N5f2ac73G8gtSAH6d3xe/n492DdXivRkr7DDMbgBfQg+M6H8CD8ytxvsfjsv9MVtDvjFcU0h3Si2Oaesd1zCe73HYUXpF7Jm7jyXH/vRmnW4Ef7/QrG+J+PBIvmFOl8mE87y/Dz6EReKH7YBxOFa/d8Zsp7zKzXfD7Hq7GW7jD8Zsyf4ufmz9NZVNcR2r5joqfLzazEXG92+IFdB1eqdkz7q+B+Ln9BB6QjsQrSOvjulPefV/chwEvR1KwT/eV9Ij7/PU4/ELc1h5xvy+KaRoRp095cRCehxbjNzmuise2b9y/g/DzZigeDJ/H80Vf/CbXbeJ2PRzTsgt+rjTG4/lC3HfE4XTPzQKyt4S+RHbz7BK8jE43Er8e0z4tpicFx9vJLjX8Hc8DDXgXerrH5YH4XQ+8MvsX/Hx8ObfPXsTPm/7x83V446kH3mofRnaPx6FxudfhZWd/vGxL98Q8hVc4hsZ5U6VlEX4u98LLttQTlPJDKl/GxvHT8TzxJh5rFuGVhv3w8+DJEMI5eKV0HV6ZvcXMtgYwsw/hx/J+Nqarr73Ha1PvxAvOf+IZZG4cnh8PTBpeH/82kNVAS6+HlP6l79J0azcybbpm81IL363NLS+/znT9p3R8ftrUu7CGt6Z3dS5968gydbrGtj7ug7Vx/n+SXb9pKU2lw+mvjqx1vrHpU4sz3TGav1aX1j0jN7w+N29DbvrUvVZfZh1pv+T3SWOZv/z49LOi+tzy0zJLr283lFlWyjehZPml291YZhkNZabZ2P7Lf15D1sNR+l3qks9va8oH6caytM3l0p3/S92dadq1JePz66/LLetPeABKeXFj50h+n+Rbjum7ls6FOrL8kL9GWzp9Po1zSpYR8IKwocz4OrIu33Q/ThqfzyMpv52Dly91eEGbguv43HKXx3leyaU3rS9VKP9F1kpL60h5bD1eYDfgPYH5bVwb/79EVkHckFt3uTzcUhmXz9/pPCmXD0vzbbky7tkWvluXW8+6Mt/n822579L+SHfop32QhleTXbcuvbcgzb8EbwSV3j+Tv6RZbv0h991CsnsI8mVJfh/k7w9KxyWtozT/t1Zm1fHW45f/nMqkZXj8eyW3riX4pZDZZA3ZmXgF9sOtxVY9yU5ERKSAukUXvYiIiFSXAryIiEgBKcCLiIgUkAK8iIhIASnAi4iIFJACvIiISAEpwItswszsdDPr3475VrVjni+b2S8rnU9EuoYCvMim7XT8aVubLDPr1fpUIlIpBXiRTUR8pPM0M/unmf3LzL6PP5v8ITN7KE6zKjf9RDO7Lg7vYGaPm9kMMzs/N81vzezg3OcbzWwcLXubmd1nZi+a2aTcfEfEZf/LzC7IjW8pPdeZ2RVm9gQwycw+aWbPxL+nc4+HFZF2Us1ZZNOxPzAnhDAGwMy2BL4C7BNCWNTKvBcDl4cQfmtmJ+fGXwN8E7gzLm9P4JiNLOfD+LPG1wP/NrNL8cdqXoA/L3spcL+ZHRxCuLOVNI0G9gwhNJjZH4CTQwiPmtlA/JGnItIBasGLbDpmAJ8xswvM7D9DCMtbnSOzF/6KScjehkcI4WFgx/hipyOAKSGE+jLzJw+GEJaHENbhz8R+B/72rL+EEBbGeW/EX6TRmttCCOnlMY8CF5rZqcBWraRBRNpAAV5kExFCeAF/C9YM4Hwz+165yXLDfTfyXd5v8XdffwV/r/fGrM8NN9B6L+DG0rO6aaIQfgoch79Q41Ez26mV5YpIKxTgRTYRZvY2YE0I4Qb8Vb8fwd/klr9ePd/M3mdmPfC3oiWP4u8hB389a951+M16hBBmtiNpfwc+aWbDzKwn3hPwcCvpKd22d4UQZoQQLsBfb6wAL9JBugYvsun4IPAzM0uvmD0Rf6/5fWY2J4SwD/4K1Kn4KzGn4++xBjgN+L2ZfRu4K7/QEMJ8M3sOaO2aeVkhhLlmdg7+3mwDpoUQ0jpaSk+p081sH/wVms8C97YnLSKS0etiRTZz8Xf0M4CPVHhdX0S6MXXRi2zGzOzTwHPApQruIsWiFryINGNmn8N/9pb3SgihxWvoItL9KMCLiIgUkLroRURECkgBXkREpIAU4EVERApIAV5ERKSAFOBFREQK6P8DDAMr4oV8vFMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bw6M0Eb24gj2"
      },
      "source": [
        "#split dataset\n",
        "#matrix = X = Independent Variable = student_hours\n",
        "#vector = y = Dependent Variable = student_marks"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZM6MJ8R5_Oa",
        "outputId": "2cc5dd34-b967-46b0-887f-325637dd0f0f"
      },
      "source": [
        "#split dataset\n",
        "#matrix = X = Independent Variable = student_hours\n",
        "#vector = y = Dependent Variable = student_marks\n",
        "\n",
        "X=df2.drop(\"student_marks\",axis=\"columns\")\n",
        "y=df2.drop(\"study_hours\",axis=\"columns\")\n",
        "print(\"shape of X =\",X.shape)\n",
        "print(\"shape of y =\",y.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape of X = (200, 1)\n",
            "shape of y = (200, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "FvZr4WUd-F9E",
        "outputId": "f1bf331f-c0f7-4b2e-df1b-fad34ee35a6e"
      },
      "source": [
        "sns.pairplot(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f5fbbc194a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "QeoGHXxkCOzr",
        "outputId": "5f0885da-d424-47c5-e5ea-355d12c0c1c8"
      },
      "source": [
        "sns.pairplot(df2,kind=\"reg\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f5fbb36dfd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQ3ugj_n_N2k"
      },
      "source": [
        "Importing Sklearn Library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oe4y6Ln37Vdj",
        "outputId": "9f67fdc6-b41e-48ac-e4bc-0f0b07e4aa53"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test= train_test_split(X,y,test_size=0.2,random_state=51) # taking 20% data for test. 20% is randomly\n",
        "print(\"shape of X_train =\",X_train.shape)\n",
        "print(\"shape of y_train =\",y_train.shape)\n",
        "print(\"shape of X_test =\",X_test.shape)\n",
        "print(\"shape of y_test =\",y_test.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape of X_train = (160, 1)\n",
            "shape of y_train = (160, 1)\n",
            "shape of X_test = (40, 1)\n",
            "shape of y_test = (40, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aueLXv-6APDE"
      },
      "source": [
        "#Selecting a model and train it"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrDNfxISBb7P"
      },
      "source": [
        "#Linear Regression\n",
        "# y=m*x+c   \n",
        "from sklearn.linear_model import LinearRegression\n",
        "lr=LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfk9n3jWCteW",
        "outputId": "2170bdb1-094a-42c6-8c99-718867b61bc1"
      },
      "source": [
        "lr.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "316uM4kGDXgA",
        "outputId": "e27d52b1-2a16-41ef-d955-57d2d890160e"
      },
      "source": [
        "#value of m\n",
        "lr.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3.93571802]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4bC3gQSUDlg2",
        "outputId": "929bb402-f0a8-4f68-b8a7-87c22c265539"
      },
      "source": [
        "#value of c\n",
        "lr.intercept_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([50.44735504])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWzgUfQqDsyD",
        "outputId": "6a99afa3-f93c-4a77-96ca-ed78e684cebc"
      },
      "source": [
        "#subsyituting the value of m and c in the formula \n",
        "# y = m * x + c\n",
        "\n",
        "m= 3.93571802\n",
        "c= 50.44735504\n",
        "\n",
        "y= m * 5 + c\n",
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "70.12594514"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4YPF2dWEWQf",
        "outputId": "663d5719-e9e3-4590-ea37-20a335a5e2b9"
      },
      "source": [
        "y_pred = lr.predict(X_test)\n",
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[83.11381458],\n",
              "       [78.9025963 ],\n",
              "       [84.57003024],\n",
              "       [85.82946001],\n",
              "       [84.72745896],\n",
              "       [80.75238377],\n",
              "       [72.84159055],\n",
              "       [71.66087515],\n",
              "       [73.23516235],\n",
              "       [71.66087515],\n",
              "       [73.47130543],\n",
              "       [76.38373677],\n",
              "       [73.23516235],\n",
              "       [73.58937697],\n",
              "       [82.95638585],\n",
              "       [70.40144538],\n",
              "       [73.23516235],\n",
              "       [78.74516758],\n",
              "       [75.55723598],\n",
              "       [82.68088559],\n",
              "       [76.65923703],\n",
              "       [70.48015974],\n",
              "       [74.77009238],\n",
              "       [77.98143645],\n",
              "       [85.59331693],\n",
              "       [82.56281405],\n",
              "       [76.42309395],\n",
              "       [85.0423164 ],\n",
              "       [78.39095296],\n",
              "       [81.38209865],\n",
              "       [81.73631327],\n",
              "       [83.15317176],\n",
              "       [82.20859943],\n",
              "       [81.10659839],\n",
              "       [73.58937697],\n",
              "       [71.1492318 ],\n",
              "       [71.89701823],\n",
              "       [81.53952737],\n",
              "       [72.60544747],\n",
              "       [71.93637541]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cgB2s3yfFH3B",
        "outputId": "acd45207-e617-4e4e-a12d-6a5d695af88a"
      },
      "source": [
        "#Creating a DataFrame for easy analysis\n",
        "#concatenating the test and pred value.\n",
        "pd.DataFrame(np.c_[X_test,y_test,y_pred],columns=[\"study_hours\",\"student_marks_original Percentage\",\"student_marks_predicted Percentage\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>study_hours</th>\n",
              "      <th>student_marks_original Percentage</th>\n",
              "      <th>student_marks_predicted Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.300000</td>\n",
              "      <td>82.02</td>\n",
              "      <td>83.113815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.230000</td>\n",
              "      <td>77.55</td>\n",
              "      <td>78.902596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.670000</td>\n",
              "      <td>84.19</td>\n",
              "      <td>84.570030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.990000</td>\n",
              "      <td>85.46</td>\n",
              "      <td>85.829460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.710000</td>\n",
              "      <td>84.03</td>\n",
              "      <td>84.727459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7.700000</td>\n",
              "      <td>80.81</td>\n",
              "      <td>80.752384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5.690000</td>\n",
              "      <td>73.61</td>\n",
              "      <td>72.841591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.390000</td>\n",
              "      <td>70.90</td>\n",
              "      <td>71.660875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5.790000</td>\n",
              "      <td>73.14</td>\n",
              "      <td>73.235162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5.390000</td>\n",
              "      <td>73.02</td>\n",
              "      <td>71.660875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.850000</td>\n",
              "      <td>75.02</td>\n",
              "      <td>73.471305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>6.590000</td>\n",
              "      <td>75.37</td>\n",
              "      <td>76.383737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5.790000</td>\n",
              "      <td>74.44</td>\n",
              "      <td>73.235162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>5.880000</td>\n",
              "      <td>73.40</td>\n",
              "      <td>73.589377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>8.260000</td>\n",
              "      <td>81.70</td>\n",
              "      <td>82.956386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5.070000</td>\n",
              "      <td>69.27</td>\n",
              "      <td>70.401445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.790000</td>\n",
              "      <td>73.64</td>\n",
              "      <td>73.235162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>7.190000</td>\n",
              "      <td>77.63</td>\n",
              "      <td>78.745168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>6.380000</td>\n",
              "      <td>77.01</td>\n",
              "      <td>75.557236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>8.190000</td>\n",
              "      <td>83.08</td>\n",
              "      <td>82.680886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>6.660000</td>\n",
              "      <td>76.63</td>\n",
              "      <td>76.659237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5.090000</td>\n",
              "      <td>72.22</td>\n",
              "      <td>70.480160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>6.180000</td>\n",
              "      <td>72.96</td>\n",
              "      <td>74.770092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>6.995949</td>\n",
              "      <td>76.14</td>\n",
              "      <td>77.981436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>8.930000</td>\n",
              "      <td>85.96</td>\n",
              "      <td>85.593317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>8.160000</td>\n",
              "      <td>83.36</td>\n",
              "      <td>82.562814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>6.600000</td>\n",
              "      <td>78.05</td>\n",
              "      <td>76.423094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>8.790000</td>\n",
              "      <td>84.60</td>\n",
              "      <td>85.042316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>76.76</td>\n",
              "      <td>78.390953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>7.860000</td>\n",
              "      <td>81.24</td>\n",
              "      <td>81.382099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>7.950000</td>\n",
              "      <td>80.86</td>\n",
              "      <td>81.736313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>8.310000</td>\n",
              "      <td>82.69</td>\n",
              "      <td>83.153172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>8.070000</td>\n",
              "      <td>82.30</td>\n",
              "      <td>82.208599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>7.790000</td>\n",
              "      <td>79.17</td>\n",
              "      <td>81.106598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>5.880000</td>\n",
              "      <td>73.34</td>\n",
              "      <td>73.589377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>5.260000</td>\n",
              "      <td>71.86</td>\n",
              "      <td>71.149232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>5.450000</td>\n",
              "      <td>70.06</td>\n",
              "      <td>71.897018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>80.76</td>\n",
              "      <td>81.539527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>5.630000</td>\n",
              "      <td>72.87</td>\n",
              "      <td>72.605447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>5.460000</td>\n",
              "      <td>71.10</td>\n",
              "      <td>71.936375</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    study_hours  ...  student_marks_predicted Percentage\n",
              "0      8.300000  ...                           83.113815\n",
              "1      7.230000  ...                           78.902596\n",
              "2      8.670000  ...                           84.570030\n",
              "3      8.990000  ...                           85.829460\n",
              "4      8.710000  ...                           84.727459\n",
              "5      7.700000  ...                           80.752384\n",
              "6      5.690000  ...                           72.841591\n",
              "7      5.390000  ...                           71.660875\n",
              "8      5.790000  ...                           73.235162\n",
              "9      5.390000  ...                           71.660875\n",
              "10     5.850000  ...                           73.471305\n",
              "11     6.590000  ...                           76.383737\n",
              "12     5.790000  ...                           73.235162\n",
              "13     5.880000  ...                           73.589377\n",
              "14     8.260000  ...                           82.956386\n",
              "15     5.070000  ...                           70.401445\n",
              "16     5.790000  ...                           73.235162\n",
              "17     7.190000  ...                           78.745168\n",
              "18     6.380000  ...                           75.557236\n",
              "19     8.190000  ...                           82.680886\n",
              "20     6.660000  ...                           76.659237\n",
              "21     5.090000  ...                           70.480160\n",
              "22     6.180000  ...                           74.770092\n",
              "23     6.995949  ...                           77.981436\n",
              "24     8.930000  ...                           85.593317\n",
              "25     8.160000  ...                           82.562814\n",
              "26     6.600000  ...                           76.423094\n",
              "27     8.790000  ...                           85.042316\n",
              "28     7.100000  ...                           78.390953\n",
              "29     7.860000  ...                           81.382099\n",
              "30     7.950000  ...                           81.736313\n",
              "31     8.310000  ...                           83.153172\n",
              "32     8.070000  ...                           82.208599\n",
              "33     7.790000  ...                           81.106598\n",
              "34     5.880000  ...                           73.589377\n",
              "35     5.260000  ...                           71.149232\n",
              "36     5.450000  ...                           71.897018\n",
              "37     7.900000  ...                           81.539527\n",
              "38     5.630000  ...                           72.605447\n",
              "39     5.460000  ...                           71.936375\n",
              "\n",
              "[40 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QBBF5VEgIad_"
      },
      "source": [
        "Checking the Accuracy of our Model.\n",
        "Fine-tune our Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBUxxp43GnKg",
        "outputId": "230d9685-fe86-40e3-bd1b-0404b53a7398"
      },
      "source": [
        "lr.score(X_test,y_test)                  #Getting 95% Accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9514124242154466"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "E1yCFhGFIyBq",
        "outputId": "0097dba1-a9de-4e1f-a409-7c58ae2f6810"
      },
      "source": [
        "plt.scatter(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f5fbe6f7898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "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": 337
        },
        "id": "pxITBhPIJKW1",
        "outputId": "19b6ecef-9445-4562-87b5-5167b8419ba9"
      },
      "source": [
        "plt.figure(figsize=(7,5))\n",
        "plt.scatter(X_test,y_test)\n",
        "plt.plot(X_train,lr.predict(X_train),color=\"red\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5fbe6c1c18>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1eX4r205KofP"
      },
      "source": [
        "## Save Our Macine Learning Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ErP7k_YlJViF",
        "outputId": "a093b6af-c2a3-4c1c-b0d4-4c4cd5d09cfc"
      },
      "source": [
        "import joblib\n",
        "joblib.dump(lr, \"Student_mark_prediction_model.pkl\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Student_mark_prediction_model.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VsQzU4oLNo0"
      },
      "source": [
        "model = joblib.load(\"Student_mark_prediction_model.pkl\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NlWy2zrnMFoh",
        "outputId": "a0ec1cab-5f7b-440b-800c-92006c8a348d"
      },
      "source": [
        "model.predict([[4]])[0][0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66.19022710353573"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NhYw8XYMNZd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}