MOUHTARAM_Soufiane_exam_ML_SDIA_23_ipynb_.ipynb

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        "<a href=\"https://colab.research.google.com/github/MouhtaramSoufiane/Projets-Machine-Learning/blob/master/MOUHTARAM_Soufiane_exam_ML_SDIA_23_ipynb_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "##Partie :1"
      ],
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        "id": "zkHr-8SGLtnX"
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      "source": [
        "# Soufiane MOUHTARAM\n",
        "# Importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Soufiane MOUHTARAM\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set()  #  Will import Seaborn functionalities\n",
        "# we don't like warnings\n",
        "# you can comment the following 2 lines if you'd like to\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "KhBBueL32ImC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Reading data"
      ],
      "metadata": {
        "id": "sh0NUIsm2VrJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "occupancy=pd.read_csv(\"occupancy.csv\")"
      ],
      "metadata": {
        "id": "j5QnS7t42aWD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "let s show the first rows of our dataset"
      ],
      "metadata": {
        "id": "07C-vz5g3jly"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "occupancy.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "4t2KL3ov2viS",
        "outputId": "f403b9e5-76ec-4446-a81e-d8c33b9a55cc"
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              "                    date  Temperature  Humidity       Light         CO2  \\\n",
              "140  2015-02-02 14:19:00      23.7000    26.272  585.200000  749.200000   \n",
              "141  2015-02-02 14:19:59      23.7180    26.290  578.400000  760.400000   \n",
              "142  2015-02-02 14:21:00      23.7300    26.230  572.666667  769.666667   \n",
              "143  2015-02-02 14:22:00      23.7225    26.125  493.750000  774.750000   \n",
              "144  2015-02-02 14:23:00      23.7540    26.200  488.600000  779.000000   \n",
              "\n",
              "     HumidityRatio  Occupancy  \n",
              "140       0.004764          1  \n",
              "141       0.004773          1  \n",
              "142       0.004765          1  \n",
              "143       0.004744          1  \n",
              "144       0.004767          1  "
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s have a look at data dimensionality, features names, and feature types."
      ],
      "metadata": {
        "id": "KzBJ7jsj3bYv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "occupancy.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReXAv8ym3I36",
        "outputId": "810af6d1-c597-4ad9-828b-43770eed71e4"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "(2665, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use the info() method to output some general information about the dataframe:"
      ],
      "metadata": {
        "id": "g5FDqVam3XoW"
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    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "occupancy.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2n6A56kE3TNX",
        "outputId": "87c0a634-9b94-4cf5-a0eb-806fc93b4976"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2665 entries, 140 to 2804\n",
            "Data columns (total 7 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   date           2665 non-null   object \n",
            " 1   Temperature    2665 non-null   float64\n",
            " 2   Humidity       2665 non-null   float64\n",
            " 3   Light          2665 non-null   float64\n",
            " 4   CO2            2665 non-null   float64\n",
            " 5   HumidityRatio  2665 non-null   float64\n",
            " 6   Occupancy      2665 non-null   int64  \n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 166.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "if we use Prior Knowledge we should remove the coulmn Date from dadaset because we do not need it"
      ],
      "metadata": {
        "id": "w92fesbF3wG6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "occupancy=occupancy.drop('date',axis=1)"
      ],
      "metadata": {
        "id": "6T9AJAxi3QaG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "describe our dataset"
      ],
      "metadata": {
        "id": "2c4fTzTJ4X93"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "occupancy.describe()"
      ],
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          "height": 300
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            "text/plain": [
              "       Temperature     Humidity        Light          CO2  HumidityRatio  \\\n",
              "count  2665.000000  2665.000000  2665.000000  2665.000000    2665.000000   \n",
              "mean     21.433876    25.353937   193.227556   717.906470       0.004027   \n",
              "std       1.028024     2.436842   250.210906   292.681718       0.000611   \n",
              "min      20.200000    22.100000     0.000000   427.500000       0.003303   \n",
              "25%      20.650000    23.260000     0.000000   466.000000       0.003529   \n",
              "50%      20.890000    25.000000     0.000000   580.500000       0.003815   \n",
              "75%      22.356667    26.856667   442.500000   956.333333       0.004532   \n",
              "max      24.408333    31.472500  1697.250000  1402.250000       0.005378   \n",
              "\n",
              "         Occupancy  \n",
              "count  2665.000000  \n",
              "mean      0.364728  \n",
              "std       0.481444  \n",
              "min       0.000000  \n",
              "25%       0.000000  \n",
              "50%       0.000000  \n",
              "75%       1.000000  \n",
              "max       1.000000  "
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              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Humidity</th>\n",
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              "      <th>count</th>\n",
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              "      <td>2665.000000</td>\n",
              "      <td>2665.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>21.433876</td>\n",
              "      <td>25.353937</td>\n",
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              "      <th>std</th>\n",
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              "      <td>2.436842</td>\n",
              "      <td>250.210906</td>\n",
              "      <td>292.681718</td>\n",
              "      <td>0.000611</td>\n",
              "      <td>0.481444</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>20.200000</td>\n",
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              "      <td>0.003303</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>20.650000</td>\n",
              "      <td>23.260000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>466.000000</td>\n",
              "      <td>0.003529</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>20.890000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>580.500000</td>\n",
              "      <td>0.003815</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>22.356667</td>\n",
              "      <td>26.856667</td>\n",
              "      <td>442.500000</td>\n",
              "      <td>956.333333</td>\n",
              "      <td>0.004532</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>24.408333</td>\n",
              "      <td>31.472500</td>\n",
              "      <td>1697.250000</td>\n",
              "      <td>1402.250000</td>\n",
              "      <td>0.005378</td>\n",
              "      <td>1.000000</td>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      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",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\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-c8ff2757-62c2-479c-aae4-d011fd5587f4 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-c8ff2757-62c2-479c-aae4-d011fd5587f4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "identify the feautures and target for dataset occupancy"
      ],
      "metadata": {
        "id": "V23La4cj4exI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "(occupancy['Occupancy'].value_counts().plot(\n",
        "        kind='bar',\n",
        "        figsize=(8, 6),\n",
        "        title='Distribution of Target Variable',\n",
        "    )\n",
        ");\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "0Sy-HDo24cu-",
        "outputId": "b4b9b1b3-fd73-4da4-95f8-b9c4b45af317"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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sWssHBwdrxIgReuGFFzR48GClp6ersLBQy5cv99ov/fr108cff6xnn31WGzdu1DXXXKPKykrZbDbl5ubqL3/5i/tLPK688krl5+dr8eLF+vWvf61WrVrp6quvPmMvAQEB6tOnj+bPny9Jevzxxz3m33jjjVq8eLEefPBB9e7d233MtmnTRt99910195g3X/dheXm57rvvPqWnp6ugoEBvvvmmrr32WnXr1u202/Bl3wEgzAINxsyZMyWdHG/YrFkzxcbGKjMzU7fffvtZg1PXrl1VWFioZcuW6ciRIwoPD1enTp00YsQIhYaGutc7efJkTZs2TePHj1dFRYUmTZp0zmH24Ycf1nfffaeFCxequLhYycnJevbZZz3u4/nb3/5WY8eO1eLFizVx4kRdddVVmj9/vl566SWPdfnSm8Vi0bx58zRz5kzl5ORo+fLlatmypf7whz/ogQceOKfncioXXXSRli5dqqlTp+qDDz7QsWPHFBUVpUmTJvl01wdf/PGPf5TFYtHKlSt14sQJXXPNNe7AV12///3v5XK5tHjxYr300ktq166d5s2bpwkTJnj8QWSxWDRnzhy9+uqrWrFihT799FM1adJErVq10qBBgxQVFeWuHTt2rLKysjRjxgwdP35ct91221nDrCR3mA0ODlbPnj095iUnJ+vFF1/UokWLNHHiRLVq1UpPPfWUCgsLzyvM+roPs7KytHLlSs2cOVPl5eXq1auXxo0bd8bhA77sOwBSgKs+fkoBAGAMp9PpvsPFhAkT6rodAA0MY2YBANV24sQJr0/t//Wvf9XRo0fVqVOnOuoKQEPGMAMAQLV98803mjRpkm655RY1a9ZMW7du1fvvv6/Y2Fjdcsstdd0egAaIMAsAqLaWLVuqRYsWWrp0qex2u8LCwtSvXz899dRTPn0pBgDUFMbMAgAAwFiMmQUAAICxCLMAAAAwFmEWAAAAxmqwHwBzuVxyOhkuDP+zWAI41gBcUHhdQ22wWALO+AUjVRpsmHU6XTp8uLiu28AFLijIovDwEDkcJaqocNZ1OwBw3nhdQ22JiAhRYODZwyzDDAAAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLGC6roBmMNiCZDFElDXbRglMNDi8V9Uj9PpktPpqus2AAAGIMyiWiyWADVr1pRQdo6s1iZ13YJRKiudOnq0hEALADgrwiyqxWIJUGCgRVPf+Fr7DhTVdTu4gLVqHqqn7rlWFksAYRYAcFaEWfhk34Ei7Sy013UbAAAAkvgAGAAAAAxGmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAsn8Ps7t27lZWVpX79+qlDhw7q3bv3KescDocmTJig1NRUxcfHq3v37nrllVc8asrKyvTSSy8pJSVFCQkJuv/++2Wz2bzWtXPnTt1///1KSEhQSkqKpkyZorKyMl9bBwAAwAUmyNcFduzYoXXr1unqq6+W0+mUy+XyqikpKdGgQYMUGBiozMxMXXLJJdq1a5eOHTvmUTdhwgTl5ORo7Nixat68uebPn6/77rtPq1atUmhoqCTJbrdr8ODBuvzyyzVr1iwdOHBAkydP1vHjx5WVlXWOTxsAAAAXAp/DbNeuXdW9e3dJ0tixY7VlyxavmoULF6q4uFgffvihmjZtKklKSkryqNm/f7/ef/99Pfvss7rzzjslSfHx8erSpYvefvttDR06VJL09ttvq7i4WLNnz1azZs0kSZWVlXruuec0bNgwNW/e3NenAAAAgAuEz8MMLJazL/L+++/rjjvucAfZU1m/fr2cTqduueUW97RmzZopJSVFeXl57ml5eXlKTk52B1lJSk9Pl9Pp1IYNG3xtHwAAABcQn6/Mns2+fft08OBBhYeH6+GHH9b69evVtGlT9ejRQ08//bRCQkIkSTabTZdcconCwsI8lo+JidH777/vfmyz2XTHHXd41FitVkVGRp5yfK0vgoL4/Ft1BQayr1C7OOaA+qnq3OQcRX1R42H2p59+kiS99NJL6tGjhxYtWqRdu3bp5ZdfVklJiaZNmybp5AfEqsbF/pzVapXdbnc/djgcslqtXnVhYWEedb6yWAIUHh5yzssD8C+rtUldtwDgDDhHUV/UeJh1Op2SpKioKL300kuSpOTkZAUFBWncuHEaPXq0WrduXdOb9ZnT6ZLDUVLXbRgjMNDCCxdqlcNRqspKZ123AeAXqn4fcI7C36zWJtV6B6DGw2zVsIFffuCrc+fOkk7eDaF169ayWq1edzeQTl6J/fnQA6vVqqKiIq86u93uNUTBVxUVnIRAfVVZ6eQcBeoxzlHUFzU+4KV169YKDg4+7fwTJ05IkqKjo/XTTz95DRWw2WyKjo52P46OjvYaG1tUVKSDBw961AEAAKDhqfEwGxwcrJSUFOXn53tM//LLLyVJV155pSQpNTVVFotFq1evdtfY7XatX79eaWlp7mlpaWn68ssv5XA43NNyc3NlsViUkpJS0+0DAADAID4PMygtLdW6deskSYWFhTp27Jhyc3MlSZ06dVJERISGDx+uAQMG6Mknn9Rtt92m3bt36+WXX1afPn3Upk0bSVKLFi105513asqUKbJYLGrevLkWLFig0NBQDRgwwL29AQMGaOnSpXrsscc0bNgwHThwQFOmTNGAAQO4xywAAEADF+A61Vd4ncG+ffvUrVu3U85bsmSJe6xsfn6+pk6dqu3btyssLEx9+vTR6NGjPYYglJWVafr06VqxYoWKi4t1zTXXaNy4cYqJifFY786dO/XCCy9o8+bNCgkJUb9+/bzW5avKSqcOHy4+5+UbmqAgi8LDQzRq2ufaWXjud5EAziamZZhmPHGTjhwpZjweUA9V/T7gHIW/RUSEVOsDYD6H2QsFYdY3hFnUFsIsUL8RZlFbqhtmueMxAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYy+cwu3v3bmVlZalfv37q0KGDevfufcb6zz77THFxcaesKyoqUmZmpjp16qTExESNHDlSP/74o1fdpk2b1L9/f3Xs2FFdunTRwoUL5XK5fG0dAAAAFxifw+yOHTu0bt06tW3bVjExMWesPX78uCZOnKhf/epXp5w/atQobdiwQePHj9fUqVNVUFCgoUOHqqKiwl2ze/duDRkyRJGRkVqwYIEGDx6smTNn6pVXXvG1dQAAAFxggnxdoGvXrurevbskaezYsdqyZctpaxcsWKDLLrtMrVq18qrbvHmz1q9fr+zsbKWmpkqSoqKilJGRodWrVysjI0OSlJ2drfDwcE2bNk3BwcFKTk7W4cOHNX/+fA0aNEjBwcG+PgUAAABcIHy+MmuxVG+RPXv2aPHixRo3btwp5+fl5clqtSolJcU9LTo6Wu3bt1deXp5HXbdu3TxCa0ZGhhwOhzZv3uxr+wAAALiA+O0DYC+++KL69eundu3anXK+zWZTVFSUAgICPKZHR0fLZrNJkkpKSvTDDz8oOjraqyYgIMBdBwAAgIbJ52EG1fG3v/1NmzdvVm5u7mlrHA6HQkNDvaaHhYW5hyQUFRVJkqxWq0dNcHCwmjRpIrvdfl59BgVxM4fqCgxkX6F2ccwB9VPVuck5ivqixsPsiRMnNHHiRI0YMUIRERE1vfoaY7EEKDw8pK7bAHAaVmuTum4BwBlwjqK+qPEw+9prr8lisahXr15yOBySpPLycjmdTjkcDl100UUKDg6W1WrV/v37vZa32+0KCwuTJPeV26ortFXKyspUWlrqrjsXTqdLDkfJOS/f0AQGWnjhQq1yOEpVWems6zYA/ELV7wPOUfib1dqkWu8A1HiYtdls2r17t5KTk73mXX/99Ro/frwGDhyo6Oho5efny+VyeYybLSgoUGxsrCSpadOmuvTSS73GxhYUFMjlcnmNpfVVRQUnIVBfVVY6OUeBeoxzFPVFjQ94GTp0qJYsWeLxLzU1VS1bttSSJUvUtWtXSVJaWprsdrvy8/PdyxYUFGjr1q1KS0tzT0tLS9OaNWtUXl7unpaTkyOr1arExMSabh8AAAAG8fnKbGlpqdatWydJKiws1LFjx9wf9OrUqZNiYmK8vkzhgw8+0IEDB5SUlOSelpiYqNTUVGVmZmrMmDFq3Lixpk+frri4OPXo0cNdN2TIEK1cuVJPPvmkBg4cqO3btys7O1ujR4/mHrMAAAANnM9h9tChQ3r88cc9plU9XrJkiUdgPZsZM2Zo0qRJysrKUkVFhVJTUzVu3DgFBf2/ttq2bavs7GxNnjxZDz30kCIiIjRy5Eg98MADvrYOAACAC0yAy+Vy1XUTdaGy0qnDh4vrug1jBAVZFB4eolHTPtfOwvO7JRpwJjEtwzTjiZt05Egx4/GAeqjq9wHnKPwtIiKkWh8A4yZxAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjOVzmN29e7eysrLUr18/dejQQb179/aYf+zYMc2aNUt33nmnrrvuOt1www16+OGH9d1333mtq6ioSJmZmerUqZMSExM1cuRI/fjjj151mzZtUv/+/dWxY0d16dJFCxculMvl8rV1AAAAXGB8DrM7duzQunXr1LZtW8XExHjN//777/XOO+8oJSVFM2bM0AsvvKCioiL1799fO3fu9KgdNWqUNmzYoPHjx2vq1KkqKCjQ0KFDVVFR4a7ZvXu3hgwZosjISC1YsECDBw/WzJkz9corr5zD0wUAAMCFJMjXBbp27aru3btLksaOHastW7Z4zG/VqpU+/fRTNWnSxD2tc+fO6tq1q958800988wzkqTNmzdr/fr1ys7OVmpqqiQpKipKGRkZWr16tTIyMiRJ2dnZCg8P17Rp0xQcHKzk5GQdPnxY8+fP16BBgxQcHHxuzxwAAADG8/nKrMVy5kWaNm3qEWQlKSQkRG3atPEYQpCXlyer1aqUlBT3tOjoaLVv3155eXkedd26dfMIrRkZGXI4HNq8ebOv7QMAAOACUisfAHM4HNqxY4eio6Pd02w2m6KiohQQEOBRGx0dLZvNJkkqKSnRDz/84LFcVU1AQIC7DgAAAA2Tz8MMzsWf/vQnBQQEaODAge5pDodDoaGhXrVhYWHuoQtFRUWSJKvV6lETHBysJk2ayG63n1dfQUHczKG6AgPZV6hdHHNA/VR1bnKOor7we5hdtmyZ3n33XU2ePFktWrTw9+aqzWIJUHh4SF23AeA0rNYmZy8CUGc4R1Ff+DXMrlu3TllZWXr00Ud12223ecyzWq3av3+/1zJ2u11hYWGS5L5yW3WFtkpZWZlKS0vddefC6XTJ4Sg55+UbmsBACy9cqFUOR6kqK5113QaAX6j6fcA5Cn+zWptU6x0Av4XZb775Ro8//rhuvfVWPf74417zo6OjlZ+fL5fL5TFutqCgQLGxsZJOfpjs0ksv9RobW1BQIJfL5TWW1lcVFZyEQH1VWenkHAXqMc5R1Bd+GfDyv//9T8OGDVPnzp313HPPnbImLS1Ndrtd+fn57mkFBQXaunWr0tLSPOrWrFmj8vJy97ScnBxZrVYlJib6o30AAAAYwucrs6WlpVq3bp0kqbCwUMeOHVNubq4kqVOnTnK5XBoyZIgaN26swYMHe9yH9uKLL9YVV1whSUpMTFRqaqoyMzM1ZswYNW7cWNOnT1dcXJx69OjhXmbIkCFauXKlnnzySQ0cOFDbt29Xdna2Ro8ezT1mAQAAGrgAl4/fC7tv3z5169btlPOWLFkiSbr33ntPOb9Tp05aunSp+3FRUZEmTZqkTz/9VBUVFUpNTdW4cePUvHlzj+U2bdqkyZMna9u2bYqIiNA999yjoUOHet3WyxeVlU4dPlx8zss3NEFBFoWHh2jUtM+1s/D87iIBnElMyzDNeOImHTlSzFuYQD1U9fuAcxT+FhERUq0xsz6H2QsFYdY3hFnUFsIsUL8RZlFbqhtmuUkcAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAY/kcZnfv3q2srCz169dPHTp0UO/evU9Z995776lnz56Kj49X3759tXbtWq+aoqIiZWZmqlOnTkpMTNTIkSP1448/etVt2rRJ/fv3V8eOHdWlSxctXLhQLpfL19YBAABwgfE5zO7YsUPr1q1T27ZtFRMTc8qaVatW6ZlnnlF6eroWLVqkhIQEDR8+XN98841H3ahRo7RhwwaNHz9eU6dOVUFBgYYOHaqKigp3ze7duzVkyBBFRkZqwYIFGjx4sGbOnKlXXnnF19YBAABwgQnydYGuXbuqe/fukqSxY8dqy5YtXjUzZ85Ur169NGrUKElS586dtX37ds2ZM0eLFi2SJG3evFnr169Xdna2UlNTJUlRUVHKyMjQ6tWrlZGRIUnKzs5WeHi4pk2bpuDgYCUnJ+vw4cOaP3++Bg0apODg4HN64gAAADCfz1dmLZYzL7J3717t2rVL6enpHtMzMjKUn5+vsrIySVJeXp6sVqtSUlLcNdHR0Wrfvr3y8vLc0/Ly8tStWzeP0JqRkSGHw6HNmzf72j4AAAAuIDX+ATCbzSbp5FXWn4uJiVF5ebn27t3rrouKilJAQIBHXXR0tHsdJSUl+uGHHxQdHe1VExAQ4K4DAABAw+TzMIOzsdvtkiSr1eoxvepx1XyHw6HQ0FCv5cPCwtxDF4qKik65ruDgYDVp0sS9rnMVFMTNHKorMJB9hdrFMQfUT1XnJuco6osaD7OmsFgCFB4eUtdtADgNq7VJXbcA4Aw4R1Ff1HiYDQsLk3TyqmpkZKR7usPh8JhvtVq1f/9+r+Xtdru7purKbdUV2iplZWUqLS11150Lp9Mlh6PknJdvaAIDLbxwoVY5HKWqrHTWdRsAfqHq9wHnKPzNam1SrXcAajzMVo1vtdlsHmNdbTabGjVqpNatW7vr8vPz5XK5PMbNFhQUKDY2VpLUtGlTXXrppV5jYwsKCuRyubzG0vqqooKTEKivKiudnKNAPcY5ivqixge8tG7dWpdffrlyc3M9pufk5Cg5Odl9V4K0tDTZ7Xbl5+e7awoKCrR161alpaW5p6WlpWnNmjUqLy/3WJfValViYmJNtw8AAACD+HxltrS0VOvWrZMkFRYW6tixY+7g2qlTJ0VERGjEiBF66qmn1KZNGyUlJSknJ0fffvutXn/9dfd6EhMTlZqaqszMTI0ZM0aNGzfW9OnTFRcXpx49erjrhgwZopUrV+rJJ5/UwIEDtX37dmVnZ2v06NHcYxYAAKCBC3D5+L2w+/btU7du3U45b8mSJUpKSpJ08utsFy1apO+//15RUVF64okn1KVLF4/6oqIiTZo0SZ9++qkqKiqUmpqqcePGqXnz5h51mzZt0uTJk7Vt2zZFRETonnvu0dChQ71u6+WLykqnDh8uPuflG5qgIIvCw0M0atrn2ll4fneRAM4kpmWYZjxxk44cKeYtTKAeqvp9wDkKf4uICKnWmFmfw+yFgjDrG8IsagthFqjfCLOoLdUNsw321lwAAFgsAbJYzv1dvoaI+8yeG6fTJaezQV4/9DvCLACgQbJYAtSsWVNC2Tnido2+qax06ujREgKtHxBmAQANksUSoMBAi6a+8bX2HSg6+wLAOWrVPFRP3XOtLJYAwqwfEGYBAA3avgNFfBYAMBjvrQAAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABj+S3MrlmzRnfddZcSExOVmpqqxx9/XHv37vWqe++999SzZ0/Fx8erb9++Wrt2rVdNUVGRMjMz1alTJyUmJmrkyJH68ccf/dU6AAAADOGXMLtx40YNHz5cV1xxhebMmaPMzEz997//1QMPPKDjx4+761atWqVnnnlG6enpWrRokRISEjR8+HB98803HusbNWqUNmzYoPHjx2vq1KkqKCjQ0KFDVVFR4Y/2AQAAYIggf6x01apVuuyyyzRx4kQFBARIkiIiIjR48GBt2bJF1113nSRp5syZ6tWrl0aNGiVJ6ty5s7Zv3645c+Zo0aJFkqTNmzdr/fr1ys7OVmpqqiQpKipKGRkZWr16tTIyMvzxFAAAAGAAv1yZraioUEhIiDvISlJoaKgkyeVySZL27t2rXbt2KT093WPZjIwM5efnq6ysTJKUl5cnq9WqlJQUd010dLTat2+vvLw8f7QPAAAAQ/glzN5+++3auXOn3njjDRUVFWnv3r2aNm2aOnTooGuuuUaSZLPZJJ28yvpzMTExKi8vd4+vtdlsioqK8gjG0slAW7UOAAAANEx+GWZw3XXXafbs2XryySf1/PPPS5Lat2+vv/zlLwoMDJQk2e12SZLVavVYtupx1XyHw+G+qvtzYWFh2rJly3n1GRTEzRyqKzCQfYXaxTEHf+MYQ23jmPMPv4TZTZs26Q9/+IPuvvtu3XTTTTp69Kjmzp2rhx56SG+++aYuuugif2zWJxZLgMLDQ+q6DQCnYbU2qesWAKBG8brmH34JsxMmTFDnzp01duxY97SEhATddNNNWrFihfr376+wsDBJJ2+7FRkZ6a5zOByS5J5vtVq1f/9+r23Y7XZ3zblwOl1yOErOefmGJjDQwkmIWuVwlKqy0lnXbeACxusaahuva76xWptU62q2X8Lszp071a1bN49pLVq0UHh4uPbs2SPp5JhX6eSY2Kr/r3rcqFEjtW7d2l2Xn58vl8vlMW62oKBAsbGx59VnRQUHFFBfVVY6OUcBXFB4XfMPvwzeuOyyy7R161aPaYWFhTpy5IhatmwpSWrdurUuv/xy5ebmetTl5OQoOTlZwcHBkqS0tDTZ7Xbl5+e7awoKCrR161alpaX5o30AAAAYwi9XZgcMGKCJEydqwoQJ6tq1q44ePap58+bpkksu8bgV14gRI/TUU0+pTZs2SkpKUk5Ojr799lu9/vrr7pqqbxDLzMzUmDFj1LhxY02fPl1xcXHq0aOHP9oHAACAIfwSZu+9914FBwfrrbfe0rJlyxQSEqKEhATNmDFD4eHh7rrevXurtLRUixYt0sKFCxUVFaXZs2crMTHRY30zZszQpEmTlJWVpYqKCqWmpmrcuHEKCvJL+wAAADBEgKvqWwwamMpKpw4fLq7rNowRFGRReHiIRk37XDsL7XXdDi5gMS3DNOOJm3TkSDFjy+BXvK6htvC6dm4iIkKq9QEwbngGAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWH4Nsx988IFuvfVWxcfHKykpSQ8++KCOHz/unv+3v/1Nffv2VXx8vHr27Klly5Z5raOsrEwvvfSSUlJSlJCQoPvvv182m82fbQMAAMAQfguz8+bN0wsvvKCMjAxlZ2fr+eefV6tWrVRZWSlJ+uqrrzR8+HAlJCRo0aJFSk9P1x//+Efl5uZ6rGfChAl67733NHr0aM2aNUtlZWW67777VFRU5K/WAQAAYIggf6zUZrNp9uzZmjt3rm688Ub39J49e7r/f968eerYsaOef/55SVLnzp21d+9ezZw5U7fccoskaf/+/Xr//ff17LPP6s4775QkxcfHq0uXLnr77bc1dOhQf7QPAAAAQ/jlyuzy5cvVqlUrjyD7c2VlZdq4caM7tFbJyMjQzp07tW/fPknS+vXr5XQ6PeqaNWumlJQU5eXl+aN1AAAAGMQvYfZf//qXYmNjNXfuXCUnJ+uqq67SgAED9K9//UuStGfPHpWXlys6OtpjuZiYGElyj4m12Wy65JJLFBYW5lXHuFkAAAD4ZZjBwYMHtWXLFm3fvl3PPvusmjRpovnz5+uBBx7Q6tWrZbfbJUlWq9VjuarHVfMdDodCQ0O91m+1Wt015yMoiJs5VFdgIPsKtYtjDv7GMYbaxjHnH34Jsy6XSyUlJfrzn/+sdu3aSZKuvvpqde3aVa+//rpSU1P9sVmfWCwBCg8Pqes2AJyG1dqkrlsAgBrF65p/+CXMWq1WNWvWzB1kpZNjXTt06KD//e9/6tWrlyR53ZHA4XBIkntYgdVq1bFjx7zW73A4vIYe+MrpdMnhKDmvdTQkgYEWTkLUKoejVJWVzrpuAxcwXtdQ23hd843V2qRaV7P9EmavuOIK7dmz55TzTpw4oTZt2qhRo0ay2Wz67W9/655XNQ62aixtdHS0fvrpJ9ntdo/warPZvMbbnouKCg4ooL6qrHRyjgK4oPC65h9+GbzRpUsXHT16VNu2bXNPO3LkiP7zn//oyiuvVHBwsJKSkvTJJ594LJeTk6OYmBi1atVKkpSamiqLxaLVq1e7a+x2u9avX6+0tDR/tA4AAACD+OXKbPfu3RUfH6+RI0dq9OjRaty4sRYuXKjg4GD97ne/kyQ98sgjuvfeezV+/Hilp6dr48aN+uijjzR9+nT3elq0aKE777xTU6ZMkcViUfPmzbVgwQKFhoZqwIAB/mgdAAAABvFLmLVYLFq4cKEmTZqkrKwslZeX67rrrtMbb7yhyMhISdJ1112nWbNmacaMGXr//fd12WWXacKECUpPT/dY17hx4xQSEqKXX35ZxcXFuuaaa7R48eJT3uUAAAAADYtfwqwkRURE6E9/+tMZa7p166Zu3bqdsSY4OFhjxozRmDFjarI9AAAAXAC44RkAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABj+T3MFhcXKy0tTXFxcfr3v//tMe+9995Tz549FR8fr759+2rt2rVeyxcVFSkzM1OdOnVSYmKiRo4cqR9//NHfbQMAAMAAfg+zc+fOVWVlpdf0VatW6ZlnnlF6eroWLVqkhIQEDR8+XN98841H3ahRo7RhwwaNHz9eU6dOVUFBgYYOHaqKigp/tw4AAIB6zq9hdufOnXrzzTc1YsQIr3kzZ85Ur169NGrUKHXu3FnPP/+84uPjNWfOHHfN5s2btX79er344ovKyMhQt27d9Oc//1nfffedVq9e7c/WAQAAYAC/htkJEyZowIABioqK8pi+d+9e7dq1S+np6R7TMzIylJ+fr7KyMklSXl6erFarUlJS3DXR0dFq37698vLy/Nk6AAAADOC3MJubm6vt27frscce85pns9kkySvkxsTEqLy8XHv37nXXRUVFKSAgwKMuOjravQ4AAAA0XEH+WGlpaakmT56s0aNH6+KLL/aab7fbJUlWq9VjetXjqvkOh0OhoaFey4eFhWnLli3n3WdQEDdzqK7AQPYVahfHHPyNYwy1jWPOP/wSZufNm6dLLrlEd9xxhz9WXyMslgCFh4fUdRsATsNqbVLXLQBAjeJ1zT9qPMwWFhbqlVde0Zw5c1RUVCRJKikpcf+3uLhYYWFhkk7edisyMtK9rMPhkCT3fKvVqv3793ttw263u2vOldPpksNRcl7raEgCAy2chKhVDkepKiuddd0GLmC8rqG28brmG6u1SbWuZtd4mN23b5/Ky8v10EMPec279957dfXVV+vll1+WdHJMbHR0tHu+zWZTo0aN1Lp1a0knx8bm5+fL5XJ5jJstKChQbGzsefdaUcEBBdRXlZVOzlEAFxRe1/yjxsNs+/bttWTJEo9p27Zt06RJk/Tcc88pPj5erVu31uWXX67c3Fx1797dXZeTk6Pk5GQFBwdLktLS0jR37lzl5+frhhtukHQyyG7dulUPPvhgTbcOAAAAw9R4mLVarUpKSjrlvCuvvFJXXnmlJGnEiBF66qmn1KZNGyUlJSknJ0fffvutXn/9dXd9YmKiUlNTlZmZqTFjxqhx48aaPn264uLi1KNHj5puHQAAAIbxywfAqqN3794qLS3VokWLtHDhQkVFRWn27NlKTEz0qJsxY4YmTZqkrKwsVVRUKDU1VePGjVNQUJ21DgAAgHqiVhJhUlKSvvvuO6/pd911l+66664zLhsaGqqJEydq4sSJ/moPAAAAhuKGZwAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIzllzD78ccf65FHHlFaWpoSEhLUr18/vf/++3K5XB517733nnr27Kn4+Hj17dtXa9eu9VpXUVGRMjMz1alTJyUmJmrkyJH68ccf/dE2AAAADOOXMPvqq6+qSZMmGjt2rObNm6e0tDQ988wzmjNnjrtm1apVeuaZZ5Senq5FixYpISFBw4cP1zfffOOxrlGjRmnDhg0aP368pk6dqoKCAg0dOlQVFRX+aB0AAAAGCfLHSufNm6eIiAj34+TkZB09elSLFy/Wo48+KovFopkzZ6pXr14aNWqUJKlz587avn275syZo0WLFkmSNm/erPXr1ys7O1upqamSpKioKGVkZGj16tXKyMjwR/sAAAAwhF+uzP48yFZp3769jh07ppKSEu3du1e7du1Senq6R01GRoby8/NVVlYmScrLy5PValVKSoq7Jjo6Wu3bt1deXp4/WgcAAIBBau0DYF9//bWaN2+uiy++WDabTdLJq6w/FxMTo/Lycu3du1eSZLPZFBUVpYCAAI+66Oho9zoAAADQcPllmMEvffXVV8rJydGYMWMkSXa7XZJktVo96qoeV813OBwKDQ31Wl9YWJi2bNly3n0FBXEzh+oKDGRfoXZxzMHfOMZQ2zjm/MPvYXb//v0aPXq0kpKSdO+99/p7c9VmsQQoPDykrtsAcBpWa5O6bgEAahSva/7h1zDrcDg0dOhQNWvWTLNmzZLFcvIvkrCwMEknb7sVGRnpUf/z+VarVfv37/dar91ud9ecK6fTJYej5LzW0ZAEBlo4CVGrHI5SVVY667oNXMB4XUNt43XNN1Zrk2pdzfZbmD1+/LiGDRumoqIivfPOOx7DBaKjoyWdHBNb9f9Vjxs1aqTWrVu76/Lz8+VyuTzGzRYUFCg2Nva8e6yo4IAC6qvKSifnKIALCq9r/uGXwRsVFRUaNWqUbDab/vKXv6h58+Ye81u3bq3LL79cubm5HtNzcnKUnJys4OBgSVJaWprsdrvy8/PdNQUFBdq6davS0tL80ToAAAAM4pcrs88995zWrl2rsWPH6tixYx5fhNChQwcFBwdrxIgReuqpp9SmTRslJSUpJydH3377rV5//XV3bWJiolJTU5WZmakxY8aocePGmj59uuLi4tSjRw9/tA4AAACD+CXMbtiwQZI0efJkr3lr1qxRq1at1Lt3b5WWlmrRokVauHChoqKiNHv2bCUmJnrUz5gxQ5MmTVJWVpYqKiqUmpqqcePGKSioVm7EAAAAgHrML4nwb3/7W7Xq7rrrLt11111nrAkNDdXEiRM1ceLEmmgNAAAAFxBueAYAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYRoTZnTt36v7771dCQoJSUlI0ZcoUlZWV1XVbAAAAqGNBdd3A2djtdg0ePFiXX365Zs2apQMHDmjy5Mk6fvy4srKy6ro9AAAA1KF6H2bffvttFRcXa/bs2WrWrJkkqbKyUs8995yGDRum5s2b122DAAAAqDP1fphBXl6ekpOT3UFWktLT0+V0OrVhw4a6awwAAAB1rt6HWZvNpujoaI9pVqtVkZGRstlsddQVAAAA6oN6P8zA4XDIarV6TQ8LC5Pdbj/n9VosAYqICDmf1hqUgICT/x0/NFkVlc66bQYXtKDAk39jh4U1kctVx83ggsbrGmoLr2vnxmIJqFZdvQ+z/hIQEKDAwOrtJPw/zUIb13ULaCAslnr/xhEuELyuobbwuuYf9X6vWq1WFRUVeU232+0KCwurg44AAABQX9T7MBsdHe01NraoqEgHDx70GksLAACAhqXeh9m0tDR9+eWXcjgc7mm5ubmyWCxKSUmpw84AAABQ1wJcrvo9FNlut6tXr16KiorSsGHD3F+a0KdPH740AQAAoIGr92FWOvl1ti+88II2b96skJAQ9evXT6NHj1ZwcHBdtwYAAIA6ZESYBQAAAE6l3o+ZBQAAAE6HMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGCuorhsALhQHDx7Uhg0bZLPZdPToUUlSs2bNFB0drZSUFEVGRtZtgwDgB0eOHNH//vc/XX/99XXdChoowixwnsrLy/XSSy/p7bffVmVlpSIjIxUWFiZJstvtOnjwoAIDAzVgwACNHTtWQUGcdgAuHP/4xz80atQobdu2ra5bQQPFb1XgPM2YMUMrVqxQVlaW0tPTFRoa6jH/2LFj+vjjj/WnP/1JF110kZ566qk66hQAgAtPgMvlctV1E4DJUlNT9cQTT+j2228/Y92yZcs0ffp0rV+/vpY6A4Bz16dPn2rVFRcX64cffuDKLOoMV2aB81RcXKwWLVqcta5FixYqLi6uhY4A4PzZbDZdccUV6tChwxnrCgsL9cMPP9RSV4A3wixwnhISEjR//nzFx8d7DTGocuzYMc2fP1+JiYm13B0AnJvf/OY3atu2rSZNmnTGuk8++UT//Oc/a6krwBthFjhPzzzzjAYPHqwbb7xRN9xwg6Kjo92h9tixY7LZbPryyy8VEhKiV199tW6bBYBq6tixo7744otq1TJiEXWJMbNADXA4HHrrrbf0xRdfyGazyeFwSJKsVquio6OVlpamAQMGyGq11nGnAFA9e/bs0Y4dO9StW7cz1h0/flyHDh1Sy5Yta6kzwBNhFgAAAMbiG8AAAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGP9fzOwFsrZf5+8AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "occupancy['Occupancy'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdaXU8z06EFW",
        "outputId": "b8bc435e-a5d7-4e6f-f874-985b6ee02adf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1693\n",
              "1     972\n",
              "Name: Occupancy, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Standarization data "
      ],
      "metadata": {
        "id": "zlQ4WbbA6V4N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To standarize our dataset we must Import the library: \n",
        "sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "GqAWW4Ir7fwe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "# initialize the scaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Apply the transormation\n",
        "\n",
        "\n",
        "OccupancyStand =scaler.fit_transform(occupancy.drop('Occupancy',axis=1))\n",
        "OccupancyStand\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8y5LKCyR6U9o",
        "outputId": "ac9c7daf-7cd5-413e-87c5-fb30a2168a62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.20476235, 0.37681367, 1.56686218, 0.10694007, 1.20754022],\n",
              "       [2.22227495, 0.38420166, 1.53968001, 0.14521407, 1.22146086],\n",
              "       [2.23395002, 0.35957501, 1.51676171, 0.17688126, 1.20916118],\n",
              "       ...,\n",
              "       [2.81770341, 0.14203962, 2.4934545 , 1.39390353, 1.33301272],\n",
              "       [2.84364801, 0.14203962, 2.47746498, 1.38433502, 1.34579091],\n",
              "       [2.89391566, 0.13451481, 2.41750431, 1.38775235, 1.36487235]])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "OccupancyV2=pd.DataFrame(OccupancyStand,\n",
        "                        columns=['Temperature',\t'Humidity',\t'Light',\t'CO2',\t'HumidityRatio'])\n",
        "\n",
        "OccupancyV2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "yJd6lUv86uHW",
        "outputId": "389ff3e3-15b2-4c73-b6ef-cf4aea48be1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Temperature  Humidity     Light       CO2  HumidityRatio\n",
              "0        2.204762  0.376814  1.566862  0.106940       1.207540\n",
              "1        2.222275  0.384202  1.539680  0.145214       1.221461\n",
              "2        2.233950  0.359575  1.516762  0.176881       1.209161\n",
              "3        2.226653  0.316478  1.201302  0.194253       1.174140\n",
              "4        2.257300  0.347262  1.180715  0.208776       1.211522\n",
              "...           ...       ...       ...       ...            ...\n",
              "2660     2.778787  0.142040  2.457478  1.477457       1.313879\n",
              "2661     2.817703  0.156816  2.464673  1.405522       1.344207\n",
              "2662     2.817703  0.142040  2.493454  1.393904       1.333013\n",
              "2663     2.843648  0.142040  2.477465  1.384335       1.345791\n",
              "2664     2.893916  0.134515  2.417504  1.387752       1.364872\n",
              "\n",
              "[2665 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c7613b3f-a746-4487-a05f-44899fa888c6\">\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>Temperature</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Light</th>\n",
              "      <th>CO2</th>\n",
              "      <th>HumidityRatio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.204762</td>\n",
              "      <td>0.376814</td>\n",
              "      <td>1.566862</td>\n",
              "      <td>0.106940</td>\n",
              "      <td>1.207540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.222275</td>\n",
              "      <td>0.384202</td>\n",
              "      <td>1.539680</td>\n",
              "      <td>0.145214</td>\n",
              "      <td>1.221461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.233950</td>\n",
              "      <td>0.359575</td>\n",
              "      <td>1.516762</td>\n",
              "      <td>0.176881</td>\n",
              "      <td>1.209161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.226653</td>\n",
              "      <td>0.316478</td>\n",
              "      <td>1.201302</td>\n",
              "      <td>0.194253</td>\n",
              "      <td>1.174140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.257300</td>\n",
              "      <td>0.347262</td>\n",
              "      <td>1.180715</td>\n",
              "      <td>0.208776</td>\n",
              "      <td>1.211522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2660</th>\n",
              "      <td>2.778787</td>\n",
              "      <td>0.142040</td>\n",
              "      <td>2.457478</td>\n",
              "      <td>1.477457</td>\n",
              "      <td>1.313879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2661</th>\n",
              "      <td>2.817703</td>\n",
              "      <td>0.156816</td>\n",
              "      <td>2.464673</td>\n",
              "      <td>1.405522</td>\n",
              "      <td>1.344207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2662</th>\n",
              "      <td>2.817703</td>\n",
              "      <td>0.142040</td>\n",
              "      <td>2.493454</td>\n",
              "      <td>1.393904</td>\n",
              "      <td>1.333013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2663</th>\n",
              "      <td>2.843648</td>\n",
              "      <td>0.142040</td>\n",
              "      <td>2.477465</td>\n",
              "      <td>1.384335</td>\n",
              "      <td>1.345791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2664</th>\n",
              "      <td>2.893916</td>\n",
              "      <td>0.134515</td>\n",
              "      <td>2.417504</td>\n",
              "      <td>1.387752</td>\n",
              "      <td>1.364872</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2665 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7613b3f-a746-4487-a05f-44899fa888c6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Correlation"
      ],
      "metadata": {
        "id": "A_mjuE1f70dD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Calculate Correlation\n",
        "corr_matrix = occupancy.corr()  # corr(autinsurance)\n",
        "corr_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "gpj_gYSx7z1C",
        "outputId": "2c4336e1-0a7d-4742-f7cc-b48beac420f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Temperature  Humidity     Light       CO2  HumidityRatio  \\\n",
              "Temperature       1.000000  0.716988  0.768428  0.870226       0.894548   \n",
              "Humidity          0.716988  1.000000  0.561905  0.911611       0.951914   \n",
              "Light             0.768428  0.561905  1.000000  0.769167       0.693286   \n",
              "CO2               0.870226  0.911611  0.769167  1.000000       0.964440   \n",
              "HumidityRatio     0.894548  0.951914  0.693286  0.964440       1.000000   \n",
              "Occupancy         0.705784  0.610764  0.927949  0.768030       0.700330   \n",
              "\n",
              "               Occupancy  \n",
              "Temperature     0.705784  \n",
              "Humidity        0.610764  \n",
              "Light           0.927949  \n",
              "CO2             0.768030  \n",
              "HumidityRatio   0.700330  \n",
              "Occupancy       1.000000  "
            ],
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              "      <th></th>\n",
              "      <th>Temperature</th>\n",
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              "      <th>CO2</th>\n",
              "      <th>HumidityRatio</th>\n",
              "      <th>Occupancy</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Temperature</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.768428</td>\n",
              "      <td>0.870226</td>\n",
              "      <td>0.894548</td>\n",
              "      <td>0.705784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Humidity</th>\n",
              "      <td>0.716988</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.561905</td>\n",
              "      <td>0.911611</td>\n",
              "      <td>0.951914</td>\n",
              "      <td>0.610764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Light</th>\n",
              "      <td>0.768428</td>\n",
              "      <td>0.561905</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.769167</td>\n",
              "      <td>0.693286</td>\n",
              "      <td>0.927949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CO2</th>\n",
              "      <td>0.870226</td>\n",
              "      <td>0.911611</td>\n",
              "      <td>0.769167</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.964440</td>\n",
              "      <td>0.768030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HumidityRatio</th>\n",
              "      <td>0.894548</td>\n",
              "      <td>0.951914</td>\n",
              "      <td>0.693286</td>\n",
              "      <td>0.964440</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.700330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Occupancy</th>\n",
              "      <td>0.705784</td>\n",
              "      <td>0.610764</td>\n",
              "      <td>0.927949</td>\n",
              "      <td>0.768030</td>\n",
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              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\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-3b331baa-34ac-4ed6-9287-eaf99833f582 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-3b331baa-34ac-4ed6-9287-eaf99833f582');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "let s visualize that on heatmap"
      ],
      "metadata": {
        "id": "f--j96Tz8Hvp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "plt.rcParams['figure.figsize'] = 20,10  # control plot sizeimport seaborn as sns\n",
        "sns.heatmap(occupancy.corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "yIMYh7TR8G1d",
        "outputId": "9f433b0f-0bae-4f66-de1f-642a44a68b0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Identify the features X and target"
      ],
      "metadata": {
        "id": "cDIMmX4a9fZH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data split & Scaling Data Preprocessing"
      ],
      "metadata": {
        "id": "_WOCnwXhDYaD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "x=OccupancyV2.drop('CO2',axis=1)\n",
        "y=OccupancyV2.drop(['Temperature',\t'Humidity',\t'Light',\t'HumidityRatio'],axis=1)\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "l2bSNkgWDaF7",
        "outputId": "95bd63d2-5d14-43f6-84cb-47bfa9887ef2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           CO2\n",
              "0     0.106940\n",
              "1     0.145214\n",
              "2     0.176881\n",
              "3     0.194253\n",
              "4     0.208776\n",
              "...        ...\n",
              "2660  1.477457\n",
              "2661  1.405522\n",
              "2662  1.393904\n",
              "2663  1.384335\n",
              "2664  1.387752\n",
              "\n",
              "[2665 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b116f2dd-cc3c-4e75-913b-4e2b4ecda496\">\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",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CO2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.106940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.145214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.176881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.194253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.208776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2660</th>\n",
              "      <td>1.477457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2661</th>\n",
              "      <td>1.405522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2662</th>\n",
              "      <td>1.393904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2663</th>\n",
              "      <td>1.384335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2664</th>\n",
              "      <td>1.387752</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2665 rows × 1 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Splitting the dataset into the Training set and Test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "training_features, test_features, \\\n",
        "training_target, test_target, = train_test_split(x,y,\n",
        "                                               test_size = .2,\n",
        "                                               random_state= 45)\n",
        "     "
      ],
      "metadata": {
        "id": "4JGeBSMBDuIq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "from sklearn import preprocessing\n",
        "from sklearn import utils\n",
        "\n",
        "#convert y values to categorical values\n",
        "lab = preprocessing.LabelEncoder()\n",
        "y_transformed = lab.fit_transform(training_target)\n",
        "\n",
        "#view transformed values\n"
      ],
      "metadata": {
        "id": "mxwQl91zDy9D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "test_features"
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          "height": 424
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        "id": "TXxOa3mBEFCY",
        "outputId": "2a7be818-d383-4cd6-fdfe-ec2486d38431"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      Temperature  Humidity     Light  HumidityRatio\n",
              "1538     1.416695  2.231200  1.066390       2.120712\n",
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              "1749     0.064333  1.127106 -0.772404       0.703207\n",
              "...           ...       ...       ...            ...\n",
              "1307     0.815429  0.880839  1.506102       0.893494\n",
              "2597     1.037256  0.128358  1.833887       0.488966\n",
              "1588     1.231840  2.427187  0.996436       2.154806\n",
              "634     -0.843728 -1.257575 -0.772404      -1.138725\n",
              "1322     0.930234  0.757706  1.526089       0.864956\n",
              "\n",
              "[533 rows x 4 columns]"
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              "      <td>-0.772404</td>\n",
              "      <td>-0.519712</td>\n",
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              "      <td>0.064333</td>\n",
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              "      <td>0.703207</td>\n",
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              "      <td>-1.138725</td>\n",
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              "      <th>1322</th>\n",
              "      <td>0.930234</td>\n",
              "      <td>0.757706</td>\n",
              "      <td>1.526089</td>\n",
              "      <td>0.864956</td>\n",
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              "<p>533 rows × 4 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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    {
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      "metadata": {
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          "height": 424
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        "outputId": "ed38e944-7681-472d-988c-021ed38a90cb"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           CO2\n",
              "962  -0.972478\n",
              "46    1.152811\n",
              "1832 -0.148903\n",
              "2526 -0.135519\n",
              "893  -0.965928\n",
              "...        ...\n",
              "163   0.399292\n",
              "607  -0.918940\n",
              "1916 -0.459880\n",
              "1667  1.695311\n",
              "2462 -0.858567\n",
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              "[2132 rows x 1 columns]"
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              "      <th>962</th>\n",
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      "execution_count": null,
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              "           CO2\n",
              "1538  2.293343\n",
              "1195  1.226284\n",
              "2554  0.096574\n",
              "2287 -0.836924\n",
              "1749  0.447989\n",
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              "                                                     [key], {});\n",
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              "\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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      "cell_type": "markdown",
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        "identify the features X and target Y for our dataset"
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      "metadata": {
        "id": "fl_j8asE-cDg"
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    {
      "cell_type": "code",
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            "text/plain": [
              "      Temperature  Humidity     Light       CO2  HumidityRatio\n",
              "0        2.204762  0.376814  1.566862  0.106940       1.207540\n",
              "1        2.222275  0.384202  1.539680  0.145214       1.221461\n",
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              "3        2.226653  0.316478  1.201302  0.194253       1.174140\n",
              "4        2.257300  0.347262  1.180715  0.208776       1.211522\n",
              "...           ...       ...       ...       ...            ...\n",
              "2660     2.778787  0.142040  2.457478  1.477457       1.313879\n",
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              "2662     2.817703  0.142040  2.493454  1.393904       1.333013\n",
              "2663     2.843648  0.142040  2.477465  1.384335       1.345791\n",
              "2664     2.893916  0.134515  2.417504  1.387752       1.364872\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "\n",
        "X1=training_features['Temperature'].to_numpy()\n",
        "X2=training_features['Humidity'].to_numpy()\n",
        "X3=training_features['Light'].to_numpy()\n",
        "X4=training_features['HumidityRatio'].to_numpy()\n",
        "y=training_target['CO2'].to_numpy()\n",
        "#Reshaping Dim\n",
        "X1=X1.reshape(X1.shape[0],1)\n",
        "X2=X2.reshape(X1.shape[0],1)\n",
        "X3=X3.reshape(X1.shape[0],1)\n",
        "X4=X4.reshape(X1.shape[0],1)\n",
        "\n",
        "y=y.reshape(y.shape[0],1)\n",
        "y.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZSwbR1Ce9fIb",
        "outputId": "001dbde5-bb08-40ef-9018-ec7a95eb7073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2132, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Création de la matrice X, inclut le Biais\n",
        "X = np.hstack((X1,X2,X3,X4,np.ones(X1.shape)))\n",
        "print(X.shape)\n",
        "print(X[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dsjf95Wu_aiR",
        "outputId": "d50ab963-b127-45ef-be54-89621a75e40b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2132, 5)\n",
            "[[-1.11290278 -1.06466618 -0.77240366 -1.11791575  1.        ]\n",
            " [ 2.01017788  1.07703138  1.04240614  1.61804618  1.        ]\n",
            " [-0.32483569  0.55248376 -0.77240366  0.16790055  1.        ]\n",
            " [-0.55347244  0.00454083  0.97844808 -0.26517842  1.        ]\n",
            " [-1.01561054 -1.17138166 -0.77240366 -1.14709231  1.        ]\n",
            " [-0.96209982 -0.63062149  0.83853984 -0.80643878  1.        ]\n",
            " [-0.90858909 -1.23705272 -0.77240366 -1.14898773  1.        ]\n",
            " [-0.90858909 -0.60086429  0.85719427 -0.76885103  1.        ]\n",
            " [ 1.77083899  0.13383073  1.66199979  0.82646857  1.        ]\n",
            " [ 1.80586419  0.220024   -0.08005772  0.90403103  1.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Initialisation du vecteur theta aléatoire, avec 3 éléments (car X a trois colonnes)\n",
        "theta = np.random.randn(5, 1)\n",
        "theta.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BTx_zKGCFw4r",
        "outputId": "66d6dce0-b715-45f6-c0c1-55ac0fbf30e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "def model(X, theta): # définir le modèle\n",
        "    return X.dot(theta)"
      ],
      "metadata": {
        "id": "-ILLHf8nFhmH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "def cost_function(X, y, theta): #définir la fonction coût\n",
        "    m = len(y)\n",
        "    return 1/(2*m) * np.sum((model(X, theta) - y)**2)\n",
        "\n"
      ],
      "metadata": {
        "id": "7ZPPYjwZFmCW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "print(X.shape)\n",
        "print(y.shape)\n",
        "print(theta.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XqYKACdfIx08",
        "outputId": "b0a1e17d-03d1-40af-f97c-7804a7ad1a5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2132, 5)\n",
            "(2132, 1)\n",
            "(5, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "cost_function(X, y, theta)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wda3Fz65FqBf",
        "outputId": "261ad53b-1881-4b82-f7c9-b5e8a3f9f790"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.83794000068958"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "#définir la fonction de gradient\n",
        "def grad(X, y, theta):\n",
        "    m = len(y)\n",
        "    return 1/m * X.T.dot(model(X, theta) - y)"
      ],
      "metadata": {
        "id": "l4wwOo4HI791"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Définir la descente de gradient\n",
        "def gradient_descent(X, y, theta, learning_rate, n_iterations):\n",
        "    \n",
        "    cost_history = np.zeros(n_iterations) # création d'un tableau de stockage pour enregistrer l'évolution du Cout du modele\n",
        "    \n",
        "    for i in range(0, n_iterations):\n",
        "        theta = theta - learning_rate * grad(X, y, theta) # mise a jour du parametre theta (formule du gradient descent)\n",
        "        cost_history[i] = cost_function(X, y, theta) # on enregistre la valeur du Cout au tour i dans cost_history[i]\n",
        "        \n",
        "    return theta, cost_history"
      ],
      "metadata": {
        "id": "9R_KHGohI_2x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Entrainement du modèle\n",
        "n_iterations = 1000\n",
        "learning_rate = 0.01\n",
        "\n",
        "theta_final, cost_history = gradient_descent(X, y, theta, learning_rate, n_iterations)"
      ],
      "metadata": {
        "id": "VbnBSTAbJCVq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# création d'un vecteur prédictions qui contient les prédictions de notre modele final\n",
        "predictions = model(X, theta_final)\n",
        "\n",
        "theta_final\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f644Huk4JEYy",
        "outputId": "f0d44143-8443-4794-8983-d1c40b144488"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.01190063],\n",
              "       [-0.0046227 ],\n",
              "       [ 0.1769695 ],\n",
              "       [ 0.85841547],\n",
              "       [ 0.00182444]])"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Predictions "
      ],
      "metadata": {
        "id": "hNC_1uLMKHad"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pQKrLsbGKFvL",
        "outputId": "5cc531df-2a09-47ba-e3e6-479466bdfe20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.07633775],\n",
              "       [ 1.54635324],\n",
              "       [ 0.01057276],\n",
              "       ...,\n",
              "       [-0.25185229],\n",
              "       [ 1.66181952],\n",
              "       [-0.78624425]])"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Courbe d'apprentissage\n",
        "plt.plot(range(n_iterations), cost_history)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "MFbXyEQdJHpq",
        "outputId": "241843fa-06e9-420a-cd19-626cae12c1bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Confusion Matrix\n",
        "#from sklearn.model_selection import cross_val_score, cross_val_predict\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
      ],
      "metadata": {
        "id": "GpXYD1jMJWSF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Evaluation du modèle\n",
        "def coef_determination(y, pred):\n",
        "    u = ((y - pred)**2).sum()\n",
        "    v = ((y - y.mean())**2).sum()\n",
        "    return 1 - u/v"
      ],
      "metadata": {
        "id": "8dB4EjUWKPM5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "#Accuracy\n",
        "print(\" Prediction Accuracy : {:.2f}%\".format(coef_determination(y, predictions) * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MkMZOXPGK4mV",
        "outputId": "4825e27d-849a-44f3-e9a0-cac977329d3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Prediction Accuracy : 95.18%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Partie 2"
      ],
      "metadata": {
        "id": "VlVm4SLML2kV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Logistic Regression Model (From scratch)"
      ],
      "metadata": {
        "id": "dikN99N5L4uM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "def sigmoid(x):\n",
        "    return 1 / (1 + np.exp(-x))\n",
        "\n",
        "def initialisation(X):\n",
        "    W = np.random.randn(X.shape[1], 1)\n",
        "    b = np.random.randn(1)\n",
        "    return (W, b)\n",
        "     \n",
        "\n",
        "# Modèle\n",
        "def modele(X, W, b):\n",
        "    Z = X.dot(W) + b\n",
        "    A = sigmoid(Z)\n",
        "    return A\n",
        "     \n",
        "\n",
        "def log_los(y, A):\n",
        "    return 1/len(y) * np.sum(-y * np.log(A) - (1 - y) * np.log(1 - A))\n",
        "def gradients(X, A, y):\n",
        "    dW = 1/len(y) * np.dot(X.T, A - y)\n",
        "    db = 1/len(y) * np.sum(A - y)\n",
        "    return (dW, db)\n",
        "     \n",
        "\n",
        "def optimisation(X, W, b, A, y, learning_rate):\n",
        "    dW, db = gradients(X, A, y)\n",
        "    W = W - learning_rate * dW\n",
        "    b = b - learning_rate * db\n",
        "    return (W, b)\n",
        "     \n",
        "\n",
        "def predict(X, W, b):\n",
        "    A = modele(X, W, b)\n",
        "    return A >= 0.5"
      ],
      "metadata": {
        "id": "Jfn67GtIME8N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "from sklearn.metrics import log_loss\n",
        "def regression_logistique(X, y, learning_rate=0.1, n_iter=10000):\n",
        "  # Initialisation\n",
        "    W, b = initialisation(X)\n",
        "    loss_history = []\n",
        "  # Entrainement\n",
        "    for i in range(n_iter):\n",
        "        A = modele(X, W, b)\n",
        "        A = np.clip(A, 1e-15, 1 - 1e-15)\n",
        "        loss_history.append(log_loss(y, A))\n",
        "        W, b = optimisation(X, W, b, A, y, learning_rate=0.1)\n",
        "        \n",
        "    return W,b,loss_history"
      ],
      "metadata": {
        "id": "8ZqX2k6aMZxL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "OccupancyV2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tExhRiSyOhQK",
        "outputId": "cc29ef15-8d58-46b8-ca39-c61eebf90970"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Temperature  Humidity     Light       CO2  HumidityRatio\n",
              "0        2.204762  0.376814  1.566862  0.106940       1.207540\n",
              "1        2.222275  0.384202  1.539680  0.145214       1.221461\n",
              "2        2.233950  0.359575  1.516762  0.176881       1.209161\n",
              "3        2.226653  0.316478  1.201302  0.194253       1.174140\n",
              "4        2.257300  0.347262  1.180715  0.208776       1.211522\n",
              "...           ...       ...       ...       ...            ...\n",
              "2660     2.778787  0.142040  2.457478  1.477457       1.313879\n",
              "2661     2.817703  0.156816  2.464673  1.405522       1.344207\n",
              "2662     2.817703  0.142040  2.493454  1.393904       1.333013\n",
              "2663     2.843648  0.142040  2.477465  1.384335       1.345791\n",
              "2664     2.893916  0.134515  2.417504  1.387752       1.364872\n",
              "\n",
              "[2665 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b849b233-7870-4ae9-bd6e-8abc2f6c5cba\">\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>Temperature</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Light</th>\n",
              "      <th>CO2</th>\n",
              "      <th>HumidityRatio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.204762</td>\n",
              "      <td>0.376814</td>\n",
              "      <td>1.566862</td>\n",
              "      <td>0.106940</td>\n",
              "      <td>1.207540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.222275</td>\n",
              "      <td>0.384202</td>\n",
              "      <td>1.539680</td>\n",
              "      <td>0.145214</td>\n",
              "      <td>1.221461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.233950</td>\n",
              "      <td>0.359575</td>\n",
              "      <td>1.516762</td>\n",
              "      <td>0.176881</td>\n",
              "      <td>1.209161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.226653</td>\n",
              "      <td>0.316478</td>\n",
              "      <td>1.201302</td>\n",
              "      <td>0.194253</td>\n",
              "      <td>1.174140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.257300</td>\n",
              "      <td>0.347262</td>\n",
              "      <td>1.180715</td>\n",
              "      <td>0.208776</td>\n",
              "      <td>1.211522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2660</th>\n",
              "      <td>2.778787</td>\n",
              "      <td>0.142040</td>\n",
              "      <td>2.457478</td>\n",
              "      <td>1.477457</td>\n",
              "      <td>1.313879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2661</th>\n",
              "      <td>2.817703</td>\n",
              "      <td>0.156816</td>\n",
              "      <td>2.464673</td>\n",
              "      <td>1.405522</td>\n",
              "      <td>1.344207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2662</th>\n",
              "      <td>2.817703</td>\n",
              "      <td>0.142040</td>\n",
              "      <td>2.493454</td>\n",
              "      <td>1.393904</td>\n",
              "      <td>1.333013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2663</th>\n",
              "      <td>2.843648</td>\n",
              "      <td>0.142040</td>\n",
              "      <td>2.477465</td>\n",
              "      <td>1.384335</td>\n",
              "      <td>1.345791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2664</th>\n",
              "      <td>2.893916</td>\n",
              "      <td>0.134515</td>\n",
              "      <td>2.417504</td>\n",
              "      <td>1.387752</td>\n",
              "      <td>1.364872</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2665 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b849b233-7870-4ae9-bd6e-8abc2f6c5cba')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      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",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\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-b849b233-7870-4ae9-bd6e-8abc2f6c5cba 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-b849b233-7870-4ae9-bd6e-8abc2f6c5cba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Identify the feautures X and target Y"
      ],
      "metadata": {
        "id": "Ch8l9kHDO335"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "X=OccupancyV2.drop(['Temperature',\t'Humidity',\t'HumidityRatio'],axis=1)\n",
        "y=occupancy.Occupancy\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1t4kOy-pOR6k",
        "outputId": "445455ee-fabf-4dc1-b1d1-41cb910ee04c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Light       CO2\n",
              "0     1.566862  0.106940\n",
              "1     1.539680  0.145214\n",
              "2     1.516762  0.176881\n",
              "3     1.201302  0.194253\n",
              "4     1.180715  0.208776\n",
              "...        ...       ...\n",
              "2660  2.457478  1.477457\n",
              "2661  2.464673  1.405522\n",
              "2662  2.493454  1.393904\n",
              "2663  2.477465  1.384335\n",
              "2664  2.417504  1.387752\n",
              "\n",
              "[2665 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1f168e17-5418-4f19-b828-8282dcdb9ad9\">\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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Light</th>\n",
              "      <th>CO2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.566862</td>\n",
              "      <td>0.106940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.539680</td>\n",
              "      <td>0.145214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.516762</td>\n",
              "      <td>0.176881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.201302</td>\n",
              "      <td>0.194253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.180715</td>\n",
              "      <td>0.208776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2660</th>\n",
              "      <td>2.457478</td>\n",
              "      <td>1.477457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2661</th>\n",
              "      <td>2.464673</td>\n",
              "      <td>1.405522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2662</th>\n",
              "      <td>2.493454</td>\n",
              "      <td>1.393904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2663</th>\n",
              "      <td>2.477465</td>\n",
              "      <td>1.384335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2664</th>\n",
              "      <td>2.417504</td>\n",
              "      <td>1.387752</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2665 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f168e17-5418-4f19-b828-8282dcdb9ad9')\"\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",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      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",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\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-1f168e17-5418-4f19-b828-8282dcdb9ad9 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-1f168e17-5418-4f19-b828-8282dcdb9ad9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Splitting the dataset into the Training set and Test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "training_features, test_features, \\\n",
        "training_target, test_target, = train_test_split(X,y,\n",
        "                                               test_size = .2,\n",
        "                                               random_state= 45)"
      ],
      "metadata": {
        "id": "0QXdQTiGPE1v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "X1=training_features['CO2'].to_numpy()\n",
        "X2=training_features['Light'].to_numpy()\n",
        "\n",
        "y=training_target.to_numpy()\n",
        "#Reshaping Dim\n",
        "X1=X1.reshape(X1.shape[0],1)\n",
        "X2=X2.reshape(X2.shape[0],1)\n",
        "\n",
        "\n",
        "y=y.reshape(y.shape[0],1)\n",
        "y.shape\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvVM7syTMcqC",
        "outputId": "424a3969-342b-43a7-8c21-7a852c109552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [1],\n",
              "       [0],\n",
              "       ...,\n",
              "       [0],\n",
              "       [1],\n",
              "       [0]])"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "X1_tarin=test_features['CO2'].to_numpy()\n",
        "X2_tarin=test_features['Light'].to_numpy()\n",
        "\n",
        "y_train=test_target.to_numpy()\n",
        "#Reshaping Dim\n",
        "X1_tarin=X1_tarin.reshape(X1_tarin.shape[0],1)\n",
        "X2_tarin=X2_tarin.reshape(X2_tarin.shape[0],1)\n",
        "\n",
        "\n",
        "y_train=y_train.reshape(y_train.shape[0],1)\n",
        "y_train.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5rt-nLEQ2JQ",
        "outputId": "538040d7-25f1-409f-de8b-70329e09689e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(533, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Création de la matrice X, inclut le Biais\n",
        "X_train = np.hstack((X1_tarin,X2_tarin,np.ones(X1_tarin.shape)))\n",
        "print(X_train.shape)\n",
        "print(X_train[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0R-NIR_zRClq",
        "outputId": "0635d2a8-7b2f-4056-f97f-89398989d6d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(533, 3)\n",
            "[[ 2.29334267  1.06639041  1.        ]\n",
            " [ 1.22628388  1.1223537   1.        ]\n",
            " [ 0.09657419  1.05239958  1.        ]\n",
            " [-0.83692426 -0.77240366  1.        ]\n",
            " [ 0.4479888  -0.77240366  1.        ]\n",
            " [-0.92150298 -0.77240366  1.        ]\n",
            " [-0.85144788 -0.77240366  1.        ]\n",
            " [-0.23091917 -0.77240366  1.        ]\n",
            " [ 2.25290436  0.99044022  1.        ]\n",
            " [ 1.03576818  1.11835633  1.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Création de la matrice X, inclut le Biais\n",
        "X = np.hstack((X1,X2,np.ones(X1.shape)))\n",
        "print(X.shape)\n",
        "print(X[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rrGbAln9PeLx",
        "outputId": "3d920591-a929-4980-9f64-edc2ce05e70a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(533, 3)\n",
            "[[ 2.29334267  1.06639041  1.        ]\n",
            " [ 1.22628388  1.1223537   1.        ]\n",
            " [ 0.09657419  1.05239958  1.        ]\n",
            " [-0.83692426 -0.77240366  1.        ]\n",
            " [ 0.4479888  -0.77240366  1.        ]\n",
            " [-0.92150298 -0.77240366  1.        ]\n",
            " [-0.85144788 -0.77240366  1.        ]\n",
            " [-0.23091917 -0.77240366  1.        ]\n",
            " [ 2.25290436  0.99044022  1.        ]\n",
            " [ 1.03576818  1.11835633  1.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "W,b,loss_history=regression_logistique(X,y)\n",
        "L_pred=predict(X_train,W,b)\n",
        "# Making the Confusion Matrix\n",
        "y_test_transformed = lab.fit_transform(test_target)\n",
        "\n",
        "Matrix= confusion_matrix(y_test_transformed, L_pred)\n",
        "Matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjrpPpBMPuN2",
        "outputId": "8ec0e102-ac72-48a3-f523-d77454c1a98c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[316,   9],\n",
              "       [  0, 208]])"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "y_test_transformed=y_test_transformed.reshape(y_test_transformed.shape[0],1)\n",
        "y_test_transformed.shape,L_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CkNI8hxFUyM-",
        "outputId": "61d966c9-6bdb-492a-b778-8dae086e742c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((533, 1), (533, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# Accuracy Score\n",
        "Matrix= accuracy_score(y_test_transformed, L_pred )\n",
        "\n",
        "print(\"Prediction Accuracy : {:.2f}%\".format(Matrix * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRhoDX_xSXdQ",
        "outputId": "e8b568fa-b2d8-46b7-efef-abd9ff43826e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction Accuracy : 98.31%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Confusion Matrix"
      ],
      "metadata": {
        "id": "RfxwpAUCWG5g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy\n",
        "from sklearn import metrics\n",
        "\n",
        "\n",
        "confusion_matrix = metrics.confusion_matrix(y_test_transformed, L_pred)\n",
        "\n",
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
        "\n",
        "cm_display.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "Q-9wk6rcSmZF",
        "outputId": "116d0883-80f2-439f-83cc-035399331308"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "# print(loss_history)\n",
        "plt.plot(loss_history)\n",
        "plt.xlabel('n_iteration')\n",
        "plt.ylabel('Log_loss')\n",
        "plt.title('Evolution des erreurs')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "z-WxraeFQGnI",
        "outputId": "5aa5fdf4-7507-472a-f54a-098f5da71378"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Evolution des erreurs')"
            ]
          },
          "metadata": {},
          "execution_count": 178
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soufiane MOUHTARAM\n",
        "\n",
        "X1=test_features['CO2'].to_numpy()\n",
        "X2=test_features['Light'].to_numpy()\n",
        "X3=test_features['Temperature'].to_numpy()\n",
        "X4=test_features['Humidity'].to_numpy()\n",
        "\n",
        "\n",
        "y=test_target.to_numpy()\n",
        "#Reshaping Dim\n",
        "X1_tarin=X1_tarin.reshape(X1.shape[0],1)\n",
        "X2=X2.reshape(X2.shape[0],1)\n",
        "X3=X3.reshape(X3.shape[0],1)\n",
        "X4=X4.reshape(X4.shape[0],1)\n",
        "\n",
        "\n",
        "y_train=y_train.reshape(y_train.shape[0],1)\n",
        "y_train.shape\n"
      ],
      "metadata": {
        "id": "7aFDvQdNXdfD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}