stress_level_classifier.ipynb

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        "<a href=\"https://colab.research.google.com/github/blazingbhavneek/scikit-learn_projects/blob/master/stress_level_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "id": "dc989a33",
      "metadata": {
        "id": "dc989a33"
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      "source": [
        "# Human Stress Detection in and through Sleep\n",
        "## By [Bhavneek Singh](https://bhavneek.netlify.app/), IIT Delhi\n",
        "\n",
        "Smart-Yoga Pillow (SaYoPillow) was invented to help in understanding the relationship between stress and sleep and to fully materialize the idea of “Smart-Sleeping” by proposing an edge device. An edge processor with a model analyzing the physiological changes that occur during sleep along with the sleeping habits is proposed.\n",
        "\n",
        "In SayoPillow.csv, we will see the relationship between the parameters- snoring range of the user, respiration rate, body temperature, limb movement rate, blood oxygen levels, eye movement, number of hours of sleep, heart rate and Stress Levels (0- low/normal, 1 – medium low, 2- medium, 3-medium high, 4 -high)\n",
        "\n",
        "sr-snoring rate\n",
        "\n",
        "rr-respiration rate\n",
        "\n",
        "t-body temperature \n",
        "\n",
        "lm-limb movement\n",
        "\n",
        "bo-blood oxygen\n",
        "\n",
        "rem-eye movement\n",
        "\n",
        "sr-sleeping hours\n",
        "\n",
        "hr-heart rate\n",
        "\n",
        "sl-stress level"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "35085e7b",
      "metadata": {
        "id": "35085e7b"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "id": "409afde8",
      "metadata": {
        "id": "409afde8"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"SaYoPillow.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "870cad28",
      "metadata": {
        "id": "870cad28"
      },
      "source": [
        "### Getting general idea of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "id": "2f8a9ecf",
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        "colab": {
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        },
        "id": "2f8a9ecf",
        "outputId": "16c5a11a-6de4-4cbc-ffd0-cebf1403ceea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 630 entries, 0 to 629\n",
            "Data columns (total 9 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   sr      630 non-null    float64\n",
            " 1   rr      630 non-null    float64\n",
            " 2   t       630 non-null    float64\n",
            " 3   lm      630 non-null    float64\n",
            " 4   bo      630 non-null    float64\n",
            " 5   rem     630 non-null    float64\n",
            " 6   sr.1    630 non-null    float64\n",
            " 7   hr      630 non-null    float64\n",
            " 8   sl      630 non-null    int64  \n",
            "dtypes: float64(8), int64(1)\n",
            "memory usage: 44.4 KB\n"
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      "source": [
        "df.info()"
      ]
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    {
      "cell_type": "markdown",
      "id": "5f095a58",
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        "id": "5f095a58"
      },
      "source": [
        "No NULL values, Great!"
      ]
    },
    {
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        "id": "41455275",
        "outputId": "86ffb573-8935-4ee1-b86f-ea5d8c13a500"
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              "      count  mean        std   min    25%   50%    75%    max\n",
              "sr    630.0  71.6  19.372833  45.0  52.50  70.0  91.25  100.0\n",
              "rr    630.0  21.8   3.966111  16.0  18.50  21.0  25.00   30.0\n",
              "t     630.0  92.8   3.529690  85.0  90.50  93.0  95.50   99.0\n",
              "lm    630.0  11.7   4.299629   4.0   8.50  11.0  15.75   19.0\n",
              "bo    630.0  90.9   3.902483  82.0  88.50  91.0  94.25   97.0\n",
              "rem   630.0  88.5  11.893747  60.0  81.25  90.0  98.75  105.0\n",
              "sr.1  630.0   3.7   3.054572   0.0   0.50   3.5   6.50    9.0\n",
              "hr    630.0  64.5   9.915277  50.0  56.25  62.5  72.50   85.0\n",
              "sl    630.0   2.0   1.415337   0.0   1.00   2.0   3.00    4.0"
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              "      <td>90.9</td>\n",
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              "      <td>88.50</td>\n",
              "      <td>91.0</td>\n",
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              "      <td>630.0</td>\n",
              "      <td>88.5</td>\n",
              "      <td>11.893747</td>\n",
              "      <td>60.0</td>\n",
              "      <td>81.25</td>\n",
              "      <td>90.0</td>\n",
              "      <td>98.75</td>\n",
              "      <td>105.0</td>\n",
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              "      <th>sr.1</th>\n",
              "      <td>630.0</td>\n",
              "      <td>3.7</td>\n",
              "      <td>3.054572</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6.50</td>\n",
              "      <td>9.0</td>\n",
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              "    <tr>\n",
              "      <th>hr</th>\n",
              "      <td>630.0</td>\n",
              "      <td>64.5</td>\n",
              "      <td>9.915277</td>\n",
              "      <td>50.0</td>\n",
              "      <td>56.25</td>\n",
              "      <td>62.5</td>\n",
              "      <td>72.50</td>\n",
              "      <td>85.0</td>\n",
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              "    <tr>\n",
              "      <th>sl</th>\n",
              "      <td>630.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.415337</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>4.0</td>\n",
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        "df.describe().transpose()"
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              "      sr      rr       t      lm      bo    rem   sr.1     hr  sl\n",
              "0  93.80  25.680  91.840  16.600  89.840  99.60  1.840  74.20   3\n",
              "1  91.64  25.104  91.552  15.880  89.552  98.88  1.552  72.76   3\n",
              "2  60.00  20.000  96.000  10.000  95.000  85.00  7.000  60.00   1\n",
              "3  85.76  23.536  90.768  13.920  88.768  96.92  0.768  68.84   3\n",
              "4  48.12  17.248  97.872   6.496  96.248  72.48  8.248  53.12   0\n",
              "5  56.88  19.376  95.376   9.376  94.064  83.44  6.376  58.44   1\n",
              "6  47.00  16.800  97.200   5.600  95.800  68.00  7.800  52.00   0\n",
              "7  50.00  18.000  99.000   8.000  97.000  80.00  9.000  55.00   0\n",
              "8  45.28  16.112  96.168   4.224  95.112  61.12  7.112  50.28   0\n",
              "9  55.52  19.104  95.104   9.104  93.656  82.76  6.104  57.76   1"
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              "      <td>93.80</td>\n",
              "      <td>25.680</td>\n",
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              "      <td>89.840</td>\n",
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              "      <td>1.840</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>91.64</td>\n",
              "      <td>25.104</td>\n",
              "      <td>91.552</td>\n",
              "      <td>15.880</td>\n",
              "      <td>89.552</td>\n",
              "      <td>98.88</td>\n",
              "      <td>1.552</td>\n",
              "      <td>72.76</td>\n",
              "      <td>3</td>\n",
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              "      <th>2</th>\n",
              "      <td>60.00</td>\n",
              "      <td>20.000</td>\n",
              "      <td>96.000</td>\n",
              "      <td>10.000</td>\n",
              "      <td>95.000</td>\n",
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              "      <td>7.000</td>\n",
              "      <td>60.00</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>85.76</td>\n",
              "      <td>23.536</td>\n",
              "      <td>90.768</td>\n",
              "      <td>13.920</td>\n",
              "      <td>88.768</td>\n",
              "      <td>96.92</td>\n",
              "      <td>0.768</td>\n",
              "      <td>68.84</td>\n",
              "      <td>3</td>\n",
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              "      <th>4</th>\n",
              "      <td>48.12</td>\n",
              "      <td>17.248</td>\n",
              "      <td>97.872</td>\n",
              "      <td>6.496</td>\n",
              "      <td>96.248</td>\n",
              "      <td>72.48</td>\n",
              "      <td>8.248</td>\n",
              "      <td>53.12</td>\n",
              "      <td>0</td>\n",
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              "      <th>5</th>\n",
              "      <td>56.88</td>\n",
              "      <td>19.376</td>\n",
              "      <td>95.376</td>\n",
              "      <td>9.376</td>\n",
              "      <td>94.064</td>\n",
              "      <td>83.44</td>\n",
              "      <td>6.376</td>\n",
              "      <td>58.44</td>\n",
              "      <td>1</td>\n",
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              "      <th>6</th>\n",
              "      <td>47.00</td>\n",
              "      <td>16.800</td>\n",
              "      <td>97.200</td>\n",
              "      <td>5.600</td>\n",
              "      <td>95.800</td>\n",
              "      <td>68.00</td>\n",
              "      <td>7.800</td>\n",
              "      <td>52.00</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>50.00</td>\n",
              "      <td>18.000</td>\n",
              "      <td>99.000</td>\n",
              "      <td>8.000</td>\n",
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              "      <td>80.00</td>\n",
              "      <td>9.000</td>\n",
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              "      <td>0</td>\n",
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              "      <th>8</th>\n",
              "      <td>45.28</td>\n",
              "      <td>16.112</td>\n",
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              "      <td>4.224</td>\n",
              "      <td>95.112</td>\n",
              "      <td>61.12</td>\n",
              "      <td>7.112</td>\n",
              "      <td>50.28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>55.52</td>\n",
              "      <td>19.104</td>\n",
              "      <td>95.104</td>\n",
              "      <td>9.104</td>\n",
              "      <td>93.656</td>\n",
              "      <td>82.76</td>\n",
              "      <td>6.104</td>\n",
              "      <td>57.76</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-491b4445-9df4-4720-8448-252de309fccd')\"\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-491b4445-9df4-4720-8448-252de309fccd 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-491b4445-9df4-4720-8448-252de309fccd');\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": 89
        }
      ],
      "source": [
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "id": "f0326cd1",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "f0326cd1",
        "outputId": "e3fa9e99-c1aa-407d-bc24-4d7b48597cdf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1db5bd4d30>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8),dpi=150)\n",
        "sns.heatmap(df.corr(),annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "25f79e89",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "25f79e89",
        "outputId": "607bfdc5-302b-453e-845e-8e5704c083c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f1db5af2df0>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1620x180 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.pairplot(df, y_vars='sl', x_vars=df.columns.values)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2d283ecf",
      "metadata": {
        "id": "2d283ecf"
      },
      "source": [
        "Judging from staircase shape like plots, it looks like logistic regression should yield fairly accurate results"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d516fcdd",
      "metadata": {
        "id": "d516fcdd"
      },
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "44f6acfd",
      "metadata": {
        "id": "44f6acfd"
      },
      "outputs": [],
      "source": [
        "# Separating Label\n",
        "X = df.drop('sl',axis=1)\n",
        "y = df['sl']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "d62f7275",
      "metadata": {
        "id": "d62f7275"
      },
      "outputs": [],
      "source": [
        "# TRAIN TEST SPLIT\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "id": "12c26715",
      "metadata": {
        "id": "12c26715"
      },
      "outputs": [],
      "source": [
        "# SCALE DATA\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "sX_train = scaler.transform(X_train)\n",
        "sX_test = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "id": "f78a8279",
      "metadata": {
        "id": "f78a8279"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,plot_confusion_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d64ec498",
      "metadata": {
        "id": "d64ec498"
      },
      "source": [
        "## Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "b6ccb6e8",
      "metadata": {
        "id": "b6ccb6e8"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "id": "c4b32d89",
      "metadata": {
        "id": "c4b32d89"
      },
      "outputs": [],
      "source": [
        "log_model = LogisticRegression(multi_class=\"ovr\",max_iter=5000000000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "id": "ba197d99",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ba197d99",
        "outputId": "9593d0bc-7b66-495a-db91-894a498d70d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=5000000000, multi_class='ovr')"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "log_model.fit(sX_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "id": "5b66190b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5b66190b",
        "outputId": "7f150650-9134-4b8c-ba67-d46c2237ae47"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.81690359, -0.98432225,  1.83048196, -1.27163562,  1.55156138,\n",
              "        -1.80450113,  1.64177258, -0.98432225],\n",
              "       [-5.0706216 , -0.85084529, -3.27146401,  1.33304752,  0.6158265 ,\n",
              "         3.51638181,  2.34744491, -0.85084529],\n",
              "       [ 0.1072966 , -1.44014857,  1.04748838, -2.40712448, -1.17892164,\n",
              "         4.33221106, -0.54166138, -1.44014857],\n",
              "       [ 3.90119397, -2.46937298,  1.61702812,  0.84521949,  2.19173303,\n",
              "         1.76126243, -4.74851176, -2.46937298],\n",
              "       [ 0.69886193,  1.51066636, -1.65693243,  1.06614374, -1.68096928,\n",
              "         0.63519622, -0.76422247,  1.51066636]])"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "log_model.coef_"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = log_model.predict(sX_test)"
      ],
      "metadata": {
        "id": "gFwipg1-GTDZ"
      },
      "id": "gFwipg1-GTDZ",
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uS79shqWGnGT",
        "outputId": "8e8bcb1d-9076-4e05-93c8-b3820dd3b819"
      },
      "id": "uS79shqWGnGT",
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(log_model,sX_test,y_test,normalize='true')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "sPwvz515GWQ5",
        "outputId": "c7c1d3e2-a453-4b86-90aa-f50b7c0f7e8f"
      },
      "id": "sPwvz515GWQ5",
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f1db57e44f0>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference\n",
        "As we saw from the pair plot, we had very distinct boundaries for stress level for all features. So our model was able to achieve 100% accuracy fairly easly"
      ],
      "metadata": {
        "id": "fkKIV5Y3MfmD"
      },
      "id": "fkKIV5Y3MfmD"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.15"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}