ML_LAB8_21171(proj) (1).ipynb

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      "cell_type": "code",
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"drive/My Drive/code_ques.csv\")\n",
        "df.head()"
      ],
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        "id": "MmqYvE80387K",
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      "execution_count": null,
      "outputs": [
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              "          0         1         2         3         4         5         6  \\\n",
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              "\n",
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              "4 -1.603735  0.730069  0.242844  ... -2.279187  0.451709 -1.607142 -2.184134   \n",
              "\n",
              "        763       764       765       766       767  score  \n",
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              "4  0.521283 -1.604708 -0.055002 -2.056900  1.290956    6.0  \n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  if df[col].isnull().sum()>0:\n",
        "    print(col)\n",
        "    print(df[col].isnull().sum())"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "k33BsLYV4spi",
        "outputId": "82e34478-5191-4f4c-e114-d55f3481aa1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score\n",
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "wkflWrRp4swT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(df['score'])\n",
        "column_name='score'\n",
        "# Set plot labels\n",
        "plt.xlabel('Score')\n",
        "plt.ylabel('Values')\n",
        "plt.title(f'Box Plot for {column_name}')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "vj_pUr9U9IfQ",
        "outputId": "4d9f9995-be48-4ec5-f71d-47fdebb6c6b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "z_scores = stats.zscore(df['score'])\n",
        "\n",
        "# Set a threshold (e.g., 3) to identify outliers\n",
        "threshold = 3\n",
        "outliers = df[abs(z_scores) > threshold]\n",
        "\n",
        "print(\"Outliers using Z-Score:\")\n",
        "print(outliers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzSVJAC0AC6W",
        "outputId": "0a607013-ec95-4348-d0f7-1fa34afb051d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outliers using Z-Score:\n",
            "Empty DataFrame\n",
            "Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]\n",
            "Index: []\n",
            "\n",
            "[0 rows x 769 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_value = df['score'].mean()\n",
        "\n",
        "df[column_name].fillna(mean_value, inplace=True)\n",
        "\n",
        "# Display the DataFrame after imputation\n",
        "print(\"DataFrame after imputation:\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "Jb4b1kiIAk_r",
        "outputId": "3dccb42f-6270-4851-b790-cf9571b74a6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame after imputation:\n"
          ]
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          "data": {
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              "          0         1         2         3         4         5         6  \\\n",
              "0 -1.355365  2.348287  1.030452 -1.503548 -0.380958  1.095197 -2.063201   \n",
              "1 -1.403411  2.131454  1.117223 -1.542901 -0.351417  1.042730 -2.025140   \n",
              "2 -1.388594  2.358305  0.994477 -1.480365 -0.382565  1.104627 -2.042682   \n",
              "3 -1.352139  2.155489  1.027737 -1.424550 -0.316464  1.111701 -2.039937   \n",
              "4 -1.411875  2.337363  1.050743 -1.506995 -0.321872  1.118751 -2.068671   \n",
              "\n",
              "          7         8         9  ...       759       760       761       762  \\\n",
              "0 -1.657276  0.780015  0.224140  ... -2.330601  0.542652 -1.660974 -2.150713   \n",
              "1 -1.812830  0.707280  0.165037  ... -2.183963  0.488854 -1.674375 -2.027182   \n",
              "2 -1.669257  0.800453  0.265268  ... -2.347989  0.555944 -1.651969 -2.149015   \n",
              "3 -1.789147  0.725883  0.298638  ... -2.216481  0.487809 -1.595799 -2.143987   \n",
              "4 -1.603735  0.730069  0.242844  ... -2.279187  0.451709 -1.607142 -2.184134   \n",
              "\n",
              "        763       764       765       766       767  score  \n",
              "0  0.545611 -1.614411 -0.133085 -2.134145  1.184289   10.0  \n",
              "1  0.514854 -1.625416  0.014533 -2.156347  1.088794    8.0  \n",
              "2  0.582454 -1.606852 -0.164269 -2.139201  1.158317    7.0  \n",
              "3  0.419316 -1.635483 -0.074271 -2.112344  1.094625    5.0  \n",
              "4  0.521283 -1.604708 -0.055002 -2.056900  1.290956    6.0  \n",
              "\n",
              "[5 rows x 769 columns]"
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              "      <td>-2.150713</td>\n",
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              "      <td>-1.614411</td>\n",
              "      <td>-0.133085</td>\n",
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              "      <td>1.184289</td>\n",
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              "      <th>1</th>\n",
              "      <td>-1.403411</td>\n",
              "      <td>2.131454</td>\n",
              "      <td>1.117223</td>\n",
              "      <td>-1.542901</td>\n",
              "      <td>-0.351417</td>\n",
              "      <td>1.042730</td>\n",
              "      <td>-2.025140</td>\n",
              "      <td>-1.812830</td>\n",
              "      <td>0.707280</td>\n",
              "      <td>0.165037</td>\n",
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              "      <td>-2.183963</td>\n",
              "      <td>0.488854</td>\n",
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              "      <td>-2.027182</td>\n",
              "      <td>0.514854</td>\n",
              "      <td>-1.625416</td>\n",
              "      <td>0.014533</td>\n",
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              "      <td>1.088794</td>\n",
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              "      <td>2.358305</td>\n",
              "      <td>0.994477</td>\n",
              "      <td>-1.480365</td>\n",
              "      <td>-0.382565</td>\n",
              "      <td>1.104627</td>\n",
              "      <td>-2.042682</td>\n",
              "      <td>-1.669257</td>\n",
              "      <td>0.800453</td>\n",
              "      <td>0.265268</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.347989</td>\n",
              "      <td>0.555944</td>\n",
              "      <td>-1.651969</td>\n",
              "      <td>-2.149015</td>\n",
              "      <td>0.582454</td>\n",
              "      <td>-1.606852</td>\n",
              "      <td>-0.164269</td>\n",
              "      <td>-2.139201</td>\n",
              "      <td>1.158317</td>\n",
              "      <td>7.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.352139</td>\n",
              "      <td>2.155489</td>\n",
              "      <td>1.027737</td>\n",
              "      <td>-1.424550</td>\n",
              "      <td>-0.316464</td>\n",
              "      <td>1.111701</td>\n",
              "      <td>-2.039937</td>\n",
              "      <td>-1.789147</td>\n",
              "      <td>0.725883</td>\n",
              "      <td>0.298638</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.216481</td>\n",
              "      <td>0.487809</td>\n",
              "      <td>-1.595799</td>\n",
              "      <td>-2.143987</td>\n",
              "      <td>0.419316</td>\n",
              "      <td>-1.635483</td>\n",
              "      <td>-0.074271</td>\n",
              "      <td>-2.112344</td>\n",
              "      <td>1.094625</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.411875</td>\n",
              "      <td>2.337363</td>\n",
              "      <td>1.050743</td>\n",
              "      <td>-1.506995</td>\n",
              "      <td>-0.321872</td>\n",
              "      <td>1.118751</td>\n",
              "      <td>-2.068671</td>\n",
              "      <td>-1.603735</td>\n",
              "      <td>0.730069</td>\n",
              "      <td>0.242844</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.279187</td>\n",
              "      <td>0.451709</td>\n",
              "      <td>-1.607142</td>\n",
              "      <td>-2.184134</td>\n",
              "      <td>0.521283</td>\n",
              "      <td>-1.604708</td>\n",
              "      <td>-0.055002</td>\n",
              "      <td>-2.056900</td>\n",
              "      <td>1.290956</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 769 columns</p>\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flag=False\n",
        "for col in df.columns:\n",
        "  if df[col].isnull().sum()>0:\n",
        "    flag=True\n",
        "    print(col)\n",
        "    print(df[col].isnull().sum())\n",
        "if flag==False:\n",
        "  print(\"NO NULL VALUES\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQERh5xpAylU",
        "outputId": "d261332d-d672-4e87-8052-b0b6ce68f734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NO NULL VALUES\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfgviieK4s06",
        "outputId": "2f8569da-2f62-4c28-f199-196c720dedfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1176, 769)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Final_score'] = np.where(df['score'] > 5, 1, 0)"
      ],
      "metadata": {
        "id": "po20F-Mg4s_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "DlF99EWg4tBK",
        "outputId": "693099c6-b47f-4174-d041-0dcb14509ec1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 0            1            2            3            4  \\\n",
              "count  1176.000000  1176.000000  1176.000000  1176.000000  1176.000000   \n",
              "mean     -1.253594    -0.465979     0.229329     0.372101     0.277136   \n",
              "std       1.204115     1.399195     1.165310     0.871044     0.967215   \n",
              "min      -4.964680    -4.291207    -3.836382    -2.519691    -2.327611   \n",
              "25%      -1.976430    -1.321678    -0.498208    -0.137077    -0.397124   \n",
              "50%      -1.320060    -0.528970     0.304345     0.316335     0.164286   \n",
              "75%      -0.566802     0.476583     1.044464     1.007085     0.924620   \n",
              "max       2.057063     2.867949     2.698387     2.857039     3.281687   \n",
              "\n",
              "                 5            6            7            8            9  ...  \\\n",
              "count  1176.000000  1176.000000  1176.000000  1176.000000  1176.000000  ...   \n",
              "mean      0.865536    -0.554721    -1.280188    -0.392907    -0.226238  ...   \n",
              "std       0.930031     1.271228     1.581162     1.014660     1.032897  ...   \n",
              "min      -4.050520    -3.307933    -5.572669    -3.264831    -3.251885  ...   \n",
              "25%       0.154785    -1.529384    -2.627281    -1.060415    -0.876954  ...   \n",
              "50%       0.712414    -0.673306    -1.062531    -0.610422    -0.122351  ...   \n",
              "75%       1.626414     0.208969     0.002230     0.315335     0.306526  ...   \n",
              "max       3.100244     4.445419     2.208376     2.195000     3.159068  ...   \n",
              "\n",
              "               760          761          762          763          764  \\\n",
              "count  1176.000000  1176.000000  1176.000000  1176.000000  1176.000000   \n",
              "mean     -0.091959    -0.700593     0.046954    -0.719960    -1.752216   \n",
              "std       1.109095     1.204651     1.262044     1.084299     1.040505   \n",
              "min      -3.082759    -3.774958    -2.943283    -4.615528    -3.997977   \n",
              "25%      -0.635372    -1.569121    -0.963254    -1.351840    -2.593652   \n",
              "50%       0.018466    -0.864093     0.157150    -0.634270    -1.669737   \n",
              "75%       0.522782     0.463358     1.042679     0.029348    -0.986729   \n",
              "max       3.256764     1.741923     2.987825     1.532733     1.128014   \n",
              "\n",
              "               765          766          767        score  Final_score  \n",
              "count  1176.000000  1176.000000  1176.000000  1176.000000  1176.000000  \n",
              "mean     -0.451238    -0.377730     0.211044     6.238908     0.672619  \n",
              "std       1.100149     1.154632     1.158732     3.275259     0.469457  \n",
              "min      -2.975710    -3.766625    -3.390907     0.000000     0.000000  \n",
              "25%      -1.246046    -1.236310    -0.639309     5.000000     0.000000  \n",
              "50%      -0.446756    -0.264185     0.060384     6.000000     1.000000  \n",
              "75%       0.334036     0.539263     0.954257     8.000000     1.000000  \n",
              "max       2.951406     2.301465     3.160130    88.000000     1.000000  \n",
              "\n",
              "[8 rows x 770 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa71ea82-8a99-4468-af77-9175cbc85555\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>760</th>\n",
              "      <th>761</th>\n",
              "      <th>762</th>\n",
              "      <th>763</th>\n",
              "      <th>764</th>\n",
              "      <th>765</th>\n",
              "      <th>766</th>\n",
              "      <th>767</th>\n",
              "      <th>score</th>\n",
              "      <th>Final_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "      <td>1176.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-1.253594</td>\n",
              "      <td>-0.465979</td>\n",
              "      <td>0.229329</td>\n",
              "      <td>0.372101</td>\n",
              "      <td>0.277136</td>\n",
              "      <td>0.865536</td>\n",
              "      <td>-0.554721</td>\n",
              "      <td>-1.280188</td>\n",
              "      <td>-0.392907</td>\n",
              "      <td>-0.226238</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.091959</td>\n",
              "      <td>-0.700593</td>\n",
              "      <td>0.046954</td>\n",
              "      <td>-0.719960</td>\n",
              "      <td>-1.752216</td>\n",
              "      <td>-0.451238</td>\n",
              "      <td>-0.377730</td>\n",
              "      <td>0.211044</td>\n",
              "      <td>6.238908</td>\n",
              "      <td>0.672619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.204115</td>\n",
              "      <td>1.399195</td>\n",
              "      <td>1.165310</td>\n",
              "      <td>0.871044</td>\n",
              "      <td>0.967215</td>\n",
              "      <td>0.930031</td>\n",
              "      <td>1.271228</td>\n",
              "      <td>1.581162</td>\n",
              "      <td>1.014660</td>\n",
              "      <td>1.032897</td>\n",
              "      <td>...</td>\n",
              "      <td>1.109095</td>\n",
              "      <td>1.204651</td>\n",
              "      <td>1.262044</td>\n",
              "      <td>1.084299</td>\n",
              "      <td>1.040505</td>\n",
              "      <td>1.100149</td>\n",
              "      <td>1.154632</td>\n",
              "      <td>1.158732</td>\n",
              "      <td>3.275259</td>\n",
              "      <td>0.469457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-4.964680</td>\n",
              "      <td>-4.291207</td>\n",
              "      <td>-3.836382</td>\n",
              "      <td>-2.519691</td>\n",
              "      <td>-2.327611</td>\n",
              "      <td>-4.050520</td>\n",
              "      <td>-3.307933</td>\n",
              "      <td>-5.572669</td>\n",
              "      <td>-3.264831</td>\n",
              "      <td>-3.251885</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.082759</td>\n",
              "      <td>-3.774958</td>\n",
              "      <td>-2.943283</td>\n",
              "      <td>-4.615528</td>\n",
              "      <td>-3.997977</td>\n",
              "      <td>-2.975710</td>\n",
              "      <td>-3.766625</td>\n",
              "      <td>-3.390907</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-1.976430</td>\n",
              "      <td>-1.321678</td>\n",
              "      <td>-0.498208</td>\n",
              "      <td>-0.137077</td>\n",
              "      <td>-0.397124</td>\n",
              "      <td>0.154785</td>\n",
              "      <td>-1.529384</td>\n",
              "      <td>-2.627281</td>\n",
              "      <td>-1.060415</td>\n",
              "      <td>-0.876954</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.635372</td>\n",
              "      <td>-1.569121</td>\n",
              "      <td>-0.963254</td>\n",
              "      <td>-1.351840</td>\n",
              "      <td>-2.593652</td>\n",
              "      <td>-1.246046</td>\n",
              "      <td>-1.236310</td>\n",
              "      <td>-0.639309</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-1.320060</td>\n",
              "      <td>-0.528970</td>\n",
              "      <td>0.304345</td>\n",
              "      <td>0.316335</td>\n",
              "      <td>0.164286</td>\n",
              "      <td>0.712414</td>\n",
              "      <td>-0.673306</td>\n",
              "      <td>-1.062531</td>\n",
              "      <td>-0.610422</td>\n",
              "      <td>-0.122351</td>\n",
              "      <td>...</td>\n",
              "      <td>0.018466</td>\n",
              "      <td>-0.864093</td>\n",
              "      <td>0.157150</td>\n",
              "      <td>-0.634270</td>\n",
              "      <td>-1.669737</td>\n",
              "      <td>-0.446756</td>\n",
              "      <td>-0.264185</td>\n",
              "      <td>0.060384</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-0.566802</td>\n",
              "      <td>0.476583</td>\n",
              "      <td>1.044464</td>\n",
              "      <td>1.007085</td>\n",
              "      <td>0.924620</td>\n",
              "      <td>1.626414</td>\n",
              "      <td>0.208969</td>\n",
              "      <td>0.002230</td>\n",
              "      <td>0.315335</td>\n",
              "      <td>0.306526</td>\n",
              "      <td>...</td>\n",
              "      <td>0.522782</td>\n",
              "      <td>0.463358</td>\n",
              "      <td>1.042679</td>\n",
              "      <td>0.029348</td>\n",
              "      <td>-0.986729</td>\n",
              "      <td>0.334036</td>\n",
              "      <td>0.539263</td>\n",
              "      <td>0.954257</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.057063</td>\n",
              "      <td>2.867949</td>\n",
              "      <td>2.698387</td>\n",
              "      <td>2.857039</td>\n",
              "      <td>3.281687</td>\n",
              "      <td>3.100244</td>\n",
              "      <td>4.445419</td>\n",
              "      <td>2.208376</td>\n",
              "      <td>2.195000</td>\n",
              "      <td>3.159068</td>\n",
              "      <td>...</td>\n",
              "      <td>3.256764</td>\n",
              "      <td>1.741923</td>\n",
              "      <td>2.987825</td>\n",
              "      <td>1.532733</td>\n",
              "      <td>1.128014</td>\n",
              "      <td>2.951406</td>\n",
              "      <td>2.301465</td>\n",
              "      <td>3.160130</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 770 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-PqQxNjU4tDs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nGlz5Ryq4tGi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}