train_test_data.ipynb

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          "name": "stdout",
          "text": [
            "Saving data(1).csv to data(1).csv\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "data_to_load = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import load_iris\n",
        "data=pd.read_csv('data(1).csv')\n",
        "data.head()"
      ],
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              "         id  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0    842302          1        17.99         10.38          122.80     1001.0   \n",
              "1    842517          1        20.57         17.77          132.90     1326.0   \n",
              "2  84300903          1        19.69         21.25          130.00     1203.0   \n",
              "3  84348301          1        11.42         20.38           77.58      386.1   \n",
              "4  84358402          1        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "4          0.10030           0.13280          0.1980              0.10430   \n",
              "\n",
              "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "0  ...         25.38          17.33           184.60      2019.0   \n",
              "1  ...         24.99          23.41           158.80      1956.0   \n",
              "2  ...         23.57          25.53           152.50      1709.0   \n",
              "3  ...         14.91          26.50            98.87       567.7   \n",
              "4  ...         22.54          16.67           152.20      1575.0   \n",
              "\n",
              "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "3            0.2098             0.8663           0.6869                0.2575   \n",
              "4            0.1374             0.2050           0.4000                0.1625   \n",
              "\n",
              "   symmetry_worst  fractal_dimension_worst  \n",
              "0          0.4601                  0.11890  \n",
              "1          0.2750                  0.08902  \n",
              "2          0.3613                  0.08758  \n",
              "3          0.6638                  0.17300  \n",
              "4          0.2364                  0.07678  \n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
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              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
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              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
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              "      <th>1</th>\n",
              "      <td>842517</td>\n",
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              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
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              "      <td>24.99</td>\n",
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              "      <td>0.1238</td>\n",
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              "      <td>0.2416</td>\n",
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              "      <td>0.08902</td>\n",
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              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>1</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
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              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
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              "      <td>0.2414</td>\n",
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              "      <td>14.91</td>\n",
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              "          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": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=data.diagnosis\n",
        "x=data.drop('diagnosis', axis=1)\n",
        "x_train,x_tv,y_train,y_tv=train_test_split(x,y,test_size=0.3, random_state=42)\n",
        "x_val,x_test,y_val,y_test=train_test_split(x_tv,y_tv,test_size=0.5, random_state=42)\n",
        "x_val.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "pNQVC3D_zxV-",
        "outputId": "4e4840f0-2737-4ed1-e0e9-8baf81293586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
              "count  8.500000e+01    85.000000     85.000000       85.000000    85.000000   \n",
              "mean   2.867095e+07    14.592553     19.614941       95.368118   698.474118   \n",
              "std    1.022966e+08     3.700775      4.649238       25.646762   364.455871   \n",
              "min    8.913000e+03     7.729000     10.380000       47.980000   178.800000   \n",
              "25%    8.735930e+05    11.900000     15.860000       78.070000   432.800000   \n",
              "50%    9.170920e+05    13.780000     19.110000       89.750000   585.900000   \n",
              "75%    8.912055e+06    17.270000     22.540000      112.400000   928.800000   \n",
              "max    9.111573e+08    25.220000     30.620000      171.500000  1878.000000   \n",
              "\n",
              "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "count        85.000000         85.000000       85.000000            85.000000   \n",
              "mean          0.098425          0.115487        0.101793             0.055837   \n",
              "std           0.013644          0.058125        0.088234             0.042372   \n",
              "min           0.068830          0.037290        0.000000             0.000000   \n",
              "25%           0.089460          0.070810        0.033460             0.024210   \n",
              "50%           0.098340          0.102900        0.079430             0.039800   \n",
              "75%           0.106300          0.131600        0.144000             0.079530   \n",
              "max           0.137100          0.283200        0.426400             0.184500   \n",
              "\n",
              "       symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "count      85.000000  ...     85.000000      85.000000        85.000000   \n",
              "mean        0.182849  ...     16.987024      25.892000       112.707294   \n",
              "std         0.026956  ...      5.195882       6.518776        36.920601   \n",
              "min         0.120300  ...      9.077000      15.380000        57.170000   \n",
              "25%         0.163800  ...     13.300000      20.540000        86.260000   \n",
              "50%         0.180000  ...     15.530000      25.530000       101.900000   \n",
              "75%         0.197600  ...     20.420000      30.530000       135.100000   \n",
              "max         0.255600  ...     30.790000      42.790000       211.700000   \n",
              "\n",
              "        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "count    85.000000         85.000000          85.000000        85.000000   \n",
              "mean    958.702353          0.135179           0.302990         0.317242   \n",
              "std     601.834006          0.020758           0.199645         0.249935   \n",
              "min     248.000000          0.087990           0.067440         0.000000   \n",
              "25%     533.700000          0.122200           0.165000         0.127700   \n",
              "50%     734.600000          0.135800           0.233800         0.275800   \n",
              "75%    1299.000000          0.147800           0.373500         0.410700   \n",
              "max    2782.000000          0.185300           1.058000         1.170000   \n",
              "\n",
              "       concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "count             85.000000       85.000000                85.000000  \n",
              "mean               0.129356        0.294629                 0.087582  \n",
              "std                0.072133        0.060010                 0.022598  \n",
              "min                0.000000        0.185900                 0.059740  \n",
              "25%                0.073700        0.252200                 0.071270  \n",
              "50%                0.109200        0.284900                 0.079930  \n",
              "75%                0.178900        0.321600                 0.097890  \n",
              "max                0.291000        0.460100                 0.207500  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-278d489f-7103-4211-9795-ae6dc63ceaed\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\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>id</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8.500000e+01</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>85.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.867095e+07</td>\n",
              "      <td>14.592553</td>\n",
              "      <td>19.614941</td>\n",
              "      <td>95.368118</td>\n",
              "      <td>698.474118</td>\n",
              "      <td>0.098425</td>\n",
              "      <td>0.115487</td>\n",
              "      <td>0.101793</td>\n",
              "      <td>0.055837</td>\n",
              "      <td>0.182849</td>\n",
              "      <td>...</td>\n",
              "      <td>16.987024</td>\n",
              "      <td>25.892000</td>\n",
              "      <td>112.707294</td>\n",
              "      <td>958.702353</td>\n",
              "      <td>0.135179</td>\n",
              "      <td>0.302990</td>\n",
              "      <td>0.317242</td>\n",
              "      <td>0.129356</td>\n",
              "      <td>0.294629</td>\n",
              "      <td>0.087582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.022966e+08</td>\n",
              "      <td>3.700775</td>\n",
              "      <td>4.649238</td>\n",
              "      <td>25.646762</td>\n",
              "      <td>364.455871</td>\n",
              "      <td>0.013644</td>\n",
              "      <td>0.058125</td>\n",
              "      <td>0.088234</td>\n",
              "      <td>0.042372</td>\n",
              "      <td>0.026956</td>\n",
              "      <td>...</td>\n",
              "      <td>5.195882</td>\n",
              "      <td>6.518776</td>\n",
              "      <td>36.920601</td>\n",
              "      <td>601.834006</td>\n",
              "      <td>0.020758</td>\n",
              "      <td>0.199645</td>\n",
              "      <td>0.249935</td>\n",
              "      <td>0.072133</td>\n",
              "      <td>0.060010</td>\n",
              "      <td>0.022598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.913000e+03</td>\n",
              "      <td>7.729000</td>\n",
              "      <td>10.380000</td>\n",
              "      <td>47.980000</td>\n",
              "      <td>178.800000</td>\n",
              "      <td>0.068830</td>\n",
              "      <td>0.037290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.120300</td>\n",
              "      <td>...</td>\n",
              "      <td>9.077000</td>\n",
              "      <td>15.380000</td>\n",
              "      <td>57.170000</td>\n",
              "      <td>248.000000</td>\n",
              "      <td>0.087990</td>\n",
              "      <td>0.067440</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.185900</td>\n",
              "      <td>0.059740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.735930e+05</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>15.860000</td>\n",
              "      <td>78.070000</td>\n",
              "      <td>432.800000</td>\n",
              "      <td>0.089460</td>\n",
              "      <td>0.070810</td>\n",
              "      <td>0.033460</td>\n",
              "      <td>0.024210</td>\n",
              "      <td>0.163800</td>\n",
              "      <td>...</td>\n",
              "      <td>13.300000</td>\n",
              "      <td>20.540000</td>\n",
              "      <td>86.260000</td>\n",
              "      <td>533.700000</td>\n",
              "      <td>0.122200</td>\n",
              "      <td>0.165000</td>\n",
              "      <td>0.127700</td>\n",
              "      <td>0.073700</td>\n",
              "      <td>0.252200</td>\n",
              "      <td>0.071270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.170920e+05</td>\n",
              "      <td>13.780000</td>\n",
              "      <td>19.110000</td>\n",
              "      <td>89.750000</td>\n",
              "      <td>585.900000</td>\n",
              "      <td>0.098340</td>\n",
              "      <td>0.102900</td>\n",
              "      <td>0.079430</td>\n",
              "      <td>0.039800</td>\n",
              "      <td>0.180000</td>\n",
              "      <td>...</td>\n",
              "      <td>15.530000</td>\n",
              "      <td>25.530000</td>\n",
              "      <td>101.900000</td>\n",
              "      <td>734.600000</td>\n",
              "      <td>0.135800</td>\n",
              "      <td>0.233800</td>\n",
              "      <td>0.275800</td>\n",
              "      <td>0.109200</td>\n",
              "      <td>0.284900</td>\n",
              "      <td>0.079930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.912055e+06</td>\n",
              "      <td>17.270000</td>\n",
              "      <td>22.540000</td>\n",
              "      <td>112.400000</td>\n",
              "      <td>928.800000</td>\n",
              "      <td>0.106300</td>\n",
              "      <td>0.131600</td>\n",
              "      <td>0.144000</td>\n",
              "      <td>0.079530</td>\n",
              "      <td>0.197600</td>\n",
              "      <td>...</td>\n",
              "      <td>20.420000</td>\n",
              "      <td>30.530000</td>\n",
              "      <td>135.100000</td>\n",
              "      <td>1299.000000</td>\n",
              "      <td>0.147800</td>\n",
              "      <td>0.373500</td>\n",
              "      <td>0.410700</td>\n",
              "      <td>0.178900</td>\n",
              "      <td>0.321600</td>\n",
              "      <td>0.097890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.111573e+08</td>\n",
              "      <td>25.220000</td>\n",
              "      <td>30.620000</td>\n",
              "      <td>171.500000</td>\n",
              "      <td>1878.000000</td>\n",
              "      <td>0.137100</td>\n",
              "      <td>0.283200</td>\n",
              "      <td>0.426400</td>\n",
              "      <td>0.184500</td>\n",
              "      <td>0.255600</td>\n",
              "      <td>...</td>\n",
              "      <td>30.790000</td>\n",
              "      <td>42.790000</td>\n",
              "      <td>211.700000</td>\n",
              "      <td>2782.000000</td>\n",
              "      <td>0.185300</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.170000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.460100</td>\n",
              "      <td>0.207500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-278d489f-7103-4211-9795-ae6dc63ceaed')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
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              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\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-278d489f-7103-4211-9795-ae6dc63ceaed 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-278d489f-7103-4211-9795-ae6dc63ceaed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KITZTItY2otQ",
        "outputId": "1f654e8b-7dda-459c-c8c7-13290ac72901"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(398, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression as lm\n",
        "model=lm().fit(x_train,y_train)\n",
        "predictions=model.predict(x_test)\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "gUApEPU14Sdk",
        "outputId": "54d058e7-4fb6-4426-c6e7-b52e6f7462b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fc55d7b4c50>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('True values')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "eHIacfeP4f1_",
        "outputId": "da0401a6-d21a-4b3d-c734-e77e756e3bb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'True values')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.ylabel('Predictions')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "XfVkP7jA4j8B",
        "outputId": "4b8ae0cd-770a-4078-bcc3-cab05e068f61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Predictions')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.show()"
      ],
      "metadata": {
        "id": "5Z6zZP7v4v-5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}