Breast_Cancer_Wisconsin_(Diagnostic)_Data_Set.ipynb

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        "**<h1>Breast Cancer Wisconsin (Diagnostic) Data Set</h1>**\n",
        "<h4>Data source: on <a href = 'https://www.kaggle.com/uciml/breast-cancer-wisconsin-data'>Kagge</a> <br> <h4>\n",
        "\n",
        "**<h3>Author</h3>** \n",
        "<p>Vy K Phung</p>\n",
        "\n",
        "**<h3>Date</h3>** \n",
        "<p>2022-Jan-06</p>\n",
        "\n",
        "####**Attribute Information:** <br>\n",
        "*(I get the attribute information from the above data source. If you want to know the details clearly, you can go to the link Kaggle above)*\n",
        "\n",
        "1) ID number <br>\n",
        "2) Diagnosis (M = malignant, B = benign)\n",
        "\n",
        "\n",
        "Ten real-valued attributes for each cell nucleus:\n",
        "\n",
        "| Field name  | Description                 |\n",
        "| ----------- | --------------------------- |\n",
        "| Radius      | mean of distances from center to points on the perimeter             |\n",
        "| Texture       | standard deviation of gray-scale values             |\n",
        "| Perimeter    | perimeter     |\n",
        "| Area   | area    |\n",
        "| Smoothness     | local variation in radius lengths           |\n",
        "| Compactness   | perimeter^2 / area - 1.0                    |\n",
        "| Concavity     | severity of concave portions of the contour                 |\n",
        "| Concave points  | number of concave portions of the contour             |\n",
        "| Symmetry         | Symmetry                     |\n",
        "| Fractal dimension       | \"coastline approximation\" - 1         |\n",
        "\n",
        "For 10 above attributes, there are 3 types: their mean, their se(standard error), and their worst, which as a result in the dataset you will see for example: radius_mean; radius_se; radius_worst <br> \n",
        "\n",
        "\n",
        "####**Steps:** <br>\n",
        "1. I will use multi linear regression in order to predict radius size. \n",
        "2. In order to classify benign or malignant, I will train 2 models: K-nearest neighbor (KNN) and Support Vector Machines (SVM) by using mean data.\n",
        "3. After training model, I use worst data for testing model accuracy. \n"
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        "**<h2>Table of contents</h2>**\n",
        "1. Load packages\n",
        "2. Data preprocessing\n",
        "<ul>\n",
        "<li>Data visualization</li>\n",
        "</ul>\n",
        "3. Linear regression\n",
        "<ul>\n",
        "<li>Test-train split</li>\n",
        "<li>Multi linear regression</li>\n",
        "</ul>\n",
        "4. Classification\n",
        "<ul>\n",
        "<li>Normalize data</li>\n",
        "<li>Test-train split</li>\n",
        "<li>KNN with GridSearchCV</li>\n",
        "<li>SVM with GridSearchCV</li>\n",
        "</ul>\n",
        "5. Model Evaluation\n",
        "<ul>\n",
        "<li>Prepare data</li>\n",
        "<li>Test-train split</li>\n",
        "<li>Multi linear regression accuracy</li>\n",
        "<li>KNN with GridSearchCV accuracy</li>\n",
        "<li>SVM with GridSearchCV accuracy</li>\n",
        "</ul>\n",
        "\n"
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        "**<h2>Load packages</h2>**"
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      "source": [
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import NullFormatter\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.ticker as ticker\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import classification_report, confusion_matrix, jaccard_score, f1_score, mean_squared_error\n",
        "%matplotlib inline\n",
        "import plotly.express as px"
      ]
    },
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      "source": [
        "**<h2>Data preprocessing</h2>**"
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              "         id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0    842302         M  ...                  0.11890          NaN\n",
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              "texture_worst                0\n",
              "perimeter_worst              0\n",
              "area_worst                   0\n",
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              "concave points_worst         0\n",
              "symmetry_worst               0\n",
              "fractal_dimension_worst      0\n",
              "Unnamed: 32                569\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "hjm4UviJIbp3",
        "outputId": "acf9b6a4-72eb-4e68-a067-3fdefa6ace72"
      },
      "outputs": [
        {
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          "data": {
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              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
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              "      <th>smoothness_worst</th>\n",
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              "      <th>Unnamed: 32</th>\n",
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              "      <th>count</th>\n",
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              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
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              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.037183e+07</td>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>0.405172</td>\n",
              "      <td>1.216853</td>\n",
              "      <td>2.866059</td>\n",
              "      <td>40.337079</td>\n",
              "      <td>0.007041</td>\n",
              "      <td>0.025478</td>\n",
              "      <td>0.031894</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>0.020542</td>\n",
              "      <td>0.003795</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.250206e+08</td>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>0.277313</td>\n",
              "      <td>0.551648</td>\n",
              "      <td>2.021855</td>\n",
              "      <td>45.491006</td>\n",
              "      <td>0.003003</td>\n",
              "      <td>0.017908</td>\n",
              "      <td>0.030186</td>\n",
              "      <td>0.006170</td>\n",
              "      <td>0.008266</td>\n",
              "      <td>0.002646</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.670000e+03</td>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.692180e+05</td>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>0.232400</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.002248</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.060240e+05</td>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.813129e+06</td>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>0.478900</td>\n",
              "      <td>1.474000</td>\n",
              "      <td>3.357000</td>\n",
              "      <td>45.190000</td>\n",
              "      <td>0.008146</td>\n",
              "      <td>0.032450</td>\n",
              "      <td>0.042050</td>\n",
              "      <td>0.014710</td>\n",
              "      <td>0.023480</td>\n",
              "      <td>0.004558</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.113205e+08</td>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>2.873000</td>\n",
              "      <td>4.885000</td>\n",
              "      <td>21.980000</td>\n",
              "      <td>542.200000</td>\n",
              "      <td>0.031130</td>\n",
              "      <td>0.135400</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>0.052790</td>\n",
              "      <td>0.078950</td>\n",
              "      <td>0.029840</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ],
            "text/plain": [
              "                 id  radius_mean  ...  fractal_dimension_worst  Unnamed: 32\n",
              "count  5.690000e+02   569.000000  ...               569.000000          0.0\n",
              "mean   3.037183e+07    14.127292  ...                 0.083946          NaN\n",
              "std    1.250206e+08     3.524049  ...                 0.018061          NaN\n",
              "min    8.670000e+03     6.981000  ...                 0.055040          NaN\n",
              "25%    8.692180e+05    11.700000  ...                 0.071460          NaN\n",
              "50%    9.060240e+05    13.370000  ...                 0.080040          NaN\n",
              "75%    8.813129e+06    15.780000  ...                 0.092080          NaN\n",
              "max    9.113205e+08    28.110000  ...                 0.207500          NaN\n",
              "\n",
              "[8 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_qd8S83BzKM",
        "outputId": "be6f1ea0-891f-4e60-d956-3a1dbe7ba810"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df['diagnosis'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nl6OhZtDc7oU"
      },
      "source": [
        "**<h3>Data visualization</h3>**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "SW_Wr0qQ3lpp"
      },
      "outputs": [],
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=360)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j]),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "yWeRjwkVetnK",
        "outputId": "c4705ffb-3b16-4327-bd74-46e41f5dd7be"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x1440 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure() # create figure\n",
        "\n",
        "ax0 = fig.add_subplot(3, 2, 1) \n",
        "ax1 = fig.add_subplot(3, 2, 2) \n",
        "ax2 = fig.add_subplot(3, 2, 3) \n",
        "ax3 = fig.add_subplot(3, 2, 4)\n",
        "ax4 = fig.add_subplot(3, 2, 5)\n",
        "ax5 = fig.add_subplot(3, 2, 6)\n",
        "# subplot 1: smooth ~ compact linear\n",
        "df[['smoothness_mean','compactness_mean']].plot(kind = 'scatter', x='smoothness_mean', y='compactness_mean', color='blue', figsize=(25, 20), ax = ax0)\n",
        "ax0.set_ylabel(\"compactness_mean\")\n",
        "ax0.set_xlabel(\"smoothness_mean\")\n",
        "ax0.set_title('Linear relationship between mean of smoothness and compactness')\n",
        "#subplot 2: concavity ~ texture classify \n",
        "df[df['diagnosis'] == 'B'].plot(kind='scatter', x='concavity_mean', y='texture_mean', color='DarkBlue', label='benign', figsize=(25, 20), ax =ax1);\n",
        "df[df['diagnosis'] == 'M'].plot(kind='scatter', x='concavity_mean', y='texture_mean', color='Red', label='malignant', figsize=(25, 20),ax=ax1);\n",
        "ax1.set_title('Classify benign and malignant between mean of concavity and texture')\n",
        "#subplot 3: radius ~ area linear\n",
        "df[['radius_mean','area_mean']].plot(kind = 'scatter', x='radius_mean', y='area_mean', color='blue', figsize=(25, 20),ax = ax2)\n",
        "ax2.set_title('Linear relationship between mean of radius and area')\n",
        "#subplot 4: radius ~ concave classify\n",
        "df[df['diagnosis'] == 'B'].plot(kind='scatter', x='radius_mean', y='concave points_mean', color='DarkBlue', label='benign', figsize=(25, 20),ax=ax3);\n",
        "df[df['diagnosis'] == 'M'].plot(kind='scatter', x='radius_mean', y='concave points_mean', color='Red', label='malignant', figsize=(25, 20),ax=ax3);\n",
        "ax3.set_title('Classify benign and malignant between mean of radius and concave points')\n",
        "#subplot 5: radius ~ perimeter linear\n",
        "df[['radius_mean','perimeter_mean']].plot(kind = 'scatter', x='radius_mean', y='perimeter_mean', color='blue', figsize=(25, 20),ax = ax4)\n",
        "ax4.set_title('Linear relationship between mean of radius and perimeter')\n",
        "#subplot 6: radius ~ compact classify\n",
        "df[df['diagnosis'] == 'B'].plot(kind='scatter', x='radius_mean', y='compactness_mean', color='DarkBlue', label='benign', figsize=(25, 20),ax = ax5);\n",
        "df[df['diagnosis'] == 'M'].plot(kind='scatter', x='radius_mean', y='compactness_mean', color='Red', label='malignant', figsize=(25, 20),ax=ax5);\n",
        "ax5.set_title('Classify benign and malignant between mean of radius and compactness')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wasb0pged89m"
      },
      "source": [
        "**<h2>Linear regression</h2>**\n",
        "<p>According to the figure number 3, 4, 5, we can see there is quite a linear relationship between radius_mean to other variables (area_mean, concave points_mean, perimeter_mean). <br>\n",
        "Therefore, instead of using radius_mean as independent variable (X), I will use it as Y which is depend on 3 variables (area_mean, concave points_mean, perimeter_mean) in order to predict the radius_mean </p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DyhZmkuPfX_3",
        "outputId": "87d7cd20-11bb-4ae8-aa27-5916e001872d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3c24d2d5-da32-4b6b-872d-cb94c9e92ee0\">\n",
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              "\n",
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              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.14710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.07017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.12790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.10520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10430</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c24d2d5-da32-4b6b-872d-cb94c9e92ee0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3c24d2d5-da32-4b6b-872d-cb94c9e92ee0');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   perimeter_mean  area_mean  concave points_mean\n",
              "0          122.80     1001.0              0.14710\n",
              "1          132.90     1326.0              0.07017\n",
              "2          130.00     1203.0              0.12790\n",
              "3           77.58      386.1              0.10520\n",
              "4          135.10     1297.0              0.10430"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_ra = df[['perimeter_mean',\t'area_mean','concave points_mean']]\n",
        "X_ra.head() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rytgKhS9fwLS",
        "outputId": "ce9f3c15-0dad-436b-dcb3-cd013d134cdc"
      },
      "outputs": [
        {
          "data": {
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              "  <div id=\"df-10f46091-5b64-4231-8a15-fc66a0119692\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
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              "      <td>19.69</td>\n",
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              "      <td>11.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "   radius_mean\n",
              "0        17.99\n",
              "1        20.57\n",
              "2        19.69\n",
              "3        11.42\n",
              "4        20.29"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y_ra = df[['radius_mean']]\n",
        "Y_ra.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NCgo8VndkMfA"
      },
      "source": [
        "**<h4>Test-train split</h4>**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCYt1-Y2kHUB",
        "outputId": "ec4fbcab-62fc-4e99-d97a-2aa2d51cb5b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train set: (455, 3) (455, 1)\n",
            "Test set: (114, 3) (114, 1)\n"
          ]
        }
      ],
      "source": [
        "Xra_train, Xra_test, Yra_train, Yra_test = train_test_split(X_ra, Y_ra, test_size=0.2, random_state=4)\n",
        "print ('Train set:', Xra_train.shape,  Yra_train.shape)\n",
        "print ('Test set:', Xra_test.shape,  Yra_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2bx6XtSjgIIC"
      },
      "source": [
        "**<h4>Multi linear regression</h4>**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVtqlcKCf4uQ",
        "outputId": "f962b19c-baf1-47d6-f291-22a67fc03d3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted value: [[14.50532844]\n",
            " [12.21911409]\n",
            " [19.52004677]\n",
            " [17.39865396]\n",
            " [16.40633319]]\n",
            "actual value: [[14.42]\n",
            " [12.05]\n",
            " [19.53]\n",
            " [17.29]\n",
            " [16.5 ]]\n",
            "The R-square of testing data:  0.998916869295868\n",
            "The R-square of training data:  0.9982116903428075\n",
            "The mean square error of all actual and predicted fitting data is:  0.02035123380666617\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(Xra_train,Yra_train)\n",
        "yhat = lr.predict(Xra_test)\n",
        "print('Predicted value:', yhat[0:5])\n",
        "print('actual value:', Yra_test.values[0:5])\n",
        "print('The R-square of testing data: ', lr.score(Xra_test, Yra_test))\n",
        "print('The R-square of training data: ', lr.score(Xra_train, Yra_train))\n",
        "print('The mean square error of all actual and predicted fitting data is: ', mean_squared_error(Y_ra, lr.predict(X_ra))) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YV07qIjxmwW2"
      },
      "source": [
        "<p>According to the R-square of both test and train data which are all near 1.0 and positive linear relationship, we can see that the multilinear regression model fits the data. I will evaluate model again in model evaluation section with worst data</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HuVgge7d5VhJ"
      },
      "source": [
        "**<h2>Classfication</h2>**\n",
        "<p>For classification, I will use 2 models: K-nearest neighbor (KNN) and Support Vector Machines (SVM) </p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRbyukFPpCQj"
      },
      "source": [
        "**<h3>Normalize data for classification</h3>**\n",
        "<p>In this case for training model, I will use 8 attributes, including: radius, perimeter, compactness, concave points, smoothness, area, concavity, texture, whom of them are mean. But using the characteristic worst of all 8 above attributes to be a test for testing the model accuracy</p> "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "mYvBH5thkU1q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18cb062f-d769-41bb-ce76-4fb2adcd09f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.799e+01, 1.038e+01, 1.228e+02, 1.001e+03, 1.184e-01, 2.776e-01,\n",
              "        3.001e-01, 1.471e-01],\n",
              "       [2.057e+01, 1.777e+01, 1.329e+02, 1.326e+03, 8.474e-02, 7.864e-02,\n",
              "        8.690e-02, 7.017e-02],\n",
              "       [1.969e+01, 2.125e+01, 1.300e+02, 1.203e+03, 1.096e-01, 1.599e-01,\n",
              "        1.974e-01, 1.279e-01],\n",
              "       [1.142e+01, 2.038e+01, 7.758e+01, 3.861e+02, 1.425e-01, 2.839e-01,\n",
              "        2.414e-01, 1.052e-01],\n",
              "       [2.029e+01, 1.434e+01, 1.351e+02, 1.297e+03, 1.003e-01, 1.328e-01,\n",
              "        1.980e-01, 1.043e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "x = df[['radius_mean',\t'texture_mean',\t'perimeter_mean',\t'area_mean',\t'smoothness_mean',\t'compactness_mean',\t'concavity_mean',\t'concave points_mean']].values\n",
        "x[0:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBAl47souLhS",
        "outputId": "6e2412b8-c652-43af-dbdb-2aec344de488"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.09706398, -2.07333501,  1.26993369,  0.9843749 ,  1.56846633,\n",
              "         3.28351467,  2.65287398,  2.53247522],\n",
              "       [ 1.82982061, -0.35363241,  1.68595471,  1.90870825, -0.82696245,\n",
              "        -0.48707167, -0.02384586,  0.54814416],\n",
              "       [ 1.57988811,  0.45618695,  1.56650313,  1.55888363,  0.94221044,\n",
              "         1.05292554,  1.36347845,  2.03723076],\n",
              "       [-0.76890929,  0.25373211, -0.59268717, -0.76446379,  3.28355348,\n",
              "         3.40290899,  1.91589718,  1.45170736],\n",
              "       [ 1.75029663, -1.15181643,  1.77657315,  1.82622928,  0.28037183,\n",
              "         0.53934045,  1.37101143,  1.42849277]])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "x = preprocessing.StandardScaler().fit(x).transform(x)\n",
        "x[0:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ii5xMs3TuprX",
        "outputId": "3578ff0c-7945-4f1b-dd5e-3d15b35c2327"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'M', 'M', 'M', 'M'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "y = df['diagnosis'].values\n",
        "y[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bD9ZYPRAvgtA"
      },
      "source": [
        "**<h3>Test-train split</h3>**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGQJ8sP9vuk-",
        "outputId": "7930e4ce-4bdb-4153-bf2d-2c7fe49ccf26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set: (455, 8) (455,)\n",
            "Test set: (114, 8) (114,)\n"
          ]
        }
      ],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.2, random_state=4)\n",
        "print ('Train set:', x_train.shape,  y_train.shape)\n",
        "print ('Test set:', x_test.shape,  y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PkeoaURPZrG"
      },
      "source": [
        "**<h3>KNN using GridSearchCV with cv = 10 to find best parameter for model</h3>**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-X_O2l2xPFkH",
        "outputId": "0a0b9f29-40c4-4ba3-fd2f-016321eeeaf4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'n_neighbors': 8}\n",
            "accuracy : 0.9360869565217392\n",
            "accuracy on test data: 0.9385964912280702\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import metrics\n",
        "parameters_knn = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}\n",
        "KNN = KNeighborsClassifier()\n",
        "knn_cv = GridSearchCV(KNN,parameters_knn,cv=10)\n",
        "knn_cv.fit(x_train,y_train)\n",
        "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
        "print(\"accuracy :\",knn_cv.best_score_)\n",
        "print('accuracy on test data:',knn_cv.score(x_test,y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vM3Xe8bYw0RB"
      },
      "source": [
        "<p>As we can see, the best k for model is 8 with the highest accurary on test data: 0.9385964912280702 </p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKwrTprnPgqo"
      },
      "source": [
        "**<h3>SVM using GridSearchCV with cv = 10 to find best parameter for model </h3>**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC \n",
        "parameters_svm = {'kernel':('linear','rbf','poly','sigmoid'),\n",
        "                  'C': np.logspace(-1, 1, 4),\n",
        "                  'gamma':('scale','scale','auto','auto')}\n",
        "svm = SVC()\n",
        "svm_cv = GridSearchCV(svm,parameters_svm,cv=10)\n",
        "svm_cv.fit(x_train, y_train) \n",
        "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n",
        "print(\"accuracy :\",svm_cv.best_score_)\n",
        "print('accuracy on test data:',svm_cv.score(x_test,y_test))"
      ],
      "metadata": {
        "id": "Ajeew03AxNiF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc4ac02e-6ab4-4198-ab70-45fa175e0a36"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 10.0, 'gamma': 'scale', 'kernel': 'rbf'}\n",
            "accuracy : 0.9559420289855073\n",
            "accuracy on test data: 0.956140350877193\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4MS74469xpvQ"
      },
      "source": [
        "**<h2>Model Evaluation</h2>**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Xa4WBMznUp6"
      },
      "source": [
        "**<h3>Prepare data</h3>**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fpssQa1jx0hN",
        "outputId": "f8fb48e6-8f40-4da8-f99e-2fdb18b74cfc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ],
            "text/plain": [
              "   radius_worst  texture_worst  ...  concavity_worst  concave points_worst\n",
              "0         25.38          17.33  ...           0.7119                0.2654\n",
              "1         24.99          23.41  ...           0.2416                0.1860\n",
              "2         23.57          25.53  ...           0.4504                0.2430\n",
              "3         14.91          26.50  ...           0.6869                0.2575\n",
              "4         22.54          16.67  ...           0.4000                0.1625\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "test_df = df[['radius_worst',\t'texture_worst',\t'perimeter_worst',\t'area_worst',\t'smoothness_worst',\t'compactness_worst',\t'concavity_worst',\t'concave points_worst']]\n",
        "test_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONzwrKDxyMvW",
        "outputId": "55a30512-298d-4632-dcd3-12ecbd867ae8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.88668963, -1.35929347,  2.30360062,  2.00123749,  1.30768627,\n",
              "         2.61666502,  2.10952635,  2.29607613],\n",
              "       [ 1.80592744, -0.36920322,  1.53512599,  1.89048899, -0.37561196,\n",
              "        -0.43044422, -0.14674897,  1.0870843 ],\n",
              "       [ 1.51187025, -0.02397438,  1.34747521,  1.45628455,  0.52740741,\n",
              "         1.08293217,  0.85497394,  1.95500035],\n",
              "       [-0.28146446,  0.13398409, -0.2499393 , -0.55002123,  3.3942747 ,\n",
              "         3.89339743,  1.98958826,  2.17578601],\n",
              "       [ 1.29857524, -1.46677038,  1.33853946,  1.22072425,  0.22055617,\n",
              "        -0.31339451,  0.61317876,  0.72925926]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "# normalize data\n",
        "X = test_df.values\n",
        "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
        "X[0:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gc13e4dLzMzh",
        "outputId": "3ad35827-6a49-4451-bfe9-d6c6fb8413c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'M', 'M', 'M', 'M'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "Y = df['diagnosis'].values\n",
        "Y[0:5] "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "5_d5FzwtnuHk"
      },
      "outputs": [],
      "source": [
        "X1 = df[['perimeter_worst',\t'area_worst','concave points_worst']]\n",
        "Y1 = df[['radius_worst']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9bw136xjoHn-"
      },
      "source": [
        "**<h3>Multi linear regression accuracy</h3>**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kFnscg8OoEbl",
        "outputId": "0577c869-d7fa-4b0a-d54c-ad2296381e98"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "predicted values: [[26.79101315]\n",
            " [23.5784894 ]\n",
            " [22.04086914]\n",
            " [13.36723553]\n",
            " [22.60681934]]\n",
            "actual values: [[25.38]\n",
            " [24.99]\n",
            " [23.57]\n",
            " [14.91]\n",
            " [22.54]]\n",
            "The R-square of data:  0.9852801251993204\n",
            "The mean square error:  0.34325525254669925\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- area_worst\n",
            "- concave points_worst\n",
            "- perimeter_worst\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- area_mean\n",
            "- concave points_mean\n",
            "- perimeter_mean\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- area_worst\n",
            "- concave points_worst\n",
            "- perimeter_worst\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- area_mean\n",
            "- concave points_mean\n",
            "- perimeter_mean\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        }
      ],
      "source": [
        "ypred = lr.predict(X1)\n",
        "print('predicted values:', ypred[0:5])\n",
        "print('actual values:', Y1.values[0:5])\n",
        "print('The R-square of data: ', lr.score(X1, Y1))\n",
        "print('The mean square error: ', mean_squared_error(Y1, ypred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o71uOJp6zuYx"
      },
      "source": [
        "**<h3>KNN with GridSearchCV accuracy</h3>**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eq4XR-ugM8Cx",
        "outputId": "e6368b3f-9a9e-4e63-fad1-d11da119cdd2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "predicted values: ['M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M']\n",
            "actual values: ['M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M']\n",
            "accuracy score: 0.9595782073813708\n"
          ]
        }
      ],
      "source": [
        "KNN_CV = knn_cv.predict(X)\n",
        "print('predicted values:',KNN_CV[0:10])\n",
        "print('actual values:',Y[0:10])\n",
        "print(\"accuracy score:\",knn_cv.score(X,Y))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "8R0bZj5yOC-6",
        "outputId": "fa31853b-872b-44cb-86ec-7963c2eb8783"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           B       0.97      0.96      0.97       357\n",
            "           M       0.94      0.95      0.95       212\n",
            "\n",
            "    accuracy                           0.96       569\n",
            "   macro avg       0.96      0.96      0.96       569\n",
            "weighted avg       0.96      0.96      0.96       569\n",
            "\n",
            "jaccard score of benign: 0.9373297002724795\n",
            "jaccard score of malignant: 0.8977777777777778\n",
            "weighted f1 score: 0.9596349262674452\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(classification_report(Y, KNN_CV))\n",
        "# Plot confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(confusion_matrix(Y, KNN_CV), classes=['Benign(B)','Malignant(M)'],  title='Confusion matrix for KNN with CV')\n",
        "# jaccard\n",
        "print('jaccard score of benign:', jaccard_score(Y, KNN_CV, pos_label='B'))\n",
        "print('jaccard score of malignant:', jaccard_score(Y, KNN_CV, pos_label='M'))\n",
        "# f1-score\n",
        "print('weighted f1 score:', f1_score(Y, KNN_CV, average='weighted'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TT-egrjg1Dpi"
      },
      "source": [
        "**<h3>SVM with GridSearchCV accuracy</h3>**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0KEdi2VhTTh9",
        "outputId": "7c2cee11-f0bf-4579-9d8f-825ffa7a28f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted values: ['M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M']\n",
            "actual values: ['M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M']\n"
          ]
        }
      ],
      "source": [
        "sv_cv = svm_cv.predict(X)\n",
        "print('predicted values:', sv_cv[0:10])\n",
        "print('actual values:', Y[0:10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "wRWoyP6cTpLT",
        "outputId": "786d850e-080d-47e9-be46-3571fe1ba8b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           B       0.98      0.96      0.97       357\n",
            "           M       0.94      0.97      0.95       212\n",
            "\n",
            "    accuracy                           0.96       569\n",
            "   macro avg       0.96      0.97      0.96       569\n",
            "weighted avg       0.97      0.96      0.96       569\n",
            "\n",
            "jaccard score of benign: 0.9449035812672176\n",
            "jaccard score of malignant: 0.911504424778761\n",
            "weighted f1 score: 0.9649769257251197\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "print(classification_report(Y, sv_cv))\n",
        "# Plot confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(confusion_matrix(Y, sv_cv), classes=['Benign(B)','Malignant(M)'],  title='Confusion matrix for SVM')\n",
        "# jaccard\n",
        "print('jaccard score of benign:', jaccard_score(Y, sv_cv, pos_label='B'))\n",
        "print('jaccard score of malignant:', jaccard_score(Y, sv_cv, pos_label='M'))\n",
        "# f1-score\n",
        "print('weighted f1 score:', f1_score(Y, sv_cv, average='weighted'))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Breast Cancer Wisconsin (Diagnostic) Data Set.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPMmxcJSm+AY3aKOvl2yWKC",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}