Cardiovascular_Disease_Prediction-1.ipynb

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      "source": [
        "**<h3>Libaries</h3>**\n",
        "1. Importing ***pandas*** for reading the dataset and operating on it.<br>\n",
        "2. Importing ***matplotlib.pyplot*** and ***seaborn*** for data visualization.<br>\n",
        "3. Importing ***confusion_matrix*** and ***classification_report*** from ***sklearn.metrics*** for showing **Confusion Matrix, Accuracy, Precesion, Recall, F1 Score**.<br>\n",
        "4. Importing ***plot_confusion_matrix*** to visualize the confusion matrix from ***mlxtend.plotting***. \n"
      ],
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from mlxtend.plotting import plot_confusion_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Reading the dataset**<br>\n",
        "Here, we are using ***read_csv*** module of ***pandas*** to read the csv formatted dataset named *cardio_train.csv* from Google Drive."
      ],
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        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "data = pd.read_csv('cardio_train.csv', sep = ';')\n",
        "data"
      ],
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              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
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              "// See the License for the specific language governing permissions and\n",
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          "text": [
            "Saving cardio_train.csv to cardio_train.csv\n"
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              "          id    age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  \\\n",
              "0          0  18393       2     168    62.0    110     80            1     1   \n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7115ebb3-7968-4bdd-8968-3b36e810edea 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-7115ebb3-7968-4bdd-8968-3b36e810edea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>Contents of the dataset</h3>**\n",
        "There are 13 columns and 70000 rows in this dataset.\n",
        "1. ***id***: This is unique for all the data.\n",
        "2. ***age***: Here, age is given in days.\n",
        "3. ***gender***: \n",
        "    Women = 1 and\n",
        "    Men = 2\n",
        "4. ***height***: Here, height is given in cm.\n",
        "5. ***weight***: Here, weight is given in kg.\n",
        "6. ***ap_hi***: Systolic blood pressure\n",
        "7. ***ap_lo***: Diastolic blood pressure\n",
        "8. ***cholesterol***:\n",
        "    Normal = 1, \n",
        "    Above normal = 2 and \n",
        "    Well above normal = 3\n",
        "9. ***gluc***: \n",
        "    Normal = 1,\n",
        "    Above normal = 2 and \n",
        "    Well above normal = 3\n",
        "10. ***smoke***: Smoker or not. Binary.\n",
        "11. ***alco***: Drink or not. Binary.\n",
        "12. ***active***: Does physical excercise or not. Binary.\n",
        "13. ***cardio*** [Target]: Has heart disease or not. Binary.\n"
      ],
      "metadata": {
        "id": "weUNcQYodKb-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparing the dataset.\n"
      ],
      "metadata": {
        "id": "iKI9sshrhcon"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Here the unique ID for all the data is not needed. So we can delete than column.\n",
        "2. The ***'age'*** is in **days**. Converting to **year** will give us some upperhand. "
      ],
      "metadata": {
        "id": "-kkghhKJhgvm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#deleting 'id'\n",
        "del data[\"id\"]\n",
        "\n",
        "#converting the 'age' from day to year\n",
        "data[\"age\"] //= 365"
      ],
      "metadata": {
        "id": "xzOiT6XIvOEn"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we need to check if the there is any null value in the dataset."
      ],
      "metadata": {
        "id": "r-lffyUfhzEf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().values.any() #Gives boolean value as output (i.e. True or False)"
      ],
      "metadata": {
        "id": "1jDY_zuOijeX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3923063-bced-4679-c7ae-e816c4bf209a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum() #Detailed check for null value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbgdg-xVi1WH",
        "outputId": "23893944-2772-486d-ec96-34752a407e0f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age            0\n",
              "gender         0\n",
              "height         0\n",
              "weight         0\n",
              "ap_hi          0\n",
              "ap_lo          0\n",
              "cholesterol    0\n",
              "gluc           0\n",
              "smoke          0\n",
              "alco           0\n",
              "active         0\n",
              "cardio         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see there is no null value in the dataset. So, now we can process further."
      ],
      "metadata": {
        "id": "jLoXnAi3i0dn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Quality check of the dataset."
      ],
      "metadata": {
        "id": "bh7ZFNd4jx9Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "values = data['cardio'].value_counts()\n",
        "\n",
        "values"
      ],
      "metadata": {
        "id": "E_5asa2tnjPP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54c4e197-9018-4017-ecaa-d0a5cb8a2e1b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    35021\n",
              "1    34979\n",
              "Name: cardio, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualizing\n",
        "seaborn.set(rc={'figure.figsize':(11.7,8.9)})\n",
        "seaborn.countplot(x = data['cardio'])"
      ],
      "metadata": {
        "id": "yrVgFtD-rKvK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "outputId": "8737f56c-6e2a-4ba4-c648-1049715339f9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f89b08177f0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 842.4x640.8 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the dataset has quite balanced data, where almost half of the population have cardiovascular diseases and half of them don't.<br>\n",
        "**Comment:** QC Passed"
      ],
      "metadata": {
        "id": "ZutrLxPmkyYg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data visualization\n",
        "Now we will see the hue of cardiovascular diseases in terms of age, height, weight, smoke, and so on.<br>\n",
        "This visualization will give us some general overview about the nature of the data which we are working with.<br>\n",
        "**Nerd info:**<br>\n",
        "We will use *sklearn* and *matplotlib.pyplot*.<br>\n",
        "Here, \n",
        "1. x-axis will be variable\n",
        "2. hue = \"cardio\"\n",
        "3. data = data (dataset we are working with)\n",
        "4. palette = \"colorblind\"\n",
        "5. edgecolor = \"dark\"\n",
        "<br><br>\n",
        "✔ $\\color{#c38820}{\\text{This color means, the data has cardiovascular disease.}}$<br>\n",
        "✖ $\\color{#176d9c}{\\text{This color means, the data doesn't have cardiovascular disease.}}$\n"
      ],
      "metadata": {
        "id": "Z-_LxOVLmQbJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.set(rc={'figure.figsize':(12,9)}) #setting the figure size"
      ],
      "metadata": {
        "id": "u6IHAayumP34"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h4>Age</h4>**\n",
        "Here we can see that, heart diseases increase with age. When a person is in his 60s, there is a high chance of getting cardiovascular diseases."
      ],
      "metadata": {
        "id": "rvd1DdE1JrXQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.countplot(x = \"age\", hue = \"cardio\", data = data, palette = 'colorblind', edgecolor = seaborn.color_palette('dark', n_colors = 1))"
      ],
      "metadata": {
        "id": "B1U0a9J9Jqkn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "outputId": "2dbd136b-619c-43dc-e186-6c5b57314b68"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f89b031b5e0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h4>Gender</h4>**\n",
        "Here we can see that, our dataset has a greater number of women than men, and the ratio of cardio 0:1, is almost 50-50. Which concludes that, CVDs (cardiovascular diseases) are almost gender neutral.\n"
      ],
      "metadata": {
        "id": "JZqwQYTJOZQb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.countplot(x = \"gender\", hue = \"cardio\", data = data, palette = 'colorblind', edgecolor = seaborn.color_palette('dark', n_colors = 1))"
      ],
      "metadata": {
        "id": "FGlQMJTJsIEf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "outputId": "67c6ed0c-978f-4fe5-af0c-41025b409936"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f89b0250190>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h4>BMI</h4>**\n",
        "As we already know, we don't have any attributes for BMI. But we have the attributes to calculate the BMI. \n",
        "$$\n",
        "B M I=\\frac{\\text { weight }(\\mathrm{kg})}{\\text { height }^{2}\\left(\\mathrm{~m}^{2}\\right)}\n",
        "$$\n",
        "From this equation, we can easily calculate the BMI of every population in the dataset by using height and weight attributes.<br><br>\n",
        "**Note:** In our dataset, the height is given in centimeter. We will convert it to meter by dividing by 100.<br>\n",
        "<center><img src = 'https://files.prokerala.com/health/images/bmi-category.png'></center>"
      ],
      "metadata": {
        "id": "MHJHkfNfS_gR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['bmi'] = data['weight']/((data['height']/100)**2)\n",
        "\n",
        "pd.options.mode.chained_assignment = None #To removing SettingWithCopyWarning in pandas\n",
        "\n",
        "for i in range(len(data['bmi'])):\n",
        "    if 0 < data['bmi'][i] < 18.5:\n",
        "        data['bmi'][i] = 'Underwight'\n",
        "    elif 18.5 < data['bmi'][i] < 25:\n",
        "        data['bmi'][i] = 'Normal / Optimal'\n",
        "    elif 25 < data['bmi'][i] < 30:\n",
        "        data['bmi'][i] = 'Overweight'\n",
        "    else:\n",
        "        data['bmi'][i] = 'Obese'\n",
        "        \n",
        "seaborn.countplot(x = \"bmi\", hue = \"cardio\", data = data, palette = 'colorblind', edgecolor = seaborn.color_palette('dark', n_colors = 1))"
      ],
      "metadata": {
        "id": "ZLjLykD5QrIx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "outputId": "2b9f54cc-93b4-4239-c8aa-dc81569ce52e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f89b0186c10>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here the observation is, the more risk of getting CVDs is when a person is Obese, and Overweight. "
      ],
      "metadata": {
        "id": "-mVzATbBYz7s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h4>Glucose</h4>**\n",
        "We have less data on glucose level 2 and 3. But in those data, the cardio 0:1 ratio is much higher towards cardio: 1.<br>\n",
        "This concludes that, higher the glucose level, the higher the chances of having CVDs. "
      ],
      "metadata": {
        "id": "p-Oqx6TJbzdz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.countplot(x = \"gluc\", hue = \"cardio\", data = data, palette = 'colorblind', edgecolor = seaborn.color_palette('dark', n_colors = 1))"
      ],
      "metadata": {
        "id": "GruGmtFgzPgB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "outputId": "14215dbd-23cb-4974-a3f3-d862c408b597"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f89b017c850>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h4>Cholesterol</h4>**\n",
        "We also have less data on cholestorel level 2 and 3. But in those data, the cardio 0:1 ratio is much higher towards cardio: 1.<br>\n",
        "This concludes that, higher the cholesterol level, the higher the chances of having CVDs. "
      ],
      "metadata": {
        "id": "RekoI9n6r3AW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.countplot(x = \"cholesterol\", hue = \"cardio\", data = data, palette = 'colorblind', edgecolor = seaborn.color_palette('dark', n_colors = 1))"
      ],
      "metadata": {
        "id": "Ys6Yrnqec2Xj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "outputId": "2ec2a3f6-b6e3-47f8-8c3f-35e84b794364"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f89affdf8e0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h4>Physical Excercise</h4>**\n",
        "People who don't workout have higher cardio ratio than of those who workout.<br>\n",
        "0 = Do not workout<br>\n",
        "1 = Do workout"
      ],
      "metadata": {
        "id": "RD6pTmJxsZ3H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.countplot(x = \"active\", hue = \"cardio\", data = data, palette = 'colorblind', edgecolor = seaborn.color_palette('dark', n_colors = 1))"
      ],
      "metadata": {
        "id": "C7zFSXlMdRET",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "outputId": "6b9af423-ecdb-4e2d-a305-d38186f59efa"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f89b00f9250>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is a summary of BMI and Age relating to CVDs, showing in boxplot and kdeplot."
      ],
      "metadata": {
        "id": "QpC2f14SrnCO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(2,2,figsize = (20, 15))\n",
        "data['bmi'] = data['weight']/((data['height']/100)**2)\n",
        "seaborn.boxplot(data = data, x='bmi', ax=ax[0,0])\n",
        "seaborn.boxplot(data = data, x='age', ax=ax[0,1])\n",
        "\n",
        "seaborn.kdeplot(x = data['bmi'], hue= data['cardio'], ax= ax[1,0])\n",
        "seaborn.kdeplot(x = data['age'], hue= data['cardio'], ax= ax[1,1])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "CPSFoOy4poaQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 883
        },
        "outputId": "2338cf4e-3f65-429a-a210-ccf77e6c2c08"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have visualized many of the data, we can restore the dataset to default attributes by deleting 'bmi' to work on it."
      ],
      "metadata": {
        "id": "PWKZc709tQIG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "del data[\"bmi\"]"
      ],
      "metadata": {
        "id": "hBbEMsZ8tKO2"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h4>Here is the correlation of between the attributes."
      ],
      "metadata": {
        "id": "ePAzOP0btiIn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.corr()"
      ],
      "metadata": {
        "id": "I8TipM8Ssxm-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "508e0fb2-a2a2-4b82-dd2e-9f0fd7fa982a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  age    gender    height    weight     ap_hi     ap_lo  \\\n",
              "age          1.000000 -0.022913 -0.081506  0.053561  0.020854  0.017620   \n",
              "gender      -0.022913  1.000000  0.499033  0.155406  0.006005  0.015254   \n",
              "height      -0.081506  0.499033  1.000000  0.290968  0.005488  0.006150   \n",
              "weight       0.053561  0.155406  0.290968  1.000000  0.030702  0.043710   \n",
              "ap_hi        0.020854  0.006005  0.005488  0.030702  1.000000  0.016086   \n",
              "ap_lo        0.017620  0.015254  0.006150  0.043710  0.016086  1.000000   \n",
              "cholesterol  0.154012 -0.035821 -0.050226  0.141768  0.023778  0.024019   \n",
              "gluc         0.098388 -0.020491 -0.018595  0.106857  0.011841  0.010806   \n",
              "smoke       -0.047649  0.338135  0.187989  0.067780 -0.000922  0.005186   \n",
              "alco        -0.029756  0.170966  0.094419  0.067113  0.001408  0.010601   \n",
              "active      -0.009998  0.005866 -0.006570 -0.016867 -0.000033  0.004780   \n",
              "cardio       0.237985  0.008109 -0.010821  0.181660  0.054475  0.065719   \n",
              "\n",
              "             cholesterol      gluc     smoke      alco    active    cardio  \n",
              "age             0.154012  0.098388 -0.047649 -0.029756 -0.009998  0.237985  \n",
              "gender         -0.035821 -0.020491  0.338135  0.170966  0.005866  0.008109  \n",
              "height         -0.050226 -0.018595  0.187989  0.094419 -0.006570 -0.010821  \n",
              "weight          0.141768  0.106857  0.067780  0.067113 -0.016867  0.181660  \n",
              "ap_hi           0.023778  0.011841 -0.000922  0.001408 -0.000033  0.054475  \n",
              "ap_lo           0.024019  0.010806  0.005186  0.010601  0.004780  0.065719  \n",
              "cholesterol     1.000000  0.451578  0.010354  0.035760  0.009911  0.221147  \n",
              "gluc            0.451578  1.000000 -0.004756  0.011246 -0.006770  0.089307  \n",
              "smoke           0.010354 -0.004756  1.000000  0.340094  0.025858 -0.015486  \n",
              "alco            0.035760  0.011246  0.340094  1.000000  0.025476 -0.007330  \n",
              "active          0.009911 -0.006770  0.025858  0.025476  1.000000 -0.035653  \n",
              "cardio          0.221147  0.089307 -0.015486 -0.007330 -0.035653  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0ebe8a4-c0b7-44a1-8e88-04c09c8c4aa8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
              "      <th>ap_lo</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
              "      <th>smoke</th>\n",
              "      <th>alco</th>\n",
              "      <th>active</th>\n",
              "      <th>cardio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.022913</td>\n",
              "      <td>-0.081506</td>\n",
              "      <td>0.053561</td>\n",
              "      <td>0.020854</td>\n",
              "      <td>0.017620</td>\n",
              "      <td>0.154012</td>\n",
              "      <td>0.098388</td>\n",
              "      <td>-0.047649</td>\n",
              "      <td>-0.029756</td>\n",
              "      <td>-0.009998</td>\n",
              "      <td>0.237985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>-0.022913</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499033</td>\n",
              "      <td>0.155406</td>\n",
              "      <td>0.006005</td>\n",
              "      <td>0.015254</td>\n",
              "      <td>-0.035821</td>\n",
              "      <td>-0.020491</td>\n",
              "      <td>0.338135</td>\n",
              "      <td>0.170966</td>\n",
              "      <td>0.005866</td>\n",
              "      <td>0.008109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>height</th>\n",
              "      <td>-0.081506</td>\n",
              "      <td>0.499033</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.290968</td>\n",
              "      <td>0.005488</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>-0.050226</td>\n",
              "      <td>-0.018595</td>\n",
              "      <td>0.187989</td>\n",
              "      <td>0.094419</td>\n",
              "      <td>-0.006570</td>\n",
              "      <td>-0.010821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>0.053561</td>\n",
              "      <td>0.155406</td>\n",
              "      <td>0.290968</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.030702</td>\n",
              "      <td>0.043710</td>\n",
              "      <td>0.141768</td>\n",
              "      <td>0.106857</td>\n",
              "      <td>0.067780</td>\n",
              "      <td>0.067113</td>\n",
              "      <td>-0.016867</td>\n",
              "      <td>0.181660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ap_hi</th>\n",
              "      <td>0.020854</td>\n",
              "      <td>0.006005</td>\n",
              "      <td>0.005488</td>\n",
              "      <td>0.030702</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.016086</td>\n",
              "      <td>0.023778</td>\n",
              "      <td>0.011841</td>\n",
              "      <td>-0.000922</td>\n",
              "      <td>0.001408</td>\n",
              "      <td>-0.000033</td>\n",
              "      <td>0.054475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ap_lo</th>\n",
              "      <td>0.017620</td>\n",
              "      <td>0.015254</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.043710</td>\n",
              "      <td>0.016086</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.024019</td>\n",
              "      <td>0.010806</td>\n",
              "      <td>0.005186</td>\n",
              "      <td>0.010601</td>\n",
              "      <td>0.004780</td>\n",
              "      <td>0.065719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cholesterol</th>\n",
              "      <td>0.154012</td>\n",
              "      <td>-0.035821</td>\n",
              "      <td>-0.050226</td>\n",
              "      <td>0.141768</td>\n",
              "      <td>0.023778</td>\n",
              "      <td>0.024019</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.451578</td>\n",
              "      <td>0.010354</td>\n",
              "      <td>0.035760</td>\n",
              "      <td>0.009911</td>\n",
              "      <td>0.221147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gluc</th>\n",
              "      <td>0.098388</td>\n",
              "      <td>-0.020491</td>\n",
              "      <td>-0.018595</td>\n",
              "      <td>0.106857</td>\n",
              "      <td>0.011841</td>\n",
              "      <td>0.010806</td>\n",
              "      <td>0.451578</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.004756</td>\n",
              "      <td>0.011246</td>\n",
              "      <td>-0.006770</td>\n",
              "      <td>0.089307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoke</th>\n",
              "      <td>-0.047649</td>\n",
              "      <td>0.338135</td>\n",
              "      <td>0.187989</td>\n",
              "      <td>0.067780</td>\n",
              "      <td>-0.000922</td>\n",
              "      <td>0.005186</td>\n",
              "      <td>0.010354</td>\n",
              "      <td>-0.004756</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.340094</td>\n",
              "      <td>0.025858</td>\n",
              "      <td>-0.015486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alco</th>\n",
              "      <td>-0.029756</td>\n",
              "      <td>0.170966</td>\n",
              "      <td>0.094419</td>\n",
              "      <td>0.067113</td>\n",
              "      <td>0.001408</td>\n",
              "      <td>0.010601</td>\n",
              "      <td>0.035760</td>\n",
              "      <td>0.011246</td>\n",
              "      <td>0.340094</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.025476</td>\n",
              "      <td>-0.007330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>active</th>\n",
              "      <td>-0.009998</td>\n",
              "      <td>0.005866</td>\n",
              "      <td>-0.006570</td>\n",
              "      <td>-0.016867</td>\n",
              "      <td>-0.000033</td>\n",
              "      <td>0.004780</td>\n",
              "      <td>0.009911</td>\n",
              "      <td>-0.006770</td>\n",
              "      <td>0.025858</td>\n",
              "      <td>0.025476</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.035653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cardio</th>\n",
              "      <td>0.237985</td>\n",
              "      <td>0.008109</td>\n",
              "      <td>-0.010821</td>\n",
              "      <td>0.181660</td>\n",
              "      <td>0.054475</td>\n",
              "      <td>0.065719</td>\n",
              "      <td>0.221147</td>\n",
              "      <td>0.089307</td>\n",
              "      <td>-0.015486</td>\n",
              "      <td>-0.007330</td>\n",
              "      <td>-0.035653</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0ebe8a4-c0b7-44a1-8e88-04c09c8c4aa8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c0ebe8a4-c0b7-44a1-8e88-04c09c8c4aa8 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-c0ebe8a4-c0b7-44a1-8e88-04c09c8c4aa8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h4>Here is the heatmap from the correlation."
      ],
      "metadata": {
        "id": "bGnb-fkKtqou"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.heatmap(data.corr(), annot = True)"
      ],
      "metadata": {
        "id": "c5qcTciV0ZSi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "6a05ac8d-574b-42e6-a75b-ecc808a0f120"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f89b014f760>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h4>Here is the mathematical brief description of the attributes."
      ],
      "metadata": {
        "id": "J5VS6NR_t1be"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "id": "87ENkWiG1NvR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "a6f5bd97-80be-4a13-a128-fcdfae00fafb"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                age        gender        height        weight         ap_hi  \\\n",
              "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
              "mean      52.840671      1.349571    164.359229     74.205690    128.817286   \n",
              "std        6.766774      0.476838      8.210126     14.395757    154.011419   \n",
              "min       29.000000      1.000000     55.000000     10.000000   -150.000000   \n",
              "25%       48.000000      1.000000    159.000000     65.000000    120.000000   \n",
              "50%       53.000000      1.000000    165.000000     72.000000    120.000000   \n",
              "75%       58.000000      2.000000    170.000000     82.000000    140.000000   \n",
              "max       64.000000      2.000000    250.000000    200.000000  16020.000000   \n",
              "\n",
              "              ap_lo   cholesterol          gluc         smoke          alco  \\\n",
              "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
              "mean      96.630414      1.366871      1.226457      0.088129      0.053771   \n",
              "std      188.472530      0.680250      0.572270      0.283484      0.225568   \n",
              "min      -70.000000      1.000000      1.000000      0.000000      0.000000   \n",
              "25%       80.000000      1.000000      1.000000      0.000000      0.000000   \n",
              "50%       80.000000      1.000000      1.000000      0.000000      0.000000   \n",
              "75%       90.000000      2.000000      1.000000      0.000000      0.000000   \n",
              "max    11000.000000      3.000000      3.000000      1.000000      1.000000   \n",
              "\n",
              "             active        cardio  \n",
              "count  70000.000000  70000.000000  \n",
              "mean       0.803729      0.499700  \n",
              "std        0.397179      0.500003  \n",
              "min        0.000000      0.000000  \n",
              "25%        1.000000      0.000000  \n",
              "50%        1.000000      0.000000  \n",
              "75%        1.000000      1.000000  \n",
              "max        1.000000      1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9be679f7-b8e6-4850-a15d-5e75d88e3715\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
              "      <th>ap_lo</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
              "      <th>smoke</th>\n",
              "      <th>alco</th>\n",
              "      <th>active</th>\n",
              "      <th>cardio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>52.840671</td>\n",
              "      <td>1.349571</td>\n",
              "      <td>164.359229</td>\n",
              "      <td>74.205690</td>\n",
              "      <td>128.817286</td>\n",
              "      <td>96.630414</td>\n",
              "      <td>1.366871</td>\n",
              "      <td>1.226457</td>\n",
              "      <td>0.088129</td>\n",
              "      <td>0.053771</td>\n",
              "      <td>0.803729</td>\n",
              "      <td>0.499700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.766774</td>\n",
              "      <td>0.476838</td>\n",
              "      <td>8.210126</td>\n",
              "      <td>14.395757</td>\n",
              "      <td>154.011419</td>\n",
              "      <td>188.472530</td>\n",
              "      <td>0.680250</td>\n",
              "      <td>0.572270</td>\n",
              "      <td>0.283484</td>\n",
              "      <td>0.225568</td>\n",
              "      <td>0.397179</td>\n",
              "      <td>0.500003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>-150.000000</td>\n",
              "      <td>-70.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>53.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>16020.000000</td>\n",
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              "      <td>3.000000</td>\n",
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              "      <td>1.000000</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9be679f7-b8e6-4850-a15d-5e75d88e3715 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-9be679f7-b8e6-4850-a15d-5e75d88e3715');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting the dataset for train and test\n",
        "We are using ***iloc*** from *pandas* for general split.<br>\n",
        "Than, we are going to import ***train_test_split*** from ***sklearn.model_selection***. And going to use this module to split the data into **80%** of train data naming, *xtrain* and *ytrain* and **20%** of test data naming *xtest*, *ytest*."
      ],
      "metadata": {
        "id": "mgoaymXp1JaM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.iloc[:,:-1]; x"
      ],
      "metadata": {
        "id": "o7jSG-zD1jBK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "162f80b7-c8ec-4ff8-9231-e6d0dd6f364d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
              "0       50       2     168    62.0    110     80            1     1      0   \n",
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              "\n",
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              "<p>70000 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b02372df-024e-4bfb-b0f9-c60cd85f2ed9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b02372df-024e-4bfb-b0f9-c60cd85f2ed9 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-b02372df-024e-4bfb-b0f9-c60cd85f2ed9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = data.iloc[:, -1]; y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RtPrra_uyaBv",
        "outputId": "2af76827-5fa6-4562-9f83-af94f1e837b7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        0\n",
              "1        1\n",
              "2        1\n",
              "3        1\n",
              "4        0\n",
              "        ..\n",
              "69995    0\n",
              "69996    1\n",
              "69997    1\n",
              "69998    1\n",
              "69999    0\n",
              "Name: cardio, Length: 70000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.3, random_state = 1)\n",
        "\n",
        "print(\"Size of train data:\", xtrain.shape)\n",
        "print(\"Size of test data:\", xtest.shape)"
      ],
      "metadata": {
        "id": "B2-mBIMA2JjE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70829b45-238a-4b0f-a024-5b3ab9d57915"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of train data: (49000, 11)\n",
            "Size of test data: (21000, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here 80% of 70,000 are 56,000<br>\n",
        "and 20% of 70,000 are 14,000<br><br>\n",
        "**Comment:** Splitting succesful!"
      ],
      "metadata": {
        "id": "bD851pwqzq8H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training and Testing\n",
        "Now that we are finally ready to train and test our data using some algoritms i.e. Random Forest, Logistic Regression etc.<br><br>\n",
        "We are going create an object for all the imported algorithm classifiers form ***sklearn***, and than we are going to call the ***fit*** function for the object to train the data and than we are going to test it.\n"
      ],
      "metadata": {
        "id": "7wSO7_Jl0Nco"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>1. Random Forest</h3>**\n",
        "\n",
        "**Algorithm brief:** Random forest is a Supervised Machine Learning Algorithm that is used widely in Classification and Regression problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression.<br>\n",
        "\n",
        "**After running the algoritm:** \n",
        "*   Accuracy for the train data is: **98%**\n",
        "*   Accuracy for the test data is: **71%**"
      ],
      "metadata": {
        "id": "dhTyZcEx2xA6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier #importing the algorithm\n",
        "\n",
        "rfc_object = RandomForestClassifier() #creating an object under it.\n",
        "\n",
        "rfc_object.fit(xtrain, ytrain) #training the dataset\n",
        "\n",
        "train_score = rfc_object.score(xtrain, ytrain) \n",
        "test_score = rfc_object.score(xtest, ytest)\n",
        "\n",
        "print(f\"The taining accuracy of the model is: {train_score}\")\n",
        "print(f\"The test accuracy of the model is: {test_score}\")"
      ],
      "metadata": {
        "id": "e7Hq9n6T2zfB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14a7777a-cd7e-4d4a-c643-fa8d1ae1a515"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The taining accuracy of the model is: 0.9822857142857143\n",
            "The test accuracy of the model is: 0.7082380952380952\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix and Performance calculation:**\n",
        "* Confusion matrix (Color Blue)\n",
        "* Accuracy\n",
        "* Precision\n",
        "* Recall\n",
        "* F1 Score"
      ],
      "metadata": {
        "id": "mWHIRcePBRyK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = rfc_object.predict(xtest)\n",
        "cm = confusion_matrix(ytest, pred)\n",
        "print(\"Confusion matrix:\", cm, sep = '\\n')\n",
        "print()\n",
        "print(\"Visualizing:\")\n",
        "fig, ax = plot_confusion_matrix(conf_mat = cm, show_absolute = True, show_normed = True, colorbar = True, cmap = \"Blues\")\n",
        "plt.show()\n",
        "print()\n",
        "print(\"Classification Report:\", classification_report(ytest, pred), sep = '\\n')"
      ],
      "metadata": {
        "id": "2c4G_y6527Mr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "outputId": "14db5fa5-6e34-452a-9c70-a6ae9fb87936"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix:\n",
            "[[7466 2886]\n",
            " [3241 7407]]\n",
            "\n",
            "Visualizing:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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UVFSUYXgW1nZB7ZqKje6gGfNWe49d1eESbZo/UelrXtSSt+5XA2ftM372wvDaOrj6BcOWu3G6Rg8pfGKpV+cWWv72g3J9PVW7lj2j1+JiVLOGv/fzL7yzTJPuY+0soCwdPHhQCXPe1Z13/8t77MsVy3Vpy2YKCayhntdcrd27d5/xs2lpaRo6+FY1alBPYXWCdHWXTvp2nXHR89emv6pmTRspNCRQnTq20+pVq7znHnjoEU2Nf0Z5eXnm3BxsiXXwjMrtKdonnnhCc+bMUc+ePTVnzhw9+eST5fXVKAdD+nXUklU/6fiJfElSneAAzZ12lya/9pnqXfWovk/eo9nPDT/jZ/ceOKK6nR72bu0GPCO3u0CffFG4eGpQzep6bsYSNe4xQW1unKJ6oUF65sFTQ/kbftqtwJrVdHnzBubfKHCemP3uf9WrVx/vwsaHDh3SoAE3Ku6JKdqfdliXt22nITEDz/jZnOxstW3bXmvWfaf9aYc1eMgw3Xh9X2VnZ0uSvl23To9PGKf35n6s1PSjGnb7HRo04AbvKI/T6VRkZDMlLvq0fG4WsKFyK/CaNGmijz76SElJSfroo4/UuHHj8vpqlIMenZpr5Xe/evev73aZtu50af4XG3Ui76SeemOxWl1SX5c0DDtrW7HRHbTq+1+96x5+sGSDlq3Zqtzj+crIytU789foH5ca///z9Ybt6vX7O2sB/H1Ll3yuK7t09e4v/GS+opq30E03D1C1atU0Me4J/bj5B/3y88+nfbZR48Ya/eBDcjqd8vX11R133a28vDxt++UXSdLu3b8pqnkLXd62rRwOh2KHDNWhQ4eUlpbmbaNL16u0ZPFn5t8obMPhMHezGl5VhjLRomk9bf8t1bvfvEm4Nm9L8e4fO56nnfsOqXmTs8+pjI3ucNo7bP+s8+UXa+tO4/I5v+xKVetL6pei5wDO5KctP6rpJZHe/eTkn9S69aXe/YCAADVu0kTJyT+dta0fNm1SXl6e9xVlPXv1VoHbrW/XrZPb7dasd97WpZdepvDwU2/KiGwWpR83/1CGdwScXyrVU7SwruCaNZT1p9eSBdTw16Ej2YZrMrNzDXPnzqRTmyYKrROoT77YeMbz3To2U2y/juoyZJrheFbOcQXVqn7GzwA4dxkZGapVq5Z3Pyc7WxfUrWu4JjAwSNlZWcW2k5mZqTtuG6IJj09SUFCQJKlWrVrqf+NN6n5VZ3k8HgUHB2vBos8N85xq1aqljKMZZXhHsDueojUiwUOZOJJ1TLX+VLzlHDuhWgHVDNfUCqh+2rtp/yq2X0ctWL7J+27aP+vQqqH+++wwxYyZoV/3pBnO1QqopqNZ5ryiBjgf1a5dW1l/Kt4CatZUVmam4ZqszEzV/FMR+Fe5ubm6qX8/deh4hcaMfcx7/L9vz9S7s97R9z/8pMxjeXp71hzd1D9a+/fvP9V2VpaCg4LL8I6A8wsFHsrElu0panpRqHc/eccBw5BpjWp+ahxxgZJ3FP1mkmr+VXXjNW3OODx7aWSEPnrpX7rniQR99e22085HNgozDAkD+Htatmqt7dtP/Vlr3ryFNv9pyDQnJ0c7d+5Q8+Znnvt64sQJ3XJTf9WvH6Hpr79pOPfDD5vUu0+0ml5yiXx8fNSjZy+Fhzv1zdo13mt++XmrWv1pSBg4G+bgGVHgoUwkrUpW57ZNvfuffvmDmjepp/7dL5O/XxWNv7u3tmxP0bY/zdP7q+uvvlQZWcf0v/XGAq55E6cW/uc+PRz/kRZ/veWMn72ybVMtXZ1cNjcDQD179dGqr//n3b+u/w1K/mmLPpk/T8ePH9czT01Wy1atFdms2Wmfzc/PV8zAm1WtenXNeGeWfHyM/9S0a9deSz7/TLt27pTH49HyL5Zp+/ZtatGipfealV//Tz169TbvBmE7hYWYWcukVPTdnTvm4KFMJCSu07q541TNv6qOn8jXoSPZunXMDP177AC9/dRQrd+yW0PGveO9/pUJgyRJ9z8913sstl9HvffZt6e1PXpId9WtXVOvT4rV65NiJUl7XIfV9uanJUltmzdQ9rET2vDTmdfkAnDuYocMVcd2lyk3N1fVq1dX3bp19f6H8/Tg6JEaPmyw2nfoqNkJp/78jrrvHknSq6+9oW/WrtHizxJVvXp1hV9waph1QeLn6tz5SsUOGaqdO3eoxzVXKePIEdWPiND01970Fosul0tbtybruuv7l+9NAzbi8Hg8noruRElE9onzLpuByunJkf108HCWpr/3Vbl+7/vT7tR/F6xR0ioSPKs4sn762S9ChYubOF5164Zq1OgHyvV7x455WI0bN9G/7r2vXL8X584hyb+SREXd4/+nlCPHTWm7fu1qWj6269kvrEQo8ACUOwo8wB4o8CqvSvI/CwAAQOk5HA75+LBMyh94yAIAAMBmSPAAAIDlmbmciQUDPBI8AAAAuyHBAwAAlseryoxI8AAAAGyGBA8AAFgec/CMSPAAAABshgQPAABYHnPwjEjwAAAAbIYEDwAAWB4JnhEJHgAAgM2Q4AEAAMvjKVojEjwAAACbIcEDAAA2YN4cPMl6ER4JHgAAgM2Q4AEAAMtjDp4RCR4AALC8P5ZJMWs7FydOnNCkSZPUo0cP9evXT48//rgkadeuXRo4cKB69uypgQMH6rfffvN+prTnikKBBwAAUIaef/55+fv7KykpSYsWLdLo0aMlSZMmTVJMTIySkpIUExOjuLg472dKe64oFHgAAMDy/hiiNWsrqZycHC1YsECjR4/2Jn8XXHCB0tPTlZycrOjoaElSdHS0kpOTdfjw4VKfKw5z8AAAAErA5XLJ7XYbjgUGBiowMNC7v3fvXgUHB2v69Olat26dAgICNHr0aFWrVk1hYWHy9fWVJPn6+io0NFQul0sej6dU50JCQorsKwUeAACwvPJ4VVlsbKxSUlIM50aOHKlRo0Z5991ut/bu3avmzZtr7Nix+uGHH3TPPffo5ZdfNqVvRaHAAwAAKIGEhIQzJnh/5nQ6VaVKFe+Q6qWXXqratWurWrVqSk1Nldvtlq+vr9xut9LS0uR0OuXxeEp1rjjMwQMAAJZXHnPwnE6nIiIiDNtfC7yQkBB17NhRq1evllT4BGx6eroaNmyoqKgoJSYmSpISExMVFRWlkJAQ1alTp1Tniv15eDweT1n+gM0S2SdOe1zFTygEYA1H1k+v6C4AKAMOSf6VZCzw+v98I9fR46a07QyqpoUjrijx9Xv37tX48eOVkZGhKlWq6IEHHlDXrl21Y8cOjRs3TpmZmQoMDFR8fLwaN24sSaU+VxQKPADljgIPsIfKVOD1f+0buY6eMKVtZ5C/FtxX8gKvMmCIFgAAwGYqSd0NAABQeryqzIgEDwAAwGZI8AAAgA2Ytw5e4WxDayHBAwAAsBkSPAAAYHnMwTMiwQMAALAZEjwAAGB55fEuWishwQMAALAZEjwAAGB5zMEzIsEDAACwGRI8AABgeczBM6LAAwAAlkeBZ8QQLQAAgM2Q4AEAAMvjIQsjEjwAAACbIcEDAACWxxw8IxI8AAAAmyHBAwAAtmDBoM00JHgAAAA2Q4IHAAAsjzl4RiR4AAAANkOCBwAALI918IxI8AAAAGyGBA8AAFiej8MhH5OiNrPaNRMJHgAAgM2Q4AEAAMtjDp4RCR4AAIDNkOABAADLK0zwzFoHz5RmTUWCBwAAYDMkeAAAwPIcDsmHOXheJHgAAAA2Q4IHAAAsj3fRGlHgAQAAy3PIxGVSzGnWVAzRAgAA2AwJHgAAsDzH77/MattqSPAAAABshgQPAABYno+Jy6SY1a6ZSPAAAABshgQPAABYHsukGJHgAQAA2AwJHgAAsDyHw8R18KwX4JHgAQAA2A0JHgAAsDyHwyEf5uB5keABAADYDAkeAACwPObgGZHgAQAA2AwJHgAAsDzWwTMiwQMAALAZEjwAAGB5zMEzIsEDAACwGRI8AABgeT4ybx08H1kvwiPBAwAAsBkSPAAAYHmO3zez2rYaEjwAAACbIcEDAADWZ+I6eFZ8jLbIAi8mJqZEP6iEhIQy7RAAAMC58nEUbma1bTVFFngDBgwoz34AAACgjBRZ4N1www3l2Q8AAIBSK1zo2KxXlZnSrKlK9JCFx+PRhx9+qKFDh6pfv36SpPXr12vx4sWmdg4AAADnrkQF3ssvv6yPP/5YAwcOlMvlkiSFh4drxowZpnYOAACgJP54VZlZm9WUqMD75JNP9MYbb6hv377e+DMiIkJ79+41tXMAAAA4dyVaJsXtdisgIEDSqfHtnJwc1ahRw7yeAQAAlJDDxGVSTFt+xUQlSvC6du2qZ599Vnl5eZIK5+S9/PLLuvrqq03tHAAAAM5diQq8xx57TAcPHlTbtm2VlZWlNm3aaP/+/XrkkUfM7h8AAMBZ/bEOnlmb1ZRoiLZmzZr6z3/+o/T0dKWkpMjpdKpu3bpm9w0AAAClUOJXlWVmZmr16tVKS0tTaGiounbtqqCgIDP7BgAAUDK8qsygREO0a9euVbdu3TR79mz9+OOPmjNnjrp37661a9ea3T8AAACcoxIleFOmTNHkyZPVp08f77HPP/9cTz75pJYsWWJa5wAAAErC8ftmVttWU6IELy0tTT179jQcu/baa3Xo0CFTOgUAAIDSK1GBd/311yshIcFw7P3331f//v1N6RQAAMC58JFDPg6TNgtmeEUO0cbExHgnKxYUFGju3LmaMWOGwsLClJqaqvT0dF166aXl1lEAAACUTJEF3oABAwz7t9xyi+mdAQAAKA0z3xlrwYdoiy7wbrjhhvLsBwAAAMpIidfBO3TokDZv3qwjR47I4/F4j998882mdAwAAKCkeBetUYkKvC+++EJjxozRRRddpF9//VUXX3yxtm/frssvv5wCDwAAoJIpUYH30ksv6ZlnnlHv3r3Vvn17LViwQPPmzdOvv/5qdv8AAADOijl4RiVaJmX//v3q3bu34dgNN9ygBQsWmNIpAACAc+Ewa4kUM1+BZqISFXh16tTxLmpcv359bdy4UXv27FFBQYGpnQMAAMC5K9EQ7YABA/Tdd9+pZ8+euu222zR06FD5+Pjo9ttvN7t/AAAAZ8UQrVGJCry7777b+/v+/furQ4cOys3NVZMmTUzrGAAAAEqnxMuk/Fm9evXKuh8AAAClxjIpRkUWeF27di3RDX311Vdl2Z8irfrgcRV4zn4dgMqv9hUPVHQXAJSBBs4Q/bIorqK7gTMossB7/vnny7MfAAAApeajEj45Wsq2rabIAq9Dhw7l2Q8AAACUkVLNwQMAAKhMmINnZMXUEQAAAMUgwQMAAJbnI8nHpKDNimnYOfW5oKBAaWlpZvUFAAAAZaBEBV5mZqYefvhhtW7dWj169JAkLV++XP/+979N7RwAAEBJOByFCZ4ZmwWn4JWswJs0aZJq1qypFStWqGrVqpKkNm3a6PPPPze1cwAAADh3JZqDt3btWq1cuVJVq1b1PkkSEhKi9PR0UzsHAABQEoXvojXrKVpTmjVViRK8WrVq6ciRI4Zj+/fvV926dU3pFAAAAEqvRAXegAEDdP/99+ubb75RQUGBNm7cqLFjx2rQoEFm9w8AAOCszJp/98dmNSUaor3rrrvk7++vyZMn6+TJkxo/frwGDhyoYcOGmd0/AAAAnKMSFXgOh0PDhg2joAMAAJWSw8SnXa04B6/ED1kU5R//+EeZdQYAAAB/X4kKvAkTJhj2jxw5ovz8fIWFhWn58uWmdAwAAKCkfBwO+ZgUtZnVrplKVOCtWLHCsO92u/X6668rICDAlE4BAACcC4fMe6XYuZZ33bp1k5+fn/z9/SVJjzzyiK688kpt2rRJcXFxOnHihOrXr6/nn39ederUkaRSnytKqX4Wvr6+uueeezRjxozSfBwAAMDWXnnlFS1cuFALFy7UlVdeqYKCAo0ZM0ZxcXFKSkpSu3btNG3aNEkq9bnilLrYXb16tWkLCgIAAJyLPx6yMGuTJJfLpX379hm2zMzMEvVvy5Yt8vf3V7t27SRJgwYN0pIlS/7WueKUaIi2a9euhmIuNzdXeXl5mjRpUoluCgAAwOpiY2OVkpJiODZy5EiNGjXqtGsfeeQReTwetW3bVg899DTh78kAACAASURBVJBcLpfq1avnPR8SEqKCggJlZGSU+lxwcHCRfS1Rgff8888b9qtXr65GjRqpZs2aJfk4AACAqcrjIYuEhAS53W7DucDAwNOuT0hIkNPpVF5enp5++mlNnjxZ1157rSl9K8pZCzy3261XX31VM2fOlJ+fX3n0CQAAoNJxOp3ndJ2fn59iYmJ07733aujQodq/f7/3msOHD8vHx0fBwcFyOp2lOlecs87B8/X11b59+1RQUFCimwIAAChvDpk4B+8c+nHs2DFlZWVJkjwejxYvXqyoqCi1bNlSx48f14YNGyRJc+fOVa9evSSp1OeKU6Ih2hEjRuiJJ57QqFGjFB4ebpiP5+Nj1kPJAAAA1pKenq5Ro0bJ7XaroKBATZo00aRJk+Tj46OpU6dq0qRJhuVOJJX6XHEcHo/Hc7aLmjVrVnjxnwo7j8cjh8OhrVu3luoHcK4OZuWr4Kw9BWAFDbuNqeguACgDDZwh+mVRXEV3Q5L00te7lHH8pCltB1eroge6NDKlbbOUKMHjbRUAAADWUaLx1SVLlqh+/fqnbUuXLjW7fwAAAGfl+P0pWjM2K677W6IC7z//+c8Zj7/++utl2hkAAAD8fcUO0a5du1ZS4WsyvvnmG/15ut6+fft4Fy0AAKgU/vzGCTPatppiC7wJEyZIkk6cOKHx48d7jzscDtWtW1cTJ040t3cAAAA4Z8UWeCtWrJAkPfroo5o6dWq5dAgAAOBc+TgKN7PatpoSzcGjuAMAALCOEi2TAgAAUJk5fv9lVttWw2soAAAAbIYEDwAAWB5z8IxI8AAAAGyGBA8AAFiew8QEz3br4AEAAFiBw8RXitn2VWUAAACwDhI8AABgeT4y8SELc5o1lRX7DAAAgGKQ4AEAAMtzOMx7GMKCU/BI8AAAAOyGBA8AAFiej8MhH5OiNrPaNRMJHgAAgM2Q4AEAAMtjoWMjEjwAAACbIcEDAACWx1O0RiR4AAAANkOCBwAALM9HDvnIpKdoTWrXTCR4AAAANkOCBwAALI85eEYkeAAAADZDggcAACzPx8R18Mxq10wkeAAAADZDggcAACyv8E0W5kRtzMEDAABAhSPBAwAAlueQiU/RmtOsqUjwAAAAbIYEDwAAWJ6Pw2HaHDyz2jUTBR4AALA8Fjo2YogWAADAZkjwAACA5fnIvNTKimmYFfsMAACAYpDgAQAA63M45GASnhcJHgAAgM2Q4AEAAMtzyLwFia2X35HgAQAA2A4JHgAAsDwWOjYiwQMAALAZEjwAAGB5zMEzIsEDAACwGRI8AABgebyL1ogEDwAAwGZI8AAAgOU5THyThWlvyDARCR4AAIDNkOABAADLc8i81Mp6+R0JHgAAgO2Q4AEAAMtjDp4RCR4AAIDNkOABAADL400WRhR4AADA8hwycYjWgiUeQ7QAAAA2Q4IHAAAsz0fmpVZWTMOs2GcAAAAUgwQPAABYHsukGJHgAQAA2AwJHgAAsDyWSTEiwQMAALAZEjwAAGB9Dsm0qXIWjPBI8AAAAGyGBA8AAFhe4Tp45kRtVkzDrNhnAAAAFIMEDwAAWJ7DxDl4FlwGjwQPAADAbkjwAACA5Tl+/2VW21ZDggcAAGAzJHgAAMDymINnRIIHAABgMyR4AADA8nzkMHEdPOtFeCR4AAAANkOCBwAArI930RpQ4AEAAMvjIQsjhmgBAABshgQPAABYHgsdG5HgAQAA2AwJHgAAsDwfR+FmVttWQ4IHAABgMyR4AADA8piDZ0SCBwAAYDMUeDDFiLuGqfUlDXRxRB398/LmSpj1tiTpu/XrdMv1vdXsojA1b1xPdw4dpNQDrtM+n5eXpyvbt1KbqEaG44/cf686tW0hZ7C/5ia8Wy73ApzvDn4db9iy172oF8fceNp1j93ZU7kbXtLVHS7xHvOr6qs34m5V6lfPadeSybo/9irvuUG92hraTV81VbkbXlKbZhHlcVuwGYdOrYVX5ltF31wpMEQLU4x66FG9OP0t+fv7a/u2n3Vj32vVsvVlysg4osG33aGru38g3ypVNH7MaD1w3116f36i4fOvvfyC6lxQV9nZ2YbjzVu11vU3DtCUSePL83aA81rdLmO9vw+o7qffkqZo3hebDNc0ql9HN15zqVwHjxqOT7y7ty6+8AJF9ntSYXUCteSNEdq684CWrf1Zc5d8p7lLvvNeOzi6gx67s4c2/rzP3BsCzgMkeDBFs6gW8vf3lyQ5HA45HA79tmuHul/bS9fdcLNqBQaqRo0aGn7Xffp23RrDZ3f/tkvzPnxPox569LR2h991r668qpuqVatWLvcBwKh/t0t18HCWVm/caTj+0tibNfHVRcrLdxuOD45ur2dnLFVGVq5++S1V7yxYqyH9Opyx7cHR7ZXw2XrT+g57c5j8y2oo8GCasQ+NUqPwIHVu10phYeG6pkfv0675Zs1KRTZrbjg24dEH9VjcFFWrVr28ugqghAZHt1fC4g2GYzd2v1Qn8k8qafVWw/HgWtXlrBukH7eneI/9uH2/ohqHn9Zug/Da6tymCQUeUEYo8GCa+Bdf1a8ph7VwyZfqc11/+f2e6P0hectmvRj/tOKmPOc9tnjRArndbvXp17+8uwvgLBqE19aVl1+sOYnfeo/VrOGvJ0dE65Fpn5x2fc0ahX/mj2Yf9x47mp2rWjVOT+Bj+rbX6k07tXv/YRN6jvOBw3FqLbyy3ngXLfAXvr6+6viPTnKlpGjWzDe9x3ft+FUxN1+nKfEv6Ip/dpYk5eTkaErceD099cWK6i6AYtzat53W/KUIm3h3L723eIP2uE4vzLKPnZAkBQacKugCA6op69jx066N7dveUDgC+Ht4yALl4uTJk/ptV+Gcnb17dmtA/956cMx4DRg02HvNrh3btXfPb7q+VzdJUn5enjIzj6pV0wv12Rcr1eCihhXRdQC/i+3TXtNmLTccu6r9JaofFqS7b+4kSapbu6bmPDtML767Qi/MWi7XwaNqdUk9rVi3TZLUqml9bd15wNDGPy5tJGfdQH2y/IfyuRHYEuvgGVHgocwdPJim1f/7Utf26qtq1avr6y+X65N5H+iNmbPl2p+im/v11PC77tWwO+42fK5Z85b6PvnUxO3169Zq/JgHtOzrdapzQV1JhcunFBQUyOPx6GR+vo4fPy4/Pz/5+BBGA2a6onVD1QsN0vy/PD3b577/qGoVX+/+qlkPaey/FyhpTeF8vITP1mvc8B76PnmvQkNq6fYbrtC/nnzf0EZs3/ZasGKzN/ED8PfxryLKnMPh0H/ffkttmjdS5EWhevLxsZry7Avq2aefEt59W7t/26lpz01R43q1vZskValSRaFh4d4tuHaIfHx8FBoWLl/fwn9ABt7QRw3DArV+3Vo9MvpeNQwL1NrVKyvydoHzQmx0By388vQi7PDRY0pNz/Ju7gKPjmTlKic3T5I05c3PtTMlXb8smqSlb43SS7O/1LK1P3s/7+9XRTdd24bhWfxtpq2BZ9E5eA6Px+Mx+0vi4+OVlJSklJQULVq0SJdccsnZP/QXB7PyVWB6TwGUh4bdxlR0FwCUgQbOEP2yKK6iuyFJWr/rqE6cLDClbf8qPmrfKMiUts1SLgle9+7dlZCQoPr165fH1wEAgPOMw+TNasplDl67du3K42sAAACgSjYHLzMzU/v27TNsLtfp7ykFAAD4Mx+Hw9StNKZPn67IyEht21b4FPmmTZt03XXXqWfPnho+fLjS09O915b2XJE/j1L12CSzZs1S9+7dDVtsbGxFdwsAAFRylW2I9qefftKmTZu809MKCgo0ZswYxcXFKSkpSe3atdO0adP+1rniVKoCb9iwYVq+fLlhS0hIqOhuoYSefmKC3nrtlXL/3l5X/1M/b/2p3L8XsLvJI6I18tau5f69K2c9eMbXmQFWkZeXp8mTJ+uJJ57wHtuyZYv8/f2909YGDRqkJUuW/K1zxalUBV5gYKAiIiIMm9PprOhuoQQOHTqoj+YmaMjtd3mPrfxqhTq3a6lG4UG6Mfpa7d2zu8jP3xh9rZo3rqeLI+qoW6e2WvLZp4bz8z96X21bXqxGzmDdFnOTjhw+tWr+vaMe1PNPP1n2NwWcxy4IDlBs33aaMX+N99hV7Ztq08ePKX3VVC15Y4QahNc+42cvDAvWwa/jDVvuhpc0OvYq7zUDe16uXxbF6dDKeH047Q7VDqzhPffS7C8Vd8/p764GilUOEZ7L5TptKllmZuZpXXn55Zd13XXXKSIiwnvM5XKpXr163v2QkBAVFBQoIyOj1OeKUy4F3lNPPaUuXbrowIEDuv3229W3b9/y+FqUow8S3lX3a3upevXqkqT09EMaPuQWPTrhCW39LVWXXtZW/7q96OH2p557QZu37dGv+9I17eXXNeLu25R6oHD+5c9bf9KYB0Zo+pvvaMv2fapevYbGPTzK+9meffpp9cr/KS31QFHNAzhHQ/p10JLVW3X8RL4kqU5QgOY+P1yTX1+set3G6/vkvZr97LAzfnZvaobqdhnr3doNipfbXaBPVmyWJEU1Dter42/R8Lg5uqjH4zp2PE8vj7vZ+/nEr7eoS9uLFVanlvk3CpyD2NjY06aSzZo1y3DNxo0btWXLFsXExFRQLwuVy1O0EydO1MSJE8vjq1BBVixL0q1DTv1lv/jTBYps1lzX3VD4l/Yjjz2uFo2d2r7tZzW9pNlpn2/esvWpHYdDJ/PzlZKyT2HhTs3/8H316NVX/+h0pSRp7IQndGWH1srOylLNWrVUrVo1tb7scn25fKkGxgw190aB80SPf0Zp1qfrvPvXd2utrTsOaP7vrxN76q0l2vfFU7rkolBt251WbFuxfdtr1cYd3vfVDurVVotX/qTVGwvfXPPk64u16ePHVLOGv7KPndCJvJPa+PM+XXNFMyV8tt6kO4Qdmf1KsYSEBLndbsOxwMBAw/769eu1Y8cOde/eXZJ04MAB3XHHHRoyZIj279/vve7w4cPy8fFRcHCwnE5nqc4Vp1IN0cK6tiZvUZOLI737v/ycbCjaAgICdFGjxvpla3KRbQy+pb8uCq2lPt066Z+du+qyNm3P2FbDxk1U1c9PO37d7j3WNLKZkrf8WJa3BJzXWlxcT9v/VLg1bxyuzdtP/SNz7Hiedqakq3mTs8+Vi+3bXnMSTxVqUU3C9eOf2tqVkq68fLeaNqjrPfbLrlS1voS1U1G5OJ3O06aS/bXAu/vuu7Vq1SqtWLFCK1asUHh4uGbOnKk777xTx48f14YNGyRJc+fOVa9evSRJLVu2LNW54vAuWpSJzKMZqlmrpnc/JztbdS64wHBNYGCQsrOzi2xjzocLlJ+fr6+/Wq7tv/zsfb9sTnaOAgONK4gXtpXl3a9Zs6ZSGaIFykxwrerKyjn1WrKAGv46dMT45zczO1c1a1Qrtp1OlzVWaEgtfbL81Dtsa1b319Hs3NPbCjjVVtax4wq/wFpvDkDFMvOVYmXRro+Pj6ZOnapJkybpxIkTql+/vp5//vm/da44FHgoE0HBtZWddeov/4CaNZWVlWW4JisrUzVr1vzrRw2qVq2q7tf20ozXp6tR4ybq2aefAmoGKCvLOIm1sK1T83Oys7MVFFR8XA2g5I5kHlOtAH/vfs6xE6oVYCzmagVUU/ax48W2ExvdXgtW/OB9N60kZeeeUOCZ2so51VatGtV0NMtYBAJWtGLFCu/vL7/8ci1atOiM15X2XFEYokWZaN6ilXbu2Obdj2zWXMlbNnv3c3JytHvXTkVGNS9ReydPntRvu3aesa3du3Yq78QJNbm4qffY9l9+VvOWrf7ubQD43Zbt+9W0Qah3P3nnAbW+5NSTfDWq+alxxAVK3lF0cl7Nv6puvOYyw/CsJG3dcUCtmp5qq2H9OvL3q6Ltew56j0U2CtPmbSllcSs4T1S2dfAqGgUeykT3Hr20dtVK737vftfr560/KXHhfB0/flwvxj+lqBatzviAxfZtP2v5siXKzc1Vfn6+Pv4gQd+sWal/dOoiSbrxllu1dMln+mbNKuXk5GjqM0+qT7/+qlmrMME7fvy4Nm/6Xl2vvqZ8bhY4DySt2arOlzfx7n/65WY1b+JU/26t5e9XRePv6qkt2/cX+4DF9Ve1UkZmrv63Ybvh+Nwl36nPlS3V6bLGqlHNT3H/6q2FX25W9rHCIWF/vypq0yxCK9b9Ys7NAecBCjyUiQG3DvYWaZJ0wQV1NfPdD/TclDg1uyhU33+3Xm+8Pcd7/aMPjNCjD4yQJHk8Hk17dopaXlxfLZrU0/+9Pl1vvpOg1pe1kSQ1i2qhqf+erhF3DVPLi+srOztLz73wqretpZ8n6p+duyjcWU8AykZC4nr16tRc1fyrSpIOZeTo1kff0RP39ZVrxTNq3/IiDRn/rvf6Vx4boFceG2BoIza6g95bfPpTsFt3HtD9z36od54aoj3LpqhmgL9GP/ex93zfK1to5Xe/ynXo9PXFgCIR4Rk4PB6Pp6I7URIHs/JVYImenr+eeXKiLqgbqrvvu79cv7d3t056cfqbimresly/F6XXsNuYiu4CSuDJ+/rq4JFsTX//f+X6vV//90HdM+X9Yod/UTk0cIbol0VxFd0NSdIPezOVd9KcQsGvikOXXhh49gsrEQo8AOWOAg+wh8pU4G3em2Vqgdf6QmstvM0QLQAAgM2wTAoAALC8yr4OXnkjwQMAALAZEjwAAGB5Zj7sasEAjwQPAADAbkjwAACAPVgxajMJCR4AAIDNkOABAADLc8hh4hw860WDJHgAAAA2Q4IHAAAsj3XwjCjwAACALViwDjMNQ7QAAAA2Q4IHAACsj5WODUjwAAAAbIYEDwAAWB7LpBiR4AEAANgMCR4AALA8lkkxIsEDAACwGRI8AABgeTxEa0SCBwAAYDMkeAAAwPqI8AxI8AAAAGyGBA8AAFge6+AZkeABAADYDAkeAACwPNbBMyLBAwAAsBkSPAAAYHk8RGtEggcAAGAzJHgAAMD6iPAMSPAAAABshgQPAABYHuvgGVHgAQAA6zNxmRQL1ncM0QIAANgNCR4AALA8nrEwIsEDAACwGRI8AABgD1aM2kxCggcAAGAzJHgAAMDyWCbFiAQPAADAZkjwAACA5TlMXAfPtPX1TESCBwAAYDMkeAAAwPJYB8+IBA8AAMBmSPAAAID1EeEZkOABAADYDAkeAACwPNbBMyLBAwAAsBkSPAAAYHmsg2dEggcAAGAzJHgAAMDyeIjWiAQPAADAZkjwAACA9RHhGVDgAQAAW7DiciZmYYgWAADAZkjwAACA5bFMihEJHgAAgM2Q4AEAAMvjGQsjEjwAAACbIcEDAACWxxw8IxI8AAAAmyHBAwAANmDBmM1EJHgAAAA2Q4IHAAAsjzl4RiR4AAAANkOCBwAALI918IxI8AAAAGyGBA8AAFieQybOwTOnWVOR4AEAANgMCR4AALA8hxzMwfsTEjwAAACbIcEDAADWZ2bMZsEIjwQPAADAZkjwAACALVgwaDMNBR4AALA8h8PEhY4tWDkyRAsAAGAzJHgAAMDyWCbFiAQPAADAZkjwAACA9bFMigEJHgAAgM2Q4AEAAMtzyMSnaE1q10wkeAAAADZDggcAACyPdfCMSPAAAABshgQPAABYHuvgGZHgAQAA2AwJHgAAsDzm4BmR4AEAANgMBR4AAIDNUOABAADYDHPwAACA5TEHz4gEDwAAwGZI8AAAgA2Ytw6eFZHgAQAA2AwJHgAAsDwz58mda9v33Xef9u3bJx8fH9WoUUOPP/64oqKitGvXLo0bN04ZGRkKDg5WfHy8GjZsKEmlPlcUEjwAAIAyFB8fr08//VQLFizQ8OHDNX78eEnSpEmTFBMTo6SkJMXExCguLs77mdKeKwoFHgAAsDyHydu5qFWrlvf32dnZcjgcSk9PV3JysqKjoyVJ0dHRSk5O1uHDh0t9rjgM0QIAAHsw+SkLl8slt9ttOBYYGKjAwMDTrp0wYYJWr14tj8ejGTNmyOVyKSwsTL6+vpIkX19fhYaGyuVyyePxlOpcSEhIkX2lwAMAACiB2NhYpaSkGI6NHDlSo0aNOu3ap59+WpK0YMECTZ06VaNHjy6XPv6BAg8AAFiemYuk/NFyQkLCGRO84vTv319xcXEKDw9Xamqq3G63fH195Xa7lZaWJqfTKY/HU6pzxWEOHgAAQAk4nU5FREQYtr8WeDk5OXK5XN79FStWKCgoSHXq1FFUVJQSExMlSYmJiYqKilJISEipzxXH4fF4PGV582Y5mJWvAkv0FMDZNOw2pqK7AKAMNHCG6JdFZ3+iszwcy/PIrDLBIamGX8kSwkOHDum+++5Tbm6ufHx8FBQUpLFjx6pFixbasWOHxo0bp8zMTAUGBio+Pl6NGzeWpFKfK7LPFHgAyhsFHmAPFHiVF3PwAACA5ZlZflmrtCvEHDwAAACbIcEDAADWZ8WYzUQkeAAAADZDggcAACyvPNbBsxISPAAAAJuxTILnY8XyGcAZNXAWv0AnAGuoHxpU0V3wcjismbSZxTLr4AEAAKBkGKJFpeByudStWzfD610AWBd/poGKRYGHSsHtdislJeW0lzgDsCb+TAMViwIPAADAZijwAAAAbIYCDwAAwGYo8FApBAYGauTIkQoMDKzorgAoA/yZBioWy6QAAADYDAkeAACAzVDgAQAA2AwFHircrl27NHDgQPXs2VMDBw7Ub7/9VtFdAlBK8fHx6tatmyIjI7Vt27aK7g5w3qLAQ4WbNGmSYmJilJSUpJiYGMXFxVV0lwCUUvfu3ZWQkKD69etXdFeA8xoFHipUenq6kpOTFR0dLUmKjo5WcnKyDh8+XME9A1Aa7dq1k9PprOhuAOc9CjxUKJfLpbCwMPn6+kqSfH19FRoayvsrAQD4GyjwAAAAbIYCDxXK6XQqNTXV+0Jyt9uttLQ0hngAAPgbKPBQoerUqaOoqCglJiZKkhITExUVFaWQkJAK7hkAANbFmyxQ4Xbs2KFx48YpMzNTgYGBio+PV+PGjSu6WwBK4amnntLSpUt16NAh1a5dW8HBwfrss88qulvAeYcCDwAAwGYYogUAALAZCjwAAACbocADAACwGQo8AAAAm6HAAwAAsBkKPADF6tatm9asWSNJeuONNzRhwgTTv3PdunXq0qVLkecjIyO1e/fus7azb98+RUZG6uTJk+fch7/zWQCoaFUqugMArOOee+4p0XXjxo1TWFiYHnzwQZN7BAA4ExI84DxCGgUA5wcKPMDiunXrpjfffFN9+vRR+/bt9dhjj+nEiROSTg11vvXWW+rUqZMee+wxFRQU6K233tI111yjjh07avTo0crIyPC2t2DBAl199dXq2LGjXn/9dcN3vfrqq3rkkUe8+xs2bNCgQYPUrl07de3aVfPnz9cHH3ygRYsWaebMmWrTpo039UtNTdWoUaN0xRVXqFu3bnr33Xe97Rw/flzjxo1T+/bt1adPH/34448lvv+vvvpK/fv31+WXX66uXbvq1VdfPe2aefPmqXPnzurcubNmzpzpPX62nwUAWBUFHmADfxRUy5Yt065du/Taa695zx06dEhHjx7Vl19+qSlTpmj27Nn64osvNGfOHK1cuVJBQUGaPHmyJOnXX3/Vk08+qalTp2rlypXKyMjQgQMHzvidKSkpuuuuuzR48GCtXbtWCxYsUFRUlAYOHKh+/frpjjvu0MaNG/XGG2+ooKBA9957ryIjI/X1119r1qxZmjVrllauXClJmj59uvbs2aNly5Zp5syZWrBgQYnvvXr16oqPj9eGDRv05ptv6v3339cXX3xhuGbdunVaunSpZs6cqf/7v//zziks7mcBAFZGgQfYQGxsrJxOp4KDg3Xvvfca3v3p4+Oj+++/X35+fqpWrZrmzp2rBx98UOHh4fLz89PIkSOVlJSkkydPasmSJbrqqqvUvn17+fn5afTo0fLxOfNfE4mJifrnP/+p6OhoVa1aVbVr11ZUVNQZr/3xxx91+PBhjRw5Un5+frrwwgt1yy23aPHixZKkzz//XPfcc4+Cg4PldDo1ZMiQEt97x44dFRkZKR8fHzVr1kx9+/bVt99+a7hmxIgRqlGjhiIjI3XjjTcqMTFRkor9WQCAlfGQBWADTqfT+/t69eopLS3Nu1+7dm35+/t79/fv368RI0YYCjcfHx+lp6crLS1N4eHh3uM1atRQcHDwGb/T5XKpQYMGJepfSkqK0tLS1K5dO+8xt9vt3U9LSzvtHkrqhx9+0LRp07R9+3bl5+crLy9PvXr1Mlzz57br16+vbdu2SSr+ZwEAVkaBB9iAy+Xy/n7//v0KDQ317jscDsO14eHheuaZZ9S2bdvT2gkNDdWOHTu8+7m5uUXOSXM6ndq8efMZz/31O51OpyIiIrR06dIzXl+3bl25XC41bdr0tPs5m4cffliDBw/WjBkz5O/vr6efflpHjhwxXONyudSkSRNJxp9PcT+Lffv2lbgPAFDZMEQL2MB7772nAwcOKCMjQ2+88Yb69OlT5LW33nqrXnrpJaWkpEiSDh8+7J2z1rNnT3311VfasGGD8vLy9Morr6igoOCM7fTr109r1qzR4sWLdfLkSR05ckRbt26VJNWpU8dQILVu3VoBAQF66623dPz4cbndbm3bts1bIPbu3VtvvfWWjh49qgMHDmj27NklvvecnBwFBQXJ399fmzdv9g6//tlrr72m3Nxcbd++XfPnz/f+fIr7WQCAlVHgATYQHR2t4cOH65prrlGDBg107733Fnnt0KFD1a1bNw0fPlxt2rT5//bsGMVBIAzD8Lek9wDTe4CADCKxUSystJDUYikpU4o2OUFAECxyLQuPYW+6ZZctdtkmu8P71FP8M9XL/Dqfz++h5fu++r7X9XpVHMfyPO/TyvYjY4zmedbj8ZC1VmVZalkWSVJVVVrXVUEQqG1bHQ4HzLB7zAAAAJlJREFUTdOkZVmUpqnCMFTXddq2TZJ0uVxkjFGapmqaRkVR/PjuwzDofr/reDxqHEflef7ljLVWWZaprms1TaPT6fTtWwDAf/a27/v+6iEA/F6SJLrdboqi6NWjAAD+CH7wAAAAHEPgAQAAOIYVLQAAgGP4wQMAAHAMgQcAAOAYAg8AAMAxBB4AAIBjCDwAAADHEHgAAACOeQLuVUdqJmkNmgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.70      0.72      0.71     10352\n",
            "           1       0.72      0.70      0.71     10648\n",
            "\n",
            "    accuracy                           0.71     21000\n",
            "   macro avg       0.71      0.71      0.71     21000\n",
            "weighted avg       0.71      0.71      0.71     21000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>2. Logistic Regression</h3>**\n",
        "\n",
        "**Algorithm brief:** Logistic regression estimates the probability of an event occurring, such as voted or didn't vote, based on a given dataset of independent variables. Since the outcome is a probability, the dependent variable is bounded between 0 and 1.<br>\n",
        "\n",
        "**After running the algoritm:** \n",
        "*   Accuracy for the train data is: **72%**\n",
        "*   Accuracy for the test data is: **72%**"
      ],
      "metadata": {
        "id": "y9qrPO8Q3orq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression #importing the algorithm\n",
        "\n",
        "lr_object = LogisticRegression(solver='lbfgs', max_iter=1000) #creating an object under it.\n",
        "\n",
        "lr_object.fit(xtrain, ytrain) #training the dataset\n",
        "\n",
        "train_score = lr_object.score(xtrain, ytrain)\n",
        "test_score = lr_object.score(xtest, ytest)\n",
        "\n",
        "print(f\"The taining accuracy of the model is: {train_score}\")\n",
        "print(f\"The test accuracy of the model is: {test_score}\")"
      ],
      "metadata": {
        "id": "dGaSPR2D3rqa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61c0ebb5-006d-455c-cebc-5bbd65c1ac5b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The taining accuracy of the model is: 0.7234693877551021\n",
            "The test accuracy of the model is: 0.7188571428571429\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix and Performance calculation:**\n",
        "* Confusion matrix (Color Blue)\n",
        "* Accuracy\n",
        "* Precision\n",
        "* Recall\n",
        "* F1 Score"
      ],
      "metadata": {
        "id": "_DxIgr0qJjls"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = lr_object.predict(xtest)\n",
        "cm = confusion_matrix(ytest, pred)\n",
        "print(\"Confusion matrix:\", cm, sep = '\\n')\n",
        "print()\n",
        "print(\"Visualizing:\")\n",
        "fig, ax = plot_confusion_matrix(conf_mat = cm, show_absolute = True, show_normed = True, colorbar = True, cmap = \"Blues\")\n",
        "plt.show()\n",
        "print()\n",
        "print(\"Classification Report:\", classification_report(ytest, pred), sep = '\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "ZoVA-fZQJexF",
        "outputId": "60475062-4a60-4e48-d98e-29f0747ee084"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix:\n",
            "[[7972 2380]\n",
            " [3524 7124]]\n",
            "\n",
            "Visualizing:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.77      0.73     10352\n",
            "           1       0.75      0.67      0.71     10648\n",
            "\n",
            "    accuracy                           0.72     21000\n",
            "   macro avg       0.72      0.72      0.72     21000\n",
            "weighted avg       0.72      0.72      0.72     21000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>3. Multi-layer Perceptron</h3>**\n",
        "\n",
        "**Algorithm brief:** A multilayer perceptron is a fully connected class of feedforward artificial neural network. The term MLP is used ambiguously, sometimes loosely to mean any feedforward ANN, sometimes strictly to refer to networks composed of multiple layers of perceptrons.<br>\n",
        "\n",
        "**After running the algoritm:** \n",
        "*   Accuracy for the train data is: **72%**\n",
        "*   Accuracy for the test data is: **72.6%**\n",
        "\n"
      ],
      "metadata": {
        "id": "sjnMC9WqNOJP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPClassifier #importing the algorithm\n",
        "\n",
        "MLP_object = MLPClassifier(hidden_layer_sizes=(7), activation=\"relu\", max_iter=1000) #creating an object under it.\n",
        "\n",
        "MLP_object.fit(xtrain, ytrain) #training the dataset\n",
        "\n",
        "train_score = MLP_object.score(xtrain, ytrain)\n",
        "test_score = MLP_object.score(xtest, ytest)\n",
        "\n",
        "print(f\"The taining accuracy of the model is: {train_score}\")\n",
        "print(f\"The test accuracy of the model is: {test_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0aK4YjqG8wvo",
        "outputId": "6ef4b860-0fbd-436a-a131-0844a799c74f"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The taining accuracy of the model is: 0.7228163265306122\n",
            "The test accuracy of the model is: 0.7212857142857143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix and Performance calculation:**\n",
        "* Confusion matrix (Color Blue)\n",
        "* Accuracy\n",
        "* Precision\n",
        "* Recall\n",
        "* F1 Score"
      ],
      "metadata": {
        "id": "jdSTU87kRojO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = MLP_object.predict(xtest)\n",
        "cm = confusion_matrix(ytest, pred)\n",
        "print(\"Confusion matrix:\", cm, sep = '\\n')\n",
        "print()\n",
        "print(\"Visualizing:\")\n",
        "fig, ax = plot_confusion_matrix(conf_mat = cm, show_absolute = True, show_normed = True, colorbar = True, cmap = \"Blues\")\n",
        "plt.show()\n",
        "print()\n",
        "print(\"Classification Report:\", classification_report(ytest, pred), sep = '\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "5iS6alZPRpO9",
        "outputId": "d81febee-cde1-439c-b911-0447a82761a2"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix:\n",
            "[[8191 2161]\n",
            " [3692 6956]]\n",
            "\n",
            "Visualizing:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.79      0.74     10352\n",
            "           1       0.76      0.65      0.70     10648\n",
            "\n",
            "    accuracy                           0.72     21000\n",
            "   macro avg       0.73      0.72      0.72     21000\n",
            "weighted avg       0.73      0.72      0.72     21000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>4. K-Nearest Neighbor</h3>**\n",
        "\n",
        "**Algorithm brief:** The k-nearest neighbors algorithm, also known as KNN or k-NN, is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point.<br>\n",
        "\n",
        "**After running the algoritm:** \n",
        "*   Accuracy for the train data is: **75%**\n",
        "*   Accuracy for the test data is: **71%**\n",
        "\n"
      ],
      "metadata": {
        "id": "HghChnzcS01v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier #importing the algorithm\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors = 11) #creating an object under it.\n",
        "\n",
        "knn.fit(xtrain, ytrain) #training the dataset\n",
        "\n",
        "train_score = knn.score(xtrain, ytrain)\n",
        "test_score = knn.score(xtest, ytest)\n",
        "\n",
        "print(f\"The taining accuracy of the model is: {train_score}\")\n",
        "print(f\"The test accuracy of the model is: {test_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvavU8iOLOf0",
        "outputId": "6b0943b3-ccd0-4ed2-938b-eb65995911ab"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The taining accuracy of the model is: 0.7521428571428571\n",
            "The test accuracy of the model is: 0.7081904761904761\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix and Performance calculation:**\n",
        "* Confusion matrix (Color Blue)\n",
        "* Accuracy\n",
        "* Precision\n",
        "* Recall\n",
        "* F1 Score"
      ],
      "metadata": {
        "id": "rbrAv9XCR2Rl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = knn.predict(xtest)\n",
        "cm = confusion_matrix(ytest, pred)\n",
        "print(\"Confusion matrix:\", cm, sep = '\\n')\n",
        "print()\n",
        "print(\"Visualizing:\")\n",
        "fig, ax = plot_confusion_matrix(conf_mat = cm, show_absolute = True, show_normed = True, colorbar = True, cmap = \"Blues\")\n",
        "plt.show()\n",
        "print()\n",
        "print(\"Classification Report:\", classification_report(ytest, pred), sep = '\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "bkaVLVLgR3AN",
        "outputId": "816ab9c4-3f11-4999-8de8-9ffc89d6ddbb"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix:\n",
            "[[7752 2600]\n",
            " [3528 7120]]\n",
            "\n",
            "Visualizing:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.75      0.72     10352\n",
            "           1       0.73      0.67      0.70     10648\n",
            "\n",
            "    accuracy                           0.71     21000\n",
            "   macro avg       0.71      0.71      0.71     21000\n",
            "weighted avg       0.71      0.71      0.71     21000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>5. Support Vector Machine: C-support Vector Classification</h3>**\n",
        "\n",
        "**Algorithm brief:** SVC is a nonparametric clustering algorithm that does not make any assumption on the number or shape of the clusters in the data. It is known that, this algorithm works best for low-dimensional data, so if the data is high-dimensional, a preprocessing step, e.g. using principal component analysis, is usually required.<br>\n",
        "\n",
        "**After running the algoritm:** \n",
        "*   Accuracy for the train data is: **72%**\n",
        "*   Accuracy for the test data is: **72%**\n",
        "\n"
      ],
      "metadata": {
        "id": "su_autl0Tcbm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC #importing the algorithm\n",
        "\n",
        "svc_object = SVC() #creating an object under it.\n",
        "\n",
        "svc_object.fit(xtrain, ytrain) #training the dataset\n",
        "\n",
        "train_score = svc_object.score(xtrain, ytrain)\n",
        "test_score = svc_object.score(xtest, ytest)\n",
        "\n",
        "print(f\"The taining accuracy of the model is: {train_score}\")\n",
        "print(f\"The test accuracy of the model is: {test_score}\")"
      ],
      "metadata": {
        "id": "xLvp7xno5JJa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9634997f-bbe4-482a-cb9e-5870deab61ae"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The taining accuracy of the model is: 0.7198367346938775\n",
            "The test accuracy of the model is: 0.7192857142857143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix and Performance calculation:**\n",
        "* Confusion matrix (Color Blue)\n",
        "* Accuracy\n",
        "* Precision\n",
        "* Recall\n",
        "* F1 Score"
      ],
      "metadata": {
        "id": "AywAGaEGUbRN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = svc_object.predict(xtest)\n",
        "cm = confusion_matrix(ytest, pred)\n",
        "print(\"Confusion matrix:\", cm, sep = '\\n')\n",
        "print()\n",
        "print(\"Visualizing:\")\n",
        "fig, ax = plot_confusion_matrix(conf_mat = cm, show_absolute = True, show_normed = True, colorbar = True, cmap = \"Blues\")\n",
        "plt.show()\n",
        "print()\n",
        "print(\"Classification Report:\", classification_report(ytest, pred), sep = '\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "tRJTu2WXUb0N",
        "outputId": "326dcf67-292b-4285-ad2c-91be7cba7605"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix:\n",
            "[[8501 1851]\n",
            " [4044 6604]]\n",
            "\n",
            "Visualizing:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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l3/vY2IfVpk1b3XX3PVX6vYCdVMkYvIquowbraNSgnobHXKyZ81Z59115cTtt+OgJZX37Ly15436dG96g1PM7t4vQl289oL3fTNG2JU9r3J19vW0Xd2qpRa+NUurXCfp96fNKnHybmjY68YvAi29/pUdvi1btWqS9QGXZt2+fEue8rTvu+od337KlX+nCC85XqCtQ0Vdfpd27d5d6/lPxTyrqok6qF1BLz0yaeEr7q9Nf1vnntVKTUJcu6xGlVStP/IL2wEOPaHLCcyoqKqrUe4K9MQ+eUZW9RTtx4kTNmTNH0dHRmjNnjp566qkznwTLuGlADy1ZuVmFR45KkhqGBGnu1Ds16dVP1ezKR/Vj8u+a/cJtpZ7/3+dv0coft6vZlY/qmjum6a4hV+i6Xp0kSSGuQP3fvFU6/7p4te8fp7z8I3p94gjvuXv35+q3XRmKubKTuTcJnEVmv/1f9e3bX3Xr1pUk7d+/X8OG/FVxE59WWma2unaL0k2xQ0s9v02btnr2hcnq1/+6U9q+X7NGT04Yp3fmfqiMrIO6+dbbNWzIYO8kx+Hh4Wrf/nwtWviJOTcHnAWqrMA7vo5aUlKSPvjgA7U+aY1CWN+1l3XQinXbvNvX975Iv+xI10dfrteRomI9M+MzdWoXoXYtw057fovwhpq7eK1KSjzambJfqzdsV2SbY4/wP1+VrI++XK+8/EIVFB7VjPeW69KLjP/9fPPDVvW9/ALzbhA4y3y+ZLGu6NnLu73g448U2aGjbrhxiAICAvRE3ERt/Pkn/fbrr6c9f8TImxXdt5/q1Qs+pW337l2K7NBRXbt1k8Ph0PCbRmr//v3KzMz0HtOz15Va8tmnlX9jsC2Hw9yP1bBUGSpFx/OaaeuuDO92hzZN9fOWVO/24cIi7UjZrw5twk93uqa/s0zDYy5WrVpOndeiiXp0bqVl353+L47Lu7Y9ZX3a33buNaxhC+DP2bxpo85r1967nZy8WZ07X+jdDgoKUus2bZScvLnc147u208lbre+X7NGbrdbs/7zf7rwwovUtGlT7zHtz4/Uxp9/+nM3AZzFatRbtLCukHqByjt8xLsdFOiv/QcOGY7JPVSgeoH+pz1/8YpNmjlppB64qY9q1fLTs69/pnXJv59y3AXnNdPjd/XT3x58w7A/L/+IQoLrVsKdAJCknJwcBQefSN/yDx1So8aNDce4XPV1KC+v3NcODg7WoL/eoD5XXi6Px6OQkBDNX7jYMM4pODhYOQdzKn4DOOvwFq0RCR4qxYG8wwo+qXjLP3xEwUEBhmOCg+rq0ElF4HENXIFaMP1ePffmYoVc8qDaRj+hay6N1F1DrjAc1/qcRlow/V49MuVDrVq//X+u7a+cPHOWqAHORg0aNFDeScVbUL16ysvNNRyTl5uresGnPoI9k//+31t6e9Z/9ONPm5V7uEj/N2uObhgUo7S0tBPXzstTSP2Qit8AcJajwEOl2LQ1Vee1aOLdTt6+V51PemQaGFBHrZs3UvL2U1cmadW8kdwlHr2z6Hu53SVKzczRB0nrFH15R+8x54Y30GczRuv5N5fo3U/XnnKN9q2aauNJj4QB/DkXdOqsrVu3eLc7dOion096ZJqfn68dO7arQ4eOpzu9TD/9tEH9+sfovHbt5HQ6dW10XzVtGq7vVn/rPea3X39Rp5MeCQNnwhg8Iwo8VIqklcm6vNt53u1Plv2kDm2aaVCfi+Rfp5bG39VPm7amastJ4/SO27o7Uw6HNLRvlBwOh8IaBuvG6G7atPVYwdascX0tfv1+zZj7jWZ+ePq57q7o1lafryr/WCAApxfdt79WfrPcuz1w0GAlb96kjz+ap8LCQj33zCRd0Kmz2p9//mnPP3r0qAoLC1XiKVFxcbEKCwu9b8lGRXXXksWfaueOHfJ4PPrqyy+0desWdex44kWpFd8s17V9+5l7k7CVY4WYWdOkVPfdlR8FHipF4qI16nt5BwX415Yk7T9wSH8fO1MT74tR+vLJ6t6ppW4a9x/v8S9NGKaXJgyTJOXlF2rYwzM1esRVSl8+Wd/NfVybt6XphZlLJEm3DP6LWp/TWBPu7q99q/7p/RzXtJFLka2b6pNlP1fhHQP2NvymkVqy5DMVFBwb+tC4cWO9+/48TYyboPDGDbT2+zWanTjXe/zoe+/W6Hvv9m7f+4871SC4rt6f+64Snn9WDYLr6p05s73XHvK3Ybr26ivVJNSlhx+8X9Nffd1bLKanp+uXX5I18PpBVXjHgL04PB6Pp7o74Yv2/eP0e3p2dXcDZXhq1ADty86rlpUsduzZrzc+WFGl34uKO7B2+pkPQrWLe2K8GjduUi0rWbRu3Ub/uOfeKv1elJ9Dkn8NeV2zT8JypR4oNOXaEQ0C9NVjvc58YA1CgQegylHgAfZAgVdz1ZB/LQAAABXncDjkdDJNynGMwQMAALAZEjwAAGB5Zk5nYsEAjwQPAADAbkjwAACA5bFUmREJHgAAgM2Q4AEAAMtjDJ4RCR4AAIDNkOABAADLYwyeEQkeAACAzZDgAQAAyyPBMyLBAwAAsBkSPAAAYHm8RWtEggcAAGAzJHgAAMAGzBuDJ1kvwiPBAwAAsBkSPAAAYHmMwTOiwAMAAJbHNClGPKIFAACwGRI8AABgeTyiNSLBAwAAsBkSPAAAYHmMwTMiwQMAALAZEjwAAGB5jMEzIsEDAACwGRI8AABgeccSPLPG4JlyWVOR4AEAANgMCR4AALA8xuAZkeABAADYDAkeAACwAfPmwZOsF+GR4AEAANgMCR4AALA8xuAZkeABAADYDAkeAACwPNaiNSLBAwAAsBkSPAAAYHmMwTMiwQMAALAZEjwAAGB5jMEzosADAACWR4FnxCNaAAAAmyHBAwAAlsdLFkYkeAAAADZDggcAACyPMXhGJHgAAAA2Q4EHAABs4fg4vMr+lNeRI0cUHx+va6+9VgMGDNCTTz4pSdq5c6eGDh2q6OhoDR06VLt27fKeU9G20lDgAQAAVKIpU6bI399fSUlJWrhwocaMGSNJio+PV2xsrJKSkhQbG6u4uDjvORVtKw0FHgAAsLzjY/DM+vgqPz9f8+fP15gxY7znNWrUSFlZWUpOTlZMTIwkKSYmRsnJycrOzq5wW1l4yQIAAMAH6enpcrvdhn0ul0sul8u7vWfPHoWEhGj69Olas2aNgoKCNGbMGAUEBCgsLEx+fn6SJD8/PzVp0kTp6enyeDwVagsNDS21rxR4AADA8qpiHrzhw4crNTXV0DZq1CiNHj3au+12u7Vnzx516NBBjz32mH766SfdfffdmjZtmjmdKwUFHgAAgA8SExNPm+CdLDw8XLVq1fI+Ur3wwgvVoEEDBQQEKCMjQ263W35+fnK73crMzFR4eLg8Hk+F2srCGDwAAGB5TofD1I90rHhr3ry54fO/BV5oaKh69OihVatWSTr2BmxWVpZatmypyMhILVq0SJK0aNEiRUZGKjQ0VA0bNqxQW1kcHo/HU6k/YZO07x+n39PLHlAIwBoOrJ1e3V0AUAkckvxryLPAEbPWKyPviCnXDgv215ybu/h8/J49ezR+/Hjl5OSoVq1aeuCBB9SrVy9t375d48aNU25urlwulxISEtS6dWtJqnBbaSjwAFQ5CjzAHmpSgXfT2+YWeLNH+l7g1QQ8ogUAALCZGlJ3AwAAVNyxt2jNWovWlMuaigQPAADAZkjwAACA5TkcktPkefCshAQPAADAZkjwAACA5ZV3zdjyXttqKPAAAIDlOWTiUmXmXNZUPKIFAACwGRI8AABgeY4//ph1bashwQMAALAZEjwAAGB5ThOnSTHrumYiwQMAALAZEjwAAGB5TJNiRIIHAABgMyR4AADA8hwOE+fBs16AR4IHAABgNyR4AADA8hwOh5yMwfMiwQMAALAZEjwAAGB5jMEzIsEDAACwGRI8AABgecyDZ0SCBwAAYDMkeAAAwPIYg2dEggcAAGAzJHgAAMDynDJvHjynrBfhkeABAADYDAkeAACwPMcfH7OubTUkeAAAADZDggcAAKzPxHnwrPgabakFXmxsrE8/qMTExErtEAAAQHk5Hcc+Zl3bakot8IYMGVKV/QAAAEAlKbXAGzx4cFX2AwAAoMKOTXRs1lJlplzWVD69ZOHxePT+++9r5MiRGjBggCRp7dq1+uyzz0ztHAAAAMrPpwJv2rRp+vDDDzV06FClp6dLkpo2baqZM2ea2jkAAABfHF+qzKyP1fhU4H388ceaMWOGrrvuOm/82bx5c+3Zs8fUzgEAAKD8fJomxe12KygoSNKJ59v5+fkKDAw0r2cAAAA+cpg4TYpp06+YyKcEr1evXnr++edVVFQk6diYvGnTpumqq64ytXMAAAAoP58KvMcff1z79u1Tt27dlJeXpy5duigtLU2PPPKI2f0DAAA4o+Pz4Jn1sRqfHtHWq1dPr7zyirKyspSamqrw8HA1btzY7L4BAACgAnxeqiw3N1erVq1SZmammjRpol69eql+/fpm9g0AAMA3LFVm4NMj2tWrV6t3796aPXu2Nm7cqDlz5qhPnz5avXq12f0DAABAOfmU4D399NOaNGmS+vfv7923ePFiPfXUU1qyZIlpnQMAAPCF44+PWde2Gp8SvMzMTEVHRxv2XXPNNdq/f78pnQIAAEDF+VTgXX/99UpMTDTse/fddzVo0CBTOgUAAFAeTjnkdJj0sWCGV+oj2tjYWO9gxZKSEs2dO1czZ85UWFiYMjIylJWVpQsvvLDKOgoAAADflFrgDRkyxLD9t7/9zfTOAAAAVISZa8Za8CXa0gu8wYMHV2U/AAAAUEl8ngdv//79+vnnn3XgwAF5PB7v/htvvNGUjgEAAPiKtWiNfCrwvvzyS40dO1YtWrTQtm3b1LZtW23dulVdu3alwAMAAKhhfCrwXnzxRT333HPq16+funfvrvnz52vevHnatm2b2f0DAAA4I8bgGfk0TUpaWpr69etn2Dd48GDNnz/flE4BAACUh8OsKVLMXALNRD4VeA0bNvROahwREaH169fr999/V0lJiamdAwAAQPn59Ih2yJAhWrdunaKjo3XLLbdo5MiRcjqduvXWW83uHwAAwBnxiNbIpwLvrrvu8v7zoEGDdPHFF6ugoEBt2rQxrWMAAACoGJ+nSTlZs2bNKrsfAAAAFcY0KUalFni9evXy6Ya+/vrryuxPqV6cOkqHj7qr5LsAmKtb/OfV3QUAlaBZSIC+GNuzuruB0yi1wJsyZUpV9gMAAKDCnPLxzdEKXttqSi3wLr744qrsBwAAACpJhcbgAQAA1CSMwTOyYuoIAACAMpDgAQAAy3NKcpoUtFkxDStXn0tKSpSZmWlWXwAAAFAJfCrwcnNz9fDDD6tz58669tprJUlfffWV/v3vf5vaOQAAAF84HMcSPDM+FhyC51uBFx8fr3r16mnp0qWqXbu2JKlLly5avHixqZ0DAABA+fk0Bm/16tVasWKFateu7X2TJDQ0VFlZWaZ2DgAAwBfH1qI16y1aUy5rKp8SvODgYB04cMCwLy0tTY0bNzalUwAAAFbVu3dv9e3bV9dff72uv/56rVixQpK0YcMGDRw4UNHR0brtttsMQVlF20rjU4E3ZMgQ3X///fruu+9UUlKi9evX67HHHtOwYcPKe88AAACVzqzxd8c/5fXSSy9pwYIFWrBgga644gqVlJRo7NixiouLU1JSkqKiojR16lRJqnBbmT8PXzp55513ql+/fpo0aZKKi4s1fvx49enTRzfffHP57xgAAOAss2nTJvn7+ysqKkqSNGzYMC1ZsuRPtZXFpzF4DodDN998MwUdAACokRwmvu16/Lrp6elyu92GNpfLJZfLdco5jzzyiDwej7p166aHHnpI6enpatasmbc9NDRUJSUlysnJqXBbSEhIqX32+SWL0lx66aW+XAIAAMDShg8frtTUVMO+UaNGafTo0YZ9iYmJCg8PV1FRkZ599llNmjRJ11xzTVV21bcCb8KECYbtAwcO6OjRowoLC9NXX31lSscAAAB85XQ45DQpwjt+3cTExNMmeP8rPDxcklSnTh3Fxsbqnnvu0ciRI5WWluY9Jjs7W06nUyEhIQoPD69QW1l8KvCWLrClEhEAACAASURBVF1q2Ha73XrttdcUFBTky+kAAACmcsi8JcWOl43HC7eyHD58WG63W8HBwfJ4PPrss88UGRmpCy64QIWFhfrhhx8UFRWluXPnqm/fvpJU4bayVGgtWj8/P919993q1auXbr311opcAgAAwHaysrI0evRoud1ulZSUqE2bNoqPj5fT6dTkyZMVHx+vI0eOKCIiQlOmTJGkCreVpUIFniStWrXKtAkFAQAAyqMqXrLwxTnnnKP58+eftq1r165auHBhpbaVxqcCr1evXoZirqCgQEVFRYqPjy/XlwEAAMB8PhV4/xsF1q1bV61atVK9evVM6RQAAEB5VMVLFlZyxgLP7Xbr5Zdf1ltvvaU6depURZ8AAADwJ5yxwPPz81NKSopKSkqqoj8AAADl5pCJY/DMuaypfHqj+L777tPEiROVmprqfSvk+AcAAAA1i09j8J544glJ0oIFC7z7PB6PHA6HfvnlF3N6BgAA4COn49jHrGtbjU8FHqtVAAAAWIdPj2iXLFmiiIiIUz6ff/652f0DAAA4I8cfb9Ga8bHivL8+FXivvPLKafe/9tprldoZAAAA/HllPqJdvXq1JKmkpETfffedPB6Pty0lJYW1aAEAQI1QU1ayqCnKLPAmTJggSTpy5IjGjx/v3e9wONS4cWPvyxcAAACoOcos8JYuXSpJevTRRzV58uQq6RAAAEB58RatkU9j8CjuAAAArMOnaVIAAABqMscff8y6ttX4lOABAADAOkjwAACA5TEGz4gEDwAAwGZI8AAAgOU5TEzwbDcPHgAAgBU4TFxSzLZLlQEAAMA6SPAAAIDlOWXiSxbmXNZUVuwzAAAAykCCBwAALM/hMO9lCAsOwSPBAwAAsBsSPAAAYHlOh0NOk6I2s65rJhI8AAAAmyHBAwAAlsdEx0YkeAAAADZDggcAACyPt2iNSPAAAABshgQPAABYnlMOOWXSW7QmXddMJHgAAAA2Q4IHAAAsjzF4RiR4AAAANkOCBwAALM9p4jx4Zl3XTCR4AAAANkOCBwAALO/YShbmRG2MwQMAAEC1I8EDAACW55CJb9Gac1lTkeABAADYDAkeAACwPKfDYdoYPLOuayYKPAAAYHlMdGzEI1oAAACbIcEDAACW55R5qZUV0zAr9hkAAABlIMEDAADW53DIwSA8LxI8AAAAmyHBAwAAlueQeRMSWy+/I8EDAACwHRI8AABgeUx0bESCBwAAYDMkeAAAwPIYg2dEggcAAGAzJHgAAMDyWIvWiAQPAADAZkjwAACA5TlMXMnCtBUyTESCBwAAYDMkeAAAwPIcMi+1sl5+R4IHAABgOyR4AADA8hiDZ0SCBwAAYDMkeAAAwPJYycKIAg8AAFieQyY+orVgiccjWgAAAJuhwAMAAJbnNPlTEdOnT1f79u21ZcsWSdKGDRs0cOBARUdH67bbblNWVpb32Iq2lfXzAAAAQCXavHmzNmzYoIiICElSSUmJxo4dq7i4OCUlJSkqKkpTp079U21locADAACWd3yaFLM+5VFUVKRJkyZp4sSJ3n2bNm2Sv7+/oqKiJEnDhg3TkiVL/lRbWXjJAgAAwAfp6elyu92GfS6XSy6Xy7Bv2rRpGjhwoJo3b244t1mzZt7t0NBQlZSUKCcnp8JtISEhpfaVAg8AAFheVUyTMnz4cKWmphraRo0apdGjR3u3169fr02bNumRRx4xqTe+ocADAADwQWJi4mkTvJOtXbtW27dvV58+fSRJe/fu1e23366bbrpJaWlp3uOys7PldDoVEhKi8PDwCrWVhQIPAABYn0MybUWxP64bHh5+xkPvuusu3XXXXd7t3r17a8aMGWrbtq3ef/99/fDDD4qKitLcuXPVt29fSdIFF1ygwsLCcreVhQIPAADAZE6nU5MnT1Z8fLyOHDmiiIgITZky5U+1lcXh8Xg8pt5RJVmcnKnDR91nPhBAjRf3/sbq7gKAStAsJEBfjO1Z3d2QJH356z4VHC0x5dp1azt19fmNTbm2WZgmBQAAwGZ4RAsAACzPYeIYPNPG9pmIBA8AAMBmSPAAAIDlOf74Y9a1rYYEDwAAwGZI8AAAgOUxBs+IBA8AAMBmSPAAAIDlOeWQ06SxcmZd10wkeAAAADZDggcAAKyvCtaitRIKPAAAYHm8ZGHEI1oAAACbIcEDAACWx0THRiR4AAAANkOCBwAALM/pOPYx69pWQ4IHAABgMyR4AADA8hiDZ0SCBwAAYDMUeDBV+u4d+vvFrTRt/CjvvhWffaS7+3XX8EvaKOGBW5V38IBP553slfgHdeNFzZT++07T+g7ghH6dmuqTMX/R2vg+WvzQ5eraIkSSFFDbqScHRmrl+Cv13ZNXadYd3Q3nPRR9nlZNuFKrJlyph6LPO+21B14Urs3PXqsboiJMvw/Yl0Mn5sKr9E9131wF8IgWpnrz+fFq0/FC7/aebb/p9Wce0/iXZ6tVZCe9Pmms3nzucT2UMKPM8072y/o1ykjZbWq/AZxwaZtQPRR9nh5+72dtTDmoxsH+3raJgzrIz+nQgBdX6WDBUZ0fHuxtG9K9uXpHNtFfX14tj6SZt3ZTyoECvf99ivcYV0At3Xlla23NOFSVtwTYHgkeTLNyyXwFBddXp4sv9+775rOPFNXzGnXodonqBgZp2H2P6vuvFqsg/1CZ5x3nLi7WWy88odsee6ZK7gGAdF+ftnpt2Q79vOegPB4pM/eIMnOPqFWjQF11fhNNnJ+sA4ePqsQjJaflec+7vkszzVq1Sxl/HP/flbs1qEszw7UfiD5Piat/14H8oqq+LdiMw+Q/VkOBB1McPpSn916dqlseiTfs37PjN7Vo38G73fSclqpVu7bSdu8o87zjFs15Qx26XqKW7Tqcth1A5XI6pAsiXAoNqq3FD12urx7tqQkDzpd/Lac6Na+vtJwCjerTVivHX6mPR1+qazo28Z7bNixIv6af+OXtt715ahtWz7vdqblLHSNceu/7PVV6T8DZgEe0MMXcVyar9+Bhahhm/G298PBhBdZzGfYF1nN5E7zSzpOk/XtT9cW8OUp4Z4l5HQdg0LCev2rXcuqaC8J005vfq9jt0fQRF+kfV7VWQZFb7ZoG64vNmboqYbkuPCdEr43sou2Za7RjX74C69TSocKj3mvlFRYryP/YXztOh/TEwEg9u/BXeTzVdXewE4eJ8+CxFi0gaeevm/TzmhWKGXHXKW0BgYEqOJRn2Hc4P091g+qVeZ4k/WdKvG6860EFBbtO2w6g8h056pYkvbN6j/bnFSnn8FHNWrVbPds10pGjbh0tLtHrX+/QUbdHP+w6oO93HtBf2jaUJB0uKla9gBM5Qj1/P+UfKZYkDetxjrbsPaSf9xys+psCzgIkeKh0m39YrX1pe3RP32Nv0xUezldJSYnGDrtWF/3lKu3akuw9NiNlt4qLitSsRWt99fG7pZ43Ze7n2rhmpX5d/73mvHhi/N2Emwfo1rGTdEX/v1btTQJnidzCYqXnFMpzUsx2/B9/23vqixEnH7ctI1/tmwZrY0quJKl9eLC2/fEyxSVtGiqqVQP1bNdIklS/bm1FNgvW+eHBenbhr2bdDmyMefCMKPBQ6a65Ybgu63u9d/uTt1/TvrQU3Tn+BeVm79f4mwco+cc1ah3ZSXNfnaKL+/RT3aB6ZZ4nSS9/slIlJSXe9juvvkjjps1SC8bjAaaa/2Oqhl96rlZuzVKxu0QjL2uh5b/t07pdB5R+sFB39mqlN5fvVOfm9XVx61D9c8kWSdInG9I08rIW+mbLfnk80i2XtVTid79LkibM2yT/WiceIr0Ye5G+2JyheT+kVss9AnZDgYdK5183UP51A73bAXWDVLuOv+qHNlT90Ia6a8ILemn8fcrLOaBOl1yh+5769xnPk6T6oY1O+a7gkFD5B9Q1+Y6As9uMZTvUIKiOPn3wMhUVl2jJxr16/eudKi7xaNSc9Zo0uKNu79lK6TkFevzDTdq5/7Ak6f3vU3ROg7qaP/ovkqR5P6R4p0jJKyzWyYM1jrpLdOhIsQ798QgXKK/jc9aZdW2rcXg85g9vTUhIUFJSklJTU7Vw4UK1a9eu3NdYnJypw3+MBQFgbXHvb6zuLgCoBM1CAvTF2J7V3Q1J0tqdB3WkuOTMB1aAfy2nureqb8q1zVIlL1n06dNHiYmJiohglnIAAFD5HCZ/rKZKHtFGRUVVxdcAAABANWwMXm5urnJzcw37/Pz8FB4eXk09AgAAVuB0OOQ0abCcWdc1U40q8GbNmqXp06cb9kVERGjp0qXV1CMAAGAFZj5KtV55V8MKvJtvvlmDBw827PPz86um3qC8El96TvVDGytmxJ1V+r2z/vmUmp7TUtF/u7lKvxewuweubausQ0Wa/e3vVfq9L/79Qn24LlUrt+yv0u8F7KRGFXgul0suF6sUWNHB7CwtX/ihXl64yrvv5zUrNPP58dq/N1XnXdBVoya9qMbNmp/2/Pg7btSe7b/qaFGRmkScq6H3PKKLr+rrbf/s3be0aPYbyjt4QOEtWuvWsU8psksPSdLAkXdr3Ij+6j3476pdu465NwqcJRoE1tbALs3U758rvft6tA7VEwMjFV4/QBtTDmr8vE1Kzyks9RojLj1XN/2lhULr1VF6ToFGz9mg3VmH1bN9I93Zs5XahtVTUXGJvv5tnxI+/U2Hi47NlPDWip16cmAHCjyUDxGeQZW8RfvMM8+oZ8+e2rt3r2699VZdd911VfG1qEJff/Keulze2zsnXe6BLE19+A4Nu/dR/Xd5stp06Kx/PXZ3qeff+ugkvfnFBs1etUX/eHKyXpowWgf2ZUiStmz8UYnTntPDU9/U2yt/U59Bf9eUh26X233sL4MGjcMU0bKtfvj6c/NvFDhLDOoaoRW/7fdOOxESWFvThl+ol7/cpr88u0ybUnP1z6GdSz3/hqgI/TUqQve+/aO6P/WV7pu9XjmHiyRJwf619PrXO3RVwnINeHGVwlwBeqTfiemzNqbkqp6/nzpG8As/UFFVUuA98cQT+uabb5ScnKxVq1bp008/rYqvRRVav2qZOkZd6t1e89ViNW/dTn+5doDq+Afob/c8ot1bkpW6c+tpz2/ZroP8ah0LlB1yyF1crP170yRJ+9L26Jw27dWmQ2c5HA71GjBEuQeylZt94rf7jlF/0Y8rvjTxDoGzyxXtGmntzgPe7Ws6NtG2jHx9vilDRcUlevWr7WofHqxWjQJPOdfhkO65qo0SPv1N2/flS5L2ZBfoYMGxSYw//XmvVm7NUuHREuUWFuvDtSnqcm6I4Rprdx5Qz/anTm4OlMVh0h8rqpICD/b3+7Zf1KxFG+/2nu2/qWX7jt7tgLqBCmveQnu2byn1Gs+NHqm/X9xKj990nTpGXao2HS+UJHW5rLdKStzasvFHud1uLZ3/rlq276iQRk2850a0Ps+wxi2AP+e8pvW0a3++d7tNk3r6be+JtScKjrq1J7tAbcPqnXJuU1eAwkMCdF5YPX05tqeSHr5C9/VpU+pqAFEtG2hbpnFd2x37jq1jC6BiatQYPFhXfl6u6gYFebcLC/LlatDQcExgvWAV5J+6OPlx419+W8VHj+rnNSuUunOrnM5jv3/UDaqnHn2u05O3DpLH41FQsEsTpifKcdLfFnUDg5Sfl1vapQGUU3BALeWftGxYYB0/Hcg/ajjmUOFRBdU59a+RsPr+kqS/nNdQg1/+VsEBtfTmrd2UcbBQH/7PWrOXtgnVwK7N9PfX1hj25x8pliugdmXdDs4CLFVmRIKHShEUXF8F+Sd+2w+oG6SCQ8ZiriD/kOoGnfrb/slq1a6trpf31k+rl2vt10mSpK8+fkfLFrynf89bprlrd+v+Z6fr+ftHKjtz74lrH85XUDDjdYDKkltQrCD/E8Xb4SK36gUYi7kg/1rKLzp17djCo8fG7f3fN7uUV1istJxCvf99iq74n0eunc+pr8lDO+vBd37S7qzDp1w7t9BYUALwHQUeKkWLdpFK273Du31Om/batWWzd7uw4LD2puzSOW18W4fY7S7W3j27JUm7ftusbj2vVrMWbeR0OtXlsqvUoFET/fbTD97jU3dsVct2HSrpbgBs2ZunFieNr9ueeUjtm574Ba1ubT+dExqobRmnpvK79uerqLhEZS10fn54sKaP6KInP9qsNTuyT2lv3TjI8EgYOBOWKjOiwEOl6Hp5HyWvW+3dvrh3P+3Z/pu++/JTFR0p1Aev/0stzuugiFbnnXJu6s6t+nHlUh0pLFDx0aP65tN5+mXdGnWMukSS1LbjRfpxxVfKSNktj8ejn1YvV9rvO3Ru2/bea2xet1pdLutt/o0CZ4kVW/are6tQ7/aXmzPVNqyerunYRHVqOXVP79basjdPO/cfPuXcwqMlWrxxr267oqUC6/gpzOWvG7s31/Jfj70Y1bZJPb1+S1c9t+gXff3rvtN+f1SrBkyTAvwJjMFDpegVc6MeGXqtjhQWyD+gruqHNtQjU9/UzBcm6KUJo9X2gi568IXXvMe//sxjkqR/PJEgj0d6f8Y/lbJji5xOP4Wf20oPTp6h1pHHpmDoNWCI9qbsUvwdN+hQ7kE1DAvXP56Y7C0WD+zLUMqOrereu++pHQNQIQvWp2neqEvlX8upI8UlOnD4qB545ydNGBCpF4Z00s97DuqR9372Hh93faQkadKCXyRJzy78RRMHddDX43opt6BY835I0Ufrjo2/u+XyFgoNrKOnB3fU04OPvYyVllOo61/6VpJ0QYRLh4vc2pjCuFqUA/PgGTg8Hk9ZKXqNsTg5U4ePuqu7GyhD4kvPq35oo2pZySKseQv1HXpLlX4vKi7u/Y3V3QX4YMw1bZWdXz0rWcxbl6oVJHg1XrOQAH0xtmd1d0OS9NOeXBUVm1PS1Knl0IXnWGucNwUegCpHgQfYQ00q8H7ek2dqgdf5HGtN28MYPAAAAJthDB4AALA85sEzIsEDAACwGRI8AABgebxEa0SCBwAAYDMkeAAAwB6sGLWZhAQPAADAZkjwAACA5TnkMHEMnvWiQRI8AAAAmyHBAwAAlsc8eEYUeAAAwBYsWIeZhke0AAAANkOCBwAArI+Zjg1I8AAAAGyGBA8AAFge06QYkeABAADYDAkeAACwPKZJMSLBAwAAsBkSPAAAYHm8RGtEggcAAGAzJHgAAMD6iPAMKPAAAAAq0b333quUlBQ5nU4FBgbqySefVGRkpHbu3Klx48YpJydHISEhSkhIUMuWLSWpwm2l4REtAACwPIfJf8ojISFBn3zyiebPn6/bbrtN48ePlyTFx8crNjZWSUlJio2NVVxcnPeciraVhgIPAACgEgUHB3v/+dChQ3I4HMrKylJycrJiYmIkSTExMUpOTlZ2dnaF28rCI1oAAGB5VTEPXnp6utxut6HN5XLJ5XKdcs6ECRO0atUqeTwezZw5U+np6QoLC5Ofn58kyc/PT02aNFF6ero8Hk+F2kJDQ0vtMwUeAACAD4YPH67U1FTDvlGjRmn06NGnHPvss89KkubPn6/JkydrzJgxVdLH4yjwAACA5VXFS7SJiYmnTfDKMmjQIMXFxalp06bKyMiQ2+2Wn5+f3G63MjMzFR4eLo/HU6G2sjAGDwAAwAfh4eFq3ry54fO/BV5+fr7S09O920uXLlX9+vXVsGFDRUZGatGiRZKkRYsWKTIyUqGhoRVuK4vD4/F4KvPmzbI4OVOHj7rPfCCAGi/u/Y3V3QUAlaBZSIC+GNuzurshSdqWeVhH3eaUNLX9HGrbJNCnY/fv3697771XBQUFcjqdql+/vh577DF17NhR27dv17hx45SbmyuXy6WEhAS1bt1akircVhoKPABVjgIPsAcKvJqLMXgAAMDyyj9bXfmubTUUeAAAwPpMnCbFgvUdL1kAAADYDQkeAACwvKqYJsVKSPAAAABshgQPAADYgxWjNpOQ4AEAANgMCR4AALA8pkkxIsEDAACwGRI8AABgeQ4T58EzbX49E5HgAQAA2AwJHgAAsDzmwTMiwQMAALAZEjwAAGB9RHgGJHgAAAA2Q4IHAAAsj3nwjEjwAAAAbIYEDwAAWB7z4BmR4AEAANgMCR4AALA8XqI1IsEDAACwGRI8AABgfUR4BhR4AADAFqw4nYlZeEQLAABgMyR4AADA8pgmxYgEDwAAwGZI8AAAgOXxjoURCR4AAIDNkOABAADLYwyeEQkeAACAzZDgAQAAG7BgzGYiEjwAAACbIcEDAACWxxg8IxI8AAAAmyHBAwAAlsc8eEYkeAAAADZDggcAACzPIRPH4JlzWVOR4AEAANgMCR4AALA8hxyMwTsJCR4AAIDNkOABAADrMzNms2CER4IHAABgMyR4AADAFiwYtJmGAg8AAFiew2HiRMcWrBx5RAsAAGAzJHgAAMDymCbFiAQPAADAZkjwAACA9TFNigEJHgAAgM2Q4AEAAMtzyMS3aE26rplI8AAAAGyGBA8AAFge8+AZkeABAADYDAkeAACwPObBMyLBAwAAsBkSPAAAYHmMwTMiwQMAALAZCjwAAACbocADAACwGcbgAQAAy2MMnhEJHgAAgM2Q4AEAABswbx48KyLBAwAAsBkSPAAAYHlmjpNjDB4AAMBZ7MCBA7rzzjsVHR2tAQMGaNSoUcrOzpYkbdiwQQMHDlR0dLRuu+02ZWVlec+raFtpKPAAAIDlOUz++NwPh0N33HGHkpKStHDhQp1zzjmaOnWqSkpKNHbsWMXFxSkpKUlRUVGaOnWqJFW4rSwUeAAAwB6qu7qTFBISoh49eni3L7roIqWlpWnTpk3y9/dXVFSUJGnYsGFasmSJJFW4rSyMwQMAAPBBenq63G63YZ/L5ZLL5Trt8SUlJXr33XfVu3dvpaenq1mzZt620NBQlZSUKCcnp8JtISEhpfaVAg8AAFiemZOkHL/y8OHDlZqaamgbNWqURo8efdrznn76aQUGBmrEiBH64osvTOvf6VDgAQAA+CAxMfG0Cd7pJCQkaPfu3ZoxY4acTqfCw8OVlpbmbc/OzpbT6VRISEiF28rCGDwAAGB5Doe5H0kKDw9X8+bNDZ/TFXj/+te/tGnTJr3yyiuqU6eOJOmCCy5QYWGhfvjhB0nS3Llz1bdv3z/VVubPw+PxeP7cj7RqLE7O1OGj7jMfCKDGi3t/Y3V3AUAlaBYSoC/G9qzubkiSDhd5ZFZB45AUWMe3R8Bbt25VTEyMWrZsqYCAAElS8+bN9corr+jHH39UfHy8jhw5ooiICE2ZMkWNGjWSpAq3ldpnCjwAVY0CD7CHmlTgFZhc4NX1scCrKXhECwAAYDO8ZAEAAKzPWgGb6UjwAAAAbIYEDwAAWF5VzINnJSR4AAAANmOZBK9ubWpRwC6ahQRUdxcAVIIwl391d8HL4bBm0mYWy0yTAgAAAN8Qi6FGSE9P9y7GDMD6+H8aqF4UeKgR3G63UlNTT1njD4A18f80UL0o8AAAAGyGAg8AAMBmKPAAAABshgIPNYLL5dKoUaPkcrmquysAKgH/TwPVi2lSAAAAbIYEDwAAwGYo8AAAAGyGAg/VbufOnRo6dKiio6M1dOhQ7dq1q7q7BKCCEhIS1Lt3b7Vv315btmyp7u4AZy0KPFS7+Ph4xcbGKikpSbGxsYqLi6vuLgGooD59+igxMVERERHV3RXgrEaBh2qVlZWl5ORkxcTESJJiYmKUnJys7Ozsau4ZgIqIiopSeHh4dXcDOOtR4KFapaenKywsTH5+fpIkPz8/NWnShPUrAQD4EyjwAAAAbIYCD9UqPDxcGRkZ3gXJ3W63MjMzecQDAMCfQIGHatWwYUNFRkZq0aJFkqRFixYpMjJSoaGh1dwzAACsi5UsUO22b9+ucePGKTc3Vy6XSwkJCWrdunV1dwtABTzzzDP6/PPPtX//fjVo0EAhISH69NNPq7tbwFmHAg8AAMBmeEQLAABgMxR4AAAANkOBBwAAYDMUeAAAADZDgQcAAGAzFHgAytS7d299++23kqQZM2ZowoQJpn/nmjVr1LNnz1Lb27dvr927d5/xOikpKWrfvr2Ki4vL3Yc/cy4AVLda1d0BANZx9913+3TcuHHjFBYWpgcffNDkHgEATocEDziLkEYBwNmBAg+wuN69e+v1119X//791b17dz3++OM6cuSIpBOPOt944w1ddtllevzxx1VSUqI33nhDV199tXr06KExY8YoJyfHe7358+frqquuUo8ePfTaa68Zvuvll1/WI4884t3+4YcfNGzYMEVFRalXr1766KOP9N5772nhwoV666231KVLF2/ql5GRodGjR+uSSy5R79699fbbb3uvU1hYqHHjxql79+7q37+/Nm7c6PP9f/311xo0aJC6du2qXr166eWXXz7lmHnz5unyyy/X5Zdfrrfeesu7/0w/CwCwKgo8wAaOF1RffPGFdu7cqVdffdXbtn//fh08eFDLli3T008/rdmzZ+vLL7/UnDlztGLFCtWvX1+TJk2SJG3btk1PPfWUJk+erBUrVignJ0d79+497Xempqbqzjvv1IgRI7R69WrNnz9fkZGRGjp0qAYMGKDbb79d69ev14wZM1RSUqJ77rlH7du31zfffKNZs2bp/9u7l5Co3jiM418nnDGFGhFlznjZhGibQFITKRBNvGQYEoKoIbNKHRTJZeTGBKGFiIp5QSRQV+JCrLRFEAiKm2yRaG5EHRnwEiiKeWl3aP6aDf9NeXg+q/Oe+XHO+/4Ww8N5zzCDg4N8+vQJgI6ODlZWVpiamqK/v5+xsbGg13716lVaW1uZm5vj9evXDA8P8+HDh4CamZkZJicn6e/vp7e313yn8KJeiIhcZgp4IhZQXl6OYRg4nU6qq6sD/vvTZrNRV1eH3W4nLCyMkZERGhoacLlc2O12vF4v79+/5+joiHfv3pGVlUVaWhp2u536+nps1wn2AAAAAy1JREFUtvO/JsbHx8nMzKSoqIjQ0FAiIyO5efPmubVfvnxha2sLr9eL3W4nPj6e0tJSJiYmAHj79i1Pnz7F6XRiGAaVlZVBr/3OnTskJSVhs9lITk7mwYMHzM7OBtTU1tYSHh5OUlISJSUljI+PA1zYCxGRy0w/shCxAMMwzGO3243f7zfHkZGROBwOc7y+vk5tbW1AcLPZbGxubuL3+3G5XOb58PBwnE7nuff0+XwkJCQENb+1tTX8fj+pqanmuePjY3Ps9/vPrCFYnz9/5tWrVywtLfHjxw8ODw/Jz88PqPn12rGxsSwuLgIX90JE5DJTwBOxAJ/PZx6vr68TExNjjkNCQgJqXS4XLS0t3L59+8x1YmJiWF5eNsf7+/u/fSfNMAzm5+fP/ey/9zQMg7i4OCYnJ8+tj46OxufzkZiYeGY9f/Ls2TMqKiro6+vD4XDw8uVLtre3A2p8Ph83btwAAvtzUS9WV1eDnoOIyL9GW7QiFjA0NMTGxgY7Ozt0d3dTWFj429qysjLa2tpYW1sDYGtry3xnLS8vj48fPzI3N8fh4SHt7e2cnJyce52HDx8yPT3NxMQER0dHbG9v8/XrVwCioqICAtKtW7eIiIigp6eHg4MDjo+PWVxcNANiQUEBPT09fP/+nY2NDd68eRP02vf29rh+/ToOh4P5+Xlz+/VXXV1d7O/vs7S0xOjoqNmfi3ohInKZKeCJWEBRUREej4f79++TkJBAdXX1b2ufPHlCdnY2Ho+HlJQUSktLzaCVmJjIixcvaGxs5N69e1y7di1gy/ZXbreb3t5eBgYGSE9P59GjRywsLADw+PFjvn37RmpqKjU1NVy5coXu7m4WFhbIyckhIyOD58+fs7u7C4DX68XtdpOTk4PH46G4uDjotTc1NdHe3k5KSgqdnZ0UFBScqUlPTyc3N5eqqio8Hg937979Yy9ERC6zkNPT09O/PQkR+f+ys7Npbm4mMzPzb09FRET+EXqCJyIiImIxCngiIiIiFqMtWhERERGL0RM8EREREYtRwBMRERGxGAU8EREREYtRwBMRERGxGAU8EREREYtRwBMRERGxmJ/YYIn5BVmtpgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.82      0.74     10352\n",
            "           1       0.78      0.62      0.69     10648\n",
            "\n",
            "    accuracy                           0.72     21000\n",
            "   macro avg       0.73      0.72      0.72     21000\n",
            "weighted avg       0.73      0.72      0.72     21000\n",
            "\n"
          ]
        }
      ]
    }
  ]
}