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Python Task on Generating Your Own Data Using Numpy and Pandas Libraries Dimension

Python Task on Generating Your Own Data Using Numpy and Pandas Libraries Dimension

  • 8th Oct, 2021
  • 15:57 PM

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tim.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-S5tt4tWKLjo",
        "colab_type": "code",
        "outputId": "8b9b43f5-938f-4e57-8353-1022c6a571a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import time\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6QsjnrAKbIW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame({\"Gender\" : np.repeat([\"Male\", \"Female\"],500), \n",
        "                   # Age ranges between 18-60 years\n",
        "                   \"Age\"    : np.random.randint(low=18, high=60, size=1000),       \n",
        "                   \n",
        "                   # Height ranges between 165-180cm\n",
        "                   \"Height\" : np.random.randint(low=165, high=180, size=1000),\n",
        "                   \n",
        "                   # Weight ranges betweem 50-95kg\n",
        "                   \"Weight\" : np.random.randint(low=50, high=95, size=1000),\n",
        "                   \n",
        "                   # Target varriable having 1's and 0's\n",
        "                   \"Target\" : np.where(np.random.normal(0.0, 1.0, size=1000)<=0,0,1),\n",
        "                     })\n",
        "\n",
        "\n",
        "df= df.sample(frac=1).reset_index(drop=True)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZ6tvGMHK4z3",
        "colab_type": "code",
        "outputId": "d3bf9880-819a-49a0-b4e9-de4160950b68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "print(\"Our data have {} rows and {} columns.\".format(df.shape[0], df.shape[1]))\n",
        "print()\n",
        "print(\"Data sample is as follows:\")\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Our data have 1000 rows and 5 columns.\n",
            "\n",
            "Data sample is as follows:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>27</td>\n",
              "      <td>177</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>59</td>\n",
              "      <td>166</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>46</td>\n",
              "      <td>171</td>\n",
              "      <td>86</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>34</td>\n",
              "      <td>165</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>18</td>\n",
              "      <td>175</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Age  Height  Weight  Target\n",
              "0  Female   27     177      51       0\n",
              "1  Female   59     166      65       0\n",
              "2    Male   46     171      86       1\n",
              "3  Female   34     165      58       0\n",
              "4  Female   18     175      69       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ViSI_S0nJ-D",
        "colab_type": "code",
        "outputId": "03943743-a33b-4004-f5fe-8bb5eabdaa86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "sns.countplot(x=\"Gender\", data=df, palette=\"Set3\")\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMdXi6Wlnbig",
        "colab_type": "code",
        "outputId": "50220526-57c9-4620-a9b5-66823a45620a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.distplot(df[\"Age\"])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f369dd9cd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Th0uT6VnnQa8",
        "colab_type": "code",
        "outputId": "bc359c6e-8089-486d-df9d-b083e218977a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "sns.distplot(df[\"Height\"])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f369d81a4a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zRcS7MznZhJ",
        "colab_type": "code",
        "outputId": "e217887c-8983-4c5b-c51a-093a96c85e9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.distplot(df[\"Weight\"])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f369d7f2748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8_zHKgtnl0i",
        "colab_type": "code",
        "outputId": "55a3418a-c285-4e6a-8276-be8037e46167",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "# Heatmapshowing correlation between variables\n",
        "fig, ax =plt.subplots(figsize=(8, 8))\n",
        "plt.title(\"Correlation Plot\")\n",
        "sns.heatmap(df.corr(), mask=np.zeros_like(df.corr(), dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
        "            square=True, ax=ax, annot=True,linewidths=3)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8efIyqtXLZ0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# encoding our categorical variable (Gender)\n",
        "df[\"Gender\"] = df[\"Gender\"].map({\"Male\":1, \"Female\":2})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QpGgE5U2o4p7",
        "colab_type": "code",
        "outputId": "8b40a6ee-c494-4cc8-9c5a-7bc9de48ece0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>177</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>59</td>\n",
              "      <td>166</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>171</td>\n",
              "      <td>86</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>165</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>175</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Age  Height  Weight  Target\n",
              "0       2   27     177      51       0\n",
              "1       2   59     166      65       0\n",
              "2       1   46     171      86       1\n",
              "3       2   34     165      58       0\n",
              "4       2   18     175      69       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcRXvj3smuUN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting data into X: independent and y: dependent variable\n",
        "\n",
        "X = df.drop(\"Target\",axis=1)\n",
        "y = df[\"Target\"]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1EQHhhiq-RG",
        "colab_type": "code",
        "outputId": "37e4a51c-7f69-4f93-9acf-a61489bf70c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Standardizing independent variables into same scale\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler().fit(X)\n",
        "X = scaler.transform(X)\n",
        "\n",
        "X = pd.DataFrame(X)\n",
        "X.columns = [\"Gender\", \"Age\", \"Height\", \"Weight\"]\n",
        "\n",
        "# Independent variables after standardizing\n",
        "X.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.914033</td>\n",
              "      <td>1.168520</td>\n",
              "      <td>-1.675116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.671862</td>\n",
              "      <td>-1.365231</td>\n",
              "      <td>-0.597920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.621342</td>\n",
              "      <td>-0.213526</td>\n",
              "      <td>1.017873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.348369</td>\n",
              "      <td>-1.595572</td>\n",
              "      <td>-1.136518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.641316</td>\n",
              "      <td>0.707838</td>\n",
              "      <td>-0.290150</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender       Age    Height    Weight\n",
              "0     1.0 -0.914033  1.168520 -1.675116\n",
              "1     1.0  1.671862 -1.365231 -0.597920\n",
              "2    -1.0  0.621342 -0.213526  1.017873\n",
              "3     1.0 -0.348369 -1.595572 -1.136518\n",
              "4     1.0 -1.641316  0.707838 -0.290150"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxJ1dQGGrBXq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting data into training and testing data, we use 75% to train our classification model\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,shuffle=True, stratify=y)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJNylh4LnH0D",
        "colab_type": "code",
        "outputId": "3e64e3b0-c531-41c4-cc80-075d947a556e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(\"Shape of X_train:\",X_train.shape)\n",
        "print(\"Shape of y_train:\",y_train.shape)\n",
        "print(\"Shape of X_test:\",X_test.shape)\n",
        "print(\"Shape of y_test:\",y_test.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of X_train: (750, 4)\n",
            "Shape of y_train: (750,)\n",
            "Shape of X_test: (250, 4)\n",
            "Shape of y_test: (250,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6ZU0ULLnhwI",
        "colab_type": "code",
        "outputId": "db741f1f-be29-46d4-d718-2fcd58fe1fde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# visualizing training and testing labels\n",
        "\n",
        "plt.figure(1 , figsize = (25 ,5))\n",
        "n = 0 \n",
        "for z , j in zip([y_train , y_test] , ['train labels', 'test labels']):\n",
        "    n += 1\n",
        "    plt.subplot(1 , 3  , n)\n",
        "    sns.countplot(x = z , palette=\"Set3\")\n",
        "    plt.title(j)\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zAvC39QvLMM",
        "colab_type": "text"
      },
      "source": [
        "__Naive BBayes Classifier:__"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22vbc9OymmAJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "122c248f-14cb-4053-fc6f-bf0120ac90a0"
      },
      "source": [
        "# Create the Multinomial Naive Bayes Classifier\n",
        "\n",
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "nbc = GaussianNB()\n",
        "nbc.fit(X_train,y_train)\n",
        "\n",
        "# making predictions\n",
        "y_pred = nbc.predict(X_test)\n",
        "\n",
        "\n",
        "print(\"Accuracy of Naive-Bayes Classifier = {:0.2f} %\".format(accuracy_score(y_test, y_pred)*100))\n",
        "print()\n",
        "print(\"===================================================================\")\n",
        "print()\n",
        "\n",
        "\n",
        "print(\"The classification report of Naive-Bayes Classifier is as follows:\")\n",
        "print(classification_report(y_test,y_pred))\n",
        "print()\n",
        "print(\"===================================================================\")\n",
        "print()\n",
        "\n",
        "\n",
        "cm=confusion_matrix(y_test,y_pred)\n",
        "confusion = pd.DataFrame(cm, index=[\"0\", \"1\"], columns=[\"0\", \"1\"])\n",
        "print(\"Confusion Matrix is as follows:\")\n",
        "confusion\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Naive-Bayes Classifier = 50.80 %\n",
            "\n",
            "===================================================================\n",
            "\n",
            "The classification report of Naive-Bayes Classifier is as follows:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.51      0.65      0.57       127\n",
            "           1       0.50      0.36      0.42       123\n",
            "\n",
            "    accuracy                           0.51       250\n",
            "   macro avg       0.51      0.51      0.50       250\n",
            "weighted avg       0.51      0.51      0.50       250\n",
            "\n",
            "\n",
            "===================================================================\n",
            "\n",
            "Confusion Matrix is as follows:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "    0   1\n",
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          "metadata": {
            "tags": []
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          "execution_count": 16
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkLt6dPzvDOV",
        "colab_type": "text"
      },
      "source": [
        "__Nearest Neighbors Classifier:__"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zI88IBQyqiiQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "4ee92eba-0f0f-4719-ba66-7715753aae3d"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# Create the K Nearest Neighbour\n",
        "\n",
        "clf = KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "clf.fit(X_train,y_train)\n",
        "\n",
        "# making predictions\n",
        "y_predicted = clf.predict(X_test)\n",
        "\n",
        "# Calculate the accuracy of the prediction\n",
        "print(\"Accuracy of Nearest Neighbors Classifier  = {:0.2f} %\".format(accuracy_score(y_test, y_predicted)*100))\n",
        "print()\n",
        "print(\"===================================================================\")\n",
        "print()\n",
        "\n",
        "# Cross validate the scores\n",
        "print(\"Classification Report of Nearest Neighbors Classifier: \\n {}\".format(classification_report(y_test, y_predicted)))\n",
        "print()\n",
        "print(\"===================================================================\")\n",
        "print()\n",
        "\n",
        "# Confusion matrix\n",
        "cm=np.array(confusion_matrix(y_test,y_predicted))\n",
        "\n",
        "confusion = pd.DataFrame(cm, index=[\"0\", \"1\"],\n",
        "                         columns=[\"0\", \"1\"])\n",
        "\n",
        "print(\"Confusion Matrix of Nearest Neighbors Classifier:\")\n",
        "confusion"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Nearest Neighbors Classifier  = 47.60 %\n",
            "\n",
            "===================================================================\n",
            "\n",
            "Classification Report of Nearest Neighbors Classifier: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.48      0.40      0.44       127\n",
            "           1       0.47      0.55      0.51       123\n",
            "\n",
            "    accuracy                           0.48       250\n",
            "   macro avg       0.48      0.48      0.47       250\n",
            "weighted avg       0.48      0.48      0.47       250\n",
            "\n",
            "\n",
            "===================================================================\n",
            "\n",
            "Confusion Matrix of Nearest Neighbors Classifier:\n"
          ],
          "name": "stdout"
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          "output_type": "execute_result",
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      "source": [
        "__Support Vector Machine:__"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHKIvVM9vrYo",
        "colab_type": "code",
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        "outputId": "330fc102-1fbb-4d9d-a32b-dedebd20f9da"
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      "source": [
        "# Create the Support Vector Machine\n",
        "from sklearn import svm\n",
        "\n",
        "clf = svm.SVC()\n",
        "\n",
        "clf.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "# Perform the predictions\n",
        "\n",
        "y_predicted = clf.predict(X_test)\n",
        "\n",
        "# Calculate the accuracy of the prediction\n",
        "print(\"Accuracy of Support Vector Machine = {:0.2f} %\".format(accuracy_score(y_test, y_predicted)*100))\n",
        "print(\"==========================================\")\n",
        "print()\n",
        "\n",
        "# Cross validate the scores\n",
        "print(\"Classification Report of Support Vector Machine is as follows: \\n {}\".format(classification_report(y_test, y_predicted)))\n",
        "print(\"==========================================\")\n",
        "print()\n",
        "\n",
        "# Confusion matrix\n",
        "cm=np.array(confusion_matrix(y_test,y_predicted))\n",
        "\n",
        "confusion = pd.DataFrame(cm, index=[\"0\", \"1\"],\n",
        "                         columns=[\"0\", \"1\"])\n",
        "\n",
        "print(\"Confusion Matrix of Support Vector Machine is as follows:\")\n",
        "confusion\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Support Vector Machine = 50.00 %\n",
            "==========================================\n",
            "\n",
            "Classification Report of Support Vector Machine is as follows: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.51      0.51      0.51       127\n",
            "           1       0.49      0.49      0.49       123\n",
            "\n",
            "    accuracy                           0.50       250\n",
            "   macro avg       0.50      0.50      0.50       250\n",
            "weighted avg       0.50      0.50      0.50       250\n",
            "\n",
            "==========================================\n",
            "\n",
            "Confusion Matrix of Support Vector Machine is as follows:\n"
          ],
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