Grid_Search_CV.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtGX3OkdD8g-"
      },
      "source": [
        "# **Grid Search CV** For Model optimization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0RjYOukD8hA"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split, cross_val_score"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/pima-indians-diabetes.csv')\n",
        "df"
      ],
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      "execution_count": 3,
      "outputs": [
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              "     Preg  Plas  Pres  skin  test  mass   pedi  age  class\n",
              "0       6   148    72    35     0  33.6  0.627   50      1\n",
              "1       1    85    66    29     0  26.6  0.351   31      0\n",
              "2       8   183    64     0     0  23.3  0.672   32      1\n",
              "3       1    89    66    23    94  28.1  0.167   21      0\n",
              "4       0   137    40    35   168  43.1  2.288   33      1\n",
              "..    ...   ...   ...   ...   ...   ...    ...  ...    ...\n",
              "763    10   101    76    48   180  32.9  0.171   63      0\n",
              "764     2   122    70    27     0  36.8  0.340   27      0\n",
              "765     5   121    72    23   112  26.2  0.245   30      0\n",
              "766     1   126    60     0     0  30.1  0.349   47      1\n",
              "767     1    93    70    31     0  30.4  0.315   23      0\n",
              "\n",
              "[768 rows x 9 columns]"
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              "      <th>Preg</th>\n",
              "      <th>Plas</th>\n",
              "      <th>Pres</th>\n",
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              "      <th>test</th>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('class',axis=1)\n",
        "y=df['class']"
      ],
      "metadata": {
        "id": "kCWx588ase24"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Divide your data into three(3) parts: Training, Validation and Testing data."
      ],
      "metadata": {
        "id": "Gy1vfVDvuPBf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#save 20% of your dataset for final testing\n",
        "# use 80% for training and validation process\n",
        "\n",
        "# STEPS:\n",
        "  #1. first divide our dataset into 80:20 (i.e. training(80) and testing(20))\n",
        "  #2. next divide the 80 part to training and validation (i.e. whatever ratio you prefer)\n",
        "\n",
        "\n",
        "X_train1, X_test, y_train1,y_test = train_test_split(X,y, test_size=.20,random_state=1)\n",
        "\n",
        "\n",
        "X_train2, X_val, y_train2,y_val = train_test_split(X_train1,y_train1, test_size=.25,random_state=1)"
      ],
      "metadata": {
        "id": "-12xq7m-qEJ6"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Total dataset',len(X.values))\n",
        "print('Total test data saved for future use: ',len(X_test+y_test))\n",
        "print('Total training data: ',len(X_train2+y_train2))\n",
        "print('Total validation data: ',len(X_val+y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8iNeft7ueiN",
        "outputId": "291f60ac-a34f-4d94-e25f-e5ede1038bcc"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total dataset 768\n",
            "Total test data saved for future use:  154\n",
            "Total training data:  460\n",
            "Total validation data:  154\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "154+460+154"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OuddWKw_Yrwd",
        "outputId": "a472e82b-dc20-43e2-b345-08852e345f37"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn_clf = KNeighborsClassifier()\n"
      ],
      "metadata": {
        "id": "JvbIZ6UDDHlM"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ak0uBB1YD8hC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3357c253-bed2-4fde-aa24-f365e84ab534"
      },
      "source": [
        "knn_clf.fit(X_train2,y_train2)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umttCWaoD8hE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca664d4f-5895-47b4-b07a-84c573977f7e"
      },
      "source": [
        "KNeighborsClassifier().get_params()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'algorithm': 'auto',\n",
              " 'leaf_size': 30,\n",
              " 'metric': 'minkowski',\n",
              " 'metric_params': None,\n",
              " 'n_jobs': None,\n",
              " 'n_neighbors': 5,\n",
              " 'p': 2,\n",
              " 'weights': 'uniform'}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qv2epaH3D8hH"
      },
      "source": [
        "param_grid = {'n_neighbors':list(range(1,9)),\n",
        "               'algorithm':('auto', 'ball_tree', 'kd_tree', 'brute')\n",
        "               \n",
        "                }"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "gs=GridSearchCV(knn_clf,param_grid,cv=20)\n"
      ],
      "metadata": {
        "id": "x1ehIboiv2w7"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XT-ufg0OD8hJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ddd1baa6-1246-4d56-c322-2f917ff58a83"
      },
      "source": [
        "gs.fit(X_train2,y_train2)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=20, estimator=KNeighborsClassifier(),\n",
              "             param_grid={'algorithm': ('auto', 'ball_tree', 'kd_tree', 'brute'),\n",
              "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8]})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0tFV4paD8hL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c53590f-6704-4a75-ec8f-597f6ba89215"
      },
      "source": [
        "gs.best_params_"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'algorithm': 'auto', 'n_neighbors': 8}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8s3tZueAD8hN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a69a0279-2506-43ef-8ae0-51bb04160ee5"
      },
      "source": [
        "gs.cv_results_['params']"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'algorithm': 'auto', 'n_neighbors': 1},\n",
              " {'algorithm': 'auto', 'n_neighbors': 2},\n",
              " {'algorithm': 'auto', 'n_neighbors': 3},\n",
              " {'algorithm': 'auto', 'n_neighbors': 4},\n",
              " {'algorithm': 'auto', 'n_neighbors': 5},\n",
              " {'algorithm': 'auto', 'n_neighbors': 6},\n",
              " {'algorithm': 'auto', 'n_neighbors': 7},\n",
              " {'algorithm': 'auto', 'n_neighbors': 8},\n",
              " {'algorithm': 'ball_tree', 'n_neighbors': 1},\n",
              " {'algorithm': 'ball_tree', 'n_neighbors': 2},\n",
              " {'algorithm': 'ball_tree', 'n_neighbors': 3},\n",
              " {'algorithm': 'ball_tree', 'n_neighbors': 4},\n",
              " {'algorithm': 'ball_tree', 'n_neighbors': 5},\n",
              " {'algorithm': 'ball_tree', 'n_neighbors': 6},\n",
              " {'algorithm': 'ball_tree', 'n_neighbors': 7},\n",
              " {'algorithm': 'ball_tree', 'n_neighbors': 8},\n",
              " {'algorithm': 'kd_tree', 'n_neighbors': 1},\n",
              " {'algorithm': 'kd_tree', 'n_neighbors': 2},\n",
              " {'algorithm': 'kd_tree', 'n_neighbors': 3},\n",
              " {'algorithm': 'kd_tree', 'n_neighbors': 4},\n",
              " {'algorithm': 'kd_tree', 'n_neighbors': 5},\n",
              " {'algorithm': 'kd_tree', 'n_neighbors': 6},\n",
              " {'algorithm': 'kd_tree', 'n_neighbors': 7},\n",
              " {'algorithm': 'kd_tree', 'n_neighbors': 8},\n",
              " {'algorithm': 'brute', 'n_neighbors': 1},\n",
              " {'algorithm': 'brute', 'n_neighbors': 2},\n",
              " {'algorithm': 'brute', 'n_neighbors': 3},\n",
              " {'algorithm': 'brute', 'n_neighbors': 4},\n",
              " {'algorithm': 'brute', 'n_neighbors': 5},\n",
              " {'algorithm': 'brute', 'n_neighbors': 6},\n",
              " {'algorithm': 'brute', 'n_neighbors': 7},\n",
              " {'algorithm': 'brute', 'n_neighbors': 8}]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opa0v3KsD8hP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0f15336-b4f4-4d6d-f81c-317a3692e349"
      },
      "source": [
        "gs.cv_results_['mean_test_score']"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.67391304, 0.7       , 0.7       , 0.70652174, 0.7       ,\n",
              "       0.71086957, 0.71521739, 0.72608696, 0.67391304, 0.7       ,\n",
              "       0.7       , 0.70652174, 0.7       , 0.71086957, 0.71521739,\n",
              "       0.72608696, 0.67391304, 0.7       , 0.7       , 0.70652174,\n",
              "       0.7       , 0.71086957, 0.71521739, 0.72608696, 0.67391304,\n",
              "       0.7       , 0.7       , 0.70652174, 0.7       , 0.71086957,\n",
              "       0.71521739, 0.72608696])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f10rnJuPD8hR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45ff4f46-204e-42db-ded5-bc3c42f19b24"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn_clf = KNeighborsClassifier(n_neighbors=8, algorithm='auto')\n",
        "\n",
        "knn_clf.fit(X_train2,y_train2)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=8)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZyREPimND8hT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4573aabc-01fb-43ab-c906-c1bf6594f526"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "y_pred = knn_clf.predict(X_test)\n",
        "\n",
        "print('Accuracy: ', metrics.accuracy_score(y_test,y_pred))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.7467532467532467\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "conf_matrix = confusion_matrix(y_test,y_pred)\n",
        "\n",
        "plt.figure(figsize=(6, 6))\n",
        "import seaborn as sns\n",
        "sns.heatmap(conf_matrix, annot=True)\n",
        " \n",
        "# fig, ax = plot_confusion_matrix(conf_mat=conf_matrix, figsize=(6, 6))\n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix', fontsize=18)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "0kmK-FBZ0Tvu",
        "outputId": "084bbcd9-f8e7-4cf5-a11d-a3efe4c9eaf2"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ],
            "image/png": 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3ZPq0iZxz7i95Z+48Lr/sfPr06cmY0dfzzDNT+ErZChmrQk7u0qho0BOyJa0G7MLyE6V/i4hlWY7/pKZfrLI11tuz0V2wDmjphzNVaxsLz/92TTGn+5k31NyHLBq2+iUimgAvoDWzfMjJrXe9Tt3MLIucTJQ6qJuZZeGRuplZgeRkotT3fjEzKxCP1M3MsnD6xcysOPJyRamDuplZFh6pm5kVSE6CuidKzcwKxCN1M7MscrKk0UHdzCyLnKRfHNTNzDKInAR159TNzArEI3UzsyxyMlJ3UDczy8IXH5mZFYhH6mZmBZKToO6JUjOzAvFI3cwsg0Y9z7laDupmZlnkJP3ioG5mloWDuplZcfiKUjMzqzuP1M3MssjJSN1B3cwsi3xcUOqgbmaWhXPqZmZWdx6pm5ll4ZG6mVmBNNW4ZSCph6TbJL0g6XlJu0nqKel+SS+nr+tUasNB3cwsg2iKmraMLgfuiYgtge2A54HTgLERsRkwNn3fKgd1M7Ms2nmkLunTwF7AtQAR8WFEzAMGACPTaiOBQyu146BuZlYHkoZKmliyDS2rsjHwJnCdpKclXSOpO9A3Imaldd4A+lY6jydKzcwyqHVJY0QMA4ZVqNIZ2BE4PiImSLqcslRLRISkih3xSN3MLIv2nyidAcyIiAnp+9tIgvxsSf0A0tc5lRpxUDczyyCaattW2n7EG8DrkrZIi/YDngPGAIPSskHA6ErtOP1iZpZFfW4TcDzwB0ldgGnA0SSD71slDQFeAw6v1ICDuplZBxERk4CdW9i1X9Y2HNTNzDLIkkLpCBzUzcyycFA3MyuOvIzUvfrFzKxAPFI3M8sgLyN1B3Uzswwc1M3MiiTU6B5k4qBuZpZBXkbqnig1MyuQzEFd0i6SvldWNkDSPyTNlPSLtu+emVnHEE2qaauXakbqZwGHNL+RtCFwE7Au8C5wqqSj27Z7ZmYdQ3vf0KutVBPUtwMeKXl/BCBg+4jYCrgPKL/pu5lZIUSopq1eqgnqvYDZJe+/BPw1Imam78cAm7VVx8zMOpIijtTnkT5GSdKngF2Bv5bsD2CNtuuamZlVq5oljZOA70p6ADgM6ArcW7J/Y5YfyZuZFUY9JztrUU1QP48kb/4kSS79/oiYWLL/YGBCSweameVd1PaI0rrJHNQj4jFJO5Lk0t8Fbm7eJ6kXScAf1eY9NDPrAIo4UiciXgJeaqH8beCktuqUmZmtGt8mwMwsg9yP1CU9uArtRURkfpaemVleFCGnvgnJMkUzs0+83I/UI6J/HfthZtah1fOq0Fr4Lo1mZgXiiVIzswzycj/1qoK6pHWAIcAXgHVYcaTviVIzK6SmnKRfMgd1SRsBjwLrkVx8tDbwDh8H97eAhe3QRzOzhitiTv18oAewH8ndGAX8F0lw/19gPrBnW3fQzKwjKOJDMvYDfhcRD/HxUkdFxPsRcQbwD+Citu6gmZllV+391CenPy9JX0tvtXs/cEBbdMrMrKOJqG2rl2omSt8EeqY/zwc+APqX7O+C76duZgWV+4uPWjCF5JF2RERIehL4gaQxJCP+ocALbd9FM7PGK9zqF2A0cLKkNSJiEXAuyUMyXk33B/C1Nu6fmZlVoZr7qV8JXFny/kFJuwHfApYBoyLisbbvoplZ4+VlSWNNV5SmTz6auNKKZmY5V4/JTknTSeYslwFLI2JnST2BW0jmMKcDh0fE3Nba8L1fzMwyaArVtFVhn4jYPiJ2Tt+fBoyNiM2Asen7VlVzRenwDNUiIoZkbdPMLC8amH4ZAOyd/jwSGAec2lrlatIvgzPUCZJ7w5iZWfUCuE9SAFdHxDCgb0TMSve/AfSt1EA1E6UrpGokdSJ5mMZPgH8Hvpy1PTOzPKk1py5pKMnS72bD0qBdao+ImCnpM8D9kpZbJp4uJ6/Yk1onSpcBLwPHSPoTyW0Cjq2lzay+tZOfc20r+s91d2x0F6ygal2nngbw8iBeXmdm+jpH0ihgF2C2pH4RMUtSP2BOpTbacqL0HmBgG7ZnZtZhRKimbWUkdZe0VvPPwIEkt2YZAwxKqw0iuWaoVW35kIyewJpt2J6ZWYdRhytK+wKjJEESm2+MiHsk/Q24VdIQ4DXg8EqN1BzUJfUA9gdOAv5ea3tmZp9EETGN9FYsZeVvk9wlN5NqljQ28fEtd1fYTfLAjB9nbc/MLE/qeKPFmlQzUr+eFX+vIAnmLwE3RcT8tuqYmVlHUrgbekXE4Hbsh5lZh5aXe79kXv0i6eeStqmwf2tJP2+bbpmZ2aqoZknj2cC2FfZvA5xVU2/MzDqophq3emnLJY1dgaVt2J6ZWYcR5CP9UjGoS1ob6FFS1EvShi1U7QkcCbzehn0zM+swmnKy/GVlI/WTgOY8eQD/l24tEXBKG/XLzKxDaSrCSJ3kFo+QBOyfA6OAZ8vqBLAAeMJPPjIza6yKQT0iHgYeBpC0EXBVREyoR8fMzDqSQuTUS0XE0e3ZETOzjqyeK1hqUc069eMkPVBh/32SjmmbbpmZdSyBatrqpZp16oNJ7p3empeA79TUGzMzq0k1QX0z4B8V9k9J65iZFU4RLz5aneQCo9Z0Xcl+M7PcKlxOnSS9ckCF/QcCr9TWHTOzjqmIOfWbgAMlnSepS3OhpNUlnUMS1G9s6w6amXUETaptq5dq0i+XAQcBZwDHljzlekuS2wSMBy5t2+6ZmVk1Mo/UI2IJyWj8NGAGsEO6vU5ye4D9ICer883MqtSEatrqpZr0CxGxJCIujojtI6J7uu0APAT8GvhXu/TSzKzBosatXlb51ruSegLfJlmb/u8ko/SX2qhfZmYdSl5Wv1Qd1CV9iSSQHwJ0IQnk5wC3R8SUtu2emVnH0KR8ZJczBXVJ/UkC+SBgA+At4DbgW8AZEXFHO/XPzMyqUDGnLulISWOBqcCpwETgMGB9ksfb5eOjy8ysRkXJqf8emAacCNwUEW8371BOvoqYmbWFouTUFwP9gQHAXEl3RMSidu+VmVkHU88LiGqxsiWN/UhG6b1IRu1vSLpW0l449WJm1uFUDOoRMS8ifhMROwI7AzeQ5NQfAh4hSRV9ut17aWbWYIW7+CginoqI40hG70eR3GoX4BpJkySdKWnr9uikmVmj5WWitKorSgEiYnFE3BgR+wGfBS4A1gHOBZ5p4/6ZmXUIebmhV9VBvVRETI+In5NMpn4F8Hp1MyukIj4ko1UREcA96WZmZg3SJkHdzKzo6pkXr4WDuplZBkVZp25mZtQvpy6pk6SnJd2Vvt9Y0gRJUyXdUvrkuZY4qJuZZVDHidITgOdL3l8EXBYRmwJzgSGVDnZQNzPrICRtAHwVuCZ9L2BfkrviAowEDq3UhoO6mVkGodo2SUMlTSzZhrZwmv8jeTxo8+C+FzAvIpam72eQ3CW3VZ4oNTPLoNa15hExDBjW2n5JBwNzIuLvkvZe1fM4qJuZZVCHC4h2Bw6R9BWgK7A2cDnQQ1LndLS+ATCzUiNOv5iZdQARcXpEbBAR/YEjgAcj4kiSGyh+Pa02CBhdqR0HdTOzDBp4Q69TgR9LmkqSY7+2UmWnX8zMMqjnxUcRMQ4Yl/48Ddgl67EO6mZmGRTlcXZmZkZ+grpz6mZmBeKRuplZBr5Lo5lZgeTlLo0O6mZmGeQlp+6gbmaWQV7SL54oNTMrEI/UzcwyaMrJWN1B3cwsA+fUzcwKJB/jdOfUzcwKxSN1M7MMnH4xMysQX3xkZlYgXv1iZlYg+Qjpnig1MysUj9TNzDLwRKmZWYE4p25mViD5COkO6mZmmeQl/eKJUjOzAvFI3cwsA+fUzcwKJB8h3UHdzCwT59TNzKzuPFI3M8sgcpKAcVA3M8sgL+kXB3Uzswy8+sXMrEDyEdI9UWpmVigeqedMr369+eFlJ9Kjdw8iggduvJc/X3cX/bfamO9dcCxdPrU6y5Y1cc2ZVzH1mZcb3V2rg179enPCZSfRo08PIuD+G+/hruF/4uQrTmH9TdYHoPva3Vn43kJ+fNAJDe5tfjn9Yu1i2bJlXH/+cF6dPI2u3dfgorsu5dlHnuHbpw/ij5ffzKRxT7HDPjvx7dMHcfYRZza6u1YHTcuWMeL84Uyb/Apdu6/BpXdfxqTxk7j0uIs/qjP4zO/w/vz3G9jL/MvLRKnTLzkzb85cXp08DYAPFi5i5tQZ9OzbkwjotmY3ALqt1Y25c95pZDetjubOmcu0ya8Ayb+JGVNfp9e6vZars/vBezB+9MON6F5hRI3/WxlJXSU9KekZSVMknZOWbyxpgqSpkm6R1KVSOx6p51ifDT7DxltvwsuTXmLEuddw5vVnc9QZR7PaauKMr53a6O5ZAyT/Jj7LS0+/+FHZVrtszby35jFr+qwG9iz/6jBSXwzsGxELJK0OPCLpL8CPgcsi4mZJVwFDgN+21kiHG6lLOrrCvqGSJkqaOG3B9Dr2quPp2q0rP7nqVK479xoWLVjEgd8+iBHnXcuxuw1hxLnXcuzFxze6i1ZnXbt15dSrT2f4Ob9j0YJFH5XvOWAvxo/+awN7ZllEYkH6dvV0C2Bf4La0fCRwaKV2OlxQB85pbUdEDIuInSNi503W7F/HLnUsnTp34uSrTmP8nQ/z5D1PALD3wH2Y8JfHAXj87kfZdLvNGtlFq7NOnTtxytWn89dR43jinsc/Kl+t02rs+uXdePRP4xvYu2KoNf1SOihNt6Hl55DUSdIkYA5wP/AKMC8ilqZVZgDrV+pnQ9Ivkp5tbRfQt559yaNjLz6emVNf565rxnxU9s6cd9hq12147onJbLP7trwx/V8N7KHV23GX/IgZU19nzDWjlyvfbo/tmfnKTN5+4+0G9aw4ak2/RMQwYNhK6iwDtpfUAxgFbFnteRqVU+8LfAmYW1Yu4LH6dyc/ttz5c3xx4D689vx0LvnzZQDceMkNXH3qFRx99ndZrVMnlixewtWnXdngnlq9fO7zW7HPwH2Z/vyr/OovlwNww8XX89RDf2ePQ/Zi/BhPkLaFpqjfksaImCfpIWA3oIekzulofQNgZqVjFXXs6Ecnla4FrouIR1rYd2NEfGtlbXxjowH5WDRqdbU08rLwzOpp1D//pFrbOGqjr9UUc37/2h0V+yCpD7AkDehrAPcBFwGDgNtLJkqfjYhWR20NGalHxJAK+1Ya0M3M6q0Oo8h+wEhJnUjmO2+NiLskPQfcLOl84Gng2kqNeEmjmVkG7X1FaUQ8C+zQQvk0YJes7Tiom5ll4Pupm5kVSF5mazriOnUzM1tFHqmbmWXguzSamRWIc+pmZgWSl5y6g7qZWQaNuFBzVXii1MysQDxSNzPLwBOlZmYF4py6mVmB5GX1i3PqZmYF4pG6mVkGzqmbmRVIXpY0OqibmWXgiVIzswLxRKmZmdWdR+pmZhl4otTMrEA8UWpmViB5Gak7p25mViAeqZuZZZCX1S8O6mZmGTQ5p25mVhz5COkO6mZmmXii1MzM6s4jdTOzDPIyUndQNzPLwBcfmZkViEfqZmYFkpd16p4oNTMrEI/UzcwycE7dzKxA8pJTd/rFzCyDiKhpWxlJ/ybpIUnPSZoi6YS0vKek+yW9nL6uU6kdB3Uzs45hKXByRGwF7AocJ2kr4DRgbERsBoxN37fKQd3MLIMmoqZtZSJiVkQ8lf48H3geWB8YAIxMq40EDq3UjnPqZmYZ1HNJo6T+wA7ABKBvRMxKd70B9K10rIO6mVkGtd56V9JQYGhJ0bCIGNZCvTWB24ETI+I9SR/ti4iQVLEjDupmZhnUOlJPA/gKQbyUpNVJAvofIuKOtHi2pH4RMUtSP2BOpTacUzcz6wCUDMmvBZ6PiF+V7BoDDEp/HgSMrtSOR+pmZhnU4clHuwNHAf+QNCkt+ylwIXCrpCHAa8DhlRpxUDczy6C9J0oj4hFArezeL2s7DupmZhn4GaVmZgXiuzSamVndeaRuZpaB0y9mZgWSl/SLg7qZWQYRTY3uQibOqZuZFYhH6mZmGeTlIRkO6mZmGfhxdmZmBeKRuplZgeRlpO6JUjOzAvFI3cwsA198ZGZWIL74yMysQPKSU3dQNzPLIC+rXzxRamZWIB6pm5ll4PSLmVmBePWLmVmB5GWk7py6mVmBeKRuZpZBXla/OAFPFxsAAAaFSURBVKibmWWQl/SLg7qZWQaeKDUzK5C83CbAE6VmZgXikbqZWQZOv5iZFYgnSs3MCiQvOXUHdTOzDPIyUvdEqZlZgXikbmaWQV5G6g7qZmYZ5COkg/Ly6WOtkzQ0IoY1uh/WsfjfxSeTc+rFMLTRHbAOyf8uPoEc1M3MCsRB3cysQBzUi8F5U2uJ/118Anmi1MysQDxSNzMrEAf1nJP0ZUkvSpoq6bRG98caT9JwSXMkTW50X6z+HNRzTFIn4ArgIGAr4JuStmpsr6wDGAF8udGdsMZwUM+3XYCpETEtIj4EbgYGNLhP1mAR8VfgnUb3wxrDQT3f1gdeL3k/Iy0zs08oB3UzswJxUM+3mcC/lbzfIC0zs08oB/V8+xuwmaSNJXUBjgDGNLhPZtZADuo5FhFLgR8C9wLPA7dGxJTG9soaTdJNwOPAFpJmSBrS6D5Z/fiKUjOzAvFI3cysQBzUzcwKxEHdzKxAHNTNzArEQd3MrEAc1K1DktRfUkg6u1JZe53LLK8c1G05kvZOA1zptkDS3yWdkN4ZMnfSwH22pO0b3Rez9tS50R2wDusm4M+AgPWAwcD/AVvTuKfUvwasASxdhWP7A2cB04FJbdiuWYfioG6teSoibmh+I+m3JFetflfSzyJidvkBktaKiPnt1aFIrpT7IC/tmjWC0y+WSUS8R3LpuYBNJE2XNE7SDpLulfQu8GxzfUmbSfq9pFmSPkzrXyKpe3nbkvaQ9KikRZJmS/oNsGYL9VrNfUsamPZnnqT306dB/VpSF0mDgYfSqteVpJXGVWpXUmdJp0p6TtIHkt6WNErSv7fWL0kHS/pbWn9W+jt3Lqu/taQ/SpopabGkNyQ9JOmrGf6vMKvII3XLRJKATdO3b6WvGwIPAn8EbicNxJJ2SsvnAVeT3DlyO+BHwO6SvhgRS9K6XwAeAOYDF6XHHAFcX0XfLgB+CjwHXAbMAj4LDAR+DvwV+EVaZxgwPj10hW8bZf4AHA7cD/wWWBc4Dnhc0p4R8XRZ/a8APwCuAoaTPLDkJ8Dc9PxI6pX+bUjrvQb0BnYGvgDcnfX3NmtRRHjz9tEG7A0ESTDsDfQBtgV+l5Y/ntabnr7/bgttPAO8AKxVVn5YeszgkrLHgA+BzUvKugBPpnXPLinv30LZLmnZg0DXsvOJj+9vtHf5uVfS7gFp2S3NbaTl25Hk3se3cPxCoH/Z+ScDs0rKDknrHt7o/6+9FXNz+sVacw7wJjCHJEh/h+S2voeW1HkHuK70oDQ1sS1wI/ApSb2bN+ARksB3YFr3M8BuwOiIeKm5jUgezXdZxn4emb6eHhHL5cUjlbGdcoelrxeUthERzwB/AvaQ1KfsmDsjYnrp+UnSPutKak4nvZu+HiRp7VXsm1mrHNStNcNIRqv7kwTePhExIJafIH0lIpaVHfe59LX5Q6F0mwN0B/qmdTZJX19o4fzPZeznZiQj32cy1s9qY6CJZHK43JSSOqWmtVD37fS1F0BEPEySWhoMvJXOJZzjB4ZbW3FO3VrzckQ8sJI677dQpvT1UuCeVo6bu8q9almkW6OVf8CVav67EBGDJF0CHATsCZwMnCHpxIj4TTv30QrOQd3a2svp67IMHwqvpq9btrAv68j1JZLguB1JHr411Qb9aSTfZD9Hyaqesr69yiqKiMkk+fZLJPUAJgAXSrqihpSRmdMv1uaeJglW35e0SfnOdJlgT4A0lfMEMEDS5iV1ugAnZTzfjenrL9Ljys/XPEJekL72zNjunenr6SVtIGkbksnORyLizYxtlfanp6Tl/ruLiHkkHxDdgK7VtmlWyiN1a1MREZKOIlmN8qyk4SQ56G4kSyK/BpwOjEgP+TEwDnhU0hV8vKQx07/NiHhS0kXAqcBTkm4B3iDJd3+dZHXMPJIc/XzgB5LeT8vmRMSDrbR7v6Rb076sI+kuPl7S+AHJ8sxV8d/ASZJGAVOBJcAXgS+RPI5w0Sq2awY4qFs7iIhJknYgCd6HAN8nCajTSYL52JK6j0s6ALgQOI1kdchtJOvC/5HxfKdJeobkea2nkHwDfZ3kNgfvp3UWSToCOJ/kdgefAh7m4zXjLTkSeIpkUvNSkpU7DwM/i4hMfWvBOGAH4GCgH0ke/lWS9ezOp1vN/IxSM7MCcU7dzKxAHNTNzArEQd3MrEAc1M3MCsRB3cysQBzUzcwKxEHdzKxAHNTNzArEQd3MrEAc1M3MCuT/A5TeiPqqxP25AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Classification Report\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOb1C-gX2xYR",
        "outputId": "8ad91bd9-d477-42de-b08a-ad1849272362"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.89      0.82        99\n",
            "           1       0.71      0.49      0.58        55\n",
            "\n",
            "    accuracy                           0.75       154\n",
            "   macro avg       0.73      0.69      0.70       154\n",
            "weighted avg       0.74      0.75      0.73       154\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "y_scores = knn_clf.predict_proba(X_test)\n",
        "fpr, tpr, threshold = roc_curve(y_test, y_scores[:, 1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "\n",
        "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
        "plt.legend(loc = 'lower right')\n",
        "plt.plot([0, 1], [0, 1],'r--')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.title('ROC Curve of kNN')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "fFG9lskc4Pt8",
        "outputId": "43c4e135-9dd4-4b96-fbf4-60983da8f3ec"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lurhCodl46_-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}