Codeforces_Problem_Predicter.ipynb

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  "metadata": {
    "colab": {
      "name": "Codeforces Problem Predicter.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1O5zkvU731ysfc_pUmr_qzt_sT5rj5iAt",
      "authorship_tag": "ABX9TyMim6LWZ0UHZn/1RnQWN0qV",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ShivamSinghal1/My-ML-Projects/blob/master/Codeforces_Problem_Predicter.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpNrHMPbv9ut"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Outlier detection\n",
        "from sklearn.svm import OneClassSVM\n",
        "\n",
        "# SVM Algo\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "#Naive Bayes\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "#Random Forest\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXosuApawNb0"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Dataset/codeforces_problem_stat_complete.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m5UxlOn8wVPL",
        "outputId": "b8963356-d2f8-4762-872c-ecc2fcdcd687"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7979 entries, 0 to 7978\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   code    7979 non-null   object\n",
            " 1   dacu    7979 non-null   int64 \n",
            " 2   dwau    7979 non-null   int64 \n",
            " 3   wa      7979 non-null   int64 \n",
            " 4   rating  7979 non-null   int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 311.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "x6YHgtiRwaIf",
        "outputId": "3dd3b3c9-fba8-49a9-80a4-5699fde7368a"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "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>code</th>\n",
              "      <th>dacu</th>\n",
              "      <th>dwau</th>\n",
              "      <th>wa</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1A</td>\n",
              "      <td>120239</td>\n",
              "      <td>19861</td>\n",
              "      <td>456524</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1C</td>\n",
              "      <td>3695</td>\n",
              "      <td>1445</td>\n",
              "      <td>17887</td>\n",
              "      <td>2100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1B</td>\n",
              "      <td>13500</td>\n",
              "      <td>4258</td>\n",
              "      <td>65477</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2B</td>\n",
              "      <td>5542</td>\n",
              "      <td>2673</td>\n",
              "      <td>49509</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2A</td>\n",
              "      <td>12455</td>\n",
              "      <td>6046</td>\n",
              "      <td>74079</td>\n",
              "      <td>1500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  code    dacu   dwau      wa  rating\n",
              "0   1A  120239  19861  456524    1000\n",
              "1   1C    3695   1445   17887    2100\n",
              "2   1B   13500   4258   65477    1600\n",
              "3   2B    5542   2673   49509    2000\n",
              "4   2A   12455   6046   74079    1500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOlX420xwcvx"
      },
      "source": [
        "test_df = df[df['rating'] == 0]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdcx_xDzwgcf"
      },
      "source": [
        "df = df[df['rating'] > 0]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cs0sGJ0qxpF9"
      },
      "source": [
        "rating , freq = np.unique(df['rating'] , return_counts = True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "JrblICcAxp3I",
        "outputId": "0f4fcb81-f40a-41f9-fe37-2f2c6ebf7776"
      },
      "source": [
        "plt.bar(rating , freq , 20)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 28 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KFe7SSB0+viS/CbyHwTh/m8FSy7vH3Q/DfeDJ9laIdn+w1c91KYU1dYmFqnqyveB+CvwTP3/7uibHl+R4BsH3iar6bKvuZg5nG19vcwhQVc8CtwGvYbAccehLlcN9/dk42v6XAk+xtsZ3YVtuq6r6MfAvHIX5M9wH9gCHPo3eBtw8VH9F+0T7POC59tb/S8Abkpzc3h6/odWtSodCr/lD4NCZNHuAy9sZCWcAZwJfZRVfQqKtt14L3F9VHxza1cUczjW+XuYwyUSS9a38IuD1DD5XuA24tDU7fP4OzeulwK3tndlc415Rc4zv20MHHmHwecLw/I3n9TnuT49X2w34FIO3tf/HYB1rO4M1vFuAB4H/Ak6pn3/y/REGa4LfBKaGHuePGXyIMw1cudLjWmB8H2/9v6e9mE4fav/eNr4HgIuG6rcwOFPjIeC9Kz2uoX69jsGSyz3A3e22pZc5nGd8Xcwh8FvA19s47gX+utW/jEE4TwP/BpzY6k9q29Nt/8sWGvcqHd+tbf7uBf6Vn59RM7bXp5cfkKQOuSwjSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KH/h+f0k23QFloxQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gumydqZP0Sgp",
        "outputId": "4df01b45-81ee-4831-8532-b8dae1e00a0e"
      },
      "source": [
        "rating , freq"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 800,  900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,\n",
              "        1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900,\n",
              "        3000, 3100, 3200, 3300, 3400, 3500]),\n",
              " array([388, 205, 257, 248, 301, 321, 314, 414, 485, 432, 419, 438, 440,\n",
              "        388, 374, 330, 367, 308, 259, 222, 157, 162, 130, 114, 109,  64,\n",
              "         53,  58]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMvuW8lM0apH"
      },
      "source": [
        "#rating         #category\n",
        "#800  - 1100 - 0 (Very Easy)\n",
        "#1200 - 1500 - 1 (Easy)\n",
        "#1600 - 1800 - 2 (Medium)\n",
        "#1900 - 2100 - 3 (Hard)\n",
        "#2200 - 2600 - 4 (Super Hard)\n",
        "#2700 -      - 5 (Extreme Hard) "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gS1bbJ6y30H8"
      },
      "source": [
        "df['category'] = df['rating'].replace(rating , [ 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2,\n",
        "        3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "_8LiVe8i4R83",
        "outputId": "9b3dff3e-b058-4758-c571-37b57fbd331e"
      },
      "source": [
        "category , category_freq = np.unique(df['category'] , return_counts = True)\n",
        "plt.bar(category , category_freq , 0.5)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 6 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQuTlNnp4WM2",
        "outputId": "b876b527-f310-4aff-c7e1-96efaa1c94e4"
      },
      "source": [
        "category_freq"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1098, 1350, 1336, 1266, 1379, 1328])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "GCbT1uMT4n5A",
        "outputId": "9aa56909-d6d8-4f91-e451-5728446096a5"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 14,
      "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>code</th>\n",
              "      <th>dacu</th>\n",
              "      <th>dwau</th>\n",
              "      <th>wa</th>\n",
              "      <th>rating</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1A</td>\n",
              "      <td>120239</td>\n",
              "      <td>19861</td>\n",
              "      <td>456524</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1C</td>\n",
              "      <td>3695</td>\n",
              "      <td>1445</td>\n",
              "      <td>17887</td>\n",
              "      <td>2100</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1B</td>\n",
              "      <td>13500</td>\n",
              "      <td>4258</td>\n",
              "      <td>65477</td>\n",
              "      <td>1600</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2B</td>\n",
              "      <td>5542</td>\n",
              "      <td>2673</td>\n",
              "      <td>49509</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2A</td>\n",
              "      <td>12455</td>\n",
              "      <td>6046</td>\n",
              "      <td>74079</td>\n",
              "      <td>1500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  code    dacu   dwau      wa  rating  category\n",
              "0   1A  120239  19861  456524    1000         0\n",
              "1   1C    3695   1445   17887    2100         3\n",
              "2   1B   13500   4258   65477    1600         2\n",
              "3   2B    5542   2673   49509    2000         3\n",
              "4   2A   12455   6046   74079    1500         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IbVrPxzX5lCF"
      },
      "source": [
        "X = np.array(df[['dacu','dwau','wa']])\n",
        "y = np.array(df['category'])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iRhP157V7636"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-tysuWi5u7r",
        "outputId": "09e17157-4e1e-4e76-b4c6-a3c759753ae2"
      },
      "source": [
        "X_train.shape , y_train.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6205, 3), (6205,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-CWNqpnj9zpS",
        "outputId": "a099d914-2244-4033-a939-d54a0efbe2be"
      },
      "source": [
        "ee = OneClassSVM(nu=0.01)\n",
        "yhat = ee.fit_predict(X_train)\n",
        "# select all rows that are not outliers\n",
        "mask = yhat != -1\n",
        "X_train, y_train = X_train[mask, :], y_train[mask]\n",
        "# summarize the shape of the updated training dataset\n",
        "print(X_train.shape, y_train.shape)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(6148, 3) (6148,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-CAdk_FPg3l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34a8702f-1b97-4989-fb4c-554f8dfc9f74"
      },
      "source": [
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "lof = LocalOutlierFactor()\n",
        "yhat = lof.fit_predict(X_train)\n",
        "# select all rows that are not outliers\n",
        "mask = yhat != -1\n",
        "X_train, y_train = X_train[mask, :], y_train[mask]\n",
        "# summarize the shape of the updated training dataset\n",
        "print(X_train.shape, y_train.shape)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(6042, 3) (6042,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s_IfuGHs_x7M",
        "outputId": "824d0526-6f45-4f99-f568-1b906e984bcd"
      },
      "source": [
        "from sklearn.ensemble import IsolationForest\n",
        "# identify outliers in the training dataset\n",
        "iso = IsolationForest(contamination=0.1)\n",
        "yhat = iso.fit_predict(X_train)\n",
        "# select all rows that are not outliers\n",
        "mask = yhat != -1\n",
        "X_train, y_train = X_train[mask, :], y_train[mask]\n",
        "# summarize the shape of the updated training dataset\n",
        "print(X_train.shape, y_train.shape)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5584, 3) (5584,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bM3kPT085ZFq"
      },
      "source": [
        "SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGPPeeUJH8JT",
        "outputId": "dfe3ec40-5b0b-4cc7-d4c9-a82751a8b1d2"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "X_train = StandardScaler().fit(X_train).transform(X_train)\n",
        "print(X_train)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.73818209 -0.69856221 -0.77088466]\n",
            " [-0.46713799 -0.36251393 -0.59001572]\n",
            " [ 0.84725433  2.62184147  1.82363133]\n",
            " ...\n",
            " [ 1.98152241  0.88610584  1.62343878]\n",
            " [ 2.09131242 -0.24844249 -0.28676395]\n",
            " [-0.32441097 -0.49508344 -0.54080494]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMSrHKbb5YiD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "872097e3-4741-40a1-a2c6-2d3e56836fa2"
      },
      "source": [
        "param_grid = {'C': [i for i in range(1,1000)], #1 ,10 ,100,1000,10000\n",
        "          \n",
        "              'gamma': [0.000001,0.00001,.00005,.0001,.0005,.001,.005,0.01, .1 ],\n",
        "              \n",
        "              'kernel':['linear','rbf'],\n",
        "\n",
        "              'decision_function_shape':['ovr','ovo','dag']\n",
        "              \n",
        "              }\n",
        "\n",
        "clf=RandomizedSearchCV(SVC(), param_grid, n_iter=100, cv=5, verbose=10)\n",
        "clf = clf.fit(X_train, y_train)\n",
        "clf.best_estimator_"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
            "[CV] kernel=linear, gamma=1e-05, decision_function_shape=ovr, C=922 ..\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
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            "[CV]  kernel=linear, gamma=1e-05, decision_function_shape=ovr, C=922, score=0.457, total=  10.8s\n",
            "[CV] kernel=linear, gamma=1e-05, decision_function_shape=ovr, C=922 ..\n"
          ],
          "name": "stdout"
        },
        {
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            "[CV] kernel=linear, gamma=1e-05, decision_function_shape=ovr, C=922 ..\n"
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            "[CV] kernel=rbf, gamma=5e-05, decision_function_shape=dag, C=680 .....\n"
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            "[CV] kernel=rbf, gamma=5e-05, decision_function_shape=ovr, C=994 .....\n",
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            "[CV] kernel=rbf, gamma=0.0001, decision_function_shape=ovo, C=331 ....\n",
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            "[CV] kernel=linear, gamma=0.0005, decision_function_shape=ovr, C=234 .\n",
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            "[CV] kernel=linear, gamma=0.0005, decision_function_shape=ovr, C=234 .\n",
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            "[CV] kernel=linear, gamma=0.0005, decision_function_shape=ovr, C=288 .\n",
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            "[CV] kernel=linear, gamma=0.0005, decision_function_shape=ovr, C=288 .\n",
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            "[CV] kernel=linear, gamma=0.0005, decision_function_shape=ovr, C=288 .\n",
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            "[CV] kernel=linear, gamma=0.0005, decision_function_shape=ovr, C=288 .\n",
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            "[CV] kernel=linear, gamma=0.0005, decision_function_shape=ovr, C=288 .\n",
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            "[CV] kernel=rbf, gamma=0.0001, decision_function_shape=ovo, C=256 ....\n",
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            "[CV] kernel=linear, gamma=0.001, decision_function_shape=ovr, C=63 ...\n",
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            "[CV] kernel=rbf, gamma=0.005, decision_function_shape=ovr, C=130 .....\n",
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            "[CV] kernel=linear, gamma=0.0001, decision_function_shape=ovr, C=436 .\n",
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            "[CV] kernel=rbf, gamma=0.1, decision_function_shape=ovo, C=49 ........\n",
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            "[CV]  kernel=rbf, gamma=0.0005, decision_function_shape=ovr, C=771, score=0.473, total=   0.8s\n",
            "[CV] kernel=rbf, gamma=0.0005, decision_function_shape=ovr, C=771 ....\n",
            "[CV]  kernel=rbf, gamma=0.0005, decision_function_shape=ovr, C=771, score=0.455, total=   0.8s\n",
            "[CV] kernel=rbf, gamma=0.0005, decision_function_shape=ovr, C=771 ....\n",
            "[CV]  kernel=rbf, gamma=0.0005, decision_function_shape=ovr, C=771, score=0.461, total=   0.8s\n",
            "[CV] kernel=rbf, gamma=0.0005, decision_function_shape=dag, C=967 ....\n",
            "[CV]  kernel=rbf, gamma=0.0005, decision_function_shape=dag, C=967, score=0.453, total=   0.8s\n",
            "[CV] kernel=rbf, gamma=0.0005, decision_function_shape=dag, C=967 ....\n",
            "[CV]  kernel=rbf, gamma=0.0005, decision_function_shape=dag, C=967, score=0.484, total=   0.8s\n",
            "[CV] kernel=rbf, gamma=0.0005, decision_function_shape=dag, C=967 ....\n",
            "[CV]  kernel=rbf, gamma=0.0005, decision_function_shape=dag, C=967, score=0.473, total=   0.8s\n",
            "[CV] kernel=rbf, gamma=0.0005, decision_function_shape=dag, C=967 ....\n",
            "[CV]  kernel=rbf, gamma=0.0005, decision_function_shape=dag, C=967, score=0.453, total=   0.8s\n",
            "[CV] kernel=rbf, gamma=0.0005, decision_function_shape=dag, C=967 ....\n",
            "[CV]  kernel=rbf, gamma=0.0005, decision_function_shape=dag, C=967, score=0.456, total=   0.9s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 500 out of 500 | elapsed: 27.5min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=435, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='dag', degree=3, gamma=0.1, kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OTa4A_neVIq",
        "outputId": "0e079ffb-1647-4112-d848-adeb8fbf103d"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "X_test = StandardScaler().fit(X_test).transform(X_test)\n",
        "\n",
        "y_pred_svm = clf.predict(X_test)\n",
        "print(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0], (y_test != y_pred_svm).sum()))\n",
        "print(\"Accuracy = %f\" % (( (y_test == y_pred_svm).sum())/X_test.shape[0]))\n",
        "confusion_matrix(y_test, y_pred_svm)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of mislabeled points out of a total 1552 points : 1023\n",
            "Accuracy = 0.340851\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 77,  97,  30,   0,   8,   0],\n",
              "       [  6,  87, 164,   8,  33,   0],\n",
              "       [  0,  17, 150,  19,  78,   0],\n",
              "       [  0,   0,  87,  32, 155,   0],\n",
              "       [  0,   0,  29,  10, 183,   0],\n",
              "       [  0,   0,   1,   0, 281,   0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVgzVhwU-uZP"
      },
      "source": [
        "Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ud8md6vG-vxM",
        "outputId": "5a73b124-ba46-4404-976b-1a8e1f11dd34"
      },
      "source": [
        "gnb = GaussianNB()\n",
        "y_pred_nb = gnb.fit(X_train, y_train).predict(X_test)\n",
        "print(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0], (y_test != y_pred_nb).sum()))\n",
        "print(\"Accuracy = %f\" % (( (y_test == y_pred_nb).sum())/X_test.shape[0]))\n",
        "confusion_matrix(y_test, y_pred_nb)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of mislabeled points out of a total 1552 points : 863\n",
            "Accuracy = 0.443943\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[139,  40,  15,   7,   4,   7],\n",
              "       [ 44, 128,  34,  50,  28,  14],\n",
              "       [  1,  86,  44,  39,  53,  41],\n",
              "       [  0,  25,  31,  50,  95,  73],\n",
              "       [  0,   0,  15,  21,  71, 115],\n",
              "       [  0,   1,   2,   2,  20, 257]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "baFaz52NKvDb",
        "outputId": "a9a49ba4-7018-4d4b-b0ac-fa7e80074a4f"
      },
      "source": [
        "clf = RandomForestClassifier(max_depth=8, random_state=0)\n",
        "y_pred = clf.fit(X_train, y_train).predict(X_test)\n",
        "print(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0], (y_test != y_pred).sum()))\n",
        "print(\"Accuracy = %f\" % (( (y_test == y_pred).sum())/X_test.shape[0]))\n",
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of mislabeled points out of a total 1552 points : 778\n",
            "Accuracy = 0.498711\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[142,  54,   7,   3,   4,   2],\n",
              "       [ 53, 140,  66,  23,   9,   7],\n",
              "       [  9,  57, 114,  30,  38,  16],\n",
              "       [  0,  10,  74,  61,  98,  31],\n",
              "       [  0,   1,  26,  24, 107,  64],\n",
              "       [  0,   1,   1,   0,  70, 210]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFgfrm8S-Ff5",
        "outputId": "a3005210-04f8-4b5a-ca76-3e196414e8fb"
      },
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "clf = AdaBoostClassifier(n_estimators=100, random_state=0)\n",
        "y_pred = clf.fit(X_train, y_train).predict(X_test)\n",
        "print(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0], (y_test != y_pred).sum()))\n",
        "print(\"Accuracy = %f\" % (( (y_test == y_pred).sum())/X_test.shape[0]))\n",
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of mislabeled points out of a total 1552 points : 1049\n",
            "Accuracy = 0.324098\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 99,  89,   6,  11,   7,   0],\n",
              "       [ 71, 112,  43,  44,  28,   0],\n",
              "       [ 13,  55,  65,  76,  55,   0],\n",
              "       [  3,  20,  58,  72, 119,   2],\n",
              "       [  0,   3,  33,  33, 153,   0],\n",
              "       [  0,   0,   3,   5, 272,   2]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvkKtol3oxnB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}