Merchandise_Popularity.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Merchandise Popularity",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7BdTw4YqoDD"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import warnings\r\n",
        "warnings.filterwarnings('ignore')\r\n",
        "sns.set_style('darkgrid')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "LEEMQirCrAXt",
        "outputId": "fbddf070-e3b3-4a4e-c7d4-76dc37568eb5"
      },
      "source": [
        "train_data=pd.read_csv('Train.csv')\r\n",
        "df=train_data.copy()\r\n",
        "df.head()"
      ],
      "execution_count": null,
      "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>Store_Ratio</th>\n",
              "      <th>Basket_Ratio</th>\n",
              "      <th>Category_1</th>\n",
              "      <th>Store_Score</th>\n",
              "      <th>Category_2</th>\n",
              "      <th>Store_Presence</th>\n",
              "      <th>Score_1</th>\n",
              "      <th>Score_2</th>\n",
              "      <th>Score_3</th>\n",
              "      <th>Score_4</th>\n",
              "      <th>time</th>\n",
              "      <th>popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.407</td>\n",
              "      <td>0.00380</td>\n",
              "      <td>2</td>\n",
              "      <td>-35.865</td>\n",
              "      <td>1</td>\n",
              "      <td>0.9920</td>\n",
              "      <td>0.944</td>\n",
              "      <td>0.0988</td>\n",
              "      <td>0.1100</td>\n",
              "      <td>113.911</td>\n",
              "      <td>189125</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.234</td>\n",
              "      <td>0.10500</td>\n",
              "      <td>0</td>\n",
              "      <td>-19.884</td>\n",
              "      <td>1</td>\n",
              "      <td>0.9440</td>\n",
              "      <td>0.900</td>\n",
              "      <td>0.1290</td>\n",
              "      <td>0.0382</td>\n",
              "      <td>76.332</td>\n",
              "      <td>186513</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.668</td>\n",
              "      <td>0.72600</td>\n",
              "      <td>9</td>\n",
              "      <td>-7.512</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0387</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.1340</td>\n",
              "      <td>0.4530</td>\n",
              "      <td>124.075</td>\n",
              "      <td>172143</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.184</td>\n",
              "      <td>0.00561</td>\n",
              "      <td>4</td>\n",
              "      <td>-34.357</td>\n",
              "      <td>1</td>\n",
              "      <td>0.8490</td>\n",
              "      <td>0.931</td>\n",
              "      <td>0.1110</td>\n",
              "      <td>0.0641</td>\n",
              "      <td>79.037</td>\n",
              "      <td>184000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.231</td>\n",
              "      <td>0.13100</td>\n",
              "      <td>6</td>\n",
              "      <td>-22.842</td>\n",
              "      <td>1</td>\n",
              "      <td>0.9370</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.1090</td>\n",
              "      <td>0.0677</td>\n",
              "      <td>109.560</td>\n",
              "      <td>186507</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Store_Ratio  Basket_Ratio  Category_1  ...  Score_4    time  popularity\n",
              "0        0.407       0.00380           2  ...  113.911  189125           4\n",
              "1        0.234       0.10500           0  ...   76.332  186513           4\n",
              "2        0.668       0.72600           9  ...  124.075  172143           4\n",
              "3        0.184       0.00561           4  ...   79.037  184000           5\n",
              "4        0.231       0.13100           6  ...  109.560  186507           3\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_Q29aLFrhCb",
        "outputId": "d0c0d93d-f381-4e73-dadc-2cdb787f6679"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Store_Ratio', 'Basket_Ratio', 'Category_1', 'Store_Score',\n",
              "       'Category_2', 'Store_Presence', 'Score_1', 'Score_2', 'Score_3',\n",
              "       'Score_4', 'time', 'popularity'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "Tj4vLDs3rIXg",
        "outputId": "415b2c49-e741-49d0-e901-ce79345c7eeb"
      },
      "source": [
        "df['Store_Ratio'].hist(bins=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7df4053ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Lu1psGQBreI7",
        "outputId": "061be38e-ad8d-41d4-e5d3-7abc511c067f"
      },
      "source": [
        "df['Store_Score'].hist(bins=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7df1db4e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "Smy-f7qCrmWx",
        "outputId": "f3713817-5c96-430c-a350-3e160891c534"
      },
      "source": [
        "df['Store_Presence'].hist(bins=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7df1cac160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVSIUGvusG4_"
      },
      "source": [
        "y = df[['popularity']]\r\n",
        "df = df.drop([\"popularity\"],1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5m-jtbisrHU"
      },
      "source": [
        "X = df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQc4UZCmssmy"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\r\n",
        "scaler = MinMaxScaler()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZgo6dRdsun-",
        "outputId": "4c86e371-b498-43b8-d397-2034e0b667ee"
      },
      "source": [
        "X = scaler.fit_transform(X,1)\r\n",
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.40781563, 0.00358477, 0.18181818, ..., 0.11011011, 0.51848194,\n",
              "        0.04057165],\n",
              "       [0.23446894, 0.10480664, 0.        , ..., 0.03823824, 0.34743583,\n",
              "        0.03989787],\n",
              "       [0.66933868, 0.7259408 , 0.81818182, ..., 0.45345345, 0.56474481,\n",
              "        0.03619104],\n",
              "       ...,\n",
              "       [0.16833667, 0.00886592, 0.45454545, ..., 0.3963964 , 0.31303908,\n",
              "        0.03628313],\n",
              "       [0.5991984 , 0.65792611, 0.09090909, ..., 0.47347347, 0.48614708,\n",
              "        0.03113896],\n",
              "       [0.58316633, 0.32385395, 0.18181818, ..., 0.24924925, 0.40398086,\n",
              "        0.0529969 ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvmZaMjvsvvC"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.05, random_state = 42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0CXXdI6osxaL",
        "outputId": "7d23eb35-f594-49af-97b7-20ec39abf0fd"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\r\n",
        "\r\n",
        "lr = LogisticRegression()\r\n",
        "\r\n",
        "lr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0QoFNJRszPe"
      },
      "source": [
        "\r\n",
        "pred = lr.predict(X_test)\r\n",
        "pred1 = lr.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGB38qfes6UE"
      },
      "source": [
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruCUBYzMs3aI",
        "outputId": "1e9c9f3e-e1b7-46ee-c88c-3ad5d21bc317"
      },
      "source": [
        "print (\"Accuracy of test set\",metrics.accuracy_score(y_test, pred)*100,\"%\")\r\n",
        "print (\"Accuracy of training set\",metrics.accuracy_score(y_train, pred1)*100,\"%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of test set 83.86388583973655 %\n",
            "Accuracy of training set 83.61565589408568 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2OozUUGizLcG",
        "outputId": "74087d46-6442-4699-9b87-037d51f808c6"
      },
      "source": [
        "y.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "popularity\n",
              "4             15231\n",
              "3              2166\n",
              "5               472\n",
              "1               323\n",
              "0                16\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZvR-GLwzGgC"
      },
      "source": [
        "# class_weight=dict({4:0.60,0:0.90,3:0.85,5:0.9,1:0.9})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_R7uKngM6B-i"
      },
      "source": [
        "# from imblearn.over_sampling import RandomOverSampler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOowJc0K6CJp"
      },
      "source": [
        "# params={\r\n",
        "#  \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,\r\n",
        "#  \"max_depth\"        : [ 3, 4, 5, 6, 8, 10, 12, 15],\r\n",
        "#  \"min_child_weight\" : [ 1, 3, 5, 7 ],\r\n",
        "#  \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],\r\n",
        "#  \"colsample_bytree\" : [ 0.3, 0.4, 0.5 , 0.7 ]\r\n",
        "    \r\n",
        "# }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShRea2LM6CTB"
      },
      "source": [
        "# from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\r\n",
        "# import xgboost"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0RJ4i-w6CcT"
      },
      "source": [
        "# def timer(start_time=None):\r\n",
        "#     if not start_time:\r\n",
        "#         start_time = datetime.now()\r\n",
        "#         return start_time\r\n",
        "#     elif start_time:\r\n",
        "#         thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600)\r\n",
        "#         tmin, tsec = divmod(temp_sec, 60)\r\n",
        "#         print('\\n Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0GimUnr6Ckb"
      },
      "source": [
        "# classifier=xgboost.XGBClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fl0e_CvI6Cs3"
      },
      "source": [
        "# random_search=RandomizedSearchCV(classifier,param_distributions=params,n_iter=5,scoring='acc',n_jobs=-1,cv=5,verbose=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gMxSvM9_Z83"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5IKBX7P_aa_"
      },
      "source": [
        "parameters = {\r\n",
        "    \"n_estimators\":[5,10,50,100,250,500,1000,2000],\r\n",
        "    \"max_depth\":[2,4,8,16,32,None]\r\n",
        "    \r\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "8Y56wqPD_amy",
        "outputId": "79932dac-0f43-4f35-b705-bcbf8292304a"
      },
      "source": [
        "rfc = RandomForestClassifier()\r\n",
        "from sklearn.model_selection import GridSearchCV\r\n",
        "cv = GridSearchCV(rfc,parameters,cv=5)\r\n",
        "cv.fit(X_train,y_train.values.ravel())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                              class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              max_samples=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators=100, n_jobs=None,\n",
              "                                              oob_score=False,\n",
              "                                              random_state=None, verbose=0,\n",
              "                                              warm_start=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'max_depth': [2, 4, 8, 16, 32, None],\n",
              "                         'n_estimators': [5, 10, 50, 100, 250, 500, 1000,\n",
              "                                          2000]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tinY_JqY_awp"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOJu0pqU_a6K"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pirYzEH_bCt"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNHe42cYs4s2"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "clf = RandomForestClassifier(n_estimators = 1500, max_features = 'log2',class_weight=class_weight,verbose = 1,n_jobs = -1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TlynKuHotB-a",
        "outputId": "2d03e992-69a1-4622-c2c0-5ca4557d9aa1"
      },
      "source": [
        "clf.fit(X_train, y_train)\r\n",
        "preds = clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    1.4s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:    6.0s\n",
            "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:   13.7s\n",
            "[Parallel(n_jobs=-1)]: Done 796 tasks      | elapsed:   24.5s\n",
            "[Parallel(n_jobs=-1)]: Done 1246 tasks      | elapsed:   38.3s\n",
            "[Parallel(n_jobs=-1)]: Done 1500 out of 1500 | elapsed:   46.1s finished\n",
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 796 tasks      | elapsed:    0.4s\n",
            "[Parallel(n_jobs=2)]: Done 1246 tasks      | elapsed:    0.6s\n",
            "[Parallel(n_jobs=2)]: Done 1500 out of 1500 | elapsed:    0.7s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lQVJDtOutD_T",
        "outputId": "281dcf0b-608f-4513-fc12-717014895e14"
      },
      "source": [
        "preds = clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 500 out of 500 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I86Nf5xHtJHq",
        "outputId": "14cbb7b5-bdb7-4610-c333-22fabbb15332"
      },
      "source": [
        "print (\"Accuracy of test set\",metrics.accuracy_score(y_test, preds)*100,\"%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of test set 89.13282107574094 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gc3jDhQutLcU",
        "outputId": "0a6ffadc-b75a-4b0c-8791-e64dc0313e5f"
      },
      "source": [
        "print(metrics.classification_report(y_test, preds))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         2\n",
            "           1       1.00      0.73      0.84        11\n",
            "           3       0.86      0.32      0.47       115\n",
            "           4       0.89      0.99      0.94       761\n",
            "           5       1.00      0.36      0.53        22\n",
            "\n",
            "    accuracy                           0.89       911\n",
            "   macro avg       0.95      0.68      0.76       911\n",
            "weighted avg       0.89      0.89      0.87       911\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFKKrwRGtXJW",
        "outputId": "f020ab2a-32f1-4f81-805f-500d3bc7ee73"
      },
      "source": [
        "pred_log = clf.predict_proba(X_test)\r\n",
        "print (\"log loss of test set\",metrics.log_loss(y_test, pred_log))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.2s\n",
            "[Parallel(n_jobs=2)]: Done 796 tasks      | elapsed:    0.4s\n",
            "[Parallel(n_jobs=2)]: Done 1246 tasks      | elapsed:    0.6s\n",
            "[Parallel(n_jobs=2)]: Done 1500 out of 1500 | elapsed:    0.7s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "log loss of test set 0.3404426034593637\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wqw4glXtlXi"
      },
      "source": [
        "df = pd.read_csv(\"Test.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wp4hQmJWtrcR"
      },
      "source": [
        "X = df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_kmb8lrttu2",
        "outputId": "ed08b791-cfe8-4dbc-9955-5a96d9d3435f"
      },
      "source": [
        "X = scaler.transform(X)\r\n",
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.64328657e-01, 9.93998704e-01, 9.09090909e-02, ...,\n",
              "        1.00100100e-05, 5.80798449e-01, 5.27758309e-02],\n",
              "       [5.87174349e-01, 6.35921359e-01, 3.63636364e-01, ...,\n",
              "        3.55355355e-01, 6.20557030e-01, 5.72113925e-02],\n",
              "       [4.57915832e-01, 7.42944476e-01, 0.00000000e+00, ...,\n",
              "        6.88688689e-01, 7.05058238e-01, 4.74188488e-02],\n",
              "       ...,\n",
              "       [5.73146293e-01, 8.51968025e-01, 5.45454545e-01, ...,\n",
              "        3.78378378e-01, 5.91572182e-01, 4.76102525e-02],\n",
              "       [7.62525050e-01, 5.24897378e-01, 1.00000000e+00, ...,\n",
              "        5.31531532e-01, 3.68091178e-01, 5.33869298e-02],\n",
              "       [3.41683367e-01, 1.96826515e-01, 8.18181818e-01, ...,\n",
              "        9.65965966e-01, 5.23925699e-01, 1.06164566e-02]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sXLwDMftxSK",
        "outputId": "e11ed9a0-91e7-49af-b09c-91ca09122454"
      },
      "source": [
        "pred_log = clf.predict_proba(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.1s\n",
            "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:    0.4s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    0.8s\n",
            "[Parallel(n_jobs=2)]: Done 796 tasks      | elapsed:    1.5s\n",
            "[Parallel(n_jobs=2)]: Done 1246 tasks      | elapsed:    2.3s\n",
            "[Parallel(n_jobs=2)]: Done 1500 out of 1500 | elapsed:    2.7s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "havBBWsjGmgL"
      },
      "source": [
        "pred=np.clip(pred_log,0.025,0.975)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "TTCHE2olt1sZ",
        "outputId": "ba5b2bff-4dd6-41a5-e804-c66ae24a192c"
      },
      "source": [
        "prediction  = pd.DataFrame(pred,columns =['0','1','3','4','5'])\r\n",
        "prediction"
      ],
      "execution_count": null,
      "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.025</td>\n",
              "      <td>0.092</td>\n",
              "      <td>0.302000</td>\n",
              "      <td>0.503333</td>\n",
              "      <td>0.102667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025000</td>\n",
              "      <td>0.970667</td>\n",
              "      <td>0.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.029333</td>\n",
              "      <td>0.957333</td>\n",
              "      <td>0.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.039333</td>\n",
              "      <td>0.944667</td>\n",
              "      <td>0.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025000</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12135</th>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.098000</td>\n",
              "      <td>0.872667</td>\n",
              "      <td>0.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12136</th>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025000</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12137</th>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025000</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12138</th>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.025000</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12139</th>\n",
              "      <td>0.025</td>\n",
              "      <td>0.078</td>\n",
              "      <td>0.289333</td>\n",
              "      <td>0.590000</td>\n",
              "      <td>0.042667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12140 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           0      1         3         4         5\n",
              "0      0.025  0.092  0.302000  0.503333  0.102667\n",
              "1      0.025  0.025  0.025000  0.970667  0.025000\n",
              "2      0.025  0.025  0.029333  0.957333  0.025000\n",
              "3      0.025  0.025  0.039333  0.944667  0.025000\n",
              "4      0.025  0.025  0.025000  0.975000  0.025000\n",
              "...      ...    ...       ...       ...       ...\n",
              "12135  0.025  0.025  0.098000  0.872667  0.025000\n",
              "12136  0.025  0.025  0.025000  0.975000  0.025000\n",
              "12137  0.025  0.025  0.025000  0.975000  0.025000\n",
              "12138  0.025  0.025  0.025000  0.975000  0.025000\n",
              "12139  0.025  0.078  0.289333  0.590000  0.042667\n",
              "\n",
              "[12140 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUC0IrlOt4cJ"
      },
      "source": [
        "prediction.to_csv('result_MP3.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "voi0-k2ht8xJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}