Merchandise_Popularity (1).ipynb

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  "metadata": {
    "colab": {
      "name": "Merchandise_Popularity.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "ZF6hLsuTqbBt"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "71eKxkApVD1_"
      },
      "source": [
        "from google.colab import drive"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fP0tFzQcVi1s"
      },
      "source": [
        "#drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELYwxL3pVkKw"
      },
      "source": [
        "import seaborn as sns\r\n",
        "import numpy as np\r\n",
        "from sklearn.metrics import log_loss\r\n",
        "import pandas as pd\r\n",
        "from scipy.stats import skew,kurtosis,zscore\r\n",
        "from sklearn.metrics import accuracy_score\r\n",
        "import matplotlib.pyplot as plt\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZWUiggXVxUu"
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/MPP_Dataset/Train.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "IzePLwqjV6qz",
        "outputId": "c4c1d6ba-8d5b-4a3c-ec58-07ddc7d6e54d"
      },
      "source": [
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orfiAePaH1rr"
      },
      "source": [
        "df['hour'] = pd.to_datetime(df['time'].astype(int), unit='s').dt.hour\r\n",
        "df['month'] = pd.to_datetime(df['time'].astype(int), unit='s').dt.month\r\n",
        "df['day'] = pd.to_datetime(df['time'].astype(int), unit='s').dt.day\r\n",
        "df['year'] = pd.to_datetime(df['time'].astype(int), unit='s').dt.year\r\n",
        "df['minute'] = pd.to_datetime(df['time'].astype(int), unit='s').dt.minute"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "3Yam7UMU_zFR",
        "outputId": "0066c4df-438b-4b77-d50b-647f81366c37"
      },
      "source": [
        "'''if df.week.all() < 5:\r\n",
        "\r\n",
        "  df['week'] = 0\r\n",
        "else:\r\n",
        "  df['week'] = 1'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"if df.week.all() < 5:\\n\\n  df['week'] = 0\\nelse:\\n  df['week'] = 1\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "WutSykOD_tET",
        "outputId": "4cf25a02-a932-4d16-d7f3-911bf27a6b13"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .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",
              "      <th>hour</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>year</th>\n",
              "      <th>minute</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.407</td>\n",
              "      <td>0.0038</td>\n",
              "      <td>2</td>\n",
              "      <td>-35.865</td>\n",
              "      <td>1</td>\n",
              "      <td>0.992</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",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1970</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.234</td>\n",
              "      <td>0.1050</td>\n",
              "      <td>0</td>\n",
              "      <td>-19.884</td>\n",
              "      <td>1</td>\n",
              "      <td>0.944</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",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1970</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Store_Ratio  Basket_Ratio  Category_1  Store_Score  ...  month  day  year  minute\n",
              "0        0.407        0.0038           2      -35.865  ...      1    3  1970      32\n",
              "1        0.234        0.1050           0      -19.884  ...      1    3  1970      48\n",
              "\n",
              "[2 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLTHDi9RGHZo",
        "outputId": "e85fb2b3-4e45-48c3-d7e2-8b12f3cc5fad"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 18208 entries, 0 to 18207\n",
            "Data columns (total 17 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Store_Ratio     18208 non-null  float64\n",
            " 1   Basket_Ratio    18208 non-null  float64\n",
            " 2   Category_1      18208 non-null  int64  \n",
            " 3   Store_Score     18208 non-null  float64\n",
            " 4   Category_2      18208 non-null  int64  \n",
            " 5   Store_Presence  18208 non-null  float64\n",
            " 6   Score_1         18208 non-null  float64\n",
            " 7   Score_2         18208 non-null  float64\n",
            " 8   Score_3         18208 non-null  float64\n",
            " 9   Score_4         18208 non-null  float64\n",
            " 10  time            18208 non-null  int64  \n",
            " 11  popularity      18208 non-null  int64  \n",
            " 12  hour            18208 non-null  int64  \n",
            " 13  month           18208 non-null  int64  \n",
            " 14  day             18208 non-null  int64  \n",
            " 15  year            18208 non-null  int64  \n",
            " 16  minute          18208 non-null  int64  \n",
            "dtypes: float64(8), int64(9)\n",
            "memory usage: 2.4 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WwMsluQTV8F5",
        "outputId": "5c636cac-ebb8-4fc5-c1b2-deead7562f4f"
      },
      "source": [
        "df.skew()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Store_Ratio       -0.460388\n",
              "Basket_Ratio      -0.124140\n",
              "Category_1         0.040952\n",
              "Store_Score       -1.134367\n",
              "Category_2        -0.622149\n",
              "Store_Presence     0.111894\n",
              "Score_1            0.623296\n",
              "Score_2            2.863491\n",
              "Score_3            0.266906\n",
              "Score_4            0.385495\n",
              "time               8.680522\n",
              "popularity        -3.051638\n",
              "hour               0.036255\n",
              "month             95.407022\n",
              "day                3.704050\n",
              "year               0.000000\n",
              "minute             0.014676\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbPfd8aqWlHG"
      },
      "source": [
        "def distribution_check(df,Cols):\r\n",
        "    plt.figure(figsize=(12,7))\r\n",
        "    fig = 1\r\n",
        "    i = (len(Cols)//3)+1\r\n",
        "    for col in Cols:\r\n",
        "        sk = \" Skewness : \" + str(skew(df[col])) +\"\\nKurtosis :\" + str(kurtosis(df[col]))\r\n",
        "        plt.subplot(i, 3, fig)\r\n",
        "        sns.distplot(df[col]).set_title(sk)\r\n",
        "        fig = fig+1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CuLVhSdwWrpW",
        "outputId": "18250492-cc1f-4de7-bc2a-c8d53ec50080"
      },
      "source": [
        "distribution_check(df,['Score_2'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPUJ1JJUWKbA"
      },
      "source": [
        "#df['Score_2'] = np.log(df['Score_2'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJHF6zx9WgEW",
        "outputId": "e454ea89-7ace-4e4b-8cba-3b1eefe9688e"
      },
      "source": [
        "distribution_check(df,['time'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTHHZKqKWoFh",
        "outputId": "ed162d44-d859-4b52-9ca0-aab12345c1ca"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Store_Ratio       0\n",
              "Basket_Ratio      0\n",
              "Category_1        0\n",
              "Store_Score       0\n",
              "Category_2        0\n",
              "Store_Presence    0\n",
              "Score_1           0\n",
              "Score_2           0\n",
              "Score_3           0\n",
              "Score_4           0\n",
              "time              0\n",
              "popularity        0\n",
              "hour              0\n",
              "month             0\n",
              "day               0\n",
              "year              0\n",
              "minute            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EI2wqhCMdYzG",
        "outputId": "3b939d05-1588-4b47-c8c4-a25c8970b465"
      },
      "source": [
        "df.popularity.nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26ahGeSaWx0g"
      },
      "source": [
        "y = df[['popularity']]\r\n",
        "df = df.drop([\"popularity\",\"time\",\"Basket_Ratio\"],1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X5-BB3JeXDcS"
      },
      "source": [
        "X = df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "F3ZmWFxecdHy",
        "outputId": "2872cb0f-9bf2-4d08-ee24-ef0f88a70a98"
      },
      "source": [
        "import seaborn as sns\r\n",
        "corr = X.corr()\r\n",
        "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True), square=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f18dd4310f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "WgmDVaW7XHdw"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\r\n",
        "scaler = MinMaxScaler()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "lyXXCR7Kq1Iv"
      },
      "source": [
        "#y = np.array(y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "1y0YXcnFuNZg"
      },
      "source": [
        "from sklearn import preprocessing\r\n",
        "#y = preprocessing.label_binarize(y, classes=[0, 1, 3,4,5])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "LxlU0tZlXOU3",
        "outputId": "26b92168-e346-4ec4-d8a2-aa54cdbc33e2"
      },
      "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, ..., 0.17391304, 0.1       ,\n",
              "        0.54237288],\n",
              "       [0.23446894, 0.10480664, 0.        , ..., 0.13043478, 0.1       ,\n",
              "        0.81355932],\n",
              "       [0.66933868, 0.7259408 , 0.81818182, ..., 1.        , 0.05      ,\n",
              "        0.83050847],\n",
              "       ...,\n",
              "       [0.16833667, 0.00886592, 0.45454545, ..., 1.        , 0.05      ,\n",
              "        0.93220339],\n",
              "       [0.5991984 , 0.65792611, 0.09090909, ..., 0.7826087 , 0.05      ,\n",
              "        0.37288136],\n",
              "       [0.58316633, 0.32385395, 0.18181818, ..., 0.73913043, 0.1       ,\n",
              "        0.91525424]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ZE75ryJFXSUM"
      },
      "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": "markdown",
      "metadata": {
        "id": "CgxW80YB7y1j"
      },
      "source": [
        "SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "kzF2dAAs7x08"
      },
      "source": [
        "from imblearn.over_sampling import ADASYN\r\n",
        "from collections import Counter"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "vMMRq51O7x37"
      },
      "source": [
        "counter = Counter(y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "egDmHfvA7_qI",
        "outputId": "3e99f83e-d7ae-4fac-d79c-2adb74e0ce08"
      },
      "source": [
        "smt = ADASYN()\r\n",
        "X_train_sm,y_train_sm = smt.fit_resample(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "sQlldGVw7_t2"
      },
      "source": [
        "counter = Counter(y_train_sm)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "hlG-Q2rv9GeN",
        "outputId": "182ac182-14f6-454e-9341-6fb8a8a5f79e"
      },
      "source": [
        "print(counter)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Counter({3: 14546, 5: 14540, 0: 14474, 4: 14470, 1: 14456})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EL2edfBC1vt6"
      },
      "source": [
        "# RF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "CuYppYYo1-HO"
      },
      "source": [
        "class_weight=dict({4:60,0:100,3:85,5:90,1:90})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "fHnVEIfVX1qv"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "clf = RandomForestClassifier(n_estimators = 1000,class_weight=class_weight,verbose = 1,n_jobs = -1,random_state = 42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "o9o367MxYCZt",
        "outputId": "8d606c44-d139-4965-bd07-3d32de169e3e"
      },
      "source": [
        "%%time\r\n",
        "\r\n",
        "clf.fit(X_train, y_train)\r\n",
        "\r\n",
        "pred_new = clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n",
            "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    1.2s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:    5.0s\n",
            "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:   11.2s\n",
            "[Parallel(n_jobs=-1)]: Done 796 tasks      | elapsed:   20.0s\n",
            "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   25.2s 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:    0.3s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 50.2 s, sys: 212 ms, total: 50.4 s\n",
            "Wall time: 26 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Done 1000 out of 1000 | elapsed:    0.4s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "AUy2ipAOYDTL",
        "outputId": "071a1e73-6569-4b74-f867-f0bd76d7325b"
      },
      "source": [
        "pred_new = 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 796 tasks      | elapsed:    0.3s\n",
            "[Parallel(n_jobs=2)]: Done 1000 out of 1000 | elapsed:    0.4s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "tJKamCE9YENr",
        "outputId": "98f0dd95-d530-4451-9110-ca682dae55fb"
      },
      "source": [
        "print (\"Accuracy of test set\",accuracy_score(y_test, pred_new)*100,\"%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of test set 88.9132821075741 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "CCzB1HAbYFVz",
        "outputId": "6e2533e3-aacd-43fb-f57a-93cef2826b0a"
      },
      "source": [
        "pred_log = clf.predict_proba(X_test)\r\n",
        "print (\"log loss of test set\",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:    0.3s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "log loss of test set 0.34194010456811974\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=2)]: Done 1000 out of 1000 | elapsed:    0.4s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Zy1lxMjb0l_X",
        "outputId": "f822f71f-8e92-42ad-be1d-b9db0fe32ec9"
      },
      "source": [
        "0.3362938412561886"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3362938412561886"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QwncS21leD7C"
      },
      "source": [
        "Hyper parameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wv2XNvHpeC-g"
      },
      "source": [
        "param_grid = { \r\n",
        "    'n_estimators': [200, 500,300],\r\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\r\n",
        "    'max_depth' : [4,5,6,7,8,10],\r\n",
        "    'criterion' :['gini', 'entropy']\r\n",
        "}\r\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_vtoFSseDCl"
      },
      "source": [
        "CV_rfc = GridSearchCV(estimator=clf, param_grid=param_grid, cv= 3)\r\n",
        "CV_rfc.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9VlvOfVaYjhe"
      },
      "source": [
        "CV_rfc.best_params_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZF6hLsuTqbBt"
      },
      "source": [
        "# Feature selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6DvnTAdqd0V",
        "outputId": "d2eec00d-1570-457c-c316-b9ce305f43bb"
      },
      "source": [
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\r\n",
        "sfs1 = SFS(RandomForestClassifier(n_jobs=-1), \r\n",
        "           k_features=X_train.shape[1], \r\n",
        "           forward=True, \r\n",
        "           floating=False, \r\n",
        "           verbose=2,\r\n",
        "           scoring='roc_auc',\r\n",
        "           cv=3,\r\n",
        "           n_jobs=-1)\r\n",
        "\r\n",
        "sfs1 = sfs1.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "\n",
            "STOPPING EARLY DUE TO KEYBOARD INTERRUPT..."
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o61cgUHmqd3M"
      },
      "source": [
        "selected_feat= df.columns[list(sfs1.k_feature_idx_)]\r\n",
        "selected_feat"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxpnUTZVwlR2"
      },
      "source": [
        "\r\n",
        "pd.DataFrame.from_dict(sfs1.get_metric_dict()).T"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWi6ydTBXUts"
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tJWRswbXje3"
      },
      "source": [
        "pred = lr.predict(X_test)\r\n",
        "pred1 = lr.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2-wSAEPXmZ3"
      },
      "source": [
        "print (\"Accuracy of test set\",accuracy_score(y_test, pred)*100,\"%\")\r\n",
        "print (\"Accuracy of training set\",accuracy_score(y_train, pred1)*100,\"%\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0vXs-sXdA89"
      },
      "source": [
        "Light gbm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-JFMVePjdDUU"
      },
      "source": [
        "import lightgbm as lgb\r\n",
        "train = lgb.Dataset(X_train, label=y_train)\r\n",
        "test = lgb.Dataset(X_test, label=y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0juV8-jdDXT"
      },
      "source": [
        "param = {'objective': 'multiclass',\r\n",
        "         'num_iterations': 1000,\r\n",
        "         'learning_rate': 0.01,  \r\n",
        "         'num_leaves': 23,\r\n",
        "         'verbose':0,\r\n",
        "         'max_depth': 15, \r\n",
        "         'min_data_in_leaf': 25, \r\n",
        "         'max_bin': 10, \r\n",
        "         'min_data_in_bin': 2,   \r\n",
        "         'num_class': 6,\r\n",
        "         'metric': 'multi_logloss'\r\n",
        "         }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yK9eYHAdDbB"
      },
      "source": [
        "lgbm = lgb.train(params=param,\r\n",
        "                 train_set=train,\r\n",
        "                 num_boost_round=200,\r\n",
        "                 valid_sets=[test])\r\n",
        "\r\n",
        "y_pred_class = lgbm.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3apwdYPdLux"
      },
      "source": [
        "predictions = []\r\n",
        "for x in y_pred_class:\r\n",
        "    predictions.append(np.argmax(x))\r\n",
        "\r\n",
        "print('accuracy:', accuracy_score(y_test, predictions)*100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_OJgAvOZIF6"
      },
      "source": [
        "# Fitting on Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98QYmww1ZLQ7"
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/MPP_Dataset/Test.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1LxmwBuZO5q"
      },
      "source": [
        "#df['time'] = np.log(df['time'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyh2bCRsJoB9"
      },
      "source": [
        "df['hour'] = pd.to_datetime(df['time'].astype(int), unit='s').dt.hour\r\n",
        "df['month'] = pd.to_datetime(df['time'].astype(int), unit='s').dt.month\r\n",
        "df['day'] = pd.to_datetime(df['time'].astype(int), unit='s').dt.day\r\n",
        "df['year'] = pd.to_datetime(df['time'].astype(int), unit='s').dt.year\r\n",
        "#df['minute'] = pd.to_datetime(df['time'].astype(int), unit='s').dt.minute\r\n",
        "df = df.drop([\"time\"],1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZADyAvpZSDR"
      },
      "source": [
        "X = df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "aGtuIHQXJyFE",
        "outputId": "fcc87c4c-5ece-400f-a8dc-597c8ac0732f"
      },
      "source": [
        "df"
      ],
      "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>hour</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>year</th>\n",
              "      <th>minute</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.164</td>\n",
              "      <td>0.994</td>\n",
              "      <td>1</td>\n",
              "      <td>-23.718</td>\n",
              "      <td>0</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>0.991000</td>\n",
              "      <td>0.7890</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>127.602</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1970</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.586</td>\n",
              "      <td>0.636</td>\n",
              "      <td>4</td>\n",
              "      <td>-7.710</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00208</td>\n",
              "      <td>0.322000</td>\n",
              "      <td>0.1070</td>\n",
              "      <td>0.35500</td>\n",
              "      <td>136.337</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1970</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.457</td>\n",
              "      <td>0.743</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.591</td>\n",
              "      <td>1</td>\n",
              "      <td>0.40400</td>\n",
              "      <td>0.009310</td>\n",
              "      <td>0.2010</td>\n",
              "      <td>0.68800</td>\n",
              "      <td>154.902</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1970</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.423</td>\n",
              "      <td>0.748</td>\n",
              "      <td>5</td>\n",
              "      <td>-9.832</td>\n",
              "      <td>1</td>\n",
              "      <td>0.03000</td>\n",
              "      <td>0.221000</td>\n",
              "      <td>0.1690</td>\n",
              "      <td>0.42800</td>\n",
              "      <td>93.977</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1970</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.802</td>\n",
              "      <td>0.756</td>\n",
              "      <td>2</td>\n",
              "      <td>-10.791</td>\n",
              "      <td>0</td>\n",
              "      <td>0.08430</td>\n",
              "      <td>0.007650</td>\n",
              "      <td>0.0521</td>\n",
              "      <td>0.96300</td>\n",
              "      <td>131.715</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1970</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\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>0.171</td>\n",
              "      <td>0.118</td>\n",
              "      <td>2</td>\n",
              "      <td>-25.360</td>\n",
              "      <td>1</td>\n",
              "      <td>0.86100</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>0.0932</td>\n",
              "      <td>0.05390</td>\n",
              "      <td>78.303</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1970</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12136</th>\n",
              "      <td>0.744</td>\n",
              "      <td>0.859</td>\n",
              "      <td>11</td>\n",
              "      <td>-3.166</td>\n",
              "      <td>1</td>\n",
              "      <td>0.02560</td>\n",
              "      <td>0.000063</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>0.78700</td>\n",
              "      <td>91.977</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1970</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12137</th>\n",
              "      <td>0.572</td>\n",
              "      <td>0.852</td>\n",
              "      <td>6</td>\n",
              "      <td>-4.712</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00105</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0844</td>\n",
              "      <td>0.37800</td>\n",
              "      <td>129.969</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1970</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12138</th>\n",
              "      <td>0.761</td>\n",
              "      <td>0.525</td>\n",
              "      <td>11</td>\n",
              "      <td>-6.900</td>\n",
              "      <td>1</td>\n",
              "      <td>0.44000</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.0921</td>\n",
              "      <td>0.53100</td>\n",
              "      <td>80.870</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1970</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12139</th>\n",
              "      <td>0.341</td>\n",
              "      <td>0.197</td>\n",
              "      <td>9</td>\n",
              "      <td>-19.350</td>\n",
              "      <td>1</td>\n",
              "      <td>0.98600</td>\n",
              "      <td>0.963000</td>\n",
              "      <td>0.0800</td>\n",
              "      <td>0.96500</td>\n",
              "      <td>115.107</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1970</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12140 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Store_Ratio  Basket_Ratio  Category_1  ...  day  year  minute\n",
              "0            0.164         0.994           1  ...    3  1970      40\n",
              "1            0.586         0.636           4  ...    3  1970      27\n",
              "2            0.457         0.743           0  ...    3  1970      54\n",
              "3            0.423         0.748           5  ...    4  1970      20\n",
              "4            0.802         0.756           2  ...    4  1970       4\n",
              "...            ...           ...         ...  ...  ...   ...     ...\n",
              "12135        0.171         0.118           2  ...    3  1970      20\n",
              "12136        0.744         0.859          11  ...    3  1970      39\n",
              "12137        0.572         0.852           6  ...    3  1970       6\n",
              "12138        0.761         0.525          11  ...    3  1970      20\n",
              "12139        0.341         0.197           9  ...    1  1970      16\n",
              "\n",
              "[12140 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1077
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJKNOze-ZVIy",
        "outputId": "adc483f2-59c6-46a8-b924-cdda5325ffec"
      },
      "source": [
        "X = scaler.transform(X)\r\n",
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.16432866, 0.9939987 , 0.09090909, ..., 0.1       , 0.        ,\n",
              "        0.6779661 ],\n",
              "       [0.58717435, 0.63592136, 0.36363636, ..., 0.1       , 0.        ,\n",
              "        0.45762712],\n",
              "       [0.45791583, 0.74294448, 0.        , ..., 0.1       , 0.        ,\n",
              "        0.91525424],\n",
              "       ...,\n",
              "       [0.57314629, 0.85196803, 0.54545455, ..., 0.1       , 0.        ,\n",
              "        0.10169492],\n",
              "       [0.76252505, 0.52489738, 1.        , ..., 0.1       , 0.        ,\n",
              "        0.33898305],\n",
              "       [0.34168337, 0.19682651, 0.81818182, ..., 0.        , 0.        ,\n",
              "        0.27118644]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1078
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVJlx-VhZZm2",
        "outputId": "c36983cd-406a-493b-89be-a65f728c62b7"
      },
      "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:    0.5s\n",
            "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:    1.2s\n",
            "[Parallel(n_jobs=2)]: Done 796 tasks      | elapsed:    2.1s\n",
            "[Parallel(n_jobs=2)]: Done 1000 out of 1000 | elapsed:    2.7s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6z64aegZkcl",
        "outputId": "2b62e5f2-d75b-4436-c0e0-2fcc5e60f061"
      },
      "source": [
        "pred_log"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.   , 0.085, 0.32 , 0.515, 0.08 ],\n",
              "       [0.   , 0.002, 0.017, 0.974, 0.007],\n",
              "       [0.   , 0.009, 0.041, 0.945, 0.005],\n",
              "       ...,\n",
              "       [0.   , 0.001, 0.013, 0.981, 0.005],\n",
              "       [0.   , 0.   , 0.003, 0.997, 0.   ],\n",
              "       [0.   , 0.053, 0.274, 0.633, 0.04 ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1080
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "sP_9UHj7Zl9t",
        "outputId": "e1991d14-7a07-4fc5-d51d-950980cfc47b"
      },
      "source": [
        "prediction  = pd.DataFrame(pred_log,columns =['0','1','3','4','5'])\r\n",
        "prediction"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "         0      1      3      4      5\n",
              "0      0.0  0.085  0.320  0.515  0.080\n",
              "1      0.0  0.002  0.017  0.974  0.007\n",
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              "...    ...    ...    ...    ...    ...\n",
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              "\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "mK-Jk8jJZsF5"
      },
      "source": [
        "prediction.to_csv('result_MP.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uskBZ-9aZviO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}