Industrial_copper EDA.ipynb

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      "source": [
        "# Importing libraries\n",
        "\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n"
      ]
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      "source": [
        "# Read the CSV file into a pandas dataframe\n",
        "df = pd.read_csv('indcopper.csv')\n",
        "df.head(5)"
      ],
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              "                                     id   item_date quantity tons    customer  \\\n",
              "0  EC06F063-9DF0-440C-8764-0B0C05A4F6AE  20210401.0     54.151139  30156308.0   \n",
              "1  4E5F4B3D-DDDF-499D-AFDE-A3227EC49425  20210401.0    768.024839  30202938.0   \n",
              "2  E140FF1B-2407-4C02-A0DD-780A093B1158  20210401.0    386.127949  30153963.0   \n",
              "3  F8D507A0-9C62-4EFE-831E-33E1DA53BB50  20210401.0    202.411065  30349574.0   \n",
              "4  4E1C4E78-152B-430A-8094-ADD889C9D0AD  20210401.0    785.526262  30211560.0   \n",
              "\n",
              "   country status item type  application  thickness   width  \\\n",
              "0     28.0    Won         W         10.0       2.00  1500.0   \n",
              "1     25.0    Won         W         41.0       0.80  1210.0   \n",
              "2     30.0    Won        WI         28.0       0.38   952.0   \n",
              "3     32.0    Won         S         59.0       2.30  1317.0   \n",
              "4     28.0    Won         W         10.0       4.00  2000.0   \n",
              "\n",
              "                               material_ref  product_ref  delivery date  \\\n",
              "0                               DEQ1 S460MC   1670798778     20210701.0   \n",
              "1  0000000000000000000000000000000000104991   1668701718     20210401.0   \n",
              "2                                  S0380700       628377     20210101.0   \n",
              "3                   DX51D+ZM310MAO 2.3X1317   1668701718     20210101.0   \n",
              "4                           2_S275JR+AR-CL1       640665     20210301.0   \n",
              "\n",
              "   selling_price  \n",
              "0         854.00  \n",
              "1        1047.00  \n",
              "2         644.33  \n",
              "3         768.00  \n",
              "4         577.00  "
            ],
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>item_date</th>\n",
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              "      <th>delivery date</th>\n",
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              "      <td>EC06F063-9DF0-440C-8764-0B0C05A4F6AE</td>\n",
              "      <td>20210401.0</td>\n",
              "      <td>54.151139</td>\n",
              "      <td>30156308.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>W</td>\n",
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              "      <td>DEQ1 S460MC</td>\n",
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              "      <td>25.0</td>\n",
              "      <td>Won</td>\n",
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              "      <td>1210.0</td>\n",
              "      <td>0000000000000000000000000000000000104991</td>\n",
              "      <td>1668701718</td>\n",
              "      <td>20210401.0</td>\n",
              "      <td>1047.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>E140FF1B-2407-4C02-A0DD-780A093B1158</td>\n",
              "      <td>20210401.0</td>\n",
              "      <td>386.127949</td>\n",
              "      <td>30153963.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>WI</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.38</td>\n",
              "      <td>952.0</td>\n",
              "      <td>S0380700</td>\n",
              "      <td>628377</td>\n",
              "      <td>20210101.0</td>\n",
              "      <td>644.33</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F8D507A0-9C62-4EFE-831E-33E1DA53BB50</td>\n",
              "      <td>20210401.0</td>\n",
              "      <td>202.411065</td>\n",
              "      <td>30349574.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>S</td>\n",
              "      <td>59.0</td>\n",
              "      <td>2.30</td>\n",
              "      <td>1317.0</td>\n",
              "      <td>DX51D+ZM310MAO 2.3X1317</td>\n",
              "      <td>1668701718</td>\n",
              "      <td>20210101.0</td>\n",
              "      <td>768.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4E1C4E78-152B-430A-8094-ADD889C9D0AD</td>\n",
              "      <td>20210401.0</td>\n",
              "      <td>785.526262</td>\n",
              "      <td>30211560.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>W</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2_S275JR+AR-CL1</td>\n",
              "      <td>640665</td>\n",
              "      <td>20210301.0</td>\n",
              "      <td>577.00</td>\n",
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      "cell_type": "code",
      "source": [
        "print(len(df['item_date'].unique())) #\n",
        "print(len(df['customer'].unique())) #\n",
        "print(len(df['material_ref'].unique()))\n",
        "print(len(df['product_ref'].unique())) #\n",
        "print(len(df['delivery date'].unique())) #"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgPtS4tlKd6Z",
        "outputId": "333da767-2428-40e0-92d9-d1828aa8de97"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "253\n",
            "1170\n",
            "16564\n",
            "33\n",
            "29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Null values, shape and data types before dealing with incorrect data"
      ],
      "metadata": {
        "id": "51lhhT6LKkYC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
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        "id": "SjfFHu9rKi6s",
        "outputId": "a7b076a6-94be-481c-e732-9aea19b0c3a4"
      },
      "execution_count": 4,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(181673, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# missing values\n",
        "missing_values_count = df.isnull().sum()\n",
        "print(missing_values_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83iHvVshKs6g",
        "outputId": "097413a1-7e3f-486c-a1e2-543a890711ab"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                   2\n",
            "item_date            1\n",
            "quantity tons        0\n",
            "customer             1\n",
            "country             28\n",
            "status               2\n",
            "item type            0\n",
            "application         24\n",
            "thickness            1\n",
            "width                0\n",
            "material_ref     77919\n",
            "product_ref          0\n",
            "delivery date        1\n",
            "selling_price        1\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
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        },
        "id": "dsousQXFKw0L",
        "outputId": "bbff262f-fcc2-409f-a497-3cbe70814a41"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 181673 entries, 0 to 181672\n",
            "Data columns (total 14 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   id             181671 non-null  object \n",
            " 1   item_date      181672 non-null  float64\n",
            " 2   quantity tons  181673 non-null  object \n",
            " 3   customer       181672 non-null  float64\n",
            " 4   country        181645 non-null  float64\n",
            " 5   status         181671 non-null  object \n",
            " 6   item type      181673 non-null  object \n",
            " 7   application    181649 non-null  float64\n",
            " 8   thickness      181672 non-null  float64\n",
            " 9   width          181673 non-null  float64\n",
            " 10  material_ref   103754 non-null  object \n",
            " 11  product_ref    181673 non-null  int64  \n",
            " 12  delivery date  181672 non-null  float64\n",
            " 13  selling_price  181672 non-null  float64\n",
            "dtypes: float64(8), int64(1), object(5)\n",
            "memory usage: 19.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dealing with data in wrong format"
      ],
      "metadata": {
        "id": "VzfdIgX1K1vM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dealing with data in wrong format\n",
        "# for categorical variables, this step is ignored\n",
        "# df = df[df['status'].isin(['Won', 'Lost'])]\n",
        "df['item_date'] = pd.to_datetime(df['item_date'], format='%Y%m%d', errors='coerce').dt.date\n",
        "df['quantity tons'] = pd.to_numeric(df['quantity tons'], errors='coerce')\n",
        "df['customer'] = pd.to_numeric(df['customer'], errors='coerce')\n",
        "df['country'] = pd.to_numeric(df['country'], errors='coerce')\n",
        "df['application'] = pd.to_numeric(df['application'], errors='coerce')\n",
        "df['thickness'] = pd.to_numeric(df['thickness'], errors='coerce')\n",
        "df['width'] = pd.to_numeric(df['width'], errors='coerce')\n",
        "df['material_ref'] = df['material_ref'].str.lstrip('0')\n",
        "df['product_ref'] = pd.to_numeric(df['product_ref'], errors='coerce')\n",
        "df['delivery date'] = pd.to_datetime(df['delivery date'], format='%Y%m%d', errors='coerce').dt.date\n",
        "df['selling_price'] = pd.to_numeric(df['selling_price'], errors='coerce')"
      ],
      "metadata": {
        "id": "Z56KzDZsKzUg"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Nan values and shape after dealing with data in wrong format"
      ],
      "metadata": {
        "id": "2YIeXzEjK_-7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values_count = df.isnull().sum()\n",
        "print(missing_values_count)\n",
        "print(df.shape)\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EDGjlzvTLF93",
        "outputId": "a8ca60c3-5357-4fb0-82bc-6e42dde7060f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id               0\n",
            "item_date        2\n",
            "quantity tons    1\n",
            "customer         0\n",
            "country          0\n",
            "status           0\n",
            "item type        0\n",
            "application      0\n",
            "thickness        0\n",
            "width            0\n",
            "material_ref     0\n",
            "product_ref      0\n",
            "delivery date    2\n",
            "selling_price    0\n",
            "dtype: int64\n",
            "(181636, 14)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 181636 entries, 0 to 181672\n",
            "Data columns (total 14 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   id             181636 non-null  object \n",
            " 1   item_date      181634 non-null  object \n",
            " 2   quantity tons  181635 non-null  float64\n",
            " 3   customer       181636 non-null  float64\n",
            " 4   country        181636 non-null  float64\n",
            " 5   status         181636 non-null  object \n",
            " 6   item type      181636 non-null  object \n",
            " 7   application    181636 non-null  float64\n",
            " 8   thickness      181636 non-null  float64\n",
            " 9   width          181636 non-null  float64\n",
            " 10  material_ref   181636 non-null  object \n",
            " 11  product_ref    181636 non-null  int64  \n",
            " 12  delivery date  181634 non-null  object \n",
            " 13  selling_price  181636 non-null  float64\n",
            "dtypes: float64(7), int64(1), object(6)\n",
            "memory usage: 20.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dealing with Missing Values"
      ],
      "metadata": {
        "id": "iPrLinaFLKyS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# material_ref has large set of null values, so replacing them with unknown\n",
        "df['material_ref'].fillna('unknown', inplace=True)\n",
        "# deleting the remaining null values as they are less than 1% of data which can be neglected\n",
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "nZfNd1luLIUA"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values_count = df.isnull().sum()\n",
        "print(missing_values_count)\n",
        "print(df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_ZvC1noLSyE",
        "outputId": "494c9386-72f8-4e63-dd2b-b91329c8c53f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id               0\n",
            "item_date        0\n",
            "quantity tons    0\n",
            "customer         0\n",
            "country          0\n",
            "status           0\n",
            "item type        0\n",
            "application      0\n",
            "thickness        0\n",
            "width            0\n",
            "material_ref     0\n",
            "product_ref      0\n",
            "delivery date    0\n",
            "selling_price    0\n",
            "dtype: int64\n",
            "(181631, 14)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_p=df.copy()"
      ],
      "metadata": {
        "id": "gJ2WqkM8LVrO"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PLOTTING"
      ],
      "metadata": {
        "id": "fEsTT4HYLfmh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ['quantity tons','selling_price','application','thickness','width', 'country']\n",
        "# ['status','item type']\n",
        "\n",
        "df_p['quantity tons']   # skewed\n",
        "df_p['country']\n",
        "df_p['application']\n",
        "df_p['thickness']       # skewed\n",
        "df_p['width']\n",
        "df_p['selling_price']   # skewed\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.distplot(df_p['quantity tons'])\n",
        "plt.show()\n",
        "sns.distplot(df_p['country'])\n",
        "plt.show()\n",
        "sns.distplot(df_p['application'])\n",
        "plt.show()\n",
        "sns.distplot(df_p['thickness'])\n",
        "plt.show()\n",
        "sns.distplot(df_p['width'])\n",
        "plt.show()\n",
        "sns.distplot(df_p['selling_price'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vUU_WkeMLed9",
        "outputId": "56a95d18-f7f0-4b2c-ae46-8240e462e2be"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "mask1 = df_p['selling_price'] <= 0\n",
        "print(mask1.sum())\n",
        "df_p.loc[mask1, 'selling_price'] = np.nan\n",
        "\n",
        "mask1 = df_p['quantity tons'] <= 0\n",
        "print(mask1.sum())\n",
        "df_p.loc[mask1, 'quantity tons'] = np.nan\n",
        "\n",
        "mask1 = df_p['thickness'] <= 0\n",
        "print(mask1.sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xu6D_15bLlQ1",
        "outputId": "c82b508a-9c05-4e5c-8329-12bbd5fa035d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n",
            "4\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_p.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FjM9NNh3MeLU",
        "outputId": "e14d9748-636e-4501-a25d-5f6e60f4f3ca"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id               0\n",
              "item_date        0\n",
              "quantity tons    4\n",
              "customer         0\n",
              "country          0\n",
              "status           0\n",
              "item type        0\n",
              "application      0\n",
              "thickness        0\n",
              "width            0\n",
              "material_ref     0\n",
              "product_ref      0\n",
              "delivery date    0\n",
              "selling_price    7\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_p.dropna(inplace=True)\n",
        "len(df_p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2X-pais4Mgja",
        "outputId": "06becc2e-946c-401e-c0e5-dd2487a053e8"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "181620"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "df_p['selling_price_log'] = np.log(df_p['selling_price'])\n",
        "sns.distplot(df_p['selling_price_log'])\n",
        "plt.show()\n",
        "\n",
        "df_p['quantity tons_log'] = np.log(df_p['quantity tons'])\n",
        "sns.distplot(df_p['quantity tons_log'])\n",
        "plt.show()\n",
        "\n",
        "df_p['thickness_log'] = np.log(df_p['thickness'])\n",
        "sns.distplot(df_p['thickness_log'])\n",
        "plt.show()\n",
        "# reverts log\n",
        "# df_p['reverted_values'] = np.exp(df_p['selling_price_log'])\n",
        "# sns.distplot(df_p['reverted_values'])\n",
        "# plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ASql-1OqMj1x",
        "outputId": "53262794-5974-481d-ed6a-9aec253905c8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_p.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "QBQbvmsbMohr",
        "outputId": "3721189f-b1f7-45e3-8305-8e058e4d76a4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     id   item_date  quantity tons  \\\n",
              "0  EC06F063-9DF0-440C-8764-0B0C05A4F6AE  2021-04-01      54.151139   \n",
              "1  4E5F4B3D-DDDF-499D-AFDE-A3227EC49425  2021-04-01     768.024839   \n",
              "2  E140FF1B-2407-4C02-A0DD-780A093B1158  2021-04-01     386.127949   \n",
              "3  F8D507A0-9C62-4EFE-831E-33E1DA53BB50  2021-04-01     202.411065   \n",
              "4  4E1C4E78-152B-430A-8094-ADD889C9D0AD  2021-04-01     785.526262   \n",
              "\n",
              "     customer  country status item type  application  thickness   width  \\\n",
              "0  30156308.0     28.0    Won         W         10.0       2.00  1500.0   \n",
              "1  30202938.0     25.0    Won         W         41.0       0.80  1210.0   \n",
              "2  30153963.0     30.0    Won        WI         28.0       0.38   952.0   \n",
              "3  30349574.0     32.0    Won         S         59.0       2.30  1317.0   \n",
              "4  30211560.0     28.0    Won         W         10.0       4.00  2000.0   \n",
              "\n",
              "              material_ref  product_ref delivery date  selling_price  \\\n",
              "0              DEQ1 S460MC   1670798778    2021-07-01         854.00   \n",
              "1                   104991   1668701718    2021-04-01        1047.00   \n",
              "2                 S0380700       628377    2021-01-01         644.33   \n",
              "3  DX51D+ZM310MAO 2.3X1317   1668701718    2021-01-01         768.00   \n",
              "4          2_S275JR+AR-CL1       640665    2021-03-01         577.00   \n",
              "\n",
              "   selling_price_log  quantity tons_log  thickness_log  \n",
              "0           6.749931           3.991779       0.693147  \n",
              "1           6.953684           6.643822      -0.223144  \n",
              "2           6.468211           5.956169      -0.967584  \n",
              "3           6.643790           5.310301       0.832909  \n",
              "4           6.357842           6.666354       1.386294  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-69b895b3-ab39-4894-9ae7-fcf09e1de209\" class=\"colab-df-container\">\n",
              "    <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>id</th>\n",
              "      <th>item_date</th>\n",
              "      <th>quantity tons</th>\n",
              "      <th>customer</th>\n",
              "      <th>country</th>\n",
              "      <th>status</th>\n",
              "      <th>item type</th>\n",
              "      <th>application</th>\n",
              "      <th>thickness</th>\n",
              "      <th>width</th>\n",
              "      <th>material_ref</th>\n",
              "      <th>product_ref</th>\n",
              "      <th>delivery date</th>\n",
              "      <th>selling_price</th>\n",
              "      <th>selling_price_log</th>\n",
              "      <th>quantity tons_log</th>\n",
              "      <th>thickness_log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>EC06F063-9DF0-440C-8764-0B0C05A4F6AE</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>54.151139</td>\n",
              "      <td>30156308.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>W</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>DEQ1 S460MC</td>\n",
              "      <td>1670798778</td>\n",
              "      <td>2021-07-01</td>\n",
              "      <td>854.00</td>\n",
              "      <td>6.749931</td>\n",
              "      <td>3.991779</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4E5F4B3D-DDDF-499D-AFDE-A3227EC49425</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>768.024839</td>\n",
              "      <td>30202938.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>W</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1210.0</td>\n",
              "      <td>104991</td>\n",
              "      <td>1668701718</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>1047.00</td>\n",
              "      <td>6.953684</td>\n",
              "      <td>6.643822</td>\n",
              "      <td>-0.223144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>E140FF1B-2407-4C02-A0DD-780A093B1158</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>386.127949</td>\n",
              "      <td>30153963.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>WI</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.38</td>\n",
              "      <td>952.0</td>\n",
              "      <td>S0380700</td>\n",
              "      <td>628377</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>644.33</td>\n",
              "      <td>6.468211</td>\n",
              "      <td>5.956169</td>\n",
              "      <td>-0.967584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F8D507A0-9C62-4EFE-831E-33E1DA53BB50</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>202.411065</td>\n",
              "      <td>30349574.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>S</td>\n",
              "      <td>59.0</td>\n",
              "      <td>2.30</td>\n",
              "      <td>1317.0</td>\n",
              "      <td>DX51D+ZM310MAO 2.3X1317</td>\n",
              "      <td>1668701718</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>768.00</td>\n",
              "      <td>6.643790</td>\n",
              "      <td>5.310301</td>\n",
              "      <td>0.832909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4E1C4E78-152B-430A-8094-ADD889C9D0AD</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>785.526262</td>\n",
              "      <td>30211560.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>W</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2_S275JR+AR-CL1</td>\n",
              "      <td>640665</td>\n",
              "      <td>2021-03-01</td>\n",
              "      <td>577.00</td>\n",
              "      <td>6.357842</td>\n",
              "      <td>6.666354</td>\n",
              "      <td>1.386294</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-69b895b3-ab39-4894-9ae7-fcf09e1de209')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-69b895b3-ab39-4894-9ae7-fcf09e1de209 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-69b895b3-ab39-4894-9ae7-fcf09e1de209');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d9b1603c-1e63-419e-ae82-ca725b398fea\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d9b1603c-1e63-419e-ae82-ca725b398fea')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d9b1603c-1e63-419e-ae82-ca725b398fea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df_p[['quantity tons_log','application','thickness_log','width','selling_price_log','country','customer','product_ref']].corr()\n",
        "sns.heatmap(x, annot=True, cmap=\"YlGnBu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "NfIxu-xbMtNw",
        "outputId": "5c3564c7-e2cf-4e1b-fbe3-4113869af609"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DECISION TREE REGRESSOR\n",
        "\n",
        "-  Encode categorical features\n",
        "\n",
        "- Test and Train split\n",
        "\n",
        "- Define Hyperparameters for GridSearchCV\n",
        "\n",
        "- Train the DecisionTreeRegressor with best parameters\n",
        "\n",
        "- Evalution metrics\n",
        "\n",
        "- Predict the selling price for new values\n",
        "\n",
        "- Save the DecisionTreeRegressor model"
      ],
      "metadata": {
        "id": "kWVgj-iNM5yH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "X=df_p[['quantity tons_log','status','item type','application','thickness_log','width','country','customer','product_ref']]\n",
        "y=df_p['selling_price_log']\n",
        "# encoding categorical variables\n",
        "ohe = OneHotEncoder(handle_unknown='ignore')\n",
        "ohe.fit(X[['item type']])\n",
        "X_ohe = ohe.fit_transform(X[['item type']]).toarray()\n",
        "ohe2 = OneHotEncoder(handle_unknown='ignore')\n",
        "ohe2.fit(X[['status']])\n",
        "X_be = ohe2.fit_transform(X[['status']]).toarray()\n",
        "# independent features after encoding\n",
        "X = np.concatenate((X[['quantity tons_log', 'application', 'thickness_log', 'width','country','customer','product_ref']].values, X_ohe, X_be), axis=1)\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "# test and train split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
        "# decision tree\n",
        "dtr = DecisionTreeRegressor()\n",
        "# hyperparameters\n",
        "param_grid = {'max_depth': [2, 5, 10, 20],\n",
        "              'min_samples_split': [2, 5, 10],\n",
        "              'min_samples_leaf': [1, 2, 4],\n",
        "              'max_features': ['auto', 'sqrt', 'log2']}\n",
        "# gridsearchcv\n",
        "grid_search = GridSearchCV(estimator=dtr, param_grid=param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "print(\"Best hyperparameters:\", grid_search.best_params_)\n",
        "best_model = grid_search.best_estimator_\n",
        "y_pred = best_model.predict(X_test)\n",
        "# evalution metrics\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print('Mean squared error:', mse)\n",
        "print('R-squared:', r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLnrzP3QM2rJ",
        "outputId": "eb0ce66b-bb66-42ed-fb04-849c421ec364"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters: {'max_depth': 20, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10}\n",
            "Mean squared error: 0.022964222252982253\n",
            "R-squared: 0.928720396368807\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ['quantity tons_log', 'application', 'thickness_log', 'width','country','customer','product_ref']].values, X_ohe, X_be\n",
        "new_sample = np.array([[np.log(40), 10, np.log(250), 0, 28,30202938,1670798778,'PL','Won']])\n",
        "new_sample_ohe = ohe.transform(new_sample[:, [7]]).toarray()\n",
        "new_sample_be = ohe2.transform(new_sample[:, [8]]).toarray()\n",
        "new_sample = np.concatenate((new_sample[:, [0,1,2, 3, 4, 5, 6,]], new_sample_ohe, new_sample_be), axis=1)\n",
        "new_sample1 = scaler.transform(new_sample)\n",
        "new_pred = best_model.predict(new_sample1)\n",
        "print('Predicted selling price:', np.exp(new_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69_ntbYXOZh4",
        "outputId": "40308a8c-082d-4aab-af5f-fb5551b0f0fa"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted selling price: [706.961582]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving the model\n",
        "import pickle\n",
        "with open('model.pkl', 'wb') as file:\n",
        "    pickle.dump(best_model, file)\n",
        "with open('scaler.pkl', 'wb') as f:\n",
        "    pickle.dump(scaler, f)\n",
        "with open('t.pkl', 'wb') as f:\n",
        "    pickle.dump(ohe, f)\n",
        "with open('s.pkl', 'wb') as f:\n",
        "    pickle.dump(ohe2, f)"
      ],
      "metadata": {
        "id": "lL1a4a5rRoIT"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df_p))\n",
        "df_p.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "uC1pd8DXRv6W",
        "outputId": "afe01ae4-5ff5-45d5-e5d6-c093b5c7974b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "181620\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     id   item_date  quantity tons  \\\n",
              "0  EC06F063-9DF0-440C-8764-0B0C05A4F6AE  2021-04-01      54.151139   \n",
              "1  4E5F4B3D-DDDF-499D-AFDE-A3227EC49425  2021-04-01     768.024839   \n",
              "2  E140FF1B-2407-4C02-A0DD-780A093B1158  2021-04-01     386.127949   \n",
              "\n",
              "     customer  country status item type  application  thickness   width  \\\n",
              "0  30156308.0     28.0    Won         W         10.0       2.00  1500.0   \n",
              "1  30202938.0     25.0    Won         W         41.0       0.80  1210.0   \n",
              "2  30153963.0     30.0    Won        WI         28.0       0.38   952.0   \n",
              "\n",
              "  material_ref  product_ref delivery date  selling_price  selling_price_log  \\\n",
              "0  DEQ1 S460MC   1670798778    2021-07-01         854.00           6.749931   \n",
              "1       104991   1668701718    2021-04-01        1047.00           6.953684   \n",
              "2     S0380700       628377    2021-01-01         644.33           6.468211   \n",
              "\n",
              "   quantity tons_log  thickness_log  \n",
              "0           3.991779       0.693147  \n",
              "1           6.643822      -0.223144  \n",
              "2           5.956169      -0.967584  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4db350c2-7b7e-482c-acd3-16fe19257b77\" class=\"colab-df-container\">\n",
              "    <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",
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              "\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>id</th>\n",
              "      <th>item_date</th>\n",
              "      <th>quantity tons</th>\n",
              "      <th>customer</th>\n",
              "      <th>country</th>\n",
              "      <th>status</th>\n",
              "      <th>item type</th>\n",
              "      <th>application</th>\n",
              "      <th>thickness</th>\n",
              "      <th>width</th>\n",
              "      <th>material_ref</th>\n",
              "      <th>product_ref</th>\n",
              "      <th>delivery date</th>\n",
              "      <th>selling_price</th>\n",
              "      <th>selling_price_log</th>\n",
              "      <th>quantity tons_log</th>\n",
              "      <th>thickness_log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>EC06F063-9DF0-440C-8764-0B0C05A4F6AE</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>54.151139</td>\n",
              "      <td>30156308.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>W</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>DEQ1 S460MC</td>\n",
              "      <td>1670798778</td>\n",
              "      <td>2021-07-01</td>\n",
              "      <td>854.00</td>\n",
              "      <td>6.749931</td>\n",
              "      <td>3.991779</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4E5F4B3D-DDDF-499D-AFDE-A3227EC49425</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>768.024839</td>\n",
              "      <td>30202938.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>W</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1210.0</td>\n",
              "      <td>104991</td>\n",
              "      <td>1668701718</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>1047.00</td>\n",
              "      <td>6.953684</td>\n",
              "      <td>6.643822</td>\n",
              "      <td>-0.223144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>E140FF1B-2407-4C02-A0DD-780A093B1158</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>386.127949</td>\n",
              "      <td>30153963.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Won</td>\n",
              "      <td>WI</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.38</td>\n",
              "      <td>952.0</td>\n",
              "      <td>S0380700</td>\n",
              "      <td>628377</td>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>644.33</td>\n",
              "      <td>6.468211</td>\n",
              "      <td>5.956169</td>\n",
              "      <td>-0.967584</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4db350c2-7b7e-482c-acd3-16fe19257b77 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4db350c2-7b7e-482c-acd3-16fe19257b77');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6ffcbb1f-bc37-4b0e-ba53-15092a3629c6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6ffcbb1f-bc37-4b0e-ba53-15092a3629c6')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6ffcbb1f-bc37-4b0e-ba53-15092a3629c6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_c = df_p[df_p['status'].isin(['Won', 'Lost'])]\n",
        "len(df_c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0GQ1aMySCxi",
        "outputId": "0528b560-24f3-467f-e0c6-0cfbcc4890d9"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "150427"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DECISION TREE CLASSIFIER"
      ],
      "metadata": {
        "id": "pm67-xUVSHlc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder,LabelBinarizer\n",
        "\n",
        "Y = df_c['status']\n",
        "X= df_c[['quantity tons_log','selling_price_log','item type','application','thickness_log','width','country','customer','product_ref']]\n",
        "\n",
        "# encoding categorical variables\n",
        "ohe = OneHotEncoder(handle_unknown='ignore')\n",
        "ohe.fit(X[['item type']])\n",
        "X_ohe = ohe.fit_transform(X[['item type']]).toarray()\n",
        "be = LabelBinarizer()\n",
        "be.fit(Y)\n",
        "y = be.fit_transform(Y)\n",
        "# independent features after encoding\n",
        "X = np.concatenate((X[['quantity tons_log', 'selling_price_log','application', 'thickness_log', 'width','country','customer','product_ref']].values, X_ohe), axis=1)\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# decision tree classifier\n",
        "dtc = DecisionTreeClassifier()\n",
        "dtc.fit(X_train, y_train)\n",
        "y_pred = dtc.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(f\"Confusion Matrix:\\n{cm}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-8mGv6SSF4L",
        "outputId": "001db423-15d7-475b-f1cf-f13c00473b29"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9113541181945091\n",
            "Confusion Matrix:\n",
            "[[ 5668  1334]\n",
            " [ 1333 21751]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evalution Metrics\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc\n",
        "import matplotlib.pyplot as plt\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "# ROC curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver operating characteristic')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "7revy5GGSTjn",
        "outputId": "d9e4af63-16b1-463d-e0fd-c2995b7f91c5"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[ 5668  1334]\n",
            " [ 1333 21751]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.81      0.81      7002\n",
            "           1       0.94      0.94      0.94     23084\n",
            "\n",
            "    accuracy                           0.91     30086\n",
            "   macro avg       0.88      0.88      0.88     30086\n",
            "weighted avg       0.91      0.91      0.91     30086\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the status for a new sample\n",
        "# 'quantity tons_log', 'selling_price_log','application', 'thickness_log', 'width','country','customer','product_ref']].values, X_ohe\n",
        "new_sample = np.array([[np.log(700), np.log(956), 10, np.log(2),1500,28.0,30202938,1670798778,'W']])\n",
        "new_sample_ohe = ohe.transform(new_sample[:, [8]]).toarray()\n",
        "new_sample = np.concatenate((new_sample[:, [0,1,2, 3, 4, 5, 6,7]], new_sample_ohe), axis=1)\n",
        "new_sample = scaler.transform(new_sample)\n",
        "new_pred = dtc.predict(new_sample)\n",
        "if new_pred==1:\n",
        "    print('The status is: Won')\n",
        "else:\n",
        "    print('The status is: Lost')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YGT1RRh-Sbrf",
        "outputId": "f07639c9-d4cd-40b8-99e7-31f668d48485"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The status is: Won\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving the model\n",
        "import pickle\n",
        "with open('clsmodel.pkl', 'wb') as file:\n",
        "    pickle.dump(dtc, file)\n",
        "with open('cscaler.pkl', 'wb') as f:\n",
        "    pickle.dump(scaler, f)\n",
        "with open('ct.pkl', 'wb') as f:\n",
        "    pickle.dump(ohe, f)"
      ],
      "metadata": {
        "id": "XfvNDOfNSfme"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "# List of file paths to download\n",
        "file_paths = ['/content/model.pkl', '/content/scaler.pkl', '/content/t.pkl','/content/s.pkl','/content/clsmodel.pkl','/content/cscaler.pkl','/content/ct.pkl']\n",
        "\n",
        "# Loop through each file path and download it\n",
        "for file_path in file_paths:\n",
        "    files.download(file_path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "wycLhwzfSjLx",
        "outputId": "7141a647-58a6-425d-e1a7-b5856e4b499a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_1924c709-76c7-41a4-aa63-ec72422a8506\", \"model.pkl\", 2019110)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_04b52d02-3205-4728-a85d-a68abeb250b4\", \"scaler.pkl\", 1001)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_3fbfd077-fd0d-4140-9cc1-af504b3ad490\", \"t.pkl\", 606)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_dad91688-6136-42e6-b786-a0f0ec37f695\", \"s.pkl\", 662)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_d4291583-7d5b-4a97-ace1-498ce2015e66\", \"clsmodel.pkl\", 1356051)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_978c223b-6311-47f5-9c52-ca2d33928ad0\", \"cscaler.pkl\", 809)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_bd4cc171-8299-4159-adec-9299f218a7ac\", \"ct.pkl\", 606)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1nueZHgUUKt5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}