{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m23ypqC8r6Xk"
   },
   "source": [
    "## Logistic Regression:\n",
    "\n",
    "### **Business Problem Definition:**\n",
    "The business problem revolves around **predicting the likelihood of order cancellations** for a product fulfillment company based on historical order data. This prediction helps the company:\n",
    "\n",
    "- `Proactively address potential cancellations,`\n",
    "- `Optimize operations, and`\n",
    "- `Improve customer satisfaction.`\n",
    "\n",
    "By identifying high-risk orders early, the business `can take preventive actions`, such as offering incentives or expedited shipping, to reduce the likelihood of cancellations.\n",
    "\n",
    "This is a `binary classification` problem where the target variable is the `Order_Cancelled` column.\n",
    "\n",
    "The model needs to classify whether an order will be cancelled based on features like the delivery time, order value, region, and other order-related attributes.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jw4-U7HQXeFa"
   },
   "source": [
    "## Dataset Description:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MLcEsAn-0qtm"
   },
   "source": [
    "Explanation of Attributes/Target Variable:\n",
    "\n",
    "\\\n",
    "Each row represents an `order placed by the customer` while columms represent the varied specifics of that order.\n",
    "\n",
    "\\\n",
    "\n",
    "- `Days_to_Delivery` (numeric):  \n",
    "  The number of days it takes for the order to be delivered, generated based on a normal distribution with a mean of 5 days and a standard deviation of 2 days.\n",
    "\n",
    "- `Num_Items_Ordered` (numeric):  \n",
    "  The total number of items in the order, represented as an integer between 1 and 20, reflecting the quantity of products ordered.\n",
    "\n",
    "- `Order_Value` (numeric):  \n",
    "  The total value of the order in USD. It is generated from a normal distribution centered around $500, with a standard deviation of $100.\n",
    "\n",
    "- `Discount_Rate` (numeric):  \n",
    "  The discount rate applied to the order, represented as a value between 0 and 0.5, indicating various discounts offered during sales.\n",
    "\n",
    "- `Num_Previous_Orders` (numeric):  \n",
    "  The number of previous orders placed by the customer. It is an integer between 0 and 10.\n",
    "\n",
    "- `Delivery_Time_Variation` (numeric):  \n",
    "  The variation between the estimated and actual delivery time, measured in days, with values ranging from 0 to 3 days.\n",
    "\n",
    "- `Region` (categorical):  \n",
    "  The geographic region of the customer. Possible values:\n",
    "  - North America\n",
    "  - EMEA (Europe, Middle East, Africa)\n",
    "  - APAC (Asia-Pacific)\n",
    "  - LATAM (Latin America)\n",
    "\n",
    "- `Product_Category` (categorical)\n",
    "  The category of the product ordered. Possible values:\n",
    "  - Cloud\n",
    "  - On-premise\n",
    "  - SaaS (Software as a Service)\n",
    "  - Hardware\n",
    "\n",
    "- `Order_Priority` (categorical):  \n",
    "  The urgency level of the order. Possible values:\n",
    "  - Low\n",
    "  - Medium\n",
    "  - High\n",
    "\n",
    "- `Payment_Method` (categorical):  \n",
    "  The method used for payment in the order. Possible values include:\n",
    "  - Credit Card\n",
    "  - Bank Transfer\n",
    "  - PayPal\n",
    "  - Bitcoin\n",
    "\n",
    "- `Correlated_Order_Value` (numeric):  \n",
    "  Represents an alternative estimation of the total order value, calculated by factoring in **historical customer spending behavior** and **product pricing trends**.It incorporates additional business insights such as customer loyalty and purchasing history.\n",
    "\n",
    "\n",
    "- `Order_Cancelled (Target):` This is the target column indicating whether the order was cancelled or not. Values are \"Cancelled\" or \"Not-Cancelled\".\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OzrRK5hmMpq1"
   },
   "source": [
    "### Comprehensive Data Science Project Workflow: From Business Understanding to Model Monitoring:\n",
    "\n",
    "1. `Business Understanding` (Define project goals and objectives.)\n",
    "\n",
    "2. `Data Requirement` (Identify necessary data for analysis)\n",
    "\n",
    "3. `Data Collection` (Data gathering from different sources with varied tools and technologies)\n",
    "\n",
    "4. `Data Preparation` (EDA/Data Preparation/Data Cleaning/Data Munging)\n",
    "\n",
    "5. `Data Modeling` ( Clean Data + Algorithms = Model)\n",
    "\n",
    "6. `Model Evaluation`  (Test Model perf)\n",
    "\n",
    "7. `Model Tuning`(Optimize model hyperparameters)\n",
    "\n",
    "8. `Model Deployment`(Deploy model for real-time use)\n",
    "\n",
    "9. `Monitoring`(Track model performance over time)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wESk_sbeMvmu"
   },
   "source": [
    "### EDA/ Data Preparation/Data Cleaning Steps:\n",
    "\n",
    "1. `Removing Duplicate data`\n",
    "2. `Missing Value Treatment`\n",
    "3. `Outlier Treatment`\n",
    "4. `Categorical to Numerical Conversion`\n",
    "5. `Numerical to Categorical Conversion`\n",
    "6. `Feature Scaling`\n",
    "7. `Feature Transformation`\n",
    "8. `Feature selection`\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-1Tl3l3pMzFp"
   },
   "source": [
    "1. Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "BZ1UmMexcFEh",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "80c1f9b1-9f3f-45b9-ce42-312c1107ef1d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting statsmodels\n",
      "  Downloading statsmodels-0.14.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.2 kB)\n",
      "Requirement already satisfied: numpy<3,>=1.22.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.26.4)\n",
      "Requirement already satisfied: scipy!=1.9.2,>=1.8 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.13.1)\n",
      "Requirement already satisfied: pandas!=2.1.0,>=1.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (2.1.4)\n",
      "Collecting patsy>=0.5.6 (from statsmodels)\n",
      "  Downloading patsy-0.5.6-py2.py3-none-any.whl.metadata (3.5 kB)\n",
      "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (24.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas!=2.1.0,>=1.4->statsmodels) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=2.1.0,>=1.4->statsmodels) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=2.1.0,>=1.4->statsmodels) (2024.1)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.6->statsmodels) (1.16.0)\n",
      "Downloading statsmodels-0.14.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.8 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.8/10.8 MB\u001b[0m \u001b[31m89.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading patsy-0.5.6-py2.py3-none-any.whl (233 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m233.9/233.9 kB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: patsy, statsmodels\n",
      "Successfully installed patsy-0.5.6 statsmodels-0.14.3\n"
     ]
    }
   ],
   "source": [
    "# %pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "_CcitolYMyph"
   },
   "outputs": [],
   "source": [
    "# Importing all necessary Libaries: Data Science Packages\n",
    "\n",
    "import numpy as np # numpy used for mathematical operation on array\n",
    "import pandas as pd  # pandas used for data manipulation on dataframe\n",
    "import seaborn as sns # seaborn used for data visualization\n",
    "import matplotlib.pyplot as plt # matplotlib used for data visualization\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "tjP2FYHmM1_B"
   },
   "outputs": [],
   "source": [
    "# Read the data with pandas\n",
    "\n",
    "df = pd.read_csv(\"order_mgmt_binary_classification.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "id": "O-bgGSmiM2B6",
    "outputId": "bf28022a-3881-4b83-ad1f-ecae22b9e45f"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Days_to_Delivery</th>\n",
       "      <th>Num_Items_Ordered</th>\n",
       "      <th>Order_Value</th>\n",
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       "      <td>On-premise</td>\n",
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       "      <td>Not-Cancelled</td>\n",
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       "      <td>PayPal</td>\n",
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       "      <td>Not-Cancelled</td>\n",
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       "      <td>0.465772</td>\n",
       "      <td>2</td>\n",
       "      <td>2.011061</td>\n",
       "      <td>APAC</td>\n",
       "      <td>Hardware</td>\n",
       "      <td>High</td>\n",
       "      <td>Bank Transfer</td>\n",
       "      <td>581.693494</td>\n",
       "      <td>Not-Cancelled</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Days_to_Delivery  Num_Items_Ordered  Order_Value  Discount_Rate  \\\n",
       "0          5.993428                  6   716.505607       0.098621   \n",
       "1          4.723471                 11   619.054859       0.106769   \n",
       "2          6.295377                  3   521.257403       0.338047   \n",
       "3          8.046060                  8   602.698626       0.202501   \n",
       "4          4.531693                  3   610.590042       0.465772   \n",
       "\n",
       "   Num_Previous_Orders  Delivery_Time_Variation Region Product_Category  \\\n",
       "0                    5                 1.129165   EMEA       On-premise   \n",
       "1                    7                 0.889863   APAC            Cloud   \n",
       "2                    0                 2.668340  LATAM       On-premise   \n",
       "3                    4                 2.998095  LATAM       On-premise   \n",
       "4                    2                 2.011061   APAC         Hardware   \n",
       "\n",
       "  Order_Priority Payment_Method  Correlated_Order_Value Order_Cancelled  \n",
       "0            Low        Bitcoin              677.114236   Not-Cancelled  \n",
       "1         Medium        Bitcoin              591.127949   Not-Cancelled  \n",
       "2         Medium  Bank Transfer              502.195055   Not-Cancelled  \n",
       "3           High         PayPal              566.850797   Not-Cancelled  \n",
       "4           High  Bank Transfer              581.693494   Not-Cancelled  "
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading first 5 Rows of the data\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "id": "HiLVof1DM2Ew",
    "outputId": "fa6f4568-ccd0-41fe-8e31-bac8049bd864"
   },
   "outputs": [
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       "      <td>532.405465</td>\n",
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       "      Days_to_Delivery  Num_Items_Ordered  Order_Value  Discount_Rate  \\\n",
       "3995          5.471104                 17   432.846658       0.041176   \n",
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       "      Num_Previous_Orders  Delivery_Time_Variation Region Product_Category  \\\n",
       "3995                    6                 0.124429  LATAM       On-premise   \n",
       "3996                    7                 1.824347   EMEA            Cloud   \n",
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       "3995         Medium        Bitcoin              410.113830       Cancelled  \n",
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       "3997         Medium         PayPal              402.431913       Cancelled  \n",
       "3998            Low         PayPal              466.154060       Cancelled  \n",
       "3999         Medium        Bitcoin              532.405465       Cancelled  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading last 5 Rows of the data\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CeeludwGM2Hp",
    "outputId": "680afd73-d9d9-49ab-ec11-48c3a5e70bd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the shape of the data\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zeI-xGVtM2J-",
    "outputId": "2124dda5-8b7f-44d7-f32b-92428fe1dd59"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the shape of the data\n",
    "\n",
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ruMfNGlGLg74",
    "outputId": "71c745fd-3fca-47bb-e94e-087c03a19479"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the shape of the data\n",
    "\n",
    "df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I5gImKt3NN9t",
    "outputId": "7cc7f093-73a2-49cb-ec5f-74f89365ad5b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Days_to_Delivery', 'Num_Items_Ordered', 'Order_Value', 'Discount_Rate',\n",
       "       'Num_Previous_Orders', 'Delivery_Time_Variation', 'Region',\n",
       "       'Product_Category', 'Order_Priority', 'Payment_Method',\n",
       "       'Correlated_Order_Value', 'Order_Cancelled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading the name of the columns\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 460
    },
    "id": "BQISHgDLNOAh",
    "outputId": "cbd91402-f6fa-485c-ad0f-283bc009cbce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Days_to_Delivery           float64\n",
       "Num_Items_Ordered            int64\n",
       "Order_Value                float64\n",
       "Discount_Rate              float64\n",
       "Num_Previous_Orders          int64\n",
       "Delivery_Time_Variation    float64\n",
       "Region                      object\n",
       "Product_Category            object\n",
       "Order_Priority              object\n",
       "Payment_Method              object\n",
       "Correlated_Order_Value     float64\n",
       "Order_Cancelled             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View datatypes of allcolumns of dataset\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eBJAd-ZJNODY",
    "outputId": "1734aa11-77b4-42c7-e2c4-175dc024baa7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4000 entries, 0 to 3999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Days_to_Delivery         4000 non-null   float64\n",
      " 1   Num_Items_Ordered        4000 non-null   int64  \n",
      " 2   Order_Value              4000 non-null   float64\n",
      " 3   Discount_Rate            4000 non-null   float64\n",
      " 4   Num_Previous_Orders      4000 non-null   int64  \n",
      " 5   Delivery_Time_Variation  4000 non-null   float64\n",
      " 6   Region                   4000 non-null   object \n",
      " 7   Product_Category         4000 non-null   object \n",
      " 8   Order_Priority           4000 non-null   object \n",
      " 9   Payment_Method           4000 non-null   object \n",
      " 10  Correlated_Order_Value   4000 non-null   float64\n",
      " 11  Order_Cancelled          4000 non-null   object \n",
      "dtypes: float64(5), int64(2), object(5)\n",
      "memory usage: 375.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# View info of Columns of the dataset such as number of entries, name of columns and data type\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "id": "rYTOY_8QNwD_",
    "outputId": "4babd1fe-d66e-4cda-ef08-8bbb07fe153e"
   },
   "outputs": [
    {
     "data": {
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       "      <th>Days_to_Delivery</th>\n",
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       "      <td>float64</td>\n",
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       "      <th>Discount_Rate</th>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>Num_Previous_Orders</th>\n",
       "      <td>int64</td>\n",
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       "    <tr>\n",
       "      <th>Delivery_Time_Variation</th>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>object</td>\n",
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       "    <tr>\n",
       "      <th>Product_Category</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order_Priority</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_Method</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Correlated_Order_Value</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order_Cancelled</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                               0\n",
       "Days_to_Delivery         float64\n",
       "Num_Items_Ordered          int64\n",
       "Order_Value              float64\n",
       "Discount_Rate            float64\n",
       "Num_Previous_Orders        int64\n",
       "Delivery_Time_Variation  float64\n",
       "Region                    object\n",
       "Product_Category          object\n",
       "Order_Priority            object\n",
       "Payment_Method            object\n",
       "Correlated_Order_Value   float64\n",
       "Order_Cancelled           object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the Data Dictionary with first column being datatype.\n",
    "\n",
    "Data_dict = pd.DataFrame(df.dtypes)\n",
    "Data_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "id": "AFEIru6ONOF_",
    "outputId": "d3011442-0ae7-4184-82d5-e4ebc1bf80e7"
   },
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>UniqueVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Days_to_Delivery</th>\n",
       "      <td>float64</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num_Items_Ordered</th>\n",
       "      <td>int64</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>Order_Value</th>\n",
       "      <td>float64</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount_Rate</th>\n",
       "      <td>float64</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num_Previous_Orders</th>\n",
       "      <td>int64</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>Delivery_Time_Variation</th>\n",
       "      <td>float64</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>object</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category</th>\n",
       "      <td>object</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order_Priority</th>\n",
       "      <td>object</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_Method</th>\n",
       "      <td>object</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Correlated_Order_Value</th>\n",
       "      <td>float64</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order_Cancelled</th>\n",
       "      <td>object</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               0  UniqueVal\n",
       "Days_to_Delivery         float64       4000\n",
       "Num_Items_Ordered          int64         19\n",
       "Order_Value              float64       4000\n",
       "Discount_Rate            float64       4000\n",
       "Num_Previous_Orders        int64         10\n",
       "Delivery_Time_Variation  float64       4000\n",
       "Region                    object          4\n",
       "Product_Category          object          4\n",
       "Order_Priority            object          3\n",
       "Payment_Method            object          4\n",
       "Correlated_Order_Value   float64       4000\n",
       "Order_Cancelled           object          2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying unique values . For this we used nunique() which returns unique elements in the object.\n",
    "\n",
    "Data_dict['UniqueVal'] = df.nunique()\n",
    "Data_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vqLry_YsN2DX"
   },
   "source": [
    "# **Discriptive Statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "7IZ56wrSNOJH",
    "outputId": "8b7ca19f-d1d6-4cbf-9ec7-42844febaa89"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Days_to_Delivery</th>\n",
       "      <th>Num_Items_Ordered</th>\n",
       "      <th>Order_Value</th>\n",
       "      <th>Discount_Rate</th>\n",
       "      <th>Num_Previous_Orders</th>\n",
       "      <th>Delivery_Time_Variation</th>\n",
       "      <th>Correlated_Order_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.042184</td>\n",
       "      <td>10.116250</td>\n",
       "      <td>501.181391</td>\n",
       "      <td>0.245425</td>\n",
       "      <td>4.534500</td>\n",
       "      <td>1.497161</td>\n",
       "      <td>476.032221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.969642</td>\n",
       "      <td>5.401438</td>\n",
       "      <td>100.872740</td>\n",
       "      <td>0.142842</td>\n",
       "      <td>2.877826</td>\n",
       "      <td>0.869979</td>\n",
       "      <td>95.924241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.482535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.510729</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>150.331593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.707940</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>433.520531</td>\n",
       "      <td>0.122622</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.757221</td>\n",
       "      <td>411.936377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.025536</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>501.165442</td>\n",
       "      <td>0.243107</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.509437</td>\n",
       "      <td>475.467864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.309499</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>569.059613</td>\n",
       "      <td>0.366216</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.238121</td>\n",
       "      <td>540.587883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.295790</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>860.283214</td>\n",
       "      <td>0.499804</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.999922</td>\n",
       "      <td>819.532127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Days_to_Delivery  Num_Items_Ordered  Order_Value  Discount_Rate  \\\n",
       "count       4000.000000        4000.000000  4000.000000    4000.000000   \n",
       "mean           5.042184          10.116250   501.181391       0.245425   \n",
       "std            1.969642           5.401438   100.872740       0.142842   \n",
       "min           -1.482535           1.000000   166.510729       0.000063   \n",
       "25%            3.707940           5.000000   433.520531       0.122622   \n",
       "50%            5.025536          10.000000   501.165442       0.243107   \n",
       "75%            6.309499          15.000000   569.059613       0.366216   \n",
       "max           13.295790          19.000000   860.283214       0.499804   \n",
       "\n",
       "       Num_Previous_Orders  Delivery_Time_Variation  Correlated_Order_Value  \n",
       "count          4000.000000              4000.000000             4000.000000  \n",
       "mean              4.534500                 1.497161              476.032221  \n",
       "std               2.877826                 0.869979               95.924241  \n",
       "min               0.000000                 0.003067              150.331593  \n",
       "25%               2.000000                 0.757221              411.936377  \n",
       "50%               5.000000                 1.509437              475.467864  \n",
       "75%               7.000000                 2.238121              540.587883  \n",
       "max               9.000000                 2.999922              819.532127  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the descriptive statistics of the dataset\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "L9xbDtWNNOLu",
    "outputId": "966985b4-6ec4-44e0-bdbc-756b14d08b6e"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Days_to_Delivery</th>\n",
       "      <th>Num_Items_Ordered</th>\n",
       "      <th>Order_Value</th>\n",
       "      <th>Discount_Rate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.042184</td>\n",
       "      <td>10.116250</td>\n",
       "      <td>501.181391</td>\n",
       "      <td>0.245425</td>\n",
       "      <td>4.534500</td>\n",
       "      <td>1.497161</td>\n",
       "      <td>476.032221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.969642</td>\n",
       "      <td>5.401438</td>\n",
       "      <td>100.872740</td>\n",
       "      <td>0.142842</td>\n",
       "      <td>2.877826</td>\n",
       "      <td>0.869979</td>\n",
       "      <td>95.924241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.482535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.510729</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>150.331593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.707940</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>433.520531</td>\n",
       "      <td>0.122622</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.757221</td>\n",
       "      <td>411.936377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.025536</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>501.165442</td>\n",
       "      <td>0.243107</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.509437</td>\n",
       "      <td>475.467864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.309499</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>569.059613</td>\n",
       "      <td>0.366216</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.238121</td>\n",
       "      <td>540.587883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.295790</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>860.283214</td>\n",
       "      <td>0.499804</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.999922</td>\n",
       "      <td>819.532127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Days_to_Delivery  Num_Items_Ordered  Order_Value  Discount_Rate  \\\n",
       "count       4000.000000        4000.000000  4000.000000    4000.000000   \n",
       "mean           5.042184          10.116250   501.181391       0.245425   \n",
       "std            1.969642           5.401438   100.872740       0.142842   \n",
       "min           -1.482535           1.000000   166.510729       0.000063   \n",
       "25%            3.707940           5.000000   433.520531       0.122622   \n",
       "50%            5.025536          10.000000   501.165442       0.243107   \n",
       "75%            6.309499          15.000000   569.059613       0.366216   \n",
       "max           13.295790          19.000000   860.283214       0.499804   \n",
       "\n",
       "       Num_Previous_Orders  Delivery_Time_Variation  Correlated_Order_Value  \n",
       "count          4000.000000              4000.000000             4000.000000  \n",
       "mean              4.534500                 1.497161              476.032221  \n",
       "std               2.877826                 0.869979               95.924241  \n",
       "min               0.000000                 0.003067              150.331593  \n",
       "25%               2.000000                 0.757221              411.936377  \n",
       "50%               5.000000                 1.509437              475.467864  \n",
       "75%               7.000000                 2.238121              540.587883  \n",
       "max               9.000000                 2.999922              819.532127  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get discriptive statistics on \"number\" datatypes\n",
    "\n",
    "df.describe(include = ['number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "agAPiTi2N7Xt",
    "outputId": "193b47cc-2de4-481e-cacf-80adc0136f5b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Order_Priority</th>\n",
       "      <th>Payment_Method</th>\n",
       "      <th>Order_Cancelled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>EMEA</td>\n",
       "      <td>SaaS</td>\n",
       "      <td>Low</td>\n",
       "      <td>Bank Transfer</td>\n",
       "      <td>Not-Cancelled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1022</td>\n",
       "      <td>1046</td>\n",
       "      <td>1356</td>\n",
       "      <td>1029</td>\n",
       "      <td>2614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Region Product_Category Order_Priority Payment_Method Order_Cancelled\n",
       "count    4000             4000           4000           4000            4000\n",
       "unique      4                4              3              4               2\n",
       "top      EMEA             SaaS            Low  Bank Transfer   Not-Cancelled\n",
       "freq     1022             1046           1356           1029            2614"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get discriptive statistics on \"objects\" datatypes\n",
    "\n",
    "df.describe(include = ['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "id": "eS24nbz9s2Jo",
    "outputId": "e48861f4-0722-4ecd-d0fa-53ca02518b89"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drawing a count plot for the target column 'Order_Status'\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "sns.countplot(x='Order_Cancelled', data=df, width=0.3)  # Default width is 0.8, 0.3 is 70% shorter\n",
    "plt.title('Count Plot of Order_Status')\n",
    "plt.xlabel('Order Status')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "_t9iG84UtK2J",
    "outputId": "aab70c7b-bafe-4b0d-dc90-323cc16b9781"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mzEltbW35tdt2221z33335Q9/+EMGDhyYadOmlR+z5IKmogYU25L38uzZszN8+PD88pe/zM9+9rPsvvvumTRpUo488si0b98+Xbp0Sb9+/fLyyy9nwoQJGT9+vKgBnzBy5MicccYZSf7fNeGaN2+eadOm5Qc/+EHeeuutbLDBBtl3330zdOjQVFRU5O67784LL7yQJKmurhY1AABWAj4lIcn/+9bwAw88kGuvvTYfffRR2rVrl0GDBmWnnXZK8+bNc/HFF2fIkCGpqKjInnvumcsvvzzTp09Pu3btGnv6fEmLFy/Odtttl+OPPz4jRozIXnvtlUMOOSRJcvjhh+edd97JlVdemSOOOCLV1dWpq6vLY489lrFjx+Y73/lOI8++uG677bYce+yxufrqq9OnT58kyY9//OM89thjmTBhQioqKtKuXbvsvPPOGT58eOrq6nLPPfekrq4uu+22WyoqKsqnLGLF98nzf//mN7/J9OnT07lz55x99tn5/ve/nx49euS+++7LHnvskaZNm+bcc8/NN77xDa8vFNhvfvObJB8f6brkvVxXV5fXX389Q4YMyfTp07PVVltlr732yhVXXJEkufHGG7PDDjukU6dOjTZvWBEtXrw4H330USZMmJDnnnsuF1xwQc4999wkycyZM9OiRYs888wzGTJkSPbYY49cfvnlSZKpU6fm9ttvz3777ZeNNtrIF0IAAFYSPi0hycff+L7rrruy9957Z4011shaa62VJ598Mt///vczfvz4bL/99unXr1+qqqpy3nnn5YEHHkiStG/fvpFnTkNo0qRJZs6cmbfeeitHHHFE7r333px55pmZMWNGkuT000/Pr371q+ywww6ZO3duNttsszzxxBO+Qfolvfrqq2nVqlXmzZtX7xv6VVVVWW211TJv3rwkyVFHHZXtttsuN910U9q2bZvHHnssTZs2FTUKZsk3Rg855JBsu+22GTFiRN57772ceuqpueuuuzJ37tzsuuuuuf/++3PllVfm4osvzqJFixp72sAymjNnTt59992ce+65+d3vfldeXllZmY022igPPfRQttxyy/Tq1SuXXXZZko9PkXP//ffn6aefbqxpwwrr/fffT6tWrXLhhRdmp512yj333JPzzz8/SdKxY8fsvvvuOfHEE7P++uvnqquuKu8jjRgxIk8//XQ222wzUQMAYCXiGhsk+fj0Nrvttlv23HPPnHfeeUk+/lbU3nvvnRdffDEvvPBCVl999dx///257rrrMnjw4HTu3LmRZ82XteQb5K+88kqaN2+eqqqqtGvXLrfccksOO+ywnHbaaTnzzDNdQ6WBffIi3+edd15GjhyZE044IWeccUbGjBmTvffeO3feeWf23HPPeo+bNWtW2rRpk4qKCtdbWMF92nVP/vd//ze9e/fO4Ycfnn79+mXOnDnZcMMNM3/+/LRp0yYXXnhhevXqlVatWuXRRx9Nhw4dsv766zfSFgBfxpLwPHXq1Fx99dW55JJLMnjw4JxwwglJkiOPPDIjR47Mvvvumz/+8Y/lnwlnnnlm/vznP+e+++5zxAZ8wm233Zaf/exnue2227Lppptm+vTp+dWvfpUJEyZkt912y/nnn59SqZRjjz02I0eOzHnnnZdFixblnXfeyc0335zHHnssm266aWNvBgAADcinYiT5+Loa//d//5cNNtggSbJgwYI0b948d9xxRzbeeOOcf/75ufTSS7Pbbrtlhx12SMuWLRt5xnxZSz54veOOO3LSSSfl7LPPzoEHHpgkOfjgg5Mkhx12WJLknHPOcS7iBrTkA6zFixfnF7/4RRYsWJArrrgiL730Uu64445cddVV2XPPPcvnY1/yjcPVVlut/DhRY8W15APN2bNnZ9asWWnatGmqq6vTrFmzHHbYYTniiCNSU1OT7bffPnvvvXeGDRuWjTfeOBdffHHmzJmTgw8+ON27d2/szQCW0eWXX54hQ4bkxRdfzDrrrJOf/vSnWbx4cc4666wsWrQoJ598cq655pq88cYbeeGFF3LeeedlrbXWyuTJk/PHP/4x48aNEzXgX7Rq1Srf/va307dv34wYMSKbbLJJzjzzzPzqV7/KmDFj0rRp05x33nm58sor881vfjP3339/Fi1alO9973uZMGFCunXr1tibAABAA3MOk6+pJQfqfPjhh0mSNddcM61atcq9996bJGnevHk5bnTr1i21tbXlx4oaK4cl5/o//PDDc+aZZ+aAAw4on1qsVCrl4IMPzvXXX5+hQ4fm7LPPLv+/QsMYPnx4fvCDHyRJBg8enD59+uSWW27Jrrvumn333TfJx0Hj00415fRTK64lUePll1/OT37ykxx++OG5/fbbU1dXl06dOuWAAw7I6quvnsGDB+f73/9+Bg8enIqKimyxxRaZPHlyRo0alYULFzb2ZgBfwuabb55FixZl5513Tl1dXb7xjW/kmGOOySmnnJKf/exnGTp0aJLkkUceye67756nn346I0eOzEcffZTx48dnk002adwNgBVQr169MmDAgKyxxho55phj8te//jXt27fPmWeeme222y5//vOfy6el+tnPfpY777wzTzzxRK688kpRAwBgJeXTsa+hJd/UHzt2bM4555w8+eSTSZLjjz8+zz33XH79618n+ThuJB9/AL7KKqtk8eLFceaylcfcuXNz2WWX5YQTTsgJJ5yQ1VZbLVOnTs1vf/vb/PGPf8yMGTNy2GGH5fLLL89NN90kbDSwTTfdNDNmzMhDDz2UJDn//PPzs5/9LM8++2yuuuqqTJ8+vZFnyBe1JGpMnjw5O+64Y9Zbb7387Gc/S9++fVNZWZmKioryKfzeeeeddOjQIa1atUqSrL766rnzzjtz/fXXl4/MAYppyy23zJ/+9Ke8//772WGHHZaKG+edd145blx22WW5++678+STT+baa6/1ASx8iiW/f+y66645+eST065duxxzzDGZNGlSOW5sv/32uffee/OLX/wiSdK2bdtUVFSkRYsWjTl1AACWI+cy+RqqqKjI7bffnsMPPzznnHNOOWDsu+++ef3113P99dfnueeeS/fu3TNx4sTcd999efrpp31LfCVTKpUyb968rLrqqnnllVdy5ZVX5q9//WsmTZqULl265C9/+UsGDx6cI444IgceeKBTUX0Jn3a9hS5duqR169Z5/PHHs8suuyT5+HobCxcuzOWXX57Zs2enf//+WWONNRpjyiyDJk2aZNq0aendu3cOPvjgDBo0qLzukxd6X3JB8Keeeiq/+93v8uqrr2bUqFEZMGBA1llnnUaZO9CwNt1009xyyy05+OCDs8MOO+Sxxx4rx43k43/vmzdvnhNOOCGrrLJKI88WVmyf3Ifabbfd0qRJkwwZMiTHHntsrrzyyvJpqX7961/nxhtvzCqrrJLTTz+9EWcMAMBXwcXDv4Zef/317LHHHunfv3/69u1bb927776bMWPGZMSIEVm0aFHatm2bX//619l4440babY0lE/7cH3AgAG5/vrr89FHH2X33XdPr169cvDBB+f444/P3Llzc8sttzTSbFdOtbW1qaqqKt8fOXJkTjnllDz++OP1vqX7P//zP5kyZUr+9Kc/LfWasWJa8v764x//mCFDhuT666/Pd7/73aXGLQkcc+bMSa9evTJ79uwsWrQoI0eOdFFTWAlNmjQpP/zhD7PmmmvmscceS2VlZd5+++1cffXVGThwYK688sr89Kc/bexpwgppyc/WSZMmZdq0aXnnnXfSu3fvtGnTJo8//ngGDx6cDz74oBw3ampqMnz48Pz0pz/Nt771rcaePgAAy5mw8TWy5JeDBx98MMcdd1zuv//+fOc730lS/9vES8ydOzdNmjTxTcKVwJLX/i9/+UvefPPNLFy4sHxh8IcffjiLFi3KrrvumkWLFqVp06Y5/vjj8+GHH+b3v/99mjdv7sP1BjBkyJA88sgj6dGjR04++eQsXrw4c+bMyf77758DDzwwJ5xwQurq6lJZWZnk/71mnxakWHGdddZZueOOO/Liiy8udYH3Ja/l7Nmzs3jx4qy66qqpra1Ns2bNnH4KCm7J+/ull17Km2++mYqKiqy33nr59re/nRdeeCE//OEPs8Yaa5TjxpQpU3LjjTdm//33z7rrrtvY04cV1p/+9KeccMIJ2XDDDfPaa6+Vj3w68sgj88ADD+T/+//+v8ycOTO//e1vs/nmm3/q7zQAAKyc7PWtpBYvXrzUf0+bNi1JMn/+/NTV1ZU/LP3kLwCPPvpoJkyYkCRp1aqVqLGSWHL6sV122SUDBw7M8ccfn1122SUfffRRdt555+y6665JkrfffjvnnHNObr755pxxxhlp0aKFD9UbyHe+85106dIlv/rVr7Ljjjtm8ODBadasWf7rv/4rI0aMyOLFi1NZWVk+TZGoUUwtWrTInDlzyv/ufvLf4iWv5S9/+ctcc801adq0adq2bStqwEpgyc/ZHj165Fe/+lUGDBiQww8/PCNHjszGG2+cW2+9NTNnzixfULxTp0457bTTRA34HM8++2xOOOGEDB48OA899FAeeuihPP3005k5c2aSpGfPnhkwYEAqKipyxhln1Pv9BgCAlZ+wsZJq0qRJXn311Vx55ZVp0qRJbrvtthx66KF57733svHGG6e2tjbDhw8vj13irrvuyj333JP58+c31tRpQEsOyProo49y3XXXZcSIERk3blzuv//+/POf/0yPHj1SW1ubJHnsscfSp0+fjB49Oo8++mg23HDDxpx6oX3yw+wl9t133wwdOjQvvvhitt5664wdOzbrrbdeSqVS/va3v+Wqq65KkjRt2rT8GL+cF8eS99qOO+6YDz/8MOedd16Sj/99XbBgQXncokWL8s4776R169aNMk9g+Xj22WdzzDHH5Nxzz83jjz+eoUOH5plnnsnUqVOTJJtttlluu+22vPrqq+nVq1eS+v/eA0t77bXXsskmm+S///u/8+qrr2aPPfbIUUcdlf79+ydJPvjgg+y888654IILcu2116aystK+EwDA14iLh6+kSqVS7r///vTr1y/PPvtsfv/73+faa6/NWmutlSS5/PLLc+SRR2bBggX56U9/miZNmuSGG27ItddemwkTJqRFixaNvAU0hIqKijz88MO5+OKLs8Yaa+QHP/hB2rdvn/bt2+euu+7Kvvvum169euXee+/NDjvskNNOOy0bbbRROnfu3NhTL6xPHgF12223ZcqUKZkzZ0769euXqqqqtGvXLhdddFEWLlyY3/3ud3nsscfSvHnz3H333Tn22GMbefYsqyUfpHz/+9/P9ttvn5tuuilt27bN6aefnubNmyf5+Gi5X/7yl3nyySfzi1/8ojGnCzSwv/71r9lqq63St2/fvPnmmznuuOPy05/+ND/72c+SJFOmTMkmm2ySRx55JK1atWrk2UIxvP7661lttdVSKpXSo0eP7LnnnhkxYkSS5M4778wrr7ySAQMGZKeddmrkmQIA0BhcY2Mlc+mll6Z37975xje+kSQ5+OCDc9ttt6VPnz657rrryqe2Wbx4ce65554cddRRWWWVVdKiRYu0aNEiN954YzbbbLNG3goa0oMPPpg+ffpk7ty5+dvf/pZvfOMb5Q/f//a3v+WHP/xh+fobq666amNPt9A+GTXOPPPM3HTTTfnud7+befPm5e23387tt9+eLbbYot5j5syZk2effTa77757br755uy///6NMXUawJJ/X6dMmZJDDjkkr7/+erbeeuscf/zxeeWVVzJp0qTcddddefjhh10oHFYyS47I++Uvf5ktttgivXr1yu9+97s0adIkDz30UCZOnJjjjz8+bdq0aeypwgppyc/QV155Ja1bt06nTp3y9NNPZ9ddd82CBQtyzDHHZOjQoeXxJ510UmpqanLNNdd4XwEAfE05FdVKpLa2NjfeeGPmzJlTXrbGGmtk7733zg033JDf/va39Q7P3nvvvTN58uSMHj06N954Yx599FFRYyXxyV65ww475KabbkqrVq1y4oknJvn49DilUikbbLBBbr311qyyyip5//33G2u6K40lUeOyyy7LyJEjc8cdd+Thhx/OaaedlqlTp+aAAw7I+PHj6z2mdevW2WGHHbLnnnvmpZdeaoxp8x/65CnGFi5cmKT+e62ioiKLFi1Kp06d8oc//CFHHXVU/v73v+eQQw7JiBEjsnDhwowfP17UgIJb8r5//fXXM3v27CTJOuusk1GjRmXDDTfMgQcemMsvv7z8M+EPf/hDXnjhBRc0hs+wJGqMHj06BxxwQIYPH56ZM2dm/fXXz/HHH5+qqqry9WimTJmSs88+OzfddFPOP/98UQMA4GvMERsrmYULF6ZZs2Z54okn8q1vfat85MZFF12Us846K0OHDs3JJ59cHv/GG2+kS5cujTVdGtiSXwxra2vL5xmurKzMggUL8thj/3979x2d8/3/f/x+ZW+jKkEkPjGKWom992joiT1iNkrVbinxtVf4oIOq0drzQwlVJWK3VkMJRUQEib2idhJJ3r8//HJ9pFS11CX5PG7n9Bx5rzxf6bnOdV2vx2v8SJs2bahVqxarVq3KcH1ycrKWH3sB27dvZ8eOHdy7d48SJUpw69YtcuXKRfv27Vm3bh0dOnQgJCSETZs2ceTIEVasWEGlSpUyPKN69eoULlyYefPmWagV8jzOnz9Pvnz5MJlMrF+/nqNHjzJ48OAMoXH6zJ2UlBTS0tKIjY3Fy8sLKysrHB0dLVi9iLyo9PfNtWvXMmDAAHr06EGfPn1wcHDgo48+Ytq0afzwww9UqlSJlJQUpkyZwrx589ixYwfFixe3dPkir61NmzbRtGlTpk2bxjvvvIOnpycAUVFRzJ8/ny+//JI8efLg5ubG/fv3WbFihQZkiYiIiPyPU7CRyaV3oKWPJLaysiIpKYkSJUpga2vL5s2byZcvH4mJiUybNo0hQ4bw2Wef0bt3b0JCQti5cyerV68mW7ZsFm6JvKj0zpYNGzYwefJk7t+/j8lkYtGiRRQpUgSArVu30rZtW+rUqcOKFSssXHHWMGfOHIYOHUrRokWJjY3l7t27+Pv7M3/+fGJjY2natCl9+/ald+/erFmzhhYtWmBjY8OBAwcoVaoUAAcPHqRVq1asXr1ao/lfYw8ePKBChQrkypWLDz/8kLZt27JixQpatWr1zPvSX5sikjWEhYXRrFkzvvjiC+rVq0fBggUBuHTpEkOHDmXp0qXkz5+fXLlyceXKFUJDQ9UBK/IHDMMgOTmZbt26kTt3bqZMmZJh6VwrKysePHhATEwM+/btw8fHh2LFipkHb4mIiIjI/y4FG5lY+of9kydP8uWXX3LhwgWqVKnCwIEDiY+Px9/fHycnJ0JDQ/H09CQxMZGZM2cyYMAAypYty4kTJ9i5cyd+fn6Wboq8JOvWraN9+/YMHDgQPz8/pk+fzokTJ5g3bx5169YFHs0uqFu3rnnfFfn75syZQ69evVi6dClNmzYlKiqKiRMnsmfPHnbt2sXu3buZPn06K1euxMPDg02bNrFx40ayZ8/OsGHDsLGxAeDmzZukpqaSK1cuC7dInubo0aOUKFECwzCIjo6matWq3L9/n1mzZtG5c2dSU1Oxtra2dJki8gqkpKTQsWNHcufOzdSpU596zebNm7l8+TI5c+akTJky6oAV+RMpKSlUrFiR2rVrM2XKFCDjoIDr16/rM5KIiIiIPEGL/WZS6aHG4cOHqVatGufPn8fe3p4hQ4YwefJkvLy8CAsL4/bt2zRt2pTz58+bl0nYs2cP3bt358iRIwo1spDTp08TEhLCuHHjGDlyJL6+vsTExJCWlkaLFi3YsmULALVr12bHjh0MHTrUwhVnbjt27KB79+4MHTqUli1bYm1tTcmSJalfvz53794lJSWFq1evcujQIR4+fMjVq1f56quvMAyDUaNGYWNjY96nIUeOHPrC/pr69ttvqVOnDrdu3cJkMuHk5MTt27extrbm22+/BcDa2jrD/hsiknUlJycTGRmJu7s7kHGfHYB79+5Rv359OnbsSOPGjRVqiPwJwzBITEzE09OTmzdv8uDBA3OoYRgGsbGxTJgwgUuXLlm6VBERERF5zSjYyITSQ40jR45QuXJlunXrxpo1a1iyZAk9evTgwoUL3L9/H09PTzZv3kxaWhoBAQGcP38egEqVKtGtWzftrZEFpHemJiYmkj17dho1akS3bt24ePEidevWpW7duhw7doySJUvywQcfEBYWBjzaUDx9eSr5e/Lly0e1atU4ePAgP/74o3lU4Z07d7Czs8PBwYGuXbtSsmRJChYsSJUqVThz5ox5JCJgnrEhr69WrVpx4MABsmXLxpUrV/Dy8iImJobdu3dz6NAhGjVqBJBhSUCFHCJZl5OTEyVKlODQoUPcvn3bvFwOPJrd9fnnn3Pv3j0LVyny+koPA3/77TcePnwIgIuLCwEBASxYsIAFCxbw4MEDAEwmEwsWLODHH3/EykpfW0VEREQkI31CzISsrKw4d+4cdevWpUmTJowfPx54NGr42rVr7NixAz8/Pxo1asSePXv47rvvMAyDOnXqcOHCBQtXLy/i9x2mVlZWLF++nNq1a+Pq6kqXLl1wcnJi4sSJFC9enKlTp+Lm5kbBggWJi4vjgw8+UIfLS1K4cGHmzp1LUlIS48ePJyYmhm3btjFo0CCmTp2Ku7s7jo6O7Nixg7lz5xISEkJkZCS2trbmmRry+goNDSU2NhYALy8vjh49ipeXF6tWraJAgQKULl2alStXcvToUfz9/TEMAysrK2bMmMGnn35q4epF5GVI74C9d+8ed+/eNR+vWLEikZGRLFmyhDt37pg7XFesWMHKlSv1PivyDCaTiXXr1lGvXj3q1atHly5dSEpKIigoiFGjRtG7d2+6detGUFAQHTt2ZNq0aXzzzTfmWVIiIiIiIukUbGRSqamp/Otf/yIpKYndu3cDMHHiRL7//ntatGjBwIEDiYuLY9iwYdy7d481a9aQPXt288goyXzSZ+r88ssvTJgwAYDz588za9YsAgMDsbW1pUCBAhiGQUxMDG+//TZOTk4AuLm5sWXLFiIiInB2drZkM7KUwoULM23aNKytrWnbti2NGjVizpw5tGzZkpSUFNLS0rCzs6Njx460bt0aa2trUlNTNVPjNWYYBlevXqVly5Z88sknxMXFAVCiRAnatWtH165d+e677wCoWrUqK1asMO/B0alTJ/r168c777xjySaIyEuQvhTOunXraNSoEbVr1+aTTz7BMAwGDhxI/fr1mTFjBm3atGHAgAG0adOGqVOnsnjxYnLnzm3p8kVeO+lB4aFDh2jTpg2NGzembNmy/Prrr5QtW5akpCSGDx/O4sWLcXFxIT4+HldXV/bs2UOZMmUsW7yIiIiIvJa0eXgmFhMTQ9++fbGzsyN37tysW7eOxYsX06BBAwDi4+MpUKAAM2bMoEePHqSkpKhDNZN6fE8VX19fRo8eTdOmTZk2bRoJCQnMnz8fZ2dn8wbGnTt3Ztu2bYSEhLBr1y5CQ0OJiIjQ8mP/kJiYGHr06MHVq1eZM2cOFStWBDJufCmZQ/r/s4MHD1KnTh3q1avHpEmT8PHxAeDDDz9k4cKFLF++nICAAADOnDnD8OHDsbW15eOPP6ZkyZKWbIKIvCS7d++mcePGBAUFYW9vz6xZs6hSpQrLli0jW7ZszJkzh59//pnjx49TvHhx+vfvz9tvv23pskVeWwcPHuTq1ascPnyYwYMHk5qayqFDh+jatSvJyckcOnQIBwcHEhMTcXBw0HcXEREREXkmBRuZ3MmTJ+nduze7du1i7NixDBgwAMMwzBsX+/v7M3z4cFq2bGnpUuVvSg81jh49SoUKFRg8eDAjR46kX79+rFq1ChcXF6KjowF4+PAhtra2XLlyhW7duhEdHY2Liwtz5szB19fXwi3J2k6dOkWfPn0AGDZsGFWrVrVwRfJ3GIbBw4cPsbOz4+DBg1StWpUOHTowePBgChUqBDw93ABISkrC3t7eUqWLyEt06tQpDh48yOnTpwkODgbg+PHj1KpVi7Jly7JkyRLeeOMN4NFr39bWVnsAiDzD9evXqVGjBidOnCA4OJiQkBDg0fvuwYMHCQoKIi0tjf379+Pg4GDhakVEREQkM9A3sEyuSJEizJw5k+rVq7N161Z++uknTCYTtra2zJ49mzt37phHj0vmkx5qpHemVKpUiWHDhgEwdOhQOnXqxI0bN8zLY9ja2mIYBu7u7qxbt47t27ezfft2hRqvQKFChczLUvXv358jR45YuiT5m+zs7FizZg1bt26lYMGCzJ07lxEjRnDmzBkAZs6cSefOnenUqRPffvut+T6FGiJZw40bN8zLzz2+t0bx4sXZsWMHBw4cICgoiPj4eODRa1+hhsizubm5MW7cOPz8/Ni0aZP5uMlkws/Pj/nz53Pr1i1q165twSpFREREJDPRjI0sIn1ZKsMwmDBhAps3b2bkyJHs2bNHndqZ1OPLT1WuXJm8efPi4OBAnz59aNWqFTlz5iQhIYGQkBB++ukn/P39GTlyJPDfmRvy6kVFRTFnzhwmT56sjq5MasuWLTRu3JgvvvgCDw8PEhIS6NOnD40bN2bSpEnmJd06duzItm3bzDOjRCTr2Lx5M506daJcuXIsXboUNzc381J1UVFRvP3227Rp04YlS5aYl4EUkWd78OABW7dupV+/fvj4+LB582bzOcMwOHz4MG5ubublH0VEREREnkXBRhYSExPDxx9/TEREBDdv3mTv3r2ULVvW0mXJC/j1118pXbo0o0ePZvjw4bz//vvs3LmTgQMH0rp1a3LkyMH169eZMGECu3fvpkmTJuYZHWJ56eGUZC79+/cnOjqajRs3mo/t27ePOnXq0KxZM8aMGUPBggUBuHz5Mh4eHpYqVUT+QeHh4TRt2pSOHTvyxRdf4OjoaA43oqOjMZlMFClSxNJlirx20l8nv/zyC4cOHcJkMlG1alWKFi1KYmIiW7ZsYeDAgXh5eREeHm7pckVEREQkk1KwkcVER0czaNAgQkJCtIFlJpecnMzQoUNxc3Nj+PDh5uPp4cYnn3xCq1atzOHGpEmT+O677+jatSuDBg2yYOUimVN6R8x7773HhQsXCA8Pz7DnxrRp0+jfvz8tWrRgypQpeHt7W7pkEXlBj3fAnj59muvXr/Pee++Z1/gPCwujefPmTw03RORJ6a+P0NBQ+vTpQ548eXByciIqKoo1a9ZQrVo1c7gRHByMk5MTERERli5bRERERDIhBRtZkJYhyjoSEhLImTMnACkpKdjY2ABPDzeuXr3KtGnTeP/99ylQoIAFqxbJ3FauXEnHjh1Zu3Yt77zzjrmTZvHixXz11VfEx8cTERGBp6enpUsVkReQ/tpes2YNPXv2xNPTkxs3bmBvb8/s2bOpVKkSdnZ2hIWF0aZNG5o0acLcuXO1sbHIY54W9O3cuZOWLVsyfvx4unfvzv79+6lYsSL29vasXr0af39/EhMT2bBhAxMnTmTVqlV4eXlZqAUiIiIiklkp2BDJRFJTU81reaeHG4MHD6Z58+bkzJlTSx+J/AXpnTEnTpzg3LlzAJQpU4Y333yT9957j7179/LZZ5/h7+8PQHBwMJ6enrz//vvq2BTJInbs2EHLli2ZNGkSQUFBnDt3Dm9vb4oWLcrUqVOpVasWtra2rFu3jh49enDw4EEtPyfy/6V/7rx27RpxcXEAlCtXjlGjRmEYBqNHj+bChQtUqVKFunXrkpqayooVKwgLC6NWrVokJSXx8OFD7VMlIiIiIn+Lgg2RTObxcKNHjx6sWrWKKVOm0KlTJ0wmk5bHEHkO6aHG6tWrGTRoEI6OjmTLlo3z58+zZcsW0tLS+Oyzz5g3bx7ly5cnLS2NY8eOsWvXLkqXLm3p8kXkL3pa8J+cnMzkyZNJSkpizJgxnDlzhjp16tC4cWOioqKIjY3lm2++oUaNGtjb23Pv3j2cnZ0t1AKR10v6a+r48eN0794dV1dXHB0dCQ0N5ZdffiE5OZmSJUtSv359SpUqxezZs9m9ezfVq1cHHi3z1qBBAwu3QkREREQyMxtLFyAi//U863ZbW1ubw41Zs2Zhb29PtWrVNFND5C8wmUzs3buXoKAgJk+eTPfu3dm+fTt169Zl1apVDBkyhEmTJhEQEMC2bdtwdXVlwYIFFC1a1NKli8hflN4Be+7cObZu3crdu3dp3749OXLkoEaNGuTMmZPbt2/ToUMH6tWrx/Tp0zl+/DilS5emU6dOLFu2jNq1ayvUEPn/DMPAysqKY8eOUa1aNXr27MkHH3xAvnz5AChbtiwAv/zyC6mpqfTv3x+A7Nmz06pVK7y9vcmfP7+lyhcRERGRLELBhshrIL3TxWQysXXrVgzDoF69en94/ePhxtSpU19hpSJZx+HDh2nevDndu3cnPj6ezp0707NnT4YMGQI8Cj/8/f3NS1GJSOaT/v569OhRWrVqha+vLy4uLrz//vsA5tHje/bs4cGDB/Tu3RuAu3fv0rZtW27dumXurBWRR0wmEwkJCfTo0YOOHTsyfvx487nHZ0ddu3aNAwcOkJqaCsB//vMf7t69y6hRo3BycrJI7SIiIiKSdWiIt4gF7d+/n6SkJKysrEhJSSEpKYkePXpw9+7dP703fTkqEXk+6SsvHj9+nDt37nDp0iXu3btHXFwc1apV45133uHLL78E4Pvvv+ezzz4jMTHRkiWLyAuysrIiOjqaWrVq0bx5c+bPn8/XX3+Ng4MDj6/Geu7cOc6ePYuNjQ0PHz5k/fr12NnZsXbtWooUKWLBFoi8ni5fvsylS5do0aIFaWlp5uPpoYZhGNStW5emTZtSqlQpKlSowBdffEFISIhCDRERERF5KRRsiFjIDz/8QIcOHZgxYwbJycnY2NiQkpJCamoqOXLksHR5IlmOyWRi3bp1NGnShCNHjlC4cGHOnTtH5cqVadCgAbNnzwYejTYNDw/n4sWLGTprRCTzSUxMZMyYMbz77ruMGDECe3t74MmlH1u3bk2BAgWoWrUqlSpVYtq0afTu3VvLPIr8gcjISOLi4qhRowZWVlZPvF+aTCYePnxI165dCQ0NpV27dhw8eFD7VImIiIjIS6OlqEQspFatWlSsWJFVq1ZhZWVFjx49cHZ2xsXFhTx58gDw8OFDbGxsMJlMT934VET+XHoH5s2bN1m2bBn9+vWjatWqlCtXjjlz5nDz5k1atGhBUlIS9+/fZ/LkyaxcuZIdO3ZoVKlIFnDgwAF69+5tDjUAc6jx+FKQERERTJkyBUdHR9555x3N1BB5hgIFCmBjY0NoaCgtWrR46mfUBQsWsHbtWsLDwy1QoYiIiIhkdQo2RCwgNTUVZ2dnZs2aRe/evVm+fDkmk4nmzZtjY2NjXmbK1tbWfM+fbSouIo/8fiS2yWRi27ZtjBgxAjs7O6pVqwaAvb09GzZsoFatWnzyySfcuHGD4sWLc/LkScLCwihWrJilmiAiL4FhGJw9e5Zz586ZR4mnpKRgY/Pfj7/pnbEzZsygZ8+eBAcHW6RWkczG29sbNzc3Fi1aRLly5fD29gYyvgfHxsbi5+f3xPuyiIiIiMjLYDIeX2BYRF6Z9M6V+/fv06tXL06dOkXVqlWZPXs2TZs2xdXV1Txa/NatW7z11lv069dPXwxFniF99PW1a9eIi4vDysoKPz8/rly5QpkyZbhy5QorVqygVatW5o6WpKQktm3bRlRUFIULF6Z06dJ4eXlZuiki8hIkJiZSpkwZSpcuzeLFi7Gzs3uik3Xnzp0EBwezevVq8ubNa8FqRTKX0NBQAgMDad26NcHBwRQvXhyA+/fvM27cOJYtW0Z4eLhmP4mIiIjIP0LBhsgr9rRRa3fv3qVXr17s2bOH5ORkypQpQ/bs2UlNTeX+/fskJiYyZcoU8xdGEXlSeqhx/Phxunfvbg4Hly5dioODAzdu3KBcuXLkypWL+fPnU6JECUuXLCL/sNTUVPr06cPq1asZPXo0QUFB2NnZZbhmxIgRHDt2jIULF+Li4mKhSkUyn7S0NL755ht69+5NwYIFqVKlCg4ODly4cIF9+/YRFhaGr6+vpcsUERERkSxKwYbIK5Qeavz0009s2bKFPHnyUKFCBfz8/Lh37x59+vTh1KlTtGzZkj59+mh2hshzSn9tHTt2jGrVqtGzZ08++OADPD09sbKyMs+Qunr1Kn5+fvj4+DBr1ixzWKhlMkSynvTX9Z07d6hWrRoXL17k448/pm/fvjg7OxMXF8fUqVNZvHgx27dvV9gp8jdFREQwefJkYmNjcXZ2pmrVqnTt2pXChQtbujQRERERycIUbIi8Yt999x3t2rWjdOnSJCQk4OTkxNixY2nSpIl55sapU6do3LgxAwYMwN7eXp2uIs8hISGBgIAAfH19mTZtmvl4+usnPdy4cuUKZcuWpUiRInzxxReUKlXKglWLyD8lNTXVvGfVgwcPaNy4MUeOHMHR0ZHcuXNjZ2fHjRs3WLlyJWXKlLFssSKZXPqsSRERERGRV0WfPkVeoatXr3LgwAGmT5/O3r17WbBgAb6+vvTq1Yvvv/8eFxcXvvrqKzw8PNi+fTv3798HtHG4yPO4fPkyly5dokWLFqSlpZmPp79+rK2tMQwDd3d3Dhw4wJ49exgyZAjJycmWKllEXsDjr/PfS05Oxtramri4OOrVq8elS5f44YcfmDlzJoGBgVSuXJlevXqxdetWhRoiL8Hjn1U1bk5EREREXgUbSxcg8r/i8OHDdOrUCVtbW2bNmgVA5cqVcXFxwWQy0atXLwDeffddFi1axK1bt8iRI4clSxbJVCIjI4mLi6NGjRqYTKYnRo+aTCbu37/P4cOHqVy5MvHx8dy6deuJ9fZFJHOwsrLi/PnznDlzhurVq7N06VL27t3L9OnTsbOz48yZM9SsWRN/f3+8vLywsbGhVatWtGrVytKli2Q5jwcbGpAjIiIiIq+CZmyIvCLXr1/H09OTEydOcOfOHfPxkiVL8tFHH9GoUSPatWvHxo0bcXZ2Jm/evBasViTzKVCgADY2NoSGhgI8dUmMefPmMXLkSO7fv0/u3Lm1/rdIJmUYBsnJybRv357hw4czZswYOnbsaF5aLi0tjfbt21OjRg1mzpyJjY2N+b7HnyEiIiIiIiKZk2ZsiLwidevWxdbWlocPH9KzZ0/mz59PpUqVAChRogQ9e/bE3t6eQoUKWbhSkczJ29sbNzc3Fi1aRLly5fD29gYybgx+9uxZypYti6OjoyVLFZEXZDKZsLOzY926dZQrV45Ro0YRHBxM9+7dgUfB5qZNm8yzIh+/72n/FhERERERkcxFm4eL/APSO1IvXbpEWloaaWlp5M+fH4Bt27Yxffp0zp49y6xZs6hQoYL5vuTkZC2LI/ICQkNDCQwMpHXr1gQHB1O8eHEA7t+/z7hx41i2bBnh4eEUKVLEwpWKyItKTk4mKSmJChUqcO/ePUqVKsWAAQOoXbs2kDHU1MbGIiIiIiIiWYuCDZGXLL0jZd26dUyYMIGLFy9SuHBh6tWrR3BwMABbtmzhq6++4uLFi3z22WdUrVrVwlWLZA1paWl888039O7dm4IFC1KlShUcHBy4cOEC+/btIywsDF9fX0uXKSIv0Z07d7hz5w6NGjUiT548DB48mDp16li6LBEREREREfkHaeiayEtmMpn44YcfCAwMpE2bNixbtoyKFSsydOhQhg0bBkC9evXo06cPLi4uDB06lMTERK31LfISWFlZ8cEHH7B7925KlixJZGQkv/76K8WKFWPXrl0KNUQyufT3yujoaDZt2sSBAwf47bffyJs3L99++y2XLl1i8uTJbN26FYD/+7//o2/fvpYsWURERERERP4BmrEh8pKdP3+eTp060axZM/r06cP169fx9fWlQIECHDlyhF69ehESEgLAzp07KViwIJ6enhauWiTr0dIzIllL+ozI1atX069fP2xtbTEMAwcHB77++mtq1KjByZMnCQwMJC0tjWzZsnHw4EE2bdpk3tNKREREREREsgb1+Ij8TWlpaU89nitXLmrUqEHjxo25dOkSNWrUoEmTJqxZs4aAgAAmTpzIRx99BEDNmjUVaoj8Qx7fGFgZvkjm8/j7bEpKCiaTiYiICN577z2GDx/Orl27WLhwIeXLl6dhw4b89NNPFClShFWrVtGyZUsqVqzIvn37FGqIiIiIiIhkQZqxIfI3pI8Ej4+PZ9++fVy+fJnu3bvj4OAA/HcT8IkTJ7Jnzx7mz5/PG2+8wfjx41m6dClpaWns2LEDd3f3DJ2vIiIi8l9xcXF4eXlhMplITU1lwYIFLF26lC1btphnZF2+fJkBAwYQFRXFxo0bcXd3BzJuHi4iIiIiIiJZi2ZsiPxF6aHGkSNHqFWrFpMmTWLkyJH4+vry4MEDAOzs7ACIjIwkOTmZN954A4AbN24QFBTE/v378fDwUIeLiIjIH0hKSqJt27b4+PhgGAbW1tbcvn2byMhIbt++DTwKLzw8PAgMDOT69eskJCSY79d7rIiIiIiISNalYEPkL0gPNQ4fPkylSpUIDAzkhx9+ICIigjt37rB+/foM19evX59jx47Rs2dPunXrxoIFCwgICMDV1dVCLRAREckc7OzsmDx5Mi4uLvj5+WEYBgEBAeTJk4f58+dz69Ytc3hRuHBhbG1tuXPnjoWrFhERERERkVfBxtIFiGQmVlZWnDp1ikqVKjFw4EDGjh0LgLu7O//61784fPgwGzZsoEGDBtSqVYvmzZtz+fJlvvvuO7Jnz8727dspXLiwhVshIiLy+kkfPJDOZDJRpUoVvvnmG7p06ULFihWJiIigWbNmzJ8/n9TUVDp27IizszPz5s3DysqKAgUKWK4BIiIiIiIi8soo2BD5C9LS0pg3bx6urq7m5aUA814anp6enDlzhqVLl9KrVy+mTJnC0KFDCQ4OJjExEWdnZwtWLyIi8npKDzUuX77M2bNnzRt+W1lZUbZsWRYtWkTbtm2pWbMmO3fuxGQysXDhQoYPH06ZMmWIjY1l06ZN5M6d28ItERERERERkVdBm4eL/EUXL15k0qRJ7Nu3jy5dunD79m2mTJnCokWLaNCgAVZWVvTp04cFCxbw66+/avSoiIjIczh37hy+vr4kJCRQs2ZNKleuTL169Shfvjyurq7s37+frl274ubmxq5du7h8+TIbNmwgR44c+Pn54e3tbekmiIiIiIiIyCuiYEPkb7h8+TLjx49n8+bNnDp1ivDwcOrUqcODBw9wdHRkw4YN9OnThw0bNvDWW29ZulwREZHXXlxcHE2bNuXBgwe4urry9ttvs2LFCooWLUqJEiV49913MZlMDBkyBB8fHzZt2qQNwkVERERERP5HafNwkb/Bw8ODYcOG0bBhQ95++20OHToEgKOjIwDh4eG8+eabWhJDRETkOXl7e/Ptt99SvHhx8uXLx4cffkh0dDSDBw/mzJkzfPrpp3Tu3BlHR0e2bNlC8+bNAdAYHRERERERkf89mrEh8gLSZ27s37+fZs2aMXjwYMaNG8e///1vdu/eTalSpSxdooiISKYSHR1Nv379SEtLY/z48ZQvXx6A3377je+//57o6Gg2btzInDlz8PX1tXC1IiIiIiIiYgkKNkReUHq4cfjwYZKSkjhy5Ai7du2ibNmyli5NREQkU4qJiaFPnz4ADBkyhJo1a2Y4n5KSgo2NjSVKExERERERkdeAlqISeUEeHh4MHTqUQoUKkZCQwN69exVqiIiIvIDChQvz5ZdfYjKZmDBhAnv27MlwXqGGiIiIiIjI/zbN2BB5Sa5du0ZaWhru7u6WLkVERCRLiImJ4eOPP+b69et8/vnnVKpUydIliYiIiIiIyGtAMzZEXpI333xToYaIiMhLVLhwYSZPnoynpyd58+a1dDkiIiIiIiLymtCMDRERERF5rSUnJ2NnZ2fpMkREREREROQ1oWBDREREREREREREREQyDS1FJSIiIiIiIiIiIiIimYaCDRERERERERERERERyTQUbIiIiIiIiIiIiIiISKahYENERERERERERERERDINBRsiIiIiIiIiIiIiIpJpKNgQEfkLatWqRf/+/V/6c0eNGkWZMmVe+nNFRERERERERESyGgUbIplYly5dMJlMmEwmbG1t8fHxYeDAgdy7d8/Spb2QHTt2YDKZ+O233577nvS/RY8ePZ4417NnT0wmE126dPlHaxARERFYsGAB2bNnt3QZrxWTycTatWsBOHv2LCaTicjIyBd65j812EJEREREJDNQsCGSyTVq1IhLly5x+vRpxo0bx4wZMxg4cKCly7KI/Pnz85///IcHDx6YjyUmJrJ8+XK8vLwsWJmIiMjr7dy5c3Tt2pW8efNiZ2eHt7c3/fr148aNG5Yu7ZlWr15NrVq1yJYtGy4uLpQqVYoxY8aQkJBg6dJEREREROQfpGBDJJOzt7fHw8OD/PnzExgYSPv27Vm7di1LliyhXLlyuLq64uHhQWBgIFevXgXAMAwKFSrElClTMjzr6NGjWFlZERsbCzwaXTh79myaNGmCk5MTxYoVY+/evZw6dYpatWrh7OxM5cqVzden+/777ylbtiwODg74+PgwevRoUlJSzOdNJhNz5syhWbNmODk5UbhwYdatWwc8GsVYu3ZtAHLkyPGXZlr4+fnh5eVFaGio+VhoaCj58+fH19c3w7WGYTBp0iR8fHxwdHSkdOnSrFq16rlqSEtLY9CgQeTMmRMPDw9GjRqV4dnx8fEEBATg4uKCm5sbrVu35sqVKxmumThxIu7u7ri6utK1a1cSExOfq40iIiIv2+nTpylXrhwnT55k+fLlnDp1ilmzZrF161YqV678hyFBcnLyP1bTw4cP//SaoUOH0qZNG8qXL8/GjRs5evQon376KYcPH2bx4sX/WG0iIiIiImJ5CjZEshhHR0cePnxIcnIyY8eO5fDhw6xdu5YzZ86YO+dNJhNBQUHMnz8/w73z5s2jevXqFCxY0Hxs7NixdOrUicjISIoWLUpgYCAffPABQ4YM4cCBAwD07t3bfP2mTZvo0KEDffv25fjx48yePZsFCxYwfvz4DL9r9OjRtG7dmiNHjuDv70/79u1JSEggf/78rF69GoDo6GguXbrE1KlTn7v97733XoZ2zZs3j6CgoCeuGzZsGPPnz2fmzJkcO3aMjz76iA4dOrBz584/rWHhwoU4Ozvz888/M2nSJMaMGcPmzZuBR4FJ06ZNSUhIYOfOnWzevJnY2FjatGljvn/lypWMHDmS8ePHc+DAAfLkycOMGTOeu40iIiIvU69evbCzsyM8PJyaNWvi5eXFO++8w5YtW7hw4QJDhw4FoECBAowbN44uXbqQLVs2unXrBjxaesrLywsnJyeaNWv21FkezzPoYdasWQQEBODs7My4ceOeWXNERAQhISF8+umnTJ48mSpVqlCgQAHq16/P6tWr6dy5MwCxsbEEBATg7u6Oi4sL5cuXZ8uWLRmeVaBAAUJCQggKCsLV1RUvLy++/vrrDNecP3+etm3bkjNnTpydnSlXrhw///zzc7fvzxw/fhx/f39cXFxwd3enY8eOXL9+3Xz+3r17dOrUCRcXF/LkycOnn3763M8WEREREcmSDBHJtDp37mwEBASYf/7555+NN954w2jduvUT10ZERBiAcefOHcMwDOPixYuGtbW18fPPPxuGYRjJycnGm2++aSxYsMB8D2AMGzbM/PPevXsNwJg7d6752PLlyw0HBwfzz9WrVzdCQkIy/O7FixcbefLk+cPn3r171zCZTMbGjRsNwzCM7du3G4Bx8+bNv/y3uHbtmmFvb2+cOXPGOHv2rOHg4GBcu3bNCAgIMDp37mz+fQ4ODsaePXsyPKNr165Gu3btnllDzZo1jWrVqmU4Vr58eWPw4MGGYRhGeHi4YW1tbcTHx5vPHzt2zACMiIgIwzAMo3LlykaPHj0yPKNixYpG6dKln7u9IiIiL8ONGzcMk8n0xHt3um7duhk5cuQw0tLSDG9vb8PNzc2YPHmyERMTY8TExBj79u0zTCaTMWHCBCM6OtqYOnWqkT17diNbtmzmZ4SFhRlubm7GggULjNjYWCM8PNwoUKCAMWrUKPM1gJE7d25j7ty5RmxsrHH27Nln1t23b1/DxcXFSE5OfuZ1kZGRxqxZs4wjR44YJ0+eNIYOHWo4ODgYcXFx5mu8vb2NnDlzGl999ZURExNjTJgwwbCysjKioqIMwzCMO3fuGD4+Pkb16tWNn376yYiJiTFWrFhh/hzxvO1bs2aNYRiGcebMGQMwDh06ZBjGo89kuXLlMoYMGWJERUUZBw8eNOrXr2/Url3bfP+HH35oeHp6GuHh4caRI0eMJk2aGC4uLka/fv2e2X4RERERkazKxlKBioi8HOvXr8fFxYWUlBQePnxIQEAAX375JYcOHWLUqFFERkaSkJBAWloa8GiZpOLFi5MnTx4aN27MvHnzqFChAuvXrycxMZFWrVpleH6pUqXM/3Z3dwegZMmSGY4lJiZy+/Zt3Nzc+OWXX9i/f3+GGRqpqakkJiZy//59nJycnnius7Mzrq6u5qWyXkSuXLlo3LgxCxcuxDAMGjduTK5cuTJcc/z4cRITE6lfv36G48nJyU8sWfU0j9cOkCdPHnPtUVFR5M+fn/z585vPFy9enOzZsxMVFUX58uWJiop6YpPzypUrs3379r/UVhERkRcVExODYRgUK1bsqeeLFSvGzZs3uXbtGgB16tTJsJfXiBEjaNiwIcHBwQAUKVKEPXv2EBYWZr5m/PjxBAcHm2dR+Pj4MHbsWAYNGsTIkSPN1wUGBj51luUf1e3j44Otre0zrytdujSlS5c2/zxu3DjWrFnDunXrMsw49ff3p2fPngAMHjyYzz//nB07dlC0aFGWLVvGtWvX2L9/Pzlz5gSgUKFCf7l9f2TmzJn4+fkREhJiPjZv3jzy58/PyZMnyZs3L3PnzmXRokXmzy4LFy7E09PzT58tIiIiIpJVKdgQyeRq167NzJkzsbW1JW/evNja2nLv3j0aNGhAgwYNWLJkCW+++Sbx8fE0bNgww3rY77//Ph07duTzzz9n/vz5tGnTxhw8pHu8w8BkMv3hsfTgJC0tjdGjR9O8efMnanVwcHjqc9Ofk/6MFxUUFGTurPjqq6+eOJ/+e3744Qfy5cuX4Zy9vf2fPv9ZtRuGYf6bPO6PjouIiLzODMMA/vt+X65cuQzno6KiaNasWYZjlStXzhBsPO+gh98/+8/qep731Xv37jF69GjWr1/PxYsXSUlJ4cGDB8THx2e47vFBCyaTCQ8PD/OghcjISHx9fc2hxu89b/v+yC+//ML27dtxcXF54lxsbCwPHjwgOTmZypUrm4/nzJmTt95660/bLyIiIiKSVSnYEMnknJ2dM4waBDhx4gTXr19n4sSJ5pkD6fthPM7f3x9nZ2dmzpzJxo0b+fHHH1+4Hj8/P6Kjo5+o6a+ws7MDHnUK/B2NGjUyBzgNGzZ84nzx4sWxt7cnPj6emjVrvtQaihcvTnx8POfOnTP/7Y8fP86tW7fMo2GLFSvGvn376NSpk/m+ffv2/aXfIyIi8jIUKlQIk8nE8ePHadq06RPnT5w4QY4cOcyzH52dnTOcTw8+nuV5Bz38/tnPUqRIEXbt2sXDhw+fOWvjk08+YdOmTUyZMoVChQrh6OhIy5Ytn9j4/FmDFhwdHZ9Zy/O271n3v/vuu/z73/9+4lyePHmIiYn502eIiIiIiPyvUbAhkgV5eXlhZ2fHl19+SY8ePTh69Chjx4594jpra2u6dOnCkCFDKFSoUIaRgH/XiBEjaNKkCfnz56dVq1ZYWVlx5MgRfv311z/dCDSdt7c3JpOJ9evX4+/vj6Oj41NHMf4Ra2troqKizP/+PVdXVwYOHMhHH31EWloa1apV4/bt2+zZswcXFxc6d+78t2uoV68epUqVon379nzxxRekpKTQs2dPatasaR6J2q9fPzp37ky5cuWoVq0aS5cu5dixY/j4+Dx3G0VERF6GN954g/r16zNjxgw++uijDJ34ly9fZunSpXTq1OkPZ0cUL178iXD+9z+/jEEPvxcYGMi0adOYMWMG/fr1e+L8b7/9Rvbs2fnpp5/o0qWLeVbJ3bt3OXv27F/6XaVKlWLOnDkkJCQ8ddbGi7bPz8+P1atXU6BAAWxsnvx6VqhQIWxtbdm3bx9eXl4A3Lx5k5MnT/7hAA0RERERkazOytIFiMjL9+abb7JgwQK+/fZbihcvzsSJE5kyZcpTr+3atSvJycnPvab1n2nYsCHr169n8+bNlC9fnkqVKvHZZ5/h7e393M/Ily8fo0ePJjg4GHd39wxrYD8vNzc33Nzc/vD82LFjGTFiBBMmTKBYsWI0bNiQ77//nn/9618vVIPJZGLt2rXkyJGDGjVqUK9ePXx8fFixYoX5mjZt2jBixAgGDx5M2bJliYuL48MPP/zLbRQREXkZpk+fTlJSEg0bNuTHH3/k3LlzhIWFUb9+ffLly5dhiaXf69u3L2FhYUyaNImTJ08yffr0DMtQwaNBD4sWLWLUqFEcO3aMqKgoVqxYwbBhw/52zRUrVmTQoEEMGDCAQYMGsXfvXuLi4ti6dSutWrVi4cKFwKNQIDQ0lMjISA4fPkxgYOBfXvqyXbt2eHh40LRpU3bv3s3p06dZvXo1e/fufSnt69WrFwkJCbRr146IiAhOnz5NeHg4QUFBpKam4uLiQteuXfnkk0/YunUrR48epUuXLlhZ6auciIiIiPwPs+DG5SLyGti1a5dhY2NjXL582dKliIiIiIWcPXvW6NKli+Hh4WHY2toa+fPnN/r06WNcv37dfI23t7fx+eefP3Hv3LlzDU9PT8PR0dF49913jSlTphjZsmXLcE1YWJhRpUoVw9HR0XBzczMqVKhgfP311+bzgLFmzZq/XPeKFSuMGjVqGK6uroazs7NRqlQpY8yYMcbNmzcNwzCMM2fOGLVr1zYcHR2N/PnzG9OnTzdq1qxp9OvX75ntKl26tDFy5MgMf58WLVoYbm5uhpOTk1GuXDnj559//lvtO3PmjAEYhw4dMp8/efKk0axZMyN79uyGo6OjUbRoUaN///5GWlqaYRiGcefOHaNDhw6Gk5OT4e7ubkyaNOmJdoiIiIiI/C8xGcZzLIwrIllOUlIS586do3v37uTJk4elS5dauiQRERERERERERGRP6X5yyL/o5YvX85bb73FrVu3mDRpkqXLeab4+HhcXFz+8L/4+HhLlygiIiIiIiIiIiKviGZsiMhrLyUl5Zkbff7RZpsiIiKSOfXo0YMlS5Y89VyHDh2YNWvWK65IREREREReJwo2RERERETktXL16lVu37791HNubm7kzp37FVckIiIiIiKvEwUbIiIiIiIiIiIiIiKSaWiPDRERERERERERERERyTQUbIiIiIiIiIiIiIiISKahYENERERERERERERERDINBRsiIiIiIiIiIiIiIpJpKNgQEREREREREREREZFMQ8GGiIiIiIiIiIiIiIhkGgo2REREREREREREREQk01CwISIiIiIiIiIiIiIimcb/A7y60knhyOwCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x1600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Redefining the cleaned categorical columns for plotting\n",
    "categorical_columns = df.select_dtypes(include=['object']).columns.tolist()\n",
    "\n",
    "# Calculate the number of rows needed for subplots\n",
    "num_rows = int(np.ceil(len(categorical_columns) / 3))  # Calculate rows needed\n",
    "\n",
    "# Step 2: Draw count plots for each categorical column\n",
    "plt.figure(figsize=(16, 8 * num_rows))  # Adjust figure height based on rows\n",
    "for i, column in enumerate(categorical_columns, 1):\n",
    "    plt.subplot(num_rows, 3, i)  # Use calculated num_rows\n",
    "    sns.countplot(x=df[column], width=0.6)  # Reduce width by 40% (default is 0.8)\n",
    "    plt.title(f'Count Plot of {column}')\n",
    "    plt.xticks(rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "id": "tZy5sPVDYY3g",
    "outputId": "2440bb5d-6e96-4879-d4d7-1cc2687a5cae"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Days_to_Delivery</th>\n",
       "      <th>Num_Items_Ordered</th>\n",
       "      <th>Order_Value</th>\n",
       "      <th>Discount_Rate</th>\n",
       "      <th>Num_Previous_Orders</th>\n",
       "      <th>Delivery_Time_Variation</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Order_Priority</th>\n",
       "      <th>Payment_Method</th>\n",
       "      <th>Correlated_Order_Value</th>\n",
       "      <th>Order_Cancelled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.993428</td>\n",
       "      <td>6</td>\n",
       "      <td>716.505607</td>\n",
       "      <td>0.098621</td>\n",
       "      <td>5</td>\n",
       "      <td>1.129165</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>On-premise</td>\n",
       "      <td>Low</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>677.114236</td>\n",
       "      <td>Not-Cancelled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.723471</td>\n",
       "      <td>11</td>\n",
       "      <td>619.054859</td>\n",
       "      <td>0.106769</td>\n",
       "      <td>7</td>\n",
       "      <td>0.889863</td>\n",
       "      <td>APAC</td>\n",
       "      <td>Cloud</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>591.127949</td>\n",
       "      <td>Not-Cancelled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.295377</td>\n",
       "      <td>3</td>\n",
       "      <td>521.257403</td>\n",
       "      <td>0.338047</td>\n",
       "      <td>0</td>\n",
       "      <td>2.668340</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>On-premise</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Bank Transfer</td>\n",
       "      <td>502.195055</td>\n",
       "      <td>Not-Cancelled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.046060</td>\n",
       "      <td>8</td>\n",
       "      <td>602.698626</td>\n",
       "      <td>0.202501</td>\n",
       "      <td>4</td>\n",
       "      <td>2.998095</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>On-premise</td>\n",
       "      <td>High</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>566.850797</td>\n",
       "      <td>Not-Cancelled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.531693</td>\n",
       "      <td>3</td>\n",
       "      <td>610.590042</td>\n",
       "      <td>0.465772</td>\n",
       "      <td>2</td>\n",
       "      <td>2.011061</td>\n",
       "      <td>APAC</td>\n",
       "      <td>Hardware</td>\n",
       "      <td>High</td>\n",
       "      <td>Bank Transfer</td>\n",
       "      <td>581.693494</td>\n",
       "      <td>Not-Cancelled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Days_to_Delivery  Num_Items_Ordered  Order_Value  Discount_Rate  \\\n",
       "0          5.993428                  6   716.505607       0.098621   \n",
       "1          4.723471                 11   619.054859       0.106769   \n",
       "2          6.295377                  3   521.257403       0.338047   \n",
       "3          8.046060                  8   602.698626       0.202501   \n",
       "4          4.531693                  3   610.590042       0.465772   \n",
       "\n",
       "   Num_Previous_Orders  Delivery_Time_Variation Region Product_Category  \\\n",
       "0                    5                 1.129165   EMEA       On-premise   \n",
       "1                    7                 0.889863   APAC            Cloud   \n",
       "2                    0                 2.668340  LATAM       On-premise   \n",
       "3                    4                 2.998095  LATAM       On-premise   \n",
       "4                    2                 2.011061   APAC         Hardware   \n",
       "\n",
       "  Order_Priority Payment_Method  Correlated_Order_Value Order_Cancelled  \n",
       "0            Low        Bitcoin              677.114236   Not-Cancelled  \n",
       "1         Medium        Bitcoin              591.127949   Not-Cancelled  \n",
       "2         Medium  Bank Transfer              502.195055   Not-Cancelled  \n",
       "3           High         PayPal              566.850797   Not-Cancelled  \n",
       "4           High  Bank Transfer              581.693494   Not-Cancelled  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading first 5 Rows of the data\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nUdbr1o_N_gS"
   },
   "source": [
    "# **Data Cleaning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QfjjBlsFN7am",
    "outputId": "f435e4a4-651c-4207-8ccd-bca054be3edf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 460
    },
    "id": "6--mdHoFN7dH",
    "outputId": "398a2ad1-2fc8-4012-c604-aff6a95f6300"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Days_to_Delivery           0\n",
       "Num_Items_Ordered          0\n",
       "Order_Value                0\n",
       "Discount_Rate              0\n",
       "Num_Previous_Orders        0\n",
       "Delivery_Time_Variation    0\n",
       "Region                     0\n",
       "Product_Category           0\n",
       "Order_Priority             0\n",
       "Payment_Method             0\n",
       "Correlated_Order_Value     0\n",
       "Order_Cancelled            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the null values columns wise\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YTObg1D8QQFP"
   },
   "source": [
    "## **Categorical attributes: Dummy Encoding:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "id": "Lf0g2aV5uxKD"
   },
   "outputs": [],
   "source": [
    "# Encoding categorical variables\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "df_encoded = df.copy()\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "#df_encoded['Region'] = label_encoder.fit_transform(df_encoded['Region'])\n",
    "#df_encoded['Product_Category'] = label_encoder.fit_transform(df_encoded['Product_Category'])\n",
    "#df_encoded['Order_Priority'] = label_encoder.fit_transform(df_encoded['Order_Priority'])\n",
    "#df_encoded['Payment_Method'] = label_encoder.fit_transform(df_encoded['Payment_Method'])\n",
    "df_encoded['Order_Cancelled'] = label_encoder.fit_transform(df_encoded['Order_Cancelled'])\n",
    "\n",
    "ohe = OneHotEncoder()\n",
    "df_encoded[ohe.get_feature_names_out()] = ohe.fit_transform(df_encoded[['Region']]).toarray()\n",
    "df_encoded[ohe.get_feature_names_out()] = ohe.fit_transform(df_encoded[['Product_Category']]).toarray()\n",
    "df_encoded[ohe.get_feature_names_out()] = ohe.fit_transform(df_encoded[['Order_Priority']]).toarray()\n",
    "df_encoded[ohe.get_feature_names_out()] = ohe.fit_transform(df_encoded[['Payment_Method']]).toarray()\n",
    "#df_encoded[ohe.get_feature_names_out()] = ohe.fit_transform(df_encoded[['Order_Cancelled']]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YMwpQYrHQrz-",
    "outputId": "c0903189-bd37-4e01-a870-158cfa61af5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Days_to_Delivery', 'Num_Items_Ordered', 'Order_Value', 'Discount_Rate',\n",
       "       'Num_Previous_Orders', 'Delivery_Time_Variation', 'Region',\n",
       "       'Product_Category', 'Order_Priority', 'Payment_Method',\n",
       "       'Correlated_Order_Value', 'Order_Cancelled', 'Region_APAC',\n",
       "       'Region_EMEA', 'Region_LATAM', 'Region_North America',\n",
       "       'Product_Category_Cloud', 'Product_Category_Hardware',\n",
       "       'Product_Category_On-premise', 'Product_Category_SaaS',\n",
       "       'Order_Priority_High', 'Order_Priority_Low', 'Order_Priority_Medium',\n",
       "       'Payment_Method_Bank Transfer', 'Payment_Method_Bitcoin',\n",
       "       'Payment_Method_Credit Card', 'Payment_Method_PayPal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Days_to_Delivery</th>\n",
       "      <th>Num_Items_Ordered</th>\n",
       "      <th>Order_Value</th>\n",
       "      <th>Discount_Rate</th>\n",
       "      <th>Num_Previous_Orders</th>\n",
       "      <th>Delivery_Time_Variation</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Order_Priority</th>\n",
       "      <th>Payment_Method</th>\n",
       "      <th>...</th>\n",
       "      <th>Product_Category_Hardware</th>\n",
       "      <th>Product_Category_On-premise</th>\n",
       "      <th>Product_Category_SaaS</th>\n",
       "      <th>Order_Priority_High</th>\n",
       "      <th>Order_Priority_Low</th>\n",
       "      <th>Order_Priority_Medium</th>\n",
       "      <th>Payment_Method_Bank Transfer</th>\n",
       "      <th>Payment_Method_Bitcoin</th>\n",
       "      <th>Payment_Method_Credit Card</th>\n",
       "      <th>Payment_Method_PayPal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.993428</td>\n",
       "      <td>6</td>\n",
       "      <td>716.505607</td>\n",
       "      <td>0.098621</td>\n",
       "      <td>5</td>\n",
       "      <td>1.129165</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>On-premise</td>\n",
       "      <td>Low</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.723471</td>\n",
       "      <td>11</td>\n",
       "      <td>619.054859</td>\n",
       "      <td>0.106769</td>\n",
       "      <td>7</td>\n",
       "      <td>0.889863</td>\n",
       "      <td>APAC</td>\n",
       "      <td>Cloud</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.295377</td>\n",
       "      <td>3</td>\n",
       "      <td>521.257403</td>\n",
       "      <td>0.338047</td>\n",
       "      <td>0</td>\n",
       "      <td>2.668340</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>On-premise</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Bank Transfer</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.046060</td>\n",
       "      <td>8</td>\n",
       "      <td>602.698626</td>\n",
       "      <td>0.202501</td>\n",
       "      <td>4</td>\n",
       "      <td>2.998095</td>\n",
       "      <td>LATAM</td>\n",
       "      <td>On-premise</td>\n",
       "      <td>High</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.531693</td>\n",
       "      <td>3</td>\n",
       "      <td>610.590042</td>\n",
       "      <td>0.465772</td>\n",
       "      <td>2</td>\n",
       "      <td>2.011061</td>\n",
       "      <td>APAC</td>\n",
       "      <td>Hardware</td>\n",
       "      <td>High</td>\n",
       "      <td>Bank Transfer</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Days_to_Delivery  Num_Items_Ordered  Order_Value  Discount_Rate  \\\n",
       "0          5.993428                  6   716.505607       0.098621   \n",
       "1          4.723471                 11   619.054859       0.106769   \n",
       "2          6.295377                  3   521.257403       0.338047   \n",
       "3          8.046060                  8   602.698626       0.202501   \n",
       "4          4.531693                  3   610.590042       0.465772   \n",
       "\n",
       "   Num_Previous_Orders  Delivery_Time_Variation Region Product_Category  \\\n",
       "0                    5                 1.129165   EMEA       On-premise   \n",
       "1                    7                 0.889863   APAC            Cloud   \n",
       "2                    0                 2.668340  LATAM       On-premise   \n",
       "3                    4                 2.998095  LATAM       On-premise   \n",
       "4                    2                 2.011061   APAC         Hardware   \n",
       "\n",
       "  Order_Priority Payment_Method  ...  Product_Category_Hardware  \\\n",
       "0            Low        Bitcoin  ...                        0.0   \n",
       "1         Medium        Bitcoin  ...                        0.0   \n",
       "2         Medium  Bank Transfer  ...                        0.0   \n",
       "3           High         PayPal  ...                        0.0   \n",
       "4           High  Bank Transfer  ...                        1.0   \n",
       "\n",
       "   Product_Category_On-premise  Product_Category_SaaS  Order_Priority_High  \\\n",
       "0                          1.0                    0.0                  0.0   \n",
       "1                          0.0                    0.0                  0.0   \n",
       "2                          1.0                    0.0                  0.0   \n",
       "3                          1.0                    0.0                  1.0   \n",
       "4                          0.0                    0.0                  1.0   \n",
       "\n",
       "   Order_Priority_Low  Order_Priority_Medium  Payment_Method_Bank Transfer  \\\n",
       "0                 1.0                    0.0                           0.0   \n",
       "1                 0.0                    1.0                           0.0   \n",
       "2                 0.0                    1.0                           1.0   \n",
       "3                 0.0                    0.0                           0.0   \n",
       "4                 0.0                    0.0                           1.0   \n",
       "\n",
       "   Payment_Method_Bitcoin  Payment_Method_Credit Card  Payment_Method_PayPal  \n",
       "0                     1.0                         0.0                    0.0  \n",
       "1                     1.0                         0.0                    0.0  \n",
       "2                     0.0                         0.0                    0.0  \n",
       "3                     0.0                         0.0                    1.0  \n",
       "4                     0.0                         0.0                    0.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "12myDXivQJi7",
    "outputId": "290fa02b-5529-435d-f8fd-dcd06699f78a"
   },
   "outputs": [],
   "source": [
    "# Visulaizing the Pairplot of complete dataset\n",
    "\n",
    "#sns.pairplot(df_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "id": "3gOaZeOc2EEC"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, confusion_matrix, classification_report\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "id": "nLcNJky812Mj"
   },
   "outputs": [],
   "source": [
    "# Separate features and target\n",
    "X = df_encoded.drop(['Order_Cancelled', 'Correlated_Order_Value', 'Order_Value'], axis=1)\n",
    "y = df_encoded['Order_Cancelled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "id": "4IH0XfB012O4"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[109], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m vif_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame()\n\u001b[1;32m      3\u001b[0m vif_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfeature\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m----> 4\u001b[0m vif_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVIF\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m [variance_inflation_factor(X\u001b[38;5;241m.\u001b[39mvalues, i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(X\u001b[38;5;241m.\u001b[39mcolumns))]\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Arrange VIF values in descending order\u001b[39;00m\n\u001b[1;32m      6\u001b[0m vif_data \u001b[38;5;241m=\u001b[39m vif_data\u001b[38;5;241m.\u001b[39msort_values(by\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVIF\u001b[39m\u001b[38;5;124m\"\u001b[39m, ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/statsmodels/stats/outliers_influence.py:196\u001b[0m, in \u001b[0;36mvariance_inflation_factor\u001b[0;34m(exog, exog_idx)\u001b[0m\n\u001b[1;32m    194\u001b[0m mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(k_vars) \u001b[38;5;241m!=\u001b[39m exog_idx\n\u001b[1;32m    195\u001b[0m x_noti \u001b[38;5;241m=\u001b[39m exog[:, mask]\n\u001b[0;32m--> 196\u001b[0m r_squared_i \u001b[38;5;241m=\u001b[39m OLS(x_i, x_noti)\u001b[38;5;241m.\u001b[39mfit()\u001b[38;5;241m.\u001b[39mrsquared\n\u001b[1;32m    197\u001b[0m vif \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.\u001b[39m \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1.\u001b[39m \u001b[38;5;241m-\u001b[39m r_squared_i)\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m vif\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/statsmodels/regression/linear_model.py:924\u001b[0m, in \u001b[0;36mOLS.__init__\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    921\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWeights are not supported in OLS and will be ignored\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    922\u001b[0m            \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn exception will be raised in the next version.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    923\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(msg, ValueWarning)\n\u001b[0;32m--> 924\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(endog, exog, missing\u001b[38;5;241m=\u001b[39mmissing,\n\u001b[1;32m    925\u001b[0m                           hasconst\u001b[38;5;241m=\u001b[39mhasconst, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    926\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_keys:\n\u001b[1;32m    927\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_keys\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/statsmodels/regression/linear_model.py:749\u001b[0m, in \u001b[0;36mWLS.__init__\u001b[0;34m(self, endog, exog, weights, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    747\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    748\u001b[0m     weights \u001b[38;5;241m=\u001b[39m weights\u001b[38;5;241m.\u001b[39msqueeze()\n\u001b[0;32m--> 749\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(endog, exog, missing\u001b[38;5;241m=\u001b[39mmissing,\n\u001b[1;32m    750\u001b[0m                           weights\u001b[38;5;241m=\u001b[39mweights, hasconst\u001b[38;5;241m=\u001b[39mhasconst, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    751\u001b[0m nobs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    752\u001b[0m weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/statsmodels/regression/linear_model.py:203\u001b[0m, in \u001b[0;36mRegressionModel.__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, endog, exog, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 203\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(endog, exog, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpinv_wexog: Float64Array \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    205\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_attr\u001b[38;5;241m.\u001b[39mextend([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpinv_wexog\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwendog\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwexog\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/statsmodels/base/model.py:270\u001b[0m, in \u001b[0;36mLikelihoodModel.__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, endog, exog\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 270\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(endog, exog, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    271\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitialize()\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/statsmodels/base/model.py:95\u001b[0m, in \u001b[0;36mModel.__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m missing \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmissing\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     94\u001b[0m hasconst \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhasconst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m---> 95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle_data(endog, exog, missing, hasconst,\n\u001b[1;32m     96\u001b[0m                               \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     97\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mk_constant \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mk_constant\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mexog\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/statsmodels/base/model.py:135\u001b[0m, in \u001b[0;36mModel._handle_data\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_handle_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, endog, exog, missing, hasconst, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 135\u001b[0m     data \u001b[38;5;241m=\u001b[39m handle_data(endog, exog, missing, hasconst, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    136\u001b[0m     \u001b[38;5;66;03m# kwargs arrays could have changed, easier to just attach here\u001b[39;00m\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m kwargs:\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/statsmodels/base/data.py:675\u001b[0m, in \u001b[0;36mhandle_data\u001b[0;34m(endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    672\u001b[0m     exog \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(exog)\n\u001b[1;32m    674\u001b[0m klass \u001b[38;5;241m=\u001b[39m handle_data_class_factory(endog, exog)\n\u001b[0;32m--> 675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m klass(endog, exog\u001b[38;5;241m=\u001b[39mexog, missing\u001b[38;5;241m=\u001b[39mmissing, hasconst\u001b[38;5;241m=\u001b[39mhasconst,\n\u001b[1;32m    676\u001b[0m              \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/statsmodels/base/data.py:88\u001b[0m, in \u001b[0;36mModelData.__init__\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconst_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     87\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mk_constant \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m---> 88\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle_constant(hasconst)\n\u001b[1;32m     89\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_integrity()\n\u001b[1;32m     90\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache \u001b[38;5;241m=\u001b[39m {}\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/statsmodels/base/data.py:133\u001b[0m, in \u001b[0;36mModelData._handle_constant\u001b[0;34m(self, hasconst)\u001b[0m\n\u001b[1;32m    131\u001b[0m check_implicit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    132\u001b[0m exog_max \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmax(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(exog_max)\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MissingDataError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexog contains inf or nans\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    135\u001b[0m exog_min \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "\u001b[0;31mTypeError\u001b[0m: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
     ]
    }
   ],
   "source": [
    "# Check for multicollinearity using Variance Inflation Factor (VIF)\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"feature\"] = X.columns\n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]\n",
    "# Arrange VIF values in descending order\n",
    "vif_data = vif_data.sort_values(by=\"VIF\", ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "zi5sGQBQ12Rw",
    "outputId": "a5171acd-7487-40a2-f959-14fa9083fb7c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Days_to_Delivery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Num_Items_Ordered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Discount_Rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Num_Previous_Orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Delivery_Time_Variation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Region</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Product_Category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Order_Priority</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Payment_Method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Region_APAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Region_EMEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Region_LATAM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Region_North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Product_Category_Cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Product_Category_Hardware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Product_Category_On-premise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Product_Category_SaaS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Order_Priority_High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Order_Priority_Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Order_Priority_Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Payment_Method_Bank Transfer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Payment_Method_Bitcoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Payment_Method_Credit Card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Payment_Method_PayPal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Order_Cancelled_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Order_Cancelled_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         feature\n",
       "0               Days_to_Delivery\n",
       "1              Num_Items_Ordered\n",
       "2                  Discount_Rate\n",
       "3            Num_Previous_Orders\n",
       "4        Delivery_Time_Variation\n",
       "5                         Region\n",
       "6               Product_Category\n",
       "7                 Order_Priority\n",
       "8                 Payment_Method\n",
       "9                    Region_APAC\n",
       "10                   Region_EMEA\n",
       "11                  Region_LATAM\n",
       "12          Region_North America\n",
       "13        Product_Category_Cloud\n",
       "14     Product_Category_Hardware\n",
       "15   Product_Category_On-premise\n",
       "16         Product_Category_SaaS\n",
       "17           Order_Priority_High\n",
       "18            Order_Priority_Low\n",
       "19         Order_Priority_Medium\n",
       "20  Payment_Method_Bank Transfer\n",
       "21        Payment_Method_Bitcoin\n",
       "22    Payment_Method_Credit Card\n",
       "23         Payment_Method_PayPal\n",
       "24             Order_Cancelled_0\n",
       "25             Order_Cancelled_1"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "id": "tjvbGXiw12UX"
   },
   "outputs": [],
   "source": [
    "# Split dataset into training and testing\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "id": "XI3z6Wn112b8"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'APAC'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/12/8kgz6g6j7r9d3hb2nqwh_q3w0000gn/T/ipykernel_28372/4251899716.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Standardize numeric features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#scaler = StandardScaler()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mX_train_scaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mX_test_scaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/sklearn/utils/_set_output.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    311\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m         \u001b[0mdata_to_wrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_to_wrap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m             \u001b[0;31m# only wrap the first output for cross decomposition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m             return_tuple = (\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m   1094\u001b[0m                 \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1095\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1096\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1099\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1101\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    446\u001b[0m             \u001b[0mFitted\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m         \"\"\"\n\u001b[1;32m    448\u001b[0m         \u001b[0;31m# Reset internal state before fitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    449\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 450\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1469\u001b[0m                 skip_parameter_validation=(\n\u001b[1;32m   1470\u001b[0m                     \u001b[0mprefer_skip_nested_validation\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1471\u001b[0m                 \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1472\u001b[0m             \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1473\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m         \u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_namespace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         \u001b[0mfirst_pass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"n_samples_seen_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m         X = self._validate_data(\n\u001b[0m\u001b[1;32m    491\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m             \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfirst_pass\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_array_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msupported_float_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    629\u001b[0m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    631\u001b[0m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"X\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    634\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m   1009\u001b[0m                         \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1010\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1011\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1014\u001b[0m                 raise ValueError(\n\u001b[1;32m   1015\u001b[0m                     \u001b[0;34m\"Complex data not supported\\n{}\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m                 \u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/sklearn/utils/_array_api.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(array, dtype, order, copy, xp, device)\u001b[0m\n\u001b[1;32m    747\u001b[0m         \u001b[0;31m# Use NumPy API to support order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    750\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 751\u001b[0;31m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    752\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m         \u001b[0;31m# At this point array is a NumPy ndarray. We convert it to an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m         \u001b[0;31m# container that is consistent with the input's namespace.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, dtype, copy)\u001b[0m\n\u001b[1;32m   2149\u001b[0m     def __array__(\n\u001b[1;32m   2150\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool_t\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2151\u001b[0m     \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2152\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2153\u001b[0;31m         \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2154\u001b[0m         if (\n\u001b[1;32m   2155\u001b[0m             \u001b[0mastype_is_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2156\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'APAC'"
     ]
    }
   ],
   "source": [
    "# Standardize numeric features\n",
    "#scaler = StandardScaler()\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "FwZw7TSe12ex"
   },
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train_scaled, y_train)\n",
    "y_pred_log = log_reg.predict(X_test_scaled)\n",
    "y_prob_log = log_reg.predict_proba(X_test_scaled)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "v07y43RI12hY"
   },
   "outputs": [],
   "source": [
    "# ROC and AUC for Logistic Regression\n",
    "roc_auc_log = roc_auc_score(y_test, y_prob_log)\n",
    "fpr_log, tpr_log, _ = roc_curve(y_test, y_prob_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ybtCuHEr12kN",
    "outputId": "19d898c5-d339-4dcd-b99d-7f0e1c697bc3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5309127199250899\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "bo7IJj_f4FxJ"
   },
   "outputs": [],
   "source": [
    "# Confusion Matrix and Classification Report for Logistic Regression\n",
    "conf_matrix_log = confusion_matrix(y_test, y_pred_log)\n",
    "class_report_log = classification_report(y_test, y_pred_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "daorTXbW4F4d",
    "outputId": "2e89160e-a73d-4373-acba-3aeabd917f47"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0 412]\n",
      " [  0 788]]\n"
     ]
    }
   ],
   "source": [
    "print(conf_matrix_log)\n",
    "\n",
    "#[ TN FP ]\n",
    "#[ FN TP ​]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kAl3fKZk4Jer",
    "outputId": "e76468f0-fd0a-4cfb-ea98-0cc4eee22e9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       412\n",
      "           1       0.66      1.00      0.79       788\n",
      "\n",
      "    accuracy                           0.66      1200\n",
      "   macro avg       0.33      0.50      0.40      1200\n",
      "weighted avg       0.43      0.66      0.52      1200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(class_report_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "hhGaw9_S2dR2",
    "outputId": "a4831af8-b817-4e94-ce24-d39faafce307"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC Curves\n",
    "plt.figure(figsize=(9, 6))\n",
    "plt.plot(fpr_log, tpr_log, label=f'Logistic Regression (AUC = {roc_auc_log:.2f})')\n",
    "#plt.plot(fpr_nb, tpr_nb, label=f'Naive Bayes (AUC = {roc_auc_nb:.2f})')\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='grey')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate (Sensitivity/Recall)')\n",
    "plt.title('ROC Curve Comparison')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eBDRoceuwVwZ"
   },
   "source": [
    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   "metadata": {
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   },
   "source": [
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ElX1NSYQe3BC"
   },
   "source": [
    "#### `In-Class Activity - 1:` Duration: 15 minutes\n",
    "- Now instead of using `Standard scaler` for feature scaling , apply Min-Max normalization as a feature scaling and check the effect in performance of model in test.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize numeric features\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled_1 = scaler.fit_transform(X_train)\n",
    "X_test_scaled_1 = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "log_reg_1 = LogisticRegression()\n",
    "log_reg_1.fit(X_train_scaled_1, y_train)\n",
    "y_pred_log_1 = log_reg_1.predict(X_test_scaled_1)\n",
    "y_prob_log_1 = log_reg_1.predict_proba(X_test_scaled_1)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC and AUC for Logistic Regression\n",
    "roc_auc_log_1 = roc_auc_score(y_test, y_prob_log_1)\n",
    "fpr_log_1, tpr_log_1, _ = roc_curve(y_test, y_prob_log_1)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "gpuType": "V28",
   "machine_shape": "hm",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}