34-Decision-tree classification ml model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f8f10f82-ce6b-43a9-b6b4-e1c5850893ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>order_value_EUR</th>\n",
       "      <th>cost</th>\n",
       "      <th>date</th>\n",
       "      <th>category</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>sales_manager</th>\n",
       "      <th>sales_rep</th>\n",
       "      <th>device_type</th>\n",
       "      <th>order_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>98320.37</td>\n",
       "      <td>77722.25</td>\n",
       "      <td>8/23/2020</td>\n",
       "      <td>Games</td>\n",
       "      <td>Konopelski LLC</td>\n",
       "      <td>Maxie Marrow</td>\n",
       "      <td>Tarrah Castelletti</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>70-0511466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>46296.26</td>\n",
       "      <td>40319.41</td>\n",
       "      <td>5/15/2020</td>\n",
       "      <td>Games</td>\n",
       "      <td>Wisoky Inc</td>\n",
       "      <td>Othello Bowes</td>\n",
       "      <td>Amelina Piscopiello</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>77-3489084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>140337.34</td>\n",
       "      <td>115708.14</td>\n",
       "      <td>2020-04-09 00:00:00</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Hegmann Group</td>\n",
       "      <td>Celine Tumasian</td>\n",
       "      <td>Corene Shirer</td>\n",
       "      <td>PC</td>\n",
       "      <td>65-8218141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>203604.46</td>\n",
       "      <td>175344.16</td>\n",
       "      <td>6/26/2019</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Kirlin and Sons</td>\n",
       "      <td>Othello Bowes</td>\n",
       "      <td>Crysta Halls</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>29-5478106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UK</td>\n",
       "      <td>63979.04</td>\n",
       "      <td>56032.84</td>\n",
       "      <td>10/22/2019</td>\n",
       "      <td>Games</td>\n",
       "      <td>Schoen-Keeling</td>\n",
       "      <td>Jessamine Apark</td>\n",
       "      <td>Genevra Charrisson</td>\n",
       "      <td>PC</td>\n",
       "      <td>27-3437546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  order_value_EUR       cost                 date     category  \\\n",
       "0    Sweden         98320.37   77722.25            8/23/2020        Games   \n",
       "1    France         46296.26   40319.41            5/15/2020        Games   \n",
       "2  Portugal        140337.34  115708.14  2020-04-09 00:00:00   Appliances   \n",
       "3    France        203604.46  175344.16            6/26/2019  Electronics   \n",
       "4        UK         63979.04   56032.84           10/22/2019        Games   \n",
       "\n",
       "     customer_name    sales_manager            sales_rep device_type  \\\n",
       "0   Konopelski LLC     Maxie Marrow   Tarrah Castelletti      Tablet   \n",
       "1       Wisoky Inc    Othello Bowes  Amelina Piscopiello      Tablet   \n",
       "2    Hegmann Group  Celine Tumasian        Corene Shirer          PC   \n",
       "3  Kirlin and Sons    Othello Bowes         Crysta Halls      Mobile   \n",
       "4   Schoen-Keeling  Jessamine Apark   Genevra Charrisson          PC   \n",
       "\n",
       "     order_id  \n",
       "0  70-0511466  \n",
       "1  77-3489084  \n",
       "2  65-8218141  \n",
       "3  29-5478106  \n",
       "4  27-3437546  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Replace 'your_file.xlsx' with the path to your Excel file\n",
    "sales_data = 'sales data.xlsx'\n",
    "\n",
    "# Read the Excel file into a pandas DataFrame\n",
    "final_df = pd.read_excel(sales_data)\n",
    "\n",
    "# Now you can work with the DataFrame 'df'\n",
    "final_df.head()  # Display the first few rows of the DataFrame\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "894b6246-c79f-46f4-a656-d944bc19843f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a pivot table with median sales, median cost, and purchase frequency by customers\n",
    "pivot_table = final_df.pivot_table(\n",
    "                              index='customer_name', \n",
    "                              values=['order_value_EUR', 'cost'], \n",
    "                              aggfunc={'order_value_EUR': 'median', 'cost': 'median', 'customer_name': 'count'})\n",
    "\n",
    "# Rename the 'Customer' column to 'Purchase Frequency'\n",
    "pivot_table.columns=['Median Cost', 'Purchase Frequency', 'Median Cost']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dcb4e909-a4e2-4fb1-a463-e3008f6a4b64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Median Cost  Purchase Frequency   Median Cost\n",
      "customer_name                                                    \n",
      "Hegmann Group         115708.14                   1  1.403373e+05\n",
      "Kirlin and Sons       249788.24                   2  5.009896e+07\n",
      "Konopelski LLC         77722.25                   1  9.832037e+04\n",
      "Labadie and Sons      110924.13                   1  1.270754e+05\n",
      "Romaguera-Dietrich     55529.13                   3  6.968143e+04\n",
      "Schoen-Keeling         56032.84                   1  6.397904e+04\n",
      "Wisoky Inc             40319.41                   1  4.629626e+04\n"
     ]
    }
   ],
   "source": [
    "# Print the pivot table\n",
    "print(pivot_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c0009dbe-05f0-4fdf-aea4-9759af6c9f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_pivot_table = pivot_table.sort_values(by = 'Purchase Frequency', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0b160f1-f622-420a-ac43-102e95fbdfa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Median Cost</th>\n",
       "      <th>Purchase Frequency</th>\n",
       "      <th>Median Cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Romaguera-Dietrich</th>\n",
       "      <td>55529.13</td>\n",
       "      <td>3</td>\n",
       "      <td>6.968143e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kirlin and Sons</th>\n",
       "      <td>249788.24</td>\n",
       "      <td>2</td>\n",
       "      <td>5.009896e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hegmann Group</th>\n",
       "      <td>115708.14</td>\n",
       "      <td>1</td>\n",
       "      <td>1.403373e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Konopelski LLC</th>\n",
       "      <td>77722.25</td>\n",
       "      <td>1</td>\n",
       "      <td>9.832037e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labadie and Sons</th>\n",
       "      <td>110924.13</td>\n",
       "      <td>1</td>\n",
       "      <td>1.270754e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schoen-Keeling</th>\n",
       "      <td>56032.84</td>\n",
       "      <td>1</td>\n",
       "      <td>6.397904e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisoky Inc</th>\n",
       "      <td>40319.41</td>\n",
       "      <td>1</td>\n",
       "      <td>4.629626e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Median Cost  Purchase Frequency   Median Cost\n",
       "customer_name                                                    \n",
       "Romaguera-Dietrich     55529.13                   3  6.968143e+04\n",
       "Kirlin and Sons       249788.24                   2  5.009896e+07\n",
       "Hegmann Group         115708.14                   1  1.403373e+05\n",
       "Konopelski LLC         77722.25                   1  9.832037e+04\n",
       "Labadie and Sons      110924.13                   1  1.270754e+05\n",
       "Schoen-Keeling         56032.84                   1  6.397904e+04\n",
       "Wisoky Inc             40319.41                   1  4.629626e+04"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "90850a50-0988-4d0b-8fb0-3e5def30a604",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>Median Cost</th>\n",
       "      <th>Purchase Frequency</th>\n",
       "      <th>Median Cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Romaguera-Dietrich</th>\n",
       "      <td>55529.13</td>\n",
       "      <td>3</td>\n",
       "      <td>6.968143e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kirlin and Sons</th>\n",
       "      <td>249788.24</td>\n",
       "      <td>2</td>\n",
       "      <td>5.009896e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hegmann Group</th>\n",
       "      <td>115708.14</td>\n",
       "      <td>1</td>\n",
       "      <td>1.403373e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Konopelski LLC</th>\n",
       "      <td>77722.25</td>\n",
       "      <td>1</td>\n",
       "      <td>9.832037e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Median Cost  Purchase Frequency   Median Cost\n",
       "customer_name                                                    \n",
       "Romaguera-Dietrich     55529.13                   3  6.968143e+04\n",
       "Kirlin and Sons       249788.24                   2  5.009896e+07\n",
       "Hegmann Group         115708.14                   1  1.403373e+05\n",
       "Konopelski LLC         77722.25                   1  9.832037e+04"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort by range\n",
    "top_4 = sorted_pivot_table[0:4]\n",
    "top_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5e3c43b-57cf-43cd-a068-baeeb5b56948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Romaguera-Dietrich', 'Kirlin and Sons', 'Hegmann Group',\n",
       "       'Konopelski LLC'],\n",
       "      dtype='object', name='customer_name')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_4.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1210677d-9649-41b2-a401-eeddd361b3b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# List of special customer names\n",
    "loyal_customer_names = ['Romaguera-Dietrich', 'Kirlin and Sons', 'Hegmann Group', 'Konopelski LLC']\n",
    "\n",
    "# Function to assign customer type\n",
    "def customer_type(name):\n",
    "    if name in loyal_customer_names:\n",
    "        return 'Loyal customer'\n",
    "    else:\n",
    "        return 'Regular customer'\n",
    "\n",
    "# Assigning new variable 'customer_type'\n",
    "final_df['customer_type'] = final_df['customer_name'].apply(customer_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06ceac12-3834-4f9a-9dda-d55bd7eb76d5",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>order_value_EUR</th>\n",
       "      <th>cost</th>\n",
       "      <th>date</th>\n",
       "      <th>category</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>sales_manager</th>\n",
       "      <th>sales_rep</th>\n",
       "      <th>device_type</th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>98320.37</td>\n",
       "      <td>77722.25</td>\n",
       "      <td>8/23/2020</td>\n",
       "      <td>Games</td>\n",
       "      <td>Konopelski LLC</td>\n",
       "      <td>Maxie Marrow</td>\n",
       "      <td>Tarrah Castelletti</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>70-0511466</td>\n",
       "      <td>Loyal customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>46296.26</td>\n",
       "      <td>40319.41</td>\n",
       "      <td>5/15/2020</td>\n",
       "      <td>Games</td>\n",
       "      <td>Wisoky Inc</td>\n",
       "      <td>Othello Bowes</td>\n",
       "      <td>Amelina Piscopiello</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>77-3489084</td>\n",
       "      <td>Regular customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>140337.34</td>\n",
       "      <td>115708.14</td>\n",
       "      <td>2020-04-09 00:00:00</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Hegmann Group</td>\n",
       "      <td>Celine Tumasian</td>\n",
       "      <td>Corene Shirer</td>\n",
       "      <td>PC</td>\n",
       "      <td>65-8218141</td>\n",
       "      <td>Loyal customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>203604.46</td>\n",
       "      <td>175344.16</td>\n",
       "      <td>6/26/2019</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Kirlin and Sons</td>\n",
       "      <td>Othello Bowes</td>\n",
       "      <td>Crysta Halls</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>29-5478106</td>\n",
       "      <td>Loyal customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UK</td>\n",
       "      <td>63979.04</td>\n",
       "      <td>56032.84</td>\n",
       "      <td>10/22/2019</td>\n",
       "      <td>Games</td>\n",
       "      <td>Schoen-Keeling</td>\n",
       "      <td>Jessamine Apark</td>\n",
       "      <td>Genevra Charrisson</td>\n",
       "      <td>PC</td>\n",
       "      <td>27-3437546</td>\n",
       "      <td>Regular customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  order_value_EUR       cost                 date     category  \\\n",
       "0    Sweden         98320.37   77722.25            8/23/2020        Games   \n",
       "1    France         46296.26   40319.41            5/15/2020        Games   \n",
       "2  Portugal        140337.34  115708.14  2020-04-09 00:00:00   Appliances   \n",
       "3    France        203604.46  175344.16            6/26/2019  Electronics   \n",
       "4        UK         63979.04   56032.84           10/22/2019        Games   \n",
       "\n",
       "     customer_name    sales_manager            sales_rep device_type  \\\n",
       "0   Konopelski LLC     Maxie Marrow   Tarrah Castelletti      Tablet   \n",
       "1       Wisoky Inc    Othello Bowes  Amelina Piscopiello      Tablet   \n",
       "2    Hegmann Group  Celine Tumasian        Corene Shirer          PC   \n",
       "3  Kirlin and Sons    Othello Bowes         Crysta Halls      Mobile   \n",
       "4   Schoen-Keeling  Jessamine Apark   Genevra Charrisson          PC   \n",
       "\n",
       "     order_id     customer_type  \n",
       "0  70-0511466    Loyal customer  \n",
       "1  77-3489084  Regular customer  \n",
       "2  65-8218141    Loyal customer  \n",
       "3  29-5478106    Loyal customer  \n",
       "4  27-3437546  Regular customer  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9046f751-d876-4f33-8827-2c3d1531e4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_type\n",
       "Loyal customer      7\n",
       "Regular customer    3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['customer_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7cc280f6-6ff0-41b2-861d-cda632b0ebca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Give me a python code to extract day, month and a year form date variable in a dataframe"
   ]
  },
  {
   "cell_type": "raw",
   "id": "67887405-221f-4f7d-87c1-8c0151a0ff33",
   "metadata": {},
   "source": [
    "# Convert 'date' column to datetime type\n",
    "final_df['date'] = pd.to_datetime(final_df['date'])\n",
    "\n",
    "# Extract day, month, and year\n",
    "final_df['day'] = final_df['date'].dt.day\n",
    "final_df['month'] = final_df['date'].dt.month\n",
    "final_df['year'] = final_df['date'].dt.year\n",
    "\n",
    "final_df.head()\n",
    "final_df['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5eec806a-ab8c-47c5-8f4c-7c2f6108a7fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Give me a python code to assign 1 for \"loyal customer\" and 0 for \"regular customer\" in a variable CustomerType in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a9452f8-c394-4428-bb87-7a9b21bc0e31",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Gennie\\AppData\\Local\\Temp\\ipykernel_11960\\57327186.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  final_df['Customer_Type_encoded'] = final_df['customer_type'].replace({'Loyal customer': 1, 'Regular customer': 0})\n"
     ]
    }
   ],
   "source": [
    "final_df['Customer_Type_encoded'] = final_df['customer_type'].replace({'Loyal customer': 1, 'Regular customer': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "376e1e88-f203-47f0-97c3-0f2cf798d954",
   "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>country</th>\n",
       "      <th>order_value_EUR</th>\n",
       "      <th>cost</th>\n",
       "      <th>date</th>\n",
       "      <th>category</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>sales_manager</th>\n",
       "      <th>sales_rep</th>\n",
       "      <th>device_type</th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>Customer_Type_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>98320.37</td>\n",
       "      <td>77722.25</td>\n",
       "      <td>8/23/2020</td>\n",
       "      <td>Games</td>\n",
       "      <td>Konopelski LLC</td>\n",
       "      <td>Maxie Marrow</td>\n",
       "      <td>Tarrah Castelletti</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>70-0511466</td>\n",
       "      <td>Loyal customer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>46296.26</td>\n",
       "      <td>40319.41</td>\n",
       "      <td>5/15/2020</td>\n",
       "      <td>Games</td>\n",
       "      <td>Wisoky Inc</td>\n",
       "      <td>Othello Bowes</td>\n",
       "      <td>Amelina Piscopiello</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>77-3489084</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>140337.34</td>\n",
       "      <td>115708.14</td>\n",
       "      <td>2020-04-09 00:00:00</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Hegmann Group</td>\n",
       "      <td>Celine Tumasian</td>\n",
       "      <td>Corene Shirer</td>\n",
       "      <td>PC</td>\n",
       "      <td>65-8218141</td>\n",
       "      <td>Loyal customer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>203604.46</td>\n",
       "      <td>175344.16</td>\n",
       "      <td>6/26/2019</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Kirlin and Sons</td>\n",
       "      <td>Othello Bowes</td>\n",
       "      <td>Crysta Halls</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>29-5478106</td>\n",
       "      <td>Loyal customer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UK</td>\n",
       "      <td>63979.04</td>\n",
       "      <td>56032.84</td>\n",
       "      <td>10/22/2019</td>\n",
       "      <td>Games</td>\n",
       "      <td>Schoen-Keeling</td>\n",
       "      <td>Jessamine Apark</td>\n",
       "      <td>Genevra Charrisson</td>\n",
       "      <td>PC</td>\n",
       "      <td>27-3437546</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  order_value_EUR       cost                 date     category  \\\n",
       "0    Sweden         98320.37   77722.25            8/23/2020        Games   \n",
       "1    France         46296.26   40319.41            5/15/2020        Games   \n",
       "2  Portugal        140337.34  115708.14  2020-04-09 00:00:00   Appliances   \n",
       "3    France        203604.46  175344.16            6/26/2019  Electronics   \n",
       "4        UK         63979.04   56032.84           10/22/2019        Games   \n",
       "\n",
       "     customer_name    sales_manager            sales_rep device_type  \\\n",
       "0   Konopelski LLC     Maxie Marrow   Tarrah Castelletti      Tablet   \n",
       "1       Wisoky Inc    Othello Bowes  Amelina Piscopiello      Tablet   \n",
       "2    Hegmann Group  Celine Tumasian        Corene Shirer          PC   \n",
       "3  Kirlin and Sons    Othello Bowes         Crysta Halls      Mobile   \n",
       "4   Schoen-Keeling  Jessamine Apark   Genevra Charrisson          PC   \n",
       "\n",
       "     order_id     customer_type  Customer_Type_encoded  \n",
       "0  70-0511466    Loyal customer                      1  \n",
       "1  77-3489084  Regular customer                      0  \n",
       "2  65-8218141    Loyal customer                      1  \n",
       "3  29-5478106    Loyal customer                      1  \n",
       "4  27-3437546  Regular customer                      0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8fea9e2c-459b-4cd5-9171-f9d6a94e92d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# give me a python code to create a new dummy var for the variables country, category, device_type in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "316a7a6c-40cd-4198-b0a9-fc045ec94739",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummy variables for multiple categorical variables\n",
    "prep_df = pd.get_dummies(final_df, columns=['country', 'category', 'device_type'], dtype=int)\n",
    "\n",
    "# Concatenate the dummy variables with the original DataFrame\n",
    "df = pd.concat([final_df, prep_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2d725cac-09e0-4fd7-9ee0-c2fca217cd45",
   "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",
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       "    }\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>country</th>\n",
       "      <th>order_value_EUR</th>\n",
       "      <th>cost</th>\n",
       "      <th>date</th>\n",
       "      <th>category</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>sales_manager</th>\n",
       "      <th>sales_rep</th>\n",
       "      <th>device_type</th>\n",
       "      <th>order_id</th>\n",
       "      <th>...</th>\n",
       "      <th>country_Sweden</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>category_Appliances</th>\n",
       "      <th>category_Clothing</th>\n",
       "      <th>category_Electronics</th>\n",
       "      <th>category_Games</th>\n",
       "      <th>device_type_MAC</th>\n",
       "      <th>device_type_Mobile</th>\n",
       "      <th>device_type_PC</th>\n",
       "      <th>device_type_Tablet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>98320.37</td>\n",
       "      <td>77722.25</td>\n",
       "      <td>8/23/2020</td>\n",
       "      <td>Games</td>\n",
       "      <td>Konopelski LLC</td>\n",
       "      <td>Maxie Marrow</td>\n",
       "      <td>Tarrah Castelletti</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>70-0511466</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>46296.26</td>\n",
       "      <td>40319.41</td>\n",
       "      <td>5/15/2020</td>\n",
       "      <td>Games</td>\n",
       "      <td>Wisoky Inc</td>\n",
       "      <td>Othello Bowes</td>\n",
       "      <td>Amelina Piscopiello</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>77-3489084</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>140337.34</td>\n",
       "      <td>115708.14</td>\n",
       "      <td>2020-04-09 00:00:00</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Hegmann Group</td>\n",
       "      <td>Celine Tumasian</td>\n",
       "      <td>Corene Shirer</td>\n",
       "      <td>PC</td>\n",
       "      <td>65-8218141</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>203604.46</td>\n",
       "      <td>175344.16</td>\n",
       "      <td>6/26/2019</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Kirlin and Sons</td>\n",
       "      <td>Othello Bowes</td>\n",
       "      <td>Crysta Halls</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>29-5478106</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UK</td>\n",
       "      <td>63979.04</td>\n",
       "      <td>56032.84</td>\n",
       "      <td>10/22/2019</td>\n",
       "      <td>Games</td>\n",
       "      <td>Schoen-Keeling</td>\n",
       "      <td>Jessamine Apark</td>\n",
       "      <td>Genevra Charrisson</td>\n",
       "      <td>PC</td>\n",
       "      <td>27-3437546</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  order_value_EUR       cost                 date     category  \\\n",
       "0    Sweden         98320.37   77722.25            8/23/2020        Games   \n",
       "1    France         46296.26   40319.41            5/15/2020        Games   \n",
       "2  Portugal        140337.34  115708.14  2020-04-09 00:00:00   Appliances   \n",
       "3    France        203604.46  175344.16            6/26/2019  Electronics   \n",
       "4        UK         63979.04   56032.84           10/22/2019        Games   \n",
       "\n",
       "     customer_name    sales_manager            sales_rep device_type  \\\n",
       "0   Konopelski LLC     Maxie Marrow   Tarrah Castelletti      Tablet   \n",
       "1       Wisoky Inc    Othello Bowes  Amelina Piscopiello      Tablet   \n",
       "2    Hegmann Group  Celine Tumasian        Corene Shirer          PC   \n",
       "3  Kirlin and Sons    Othello Bowes         Crysta Halls      Mobile   \n",
       "4   Schoen-Keeling  Jessamine Apark   Genevra Charrisson          PC   \n",
       "\n",
       "     order_id  ... country_Sweden  country_UK  category_Appliances  \\\n",
       "0  70-0511466  ...              1           0                    0   \n",
       "1  77-3489084  ...              0           0                    0   \n",
       "2  65-8218141  ...              0           0                    1   \n",
       "3  29-5478106  ...              0           0                    0   \n",
       "4  27-3437546  ...              0           1                    0   \n",
       "\n",
       "   category_Clothing category_Electronics category_Games device_type_MAC  \\\n",
       "0                  0                    0              1               0   \n",
       "1                  0                    0              1               0   \n",
       "2                  0                    0              0               0   \n",
       "3                  0                    1              0               0   \n",
       "4                  0                    0              1               0   \n",
       "\n",
       "  device_type_Mobile device_type_PC device_type_Tablet  \n",
       "0                  0              0                  1  \n",
       "1                  0              0                  1  \n",
       "2                  0              1                  0  \n",
       "3                  1              0                  0  \n",
       "4                  0              1                  0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "28e2ffab-81be-4095-a807-adf282f0e914",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=prep_df['cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7ae187f9-95f7-42e7-bc54-03bca2c8a746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     77722.25\n",
       "1     40319.41\n",
       "2    115708.14\n",
       "3    175344.16\n",
       "4     56032.84\n",
       "5    110924.13\n",
       "6     55529.13\n",
       "7     55529.13\n",
       "8     55529.13\n",
       "9    324232.32\n",
       "Name: cost, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4d3ca571-6bc8-482a-b104-6e0701c996c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['order_value_EUR', 'cost', 'date', 'customer_name', 'sales_manager',\n",
       "       'sales_rep', 'order_id', 'customer_type', 'Customer_Type_encoded',\n",
       "       'country_France', 'country_Italy', 'country_Korea', 'country_Portugal',\n",
       "       'country_Sweden', 'country_UK', 'category_Appliances',\n",
       "       'category_Clothing', 'category_Electronics', 'category_Games',\n",
       "       'device_type_MAC', 'device_type_Mobile', 'device_type_PC',\n",
       "       'device_type_Tablet'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prep_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9470f778-f6cd-4598-b602-d4174b1b52a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# give me a python code to drop 5 categorical variables from a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d5305281-632c-4e16-aa3b-20ea5ec26ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_value_EUR</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98320.37</td>\n",
       "      <td>77722.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46296.26</td>\n",
       "      <td>40319.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>140337.34</td>\n",
       "      <td>115708.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>203604.46</td>\n",
       "      <td>175344.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63979.04</td>\n",
       "      <td>56032.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>127075.41</td>\n",
       "      <td>110924.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>69681.43</td>\n",
       "      <td>55529.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>69681.43</td>\n",
       "      <td>55529.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>69681.43</td>\n",
       "      <td>55529.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99994324.23</td>\n",
       "      <td>324232.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_value_EUR       cost\n",
       "0         98320.37   77722.25\n",
       "1         46296.26   40319.41\n",
       "2        140337.34  115708.14\n",
       "3        203604.46  175344.16\n",
       "4         63979.04   56032.84\n",
       "5        127075.41  110924.13\n",
       "6         69681.43   55529.13\n",
       "7         69681.43   55529.13\n",
       "8         69681.43   55529.13\n",
       "9      99994324.23  324232.32"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of categorical variables to drop\n",
    "categorical_vars_to_drop = [  'customer_type', 'Customer_Type_encoded',\n",
    "       'country_France', 'country_Italy', 'country_Korea', 'country_Portugal',\n",
    "       'country_Sweden', 'country_UK', 'category_Appliances',\n",
    "       'category_Clothing', 'category_Electronics', 'category_Games',\n",
    "       'device_type_MAC', 'device_type_Mobile', 'device_type_PC',\n",
    "       'device_type_Tablet', 'date', 'customer_name', 'sales_manager', 'sales_rep', 'order_id']\n",
    "\n",
    "# Drop the categorical variables from the DataFrame\n",
    "x = prep_df.drop(columns=categorical_vars_to_drop, axis=1)\n",
    "x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a77b76dd-8fca-410e-bf39-9334915de12c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['order_value_EUR', 'cost'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c2c6456b-0851-4d09-a038-2fa66f5e1ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# give me a python code to scale a dataframe with standardscaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0262101f-b5b4-450d-b973-c76863c41283",
   "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>order_value_EUR</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98320.37</td>\n",
       "      <td>77722.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46296.26</td>\n",
       "      <td>40319.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>140337.34</td>\n",
       "      <td>115708.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>203604.46</td>\n",
       "      <td>175344.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63979.04</td>\n",
       "      <td>56032.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>127075.41</td>\n",
       "      <td>110924.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>69681.43</td>\n",
       "      <td>55529.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>69681.43</td>\n",
       "      <td>55529.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>69681.43</td>\n",
       "      <td>55529.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99994324.23</td>\n",
       "      <td>324232.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_value_EUR       cost\n",
       "0         98320.37   77722.25\n",
       "1         46296.26   40319.41\n",
       "2        140337.34  115708.14\n",
       "3        203604.46  175344.16\n",
       "4         63979.04   56032.84\n",
       "5        127075.41  110924.13\n",
       "6         69681.43   55529.13\n",
       "7         69681.43   55529.13\n",
       "8         69681.43   55529.13\n",
       "9      99994324.23  324232.32"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit and transform the DataFrame\n",
    "scaled_data = scaler.fit_transform(x)\n",
    "x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5f66e56a-dcdc-4c84-8dd6-983835260c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.33334695, -0.35176779],\n",
       "       [-0.3350829 , -0.80601252],\n",
       "       [-0.33194493,  0.10955789],\n",
       "       [-0.32983382,  0.83381702],\n",
       "       [-0.33449286, -0.61517828],\n",
       "       [-0.33238745,  0.05145772],\n",
       "       [-0.33430258, -0.62129566],\n",
       "       [-0.33430258, -0.62129566],\n",
       "       [-0.33430258, -0.62129566],\n",
       "       [ 2.99999666,  2.64201294]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1c0c1cae-9f51-4564-99e0-dd1bd79248cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "850c96d6-8612-4932-8306-402c775439ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Assuming you have your data stored in variables x and y\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_x, test_x, train_y, test_y = train_test_split(scaled_data, y, test_size=0.2, random_state=42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "634321b1-28b9-4c21-a3cf-b03381153792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.33238745,  0.05145772],\n",
       "       [-0.33334695, -0.35176779],\n",
       "       [-0.33430258, -0.62129566],\n",
       "       [-0.33194493,  0.10955789],\n",
       "       [ 2.99999666,  2.64201294],\n",
       "       [-0.33449286, -0.61517828],\n",
       "       [-0.32983382,  0.83381702],\n",
       "       [-0.33430258, -0.62129566]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "59ff9254-a15e-4c94-bd36-2d4e769c2495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.33430258, -0.62129566],\n",
       "       [-0.3350829 , -0.80601252]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0baf7dc1-61f3-4d04-816e-1520c1ceaf9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    110924.13\n",
       "0     77722.25\n",
       "7     55529.13\n",
       "2    115708.14\n",
       "9    324232.32\n",
       "4     56032.84\n",
       "3    175344.16\n",
       "6     55529.13\n",
       "Name: cost, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1b289606-8ae7-448c-8fad-58628004c56a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8    55529.13\n",
       "1    40319.41\n",
       "Name: cost, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "65398fd9-0b25-4681-a0c5-8dc208d5be12",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "030ea846-9511-4472-934b-0c9ab1673e8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Give me a python code build Linear Regression ML modelon train data, \n",
    "# make prediction on test data, measure accuracy with MAPE, a plot to visualize y_pred against test_y\n",
    "# and kde plot to visualize the disturbution of a residual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bf37185d-7f54-4e4f-a04b-4376c1fc461b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3f3832e5-2874-4cec-8678-004ef21e5fee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train linear regression model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Train (fit) the model z\n",
    "model.fit(train_x, train_y)\n",
    "\n",
    "# Make predictions of the test data\n",
    "y_pred = model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "28c85e28-42ee-4700-9118-b41f248ebe03",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c044a923-6b41-4923-b158-08440d35629a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to calculate Mean Absolute Percentage Error (MAPE)\n",
    "def calculate_mape(test_y, y_pred):\n",
    "    return np.mean(np.abs((test_y - y_pred) / test_y)) * 100\n",
    "# Calculating the Mean Absolute Percentage Error (MAPE) using the custom function\n",
    "mape = calculate_mape(test_y, y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f54edae4-99e6-42fa-b112-163840677960",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "211175c2-8024-4b31-a928-0684a994f177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_pred, test_y, color=\"blue\")\n",
    "plt.xlabel('Prediction')\n",
    "plt.ylabel('Actual data')\n",
    "plt.title('Linear Regression prediction vs actual data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "aaf86767-ba96-4f4f-b6a1-d296962b975c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install seaborn\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c1e5f713-7968-4453-bfc2-b6f7e4faeeb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot KDE of residuals\n",
    "residentuals=test_y - y_pred\n",
    "sns.kdeplot(residentuals, fill=True)\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Distribution of Residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "82992d82-ad69-47b6-a3c8-84a290b1a1fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Percentage Error (MAPE): 0.00%\n"
     ]
    }
   ],
   "source": [
    "# print MAPE\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5e32df44-d0a1-43f1-8e28-c525df4048e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nGive me a python code build decision tree regressor ml model on traindata, make prediction on test data, measure\\naccuracy with MAPE, a plot to visualize y_pred against test_y, and a kde plot to visualize the disturbution of a residual\\n\\n'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Give me a python code build decision tree regressor ml model on traindata, make prediction on test data, measure\n",
    "accuracy with MAPE, a plot to visualize y_pred against test_y, and a kde plot to visualize the disturbution of a residual\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8a77c82b-e6cc-4595-bc6e-43295730432d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0699cc79-20b7-467a-8218-6bec2f87c030",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building decision tree regressor model\n",
    "model_decTree= DecisionTreeRegressor()\n",
    "model_decTree.fit(train_x, train_y)\n",
    "\n",
    "# Making predictions on test data\n",
    "y_pred_decTree = model_decTree.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a568d9a8-2be4-4cce-9874-3d3de4af9e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# line 52\n",
    "mape_decTree = calculate_mape(test_y, y_pred_decTree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dfb3d0da-17be-4538-a727-23e93a86ffb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.861535920292475"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape_decTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3dcc5c84-636f-42f3-b1fc-5cb42880d821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_pred_decTree, test_y, color=\"blue\")\n",
    "plt.xlabel('Prediction')\n",
    "plt.ylabel('Actual data')\n",
    "plt.title('Decision Tree Regression prediction vs actual data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7415feb8-8ca9-4514-890b-aead84f67506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot KDE of residuals\n",
    "decTree_residentual=test_y - y_pred_decTree\n",
    "sns.kdeplot(decTree_residentual, fill=True)\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Distribution of Residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8afaf773-3153-44ea-9b90-5c6983897d30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Percentage Error (MAPE): 18.86%\n"
     ]
    }
   ],
   "source": [
    "# print MAPE\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape_decTree:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bf9d33c0-3990-4895-9cbf-f5119a5abddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nGive me a python code build Random Forest Regressor  ml model on traindata, make prediction on test data, measure\\naccuracy with MAPE, a plot to visualize y_pred against test_y, and a kde plot to visualize the disturbution of a residual\\n\\n'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Give me a python code build Random Forest Regressor  ml model on traindata, make prediction on test data, measure\n",
    "accuracy with MAPE, a plot to visualize y_pred against test_y, and a kde plot to visualize the disturbution of a residual\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3cee8372-7f39-483f-8a4b-41abe48c544d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6fc0dab5-5eb2-4abb-aa71-e371c30ec489",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create and fit the Random Forest Regressor model\n",
    "model_rfreg= RandomForestRegressor()\n",
    "model_rfreg.fit(train_x, train_y)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred_rfreg = model_rfreg.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5323e305-3bb1-4230-8b30-2c16b7d57597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([57106.0779, 57297.4877])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rfreg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2b3a1359-8a99-489a-94e2-0dc0bb50a39f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# line 52\n",
    "mape_rfreg = calculate_mape(test_y, y_pred_rfreg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "82a35fe6-19eb-4e29-82f8-fc6708859400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.474400416431102"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape_rfreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "71da136a-e9a2-44d3-8544-c282281e598b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize y_pred against test_y\n",
    "plt.scatter(y_pred_rfreg, test_y, color=\"blue\")\n",
    "plt.xlabel('Prediction')\n",
    "plt.ylabel('Actual data')\n",
    "plt.title('Random Forest Regression prediction vs actual data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6849770b-8d6a-4c58-911f-e86e4b3488b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Percentage Error (MAPE): 22.47%\n"
     ]
    }
   ],
   "source": [
    "# print MAPE\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape_rfreg:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e6447b1c-6a7e-4e7c-82fb-4875f0afe29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ngive a python code build Support Vector Regressor ML model on train data \\nmake prediction on test data, \\nmeasure accurace with MAPE, \\na plot to visualise y_pred against test_y, \\nand a kde plot to visualise the disturbution of a residual\\n'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "give a python code build Support Vector Regressor ML model on train data \n",
    "make prediction on test data, \n",
    "measure accurace with MAPE, \n",
    "a plot to visualise y_pred against test_y, \n",
    "and a kde plot to visualise the disturbution of a residual\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a6ecde90-104e-4212-9ae7-b17b1ea638a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b3844951-8735-4547-acda-1e550e0c3c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build SVR model\n",
    "model_svr= SVR()\n",
    "model_svr.fit(train_x, train_y)\n",
    "\n",
    "# Make predictions on test data\n",
    "y_pred_svr = model_svr.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b0237287-04ae-42d5-b362-01ef07468889",
   "metadata": {},
   "outputs": [],
   "source": [
    "# line 52\n",
    "mape_svr= calculate_mape(test_y, y_pred_svr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "288b5b40-8565-447d-b17a-c1465237465d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize y_pred against test_y\n",
    "plt.scatter(y_pred_svr, test_y, color=\"blue\")\n",
    "plt.xlabel('Prediction')\n",
    "plt.ylabel('Actual data')\n",
    "plt.title('Random Forest Regression prediction vs actual data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "01cbf46a-8d8c-4700-9360-f1c6f79cbc72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot KDE of residuals\n",
    "svr_residentual=test_y - y_pred_svr\n",
    "sns.kdeplot(svr_residentual, fill=True)\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Distribution of Residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "13e8faa8-20e5-496b-b418-ddb2aba5bdf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Percentage Error (MAPE): 101.90%\n"
     ]
    }
   ],
   "source": [
    "# print MAPE\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape_svr:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d20bf5a0-d9dd-4237-aac3-826c2b72bda6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nGive me a python code build Xgboost Regressor ML model on train data, \\nmake prediction on test data, measure accurace with MAPE, a plot to visualize y_pred against test_y\\nand a kde plot to visualize the distirbution of a residual\\n\\n'"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Give me a python code build Xgboost Regressor ML model on train data, \n",
    "make prediction on test data, measure accurace with MAPE, a plot to visualize y_pred against test_y\n",
    "and a kde plot to visualize the distirbution of a residual\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "52468c24-dbc9-4045-97c2-6bffa61bf0cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b9fc78da-a0db-4e16-952c-38193cfeef1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6f4d2aa0-badf-4606-bd16-9985c0763d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train XGBoost model\n",
    "model_xgboost = xgb.XGBRegressor()\n",
    "model_xgboost .fit(train_x, train_y)\n",
    "\n",
    "# Make predictions on test data\n",
    "y_pred_xgbboost = model_xgboost .predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "adf82e73-5a2d-41f8-8ed3-2bc76a3a626e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# line 52\n",
    "mape_xgbboost= calculate_mape(test_y, y_pred_xgbboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5cd58b59-2690-4bf6-86fa-f8b875d12287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize y_pred against test_y\n",
    "plt.scatter(y_pred_xgbboost, test_y, color=\"blue\")\n",
    "plt.xlabel('Prediction')\n",
    "plt.ylabel('Actual data')\n",
    "plt.title('XGBboost prediction vs actual data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a46757e1-a2d6-4332-bfc4-720c02486793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot KDE of residuals\n",
    "xgbboost_residentual=test_y - y_pred_xgbboost\n",
    "sns.kdeplot(xgbboost_residentual, fill=True)\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Distribution of Residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "effdf4da-8ab1-4ff3-9edb-dfb3725e4a21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Percentage Error (MAPE): 18.86%\n"
     ]
    }
   ],
   "source": [
    "# print MAPE\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape_xgbboost:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "20842694-b84b-4c3f-996a-f6379cfb7cf7",
   "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>order_value_EUR</th>\n",
       "      <th>cost</th>\n",
       "      <th>date</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>sales_manager</th>\n",
       "      <th>sales_rep</th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>Customer_Type_encoded</th>\n",
       "      <th>country_France</th>\n",
       "      <th>...</th>\n",
       "      <th>country_Sweden</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>category_Appliances</th>\n",
       "      <th>category_Clothing</th>\n",
       "      <th>category_Electronics</th>\n",
       "      <th>category_Games</th>\n",
       "      <th>device_type_MAC</th>\n",
       "      <th>device_type_Mobile</th>\n",
       "      <th>device_type_PC</th>\n",
       "      <th>device_type_Tablet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98320.37</td>\n",
       "      <td>77722.25</td>\n",
       "      <td>8/23/2020</td>\n",
       "      <td>Konopelski LLC</td>\n",
       "      <td>Maxie Marrow</td>\n",
       "      <td>Tarrah Castelletti</td>\n",
       "      <td>70-0511466</td>\n",
       "      <td>Loyal customer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46296.26</td>\n",
       "      <td>40319.41</td>\n",
       "      <td>5/15/2020</td>\n",
       "      <td>Wisoky Inc</td>\n",
       "      <td>Othello Bowes</td>\n",
       "      <td>Amelina Piscopiello</td>\n",
       "      <td>77-3489084</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>140337.34</td>\n",
       "      <td>115708.14</td>\n",
       "      <td>2020-04-09 00:00:00</td>\n",
       "      <td>Hegmann Group</td>\n",
       "      <td>Celine Tumasian</td>\n",
       "      <td>Corene Shirer</td>\n",
       "      <td>65-8218141</td>\n",
       "      <td>Loyal customer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>203604.46</td>\n",
       "      <td>175344.16</td>\n",
       "      <td>6/26/2019</td>\n",
       "      <td>Kirlin and Sons</td>\n",
       "      <td>Othello Bowes</td>\n",
       "      <td>Crysta Halls</td>\n",
       "      <td>29-5478106</td>\n",
       "      <td>Loyal customer</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63979.04</td>\n",
       "      <td>56032.84</td>\n",
       "      <td>10/22/2019</td>\n",
       "      <td>Schoen-Keeling</td>\n",
       "      <td>Jessamine Apark</td>\n",
       "      <td>Genevra Charrisson</td>\n",
       "      <td>27-3437546</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_value_EUR       cost                 date    customer_name  \\\n",
       "0         98320.37   77722.25            8/23/2020   Konopelski LLC   \n",
       "1         46296.26   40319.41            5/15/2020       Wisoky Inc   \n",
       "2        140337.34  115708.14  2020-04-09 00:00:00    Hegmann Group   \n",
       "3        203604.46  175344.16            6/26/2019  Kirlin and Sons   \n",
       "4         63979.04   56032.84           10/22/2019   Schoen-Keeling   \n",
       "\n",
       "     sales_manager            sales_rep    order_id     customer_type  \\\n",
       "0     Maxie Marrow   Tarrah Castelletti  70-0511466    Loyal customer   \n",
       "1    Othello Bowes  Amelina Piscopiello  77-3489084  Regular customer   \n",
       "2  Celine Tumasian        Corene Shirer  65-8218141    Loyal customer   \n",
       "3    Othello Bowes         Crysta Halls  29-5478106    Loyal customer   \n",
       "4  Jessamine Apark   Genevra Charrisson  27-3437546  Regular customer   \n",
       "\n",
       "   Customer_Type_encoded  country_France  ...  country_Sweden  country_UK  \\\n",
       "0                      1               0  ...               1           0   \n",
       "1                      0               1  ...               0           0   \n",
       "2                      1               0  ...               0           0   \n",
       "3                      1               1  ...               0           0   \n",
       "4                      0               0  ...               0           1   \n",
       "\n",
       "   category_Appliances  category_Clothing  category_Electronics  \\\n",
       "0                    0                  0                     0   \n",
       "1                    0                  0                     0   \n",
       "2                    1                  0                     0   \n",
       "3                    0                  0                     1   \n",
       "4                    0                  0                     0   \n",
       "\n",
       "   category_Games  device_type_MAC  device_type_Mobile  device_type_PC  \\\n",
       "0               1                0                   0               0   \n",
       "1               1                0                   0               0   \n",
       "2               0                0                   0               1   \n",
       "3               0                0                   1               0   \n",
       "4               1                0                   0               1   \n",
       "\n",
       "   device_type_Tablet  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prep_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28a89f72-e22f-4941-9f21-c5cb2f0461d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "806d50d1-8081-46c6-b844-e7c3bd9e47cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_cat = prep_df['Customer_Type_encoded']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "61e12ccc-4fbd-4c1d-9873-c3a4ead14cf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "5    0\n",
       "6    1\n",
       "7    1\n",
       "8    1\n",
       "9    1\n",
       "Name: Customer_Type_encoded, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f119c1-3fff-44ec-a4de-8f57985be017",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "23d580de-4452-45b7-82fc-16dda0e7f8ba",
   "metadata": {},
   "source": [
    "# List of categorical variables to drop\n",
    "categorical_vars_to_drop = ['date', 'customer_name', 'sales_manager', 'sales_rep', 'order_id', 'customer_type', 'Customer_Type_encoded']\n",
    "\n",
    "# Drop the categorical variables from the DataFrame\n",
    "x_cat = prep_df.drop(categorical_vars_to_drop, axis=1)\n",
    "x_cat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d6e8eb12-9f4b-4a05-944f-74dea6ee2c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.33334695, -0.35176779, -0.5       , -0.33333333, -0.33333333,\n",
       "        -0.33333333,  1.22474487, -0.33333333, -0.33333333, -0.81649658,\n",
       "        -0.33333333,  1.22474487, -0.33333333, -0.33333333, -1.22474487,\n",
       "         2.        ],\n",
       "       [-0.3350829 , -0.80601252,  2.        , -0.33333333, -0.33333333,\n",
       "        -0.33333333, -0.81649658, -0.33333333, -0.33333333, -0.81649658,\n",
       "        -0.33333333,  1.22474487, -0.33333333, -0.33333333, -1.22474487,\n",
       "         2.        ],\n",
       "       [-0.33194493,  0.10955789, -0.5       , -0.33333333, -0.33333333,\n",
       "         3.        , -0.81649658, -0.33333333,  3.        , -0.81649658,\n",
       "        -0.33333333, -0.81649658, -0.33333333, -0.33333333,  0.81649658,\n",
       "        -0.5       ],\n",
       "       [-0.32983382,  0.83381702,  2.        , -0.33333333, -0.33333333,\n",
       "        -0.33333333, -0.81649658, -0.33333333, -0.33333333, -0.81649658,\n",
       "         3.        , -0.81649658, -0.33333333,  3.        , -1.22474487,\n",
       "        -0.5       ],\n",
       "       [-0.33449286, -0.61517828, -0.5       , -0.33333333, -0.33333333,\n",
       "        -0.33333333, -0.81649658,  3.        , -0.33333333, -0.81649658,\n",
       "        -0.33333333,  1.22474487, -0.33333333, -0.33333333,  0.81649658,\n",
       "        -0.5       ],\n",
       "       [-0.33238745,  0.05145772, -0.5       ,  3.        , -0.33333333,\n",
       "        -0.33333333, -0.81649658, -0.33333333, -0.33333333,  1.22474487,\n",
       "        -0.33333333, -0.81649658, -0.33333333, -0.33333333,  0.81649658,\n",
       "        -0.5       ],\n",
       "       [-0.33430258, -0.62129566, -0.5       , -0.33333333, -0.33333333,\n",
       "        -0.33333333,  1.22474487, -0.33333333, -0.33333333,  1.22474487,\n",
       "        -0.33333333, -0.81649658, -0.33333333, -0.33333333,  0.81649658,\n",
       "        -0.5       ],\n",
       "       [-0.33430258, -0.62129566, -0.5       , -0.33333333, -0.33333333,\n",
       "        -0.33333333,  1.22474487, -0.33333333, -0.33333333,  1.22474487,\n",
       "        -0.33333333, -0.81649658, -0.33333333, -0.33333333,  0.81649658,\n",
       "        -0.5       ],\n",
       "       [-0.33430258, -0.62129566, -0.5       , -0.33333333, -0.33333333,\n",
       "        -0.33333333,  1.22474487, -0.33333333, -0.33333333,  1.22474487,\n",
       "        -0.33333333, -0.81649658, -0.33333333, -0.33333333,  0.81649658,\n",
       "        -0.5       ],\n",
       "       [ 2.99999666,  2.64201294, -0.5       , -0.33333333,  3.        ,\n",
       "        -0.33333333, -0.81649658, -0.33333333, -0.33333333, -0.81649658,\n",
       "        -0.33333333,  1.22474487,  3.        , -0.33333333, -1.22474487,\n",
       "        -0.5       ]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit and transform the DataFrame\n",
    "scaled_cat = scaler.fit_transform(x_cat)\n",
    "x_cat\n",
    "scaled_cat "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e8ec9bb8-79e8-4a80-a476-afbf6f1d8d10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "train_x_cat, test_x_cat, train_y_cat, test_y_cat = train_test_split(scaled_cat, y_cat, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf0ab05-b470-440a-a365-88fe073e174f",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "give me a python code builld Logistic classification ML model on train data,\n",
    "make prediction on test data,\n",
    "accuracy classification table and\n",
    "a graph to visualize the table, \n",
    "and percentage of error in a prediction\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4991a37b-b6e5-4c22-9290-ac437bac810d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c7c74e79-e597-4e16-8924-0169778df27b",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Building the Logistic Regression model\n",
    "model = LogisticRegression()\n",
    "model.fit(train_x_cat, train_y_cat)\n",
    " \n",
    "# Making predictions on the test data\n",
    "y_pred= model.predict(test_x_cat)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "6fc5784d-62c3-460e-868f-66c3b5db83df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate accuracy classification table\n",
    "confusion = confusion_matrix(test_y_cat, y_pred)\n",
    "accuracy = accuracy_score(test_y_cat, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4d9187ce-bcf0-43d2-afb7-68da4b9a1c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [0, 1]], dtype=int64)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "becdefe9-fc73-48a8-a3fc-fd5bd3b27638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "cd4b374c-5573-40e6-b262-d796641c5b3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(50.722222222222214, 0.5, 'True labels')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the accuracy classification table\n",
    "fig, ax = plt.subplots()\n",
    "sns.heatmap(confusion, annot=True, cmap=\"Blues\", fmt='d', ax=ax)\n",
    "ax.set_title('Confusion Matrix')\n",
    "ax.set_xlabel('Predicted labels')\n",
    "ax.set_ylabel('True labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6fc52a18-f0e1-4041-b392-02149098a9e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.50\n",
      "Error Percentage:  50.00%\n"
     ]
    }
   ],
   "source": [
    "# Calculate percentage of error in predictions\n",
    "error_percentage = (1 - accuracy) * 100\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"Error Percentage: {error_percentage: .2f}%\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "2becc50a-1d97-4b1e-ae28-6f725e2a403f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "49b8d371-f76d-4426-848b-2d3216444715",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build Decision Tree Classification model\n",
    "model_descTree = DecisionTreeClassifier()\n",
    "model_descTree.fit(train_x_cat, train_y_cat)\n",
    "\n",
    "# Make predictions on test data\n",
    "y_pred_descTree = model.predict(test_x_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "44c7572f-7a07-4a5c-8c42-989fc8d55ddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1], dtype=int64)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_descTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "2d1fac23-cc0f-4774-8224-4411e8c4cb92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate accuracy classification table\n",
    "confusion_decTree = confusion_matrix(test_y_cat, y_pred_descTree)\n",
    "accuracy_decTree = accuracy_score(test_y_cat, y_pred_descTree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "dffea4d2-abe7-49c3-90ef-e27a9e3136b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(50.722222222222214, 0.5, 'True labels')"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the accuracy classification table\n",
    "fig, ax = plt.subplots()\n",
    "sns.heatmap(confusion_decTree, annot=True, cmap=\"Blues\", fmt='d', ax=ax)\n",
    "ax.set_title('Accuracy Classification Table for DecTree')\n",
    "ax.set_xlabel('Predicted labels')\n",
    "ax.set_ylabel('True labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "ae862dc3-c52d-4673-b224-8574adaea101",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.50\n",
      "Error Percentage:  50.00%\n"
     ]
    }
   ],
   "source": [
    "# Calculate percentage of error in predictions\n",
    "error_percentage_decTree = (1 - accuracy_decTree) * 100\n",
    "print(f\"Accuracy: {accuracy_decTree:.2f}\")\n",
    "print(f\"Error Percentage: {error_percentage_decTree: .2f}%\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "666c31b9-0d7b-470b-bec4-4e4f951680a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7472de77-4ecb-48c4-8469-93f12355183a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}