training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bac2d582",
   "metadata": {},
   "source": [
    "# Import needed libraries and read dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "b3f9fa1e",
   "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>Invoice ID</th>\n",
       "      <th>Branch</th>\n",
       "      <th>City</th>\n",
       "      <th>Customer type</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Product line</th>\n",
       "      <th>Unit price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Total</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Payment</th>\n",
       "      <th>cogs</th>\n",
       "      <th>gross margin percentage</th>\n",
       "      <th>gross income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>750-67-8428</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Female</td>\n",
       "      <td>Health and beauty</td>\n",
       "      <td>74.69</td>\n",
       "      <td>7</td>\n",
       "      <td>548.9715</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>13:08</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>522.83</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>26.1415</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>226-31-3081</td>\n",
       "      <td>C</td>\n",
       "      <td>Naypyitaw</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Female</td>\n",
       "      <td>Electronic accessories</td>\n",
       "      <td>15.28</td>\n",
       "      <td>5</td>\n",
       "      <td>80.2200</td>\n",
       "      <td>3/8/2019</td>\n",
       "      <td>10:29</td>\n",
       "      <td>Cash</td>\n",
       "      <td>76.40</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.8200</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>631-41-3108</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Home and lifestyle</td>\n",
       "      <td>46.33</td>\n",
       "      <td>7</td>\n",
       "      <td>340.5255</td>\n",
       "      <td>3/3/2019</td>\n",
       "      <td>13:23</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>324.31</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>16.2155</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123-19-1176</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Male</td>\n",
       "      <td>Health and beauty</td>\n",
       "      <td>58.22</td>\n",
       "      <td>8</td>\n",
       "      <td>489.0480</td>\n",
       "      <td>1/27/2019</td>\n",
       "      <td>20:33</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>465.76</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>23.2880</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>373-73-7910</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sports and travel</td>\n",
       "      <td>86.31</td>\n",
       "      <td>7</td>\n",
       "      <td>634.3785</td>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>10:37</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>604.17</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>30.2085</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>233-67-5758</td>\n",
       "      <td>C</td>\n",
       "      <td>Naypyitaw</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Health and beauty</td>\n",
       "      <td>40.35</td>\n",
       "      <td>1</td>\n",
       "      <td>42.3675</td>\n",
       "      <td>1/29/2019</td>\n",
       "      <td>13:46</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>40.35</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>2.0175</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>303-96-2227</td>\n",
       "      <td>B</td>\n",
       "      <td>Mandalay</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Female</td>\n",
       "      <td>Home and lifestyle</td>\n",
       "      <td>97.38</td>\n",
       "      <td>10</td>\n",
       "      <td>1022.4900</td>\n",
       "      <td>3/2/2019</td>\n",
       "      <td>17:16</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>973.80</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>48.6900</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>727-02-1313</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Male</td>\n",
       "      <td>Food and beverages</td>\n",
       "      <td>31.84</td>\n",
       "      <td>1</td>\n",
       "      <td>33.4320</td>\n",
       "      <td>2/9/2019</td>\n",
       "      <td>13:22</td>\n",
       "      <td>Cash</td>\n",
       "      <td>31.84</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>1.5920</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>347-56-2442</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Home and lifestyle</td>\n",
       "      <td>65.82</td>\n",
       "      <td>1</td>\n",
       "      <td>69.1110</td>\n",
       "      <td>2/22/2019</td>\n",
       "      <td>15:33</td>\n",
       "      <td>Cash</td>\n",
       "      <td>65.82</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.2910</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>849-09-3807</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Female</td>\n",
       "      <td>Fashion accessories</td>\n",
       "      <td>88.34</td>\n",
       "      <td>7</td>\n",
       "      <td>649.2990</td>\n",
       "      <td>2/18/2019</td>\n",
       "      <td>13:28</td>\n",
       "      <td>Cash</td>\n",
       "      <td>618.38</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>30.9190</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Invoice ID Branch       City Customer type  Gender  \\\n",
       "0    750-67-8428      A     Yangon        Member  Female   \n",
       "1    226-31-3081      C  Naypyitaw        Normal  Female   \n",
       "2    631-41-3108      A     Yangon        Normal    Male   \n",
       "3    123-19-1176      A     Yangon        Member    Male   \n",
       "4    373-73-7910      A     Yangon        Normal    Male   \n",
       "..           ...    ...        ...           ...     ...   \n",
       "995  233-67-5758      C  Naypyitaw        Normal    Male   \n",
       "996  303-96-2227      B   Mandalay        Normal  Female   \n",
       "997  727-02-1313      A     Yangon        Member    Male   \n",
       "998  347-56-2442      A     Yangon        Normal    Male   \n",
       "999  849-09-3807      A     Yangon        Member  Female   \n",
       "\n",
       "               Product line  Unit price  Quantity      Total       Date  \\\n",
       "0         Health and beauty       74.69         7   548.9715   1/5/2019   \n",
       "1    Electronic accessories       15.28         5    80.2200   3/8/2019   \n",
       "2        Home and lifestyle       46.33         7   340.5255   3/3/2019   \n",
       "3         Health and beauty       58.22         8   489.0480  1/27/2019   \n",
       "4         Sports and travel       86.31         7   634.3785   2/8/2019   \n",
       "..                      ...         ...       ...        ...        ...   \n",
       "995       Health and beauty       40.35         1    42.3675  1/29/2019   \n",
       "996      Home and lifestyle       97.38        10  1022.4900   3/2/2019   \n",
       "997      Food and beverages       31.84         1    33.4320   2/9/2019   \n",
       "998      Home and lifestyle       65.82         1    69.1110  2/22/2019   \n",
       "999     Fashion accessories       88.34         7   649.2990  2/18/2019   \n",
       "\n",
       "      Time      Payment    cogs  gross margin percentage  gross income  Rating  \n",
       "0    13:08      Ewallet  522.83                 4.761905       26.1415     9.1  \n",
       "1    10:29         Cash   76.40                 4.761905        3.8200     9.6  \n",
       "2    13:23  Credit card  324.31                 4.761905       16.2155     7.4  \n",
       "3    20:33      Ewallet  465.76                 4.761905       23.2880     8.4  \n",
       "4    10:37      Ewallet  604.17                 4.761905       30.2085     5.3  \n",
       "..     ...          ...     ...                      ...           ...     ...  \n",
       "995  13:46      Ewallet   40.35                 4.761905        2.0175     6.2  \n",
       "996  17:16      Ewallet  973.80                 4.761905       48.6900     4.4  \n",
       "997  13:22         Cash   31.84                 4.761905        1.5920     7.7  \n",
       "998  15:33         Cash   65.82                 4.761905        3.2910     4.1  \n",
       "999  13:28         Cash  618.38                 4.761905       30.9190     6.6  \n",
       "\n",
       "[1000 rows x 16 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DatetimeIndex as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from scipy import stats\n",
    "import scipy\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, cross_validate\n",
    "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler,StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score, precision_score, r2_score\n",
    "from sklearn.base import BaseEstimator, TransformerMixin \n",
    "from sklearn.linear_model import LinearRegression, Lasso, LogisticRegression\n",
    "import pickle\n",
    "%matplotlib inline\n",
    "plt.style.use('bmh')\n",
    "\n",
    "def compute_confidence_interval(scores):\n",
    "    \"\"\"\n",
    "    Computing the 95% confidence interval \n",
    "    \"\"\"\n",
    "    mean_score = np.mean(scores)\n",
    "    sem = stats.sem(scores)  # Standard error of the mean\n",
    "    confidence_interval = stats.t.interval(0.95, len(scores)-1, loc=mean_score, scale=sem)\n",
    "    return confidence_interval\n",
    "\n",
    "\n",
    "data = pd.read_csv('supermarket_sales.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6fc2dcc",
   "metadata": {},
   "source": [
    "### Now perform basic data parsing to check for missing data, drop unecssary data and prepare for data transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "1497c1a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Invoice ID                 0\n",
      "Branch                     0\n",
      "City                       0\n",
      "Customer type              0\n",
      "Gender                     0\n",
      "Product line               0\n",
      "Unit price                 0\n",
      "Quantity                   0\n",
      "Total                      0\n",
      "Date                       0\n",
      "Time                       0\n",
      "Payment                    0\n",
      "cogs                       0\n",
      "gross margin percentage    0\n",
      "gross income               0\n",
      "Rating                     0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Unit price'}>,\n",
       "        <Axes: title={'center': 'Quantity'}>,\n",
       "        <Axes: title={'center': 'Total'}>,\n",
       "        <Axes: title={'center': 'cogs'}>],\n",
       "       [<Axes: title={'center': 'gross margin percentage'}>,\n",
       "        <Axes: title={'center': 'gross income'}>,\n",
       "        <Axes: title={'center': 'Rating'}>, <Axes: >],\n",
       "       [<Axes: >, <Axes: >, <Axes: >, <Axes: >],\n",
       "       [<Axes: >, <Axes: >, <Axes: >, <Axes: >],\n",
       "       [<Axes: >, <Axes: >, <Axes: >, <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_data = data.isnull().sum()\n",
    "print(missing_data)\n",
    "data.hist(figsize=(10, 10), layout=(5, 4), grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6ba732",
   "metadata": {},
   "source": [
    "### No missing data or visible outliers, now time to start preprocessing data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16104ccb",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74fdf2f3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_test_split' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data_train, data_test \u001b[38;5;241m=\u001b[39m train_test_split(data, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m      4\u001b[0m train_target \u001b[38;5;241m=\u001b[39m data_train[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgross income\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      5\u001b[0m test_target \u001b[38;5;241m=\u001b[39m data_test[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgross income\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "\u001b[1;31mNameError\u001b[0m: name 'train_test_split' is not defined"
     ]
    }
   ],
   "source": [
    "data_train, data_test = train_test_split(data, test_size=0.2, random_state=0)\n",
    "\n",
    "\n",
    "train_target = data_train['gross income']\n",
    "test_target = data_test['gross income']\n",
    "\n",
    "\n",
    "\n",
    "data_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd32a786",
   "metadata": {},
   "source": [
    "## We will check for correlations between numerical values and target value 'gross income'\n",
    "- In this case, for our visualization, row or column 4 holds the relations to our target value\n",
    "- highly correlated features other than the target can pose as some potential model risks that are not "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "2502a196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.628454</td>\n",
       "      <td>-0.025594</td>\n",
       "      <td>0.628454</td>\n",
       "      <td>0.628454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001034</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.703549</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>0.703549</td>\n",
       "      <td>0.703549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.628454</td>\n",
       "      <td>0.703549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.057089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.025594</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>-0.057089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.057089</td>\n",
       "      <td>-0.057089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.628454</td>\n",
       "      <td>0.703549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.057089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.628454</td>\n",
       "      <td>0.703549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.057089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5\n",
       "0  1.000000  0.001034  0.628454 -0.025594  0.628454  0.628454\n",
       "1  0.001034  1.000000  0.703549 -0.017327  0.703549  0.703549\n",
       "2  0.628454  0.703549  1.000000 -0.057089  1.000000  1.000000\n",
       "3 -0.025594 -0.017327 -0.057089  1.000000 -0.057089 -0.057089\n",
       "4  0.628454  0.703549  1.000000 -0.057089  1.000000  1.000000\n",
       "5  0.628454  0.703549  1.000000 -0.057089  1.000000  1.000000"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numerical = pd.DataFrame(zip(data_train['Unit price'],\n",
    "                                  data_train['Quantity'],\n",
    "                                  data_train['Total'],\n",
    "                                  data_train['Rating'],\n",
    "                                  data_train['gross income'], \n",
    "                                  data_train['cogs']))\n",
    "\n",
    "correlation_matrix = data_numerical.corr()\n",
    "plt.figure(figsize=(10, 8))\n",
    "sb.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap=\"coolwarm\")\n",
    "plt.show()\n",
    "correlation_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a6cee8b",
   "metadata": {},
   "source": [
    "## Now we must get rid of the target values to not interfere with preprocessing or training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "df7277cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train1 = data_train.drop('gross income', axis = 1)\n",
    "data_test1 = data_test.drop('gross income', axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5639caad",
   "metadata": {},
   "source": [
    "## Looks like \"Unit price\" and \"Quantity\" are closely related. However, \"Total\" is extremely linearly related with 1.0, this needs to be investigated when training."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c948164",
   "metadata": {},
   "source": [
    "### Starting Encoders for Date and Time as Required\n",
    "                                                                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "35667763",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time Transformer\n",
    "class Time_Encoder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):  # got rid of format thing\n",
    "        pass\n",
    "        \n",
    "    def fit(self, X, y = None):\n",
    "        return self  # we dont need to fit anything\n",
    "    \n",
    "    def transform(self, X):\n",
    "        # ensuring X is a DataFrame and copying it\n",
    "        if not isinstance(X, pd.DataFrame):\n",
    "            X = pd.DataFrame(X)\n",
    "            \n",
    "        X_copy = X.copy()\n",
    "        \n",
    "        # performing time encoding\n",
    "        # Time Encoding \n",
    "        # Integer Encoding for Time of the Day\n",
    "        # 1. Morning (10:00 - 11:59), 2. Afternoon (12:00 - 16:59)\n",
    "        # 3. Evening (17:00 - 18:59), 4. Night (19:00 - 20:59)\n",
    "        # hour = pd.to_datetime(X_copy['Time']).dt.hour\n",
    "        hour = pd.to_datetime(X['Time'], format='mixed').dt.strftime('%H').astype('float')\n",
    "        hour_cat = pd.cut(hour, bins=[0, 12, 17, 19, 24], labels=[1, 2, 3, 4], right=False)\n",
    "        \n",
    "        # returning copy with encoded time\n",
    "        X_copy['Time'] = hour_cat.astype(int)  # make it an integer\n",
    "        return X_copy\n",
    "\n",
    "# Date Transformer\n",
    "class Date_Encoder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        if not isinstance(X, pd.DataFrame):\n",
    "            X = pd.DataFrame(X)\n",
    "\n",
    "        X_copy = X.copy()\n",
    "        X_copy['Date'] = pd.to_datetime(X_copy['Date']).dt.dayofweek\n",
    "        return X_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f54956d6",
   "metadata": {},
   "source": [
    "## Now we can start to encode and scale data through pipelines for predicting gross income.\n",
    "- Gross margin percentage is all the same value, it will not be useful and so it will be deleted. \n",
    "- We will also delete the Invoice ID for the same reason. Branch and City features seem to display the same information, we will delete the city name for simplicity. \"COGS\" is also too closely related to \"Total\", so it will also be deleted.\n",
    "- All of this will be in our transformer to pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "5084bcfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PREPROCESSOR & PIPELINE FORM \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('drop', 'drop', ['Invoice ID', 'City', 'gross margin percentage', 'cogs', 'Payment', 'Total']),\n",
    "        ('date_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Date']),\n",
    "        ('time_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Time']),\n",
    "        ('branch_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Branch']),\n",
    "        ('member_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Customer type']),\n",
    "        ('gender_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Gender']),\n",
    "        ('product_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Product line']),\n",
    "        # adding numerical scaling\n",
    "        ('num_scaler', StandardScaler(), ['Quantity', 'Unit price', 'Rating'])\n",
    "    ],\n",
    "    remainder='passthrough'  # keeps all other columns\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ee16b7",
   "metadata": {},
   "source": [
    "## Here we are constructing our multiple linear regression pipeline using previous parts (no Regularizer)\n",
    "- This model will not contain lasso regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "fc3d3ee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pipeline = Pipeline([\n",
    "    # first handle date & time\n",
    "    ('date_encode', Date_Encoder()),\n",
    "    ('time_encode', Time_Encoder()),\n",
    "    # use preprocessor\n",
    "    ('preprocessor', preprocessor),\n",
    "    # add my models\n",
    "    ('regressor', LinearRegression())\n",
    "    \n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ad846d",
   "metadata": {},
   "source": [
    "## Training and Plotting Predictions without Regularizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "cc6161a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-44 {color: black;}#sk-container-id-44 pre{padding: 0;}#sk-container-id-44 div.sk-toggleable {background-color: white;}#sk-container-id-44 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-44 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-44 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-44 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-44 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-44 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-44 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-44 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-44 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-44 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-44 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-44 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-44 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-44 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-44 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-44 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-44 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-44 div.sk-item {position: relative;z-index: 1;}#sk-container-id-44 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-44 div.sk-item::before, #sk-container-id-44 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-44 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-44 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-44 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-44 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-44 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-44 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-44 div.sk-label-container {text-align: center;}#sk-container-id-44 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-44 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-44\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;date_encode&#x27;, Date_Encoder()),\n",
       "                (&#x27;time_encode&#x27;, Time_Encoder()),\n",
       "                (&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;drop&#x27;, &#x27;drop&#x27;,\n",
       "                                                  [&#x27;Invoice ID&#x27;, &#x27;City&#x27;,\n",
       "                                                   &#x27;gross margin percentage&#x27;,\n",
       "                                                   &#x27;cogs&#x27;, &#x27;Payment&#x27;,\n",
       "                                                   &#x27;Total&#x27;]),\n",
       "                                                 (&#x27;date_one_hot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Date&#x27;]),\n",
       "                                                 (&#x27;time_one_hot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ign...\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Branch&#x27;]),\n",
       "                                                 (&#x27;member_one_hot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Customer type&#x27;]),\n",
       "                                                 (&#x27;gender_one_hot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Gender&#x27;]),\n",
       "                                                 (&#x27;product_one_hot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Product line&#x27;]),\n",
       "                                                 (&#x27;num_scaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;Quantity&#x27;, &#x27;Unit price&#x27;,\n",
       "                                                   &#x27;Rating&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1032\" type=\"checkbox\" ><label for=\"sk-estimator-id-1032\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;date_encode&#x27;, Date_Encoder()),\n",
       "                (&#x27;time_encode&#x27;, Time_Encoder()),\n",
       "                (&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;drop&#x27;, &#x27;drop&#x27;,\n",
       "                                                  [&#x27;Invoice ID&#x27;, &#x27;City&#x27;,\n",
       "                                                   &#x27;gross margin percentage&#x27;,\n",
       "                                                   &#x27;cogs&#x27;, &#x27;Payment&#x27;,\n",
       "                                                   &#x27;Total&#x27;]),\n",
       "                                                 (&#x27;date_one_hot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Date&#x27;]),\n",
       "                                                 (&#x27;time_one_hot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ign...\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Branch&#x27;]),\n",
       "                                                 (&#x27;member_one_hot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Customer type&#x27;]),\n",
       "                                                 (&#x27;gender_one_hot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Gender&#x27;]),\n",
       "                                                 (&#x27;product_one_hot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;Product line&#x27;]),\n",
       "                                                 (&#x27;num_scaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;Quantity&#x27;, &#x27;Unit price&#x27;,\n",
       "                                                   &#x27;Rating&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1033\" type=\"checkbox\" ><label for=\"sk-estimator-id-1033\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Date_Encoder</label><div class=\"sk-toggleable__content\"><pre>Date_Encoder()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1034\" type=\"checkbox\" ><label for=\"sk-estimator-id-1034\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Time_Encoder</label><div class=\"sk-toggleable__content\"><pre>Time_Encoder()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1035\" type=\"checkbox\" ><label for=\"sk-estimator-id-1035\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;drop&#x27;, &#x27;drop&#x27;,\n",
       "                                 [&#x27;Invoice ID&#x27;, &#x27;City&#x27;,\n",
       "                                  &#x27;gross margin percentage&#x27;, &#x27;cogs&#x27;, &#x27;Payment&#x27;,\n",
       "                                  &#x27;Total&#x27;]),\n",
       "                                (&#x27;date_one_hot&#x27;,\n",
       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;Date&#x27;]),\n",
       "                                (&#x27;time_one_hot&#x27;,\n",
       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;Time&#x27;]),\n",
       "                                (&#x27;branch_one_hot&#x27;,\n",
       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;Branch&#x27;]),\n",
       "                                (&#x27;member_one_hot&#x27;,\n",
       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;Customer type&#x27;]),\n",
       "                                (&#x27;gender_one_hot&#x27;,\n",
       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;Gender&#x27;]),\n",
       "                                (&#x27;product_one_hot&#x27;,\n",
       "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;Product line&#x27;]),\n",
       "                                (&#x27;num_scaler&#x27;, StandardScaler(),\n",
       "                                 [&#x27;Quantity&#x27;, &#x27;Unit price&#x27;, &#x27;Rating&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1036\" type=\"checkbox\" ><label for=\"sk-estimator-id-1036\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">drop</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Invoice ID&#x27;, &#x27;City&#x27;, &#x27;gross margin percentage&#x27;, &#x27;cogs&#x27;, &#x27;Payment&#x27;, &#x27;Total&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1037\" type=\"checkbox\" ><label for=\"sk-estimator-id-1037\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">drop</label><div class=\"sk-toggleable__content\"><pre>drop</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1038\" type=\"checkbox\" ><label for=\"sk-estimator-id-1038\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">date_one_hot</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Date&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1039\" type=\"checkbox\" ><label for=\"sk-estimator-id-1039\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1040\" type=\"checkbox\" ><label for=\"sk-estimator-id-1040\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">time_one_hot</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Time&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1041\" type=\"checkbox\" ><label for=\"sk-estimator-id-1041\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1042\" type=\"checkbox\" ><label for=\"sk-estimator-id-1042\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">branch_one_hot</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Branch&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1043\" type=\"checkbox\" ><label for=\"sk-estimator-id-1043\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1044\" type=\"checkbox\" ><label for=\"sk-estimator-id-1044\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">member_one_hot</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Customer type&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1045\" type=\"checkbox\" ><label for=\"sk-estimator-id-1045\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1046\" type=\"checkbox\" ><label for=\"sk-estimator-id-1046\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">gender_one_hot</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Gender&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1047\" type=\"checkbox\" ><label for=\"sk-estimator-id-1047\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1048\" type=\"checkbox\" ><label for=\"sk-estimator-id-1048\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">product_one_hot</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Product line&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1049\" type=\"checkbox\" ><label for=\"sk-estimator-id-1049\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1050\" type=\"checkbox\" ><label for=\"sk-estimator-id-1050\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_scaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Quantity&#x27;, &#x27;Unit price&#x27;, &#x27;Rating&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1051\" type=\"checkbox\" ><label for=\"sk-estimator-id-1051\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1052\" type=\"checkbox\" ><label for=\"sk-estimator-id-1052\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1053\" type=\"checkbox\" ><label for=\"sk-estimator-id-1053\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1054\" type=\"checkbox\" ><label for=\"sk-estimator-id-1054\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('date_encode', Date_Encoder()),\n",
       "                ('time_encode', Time_Encoder()),\n",
       "                ('preprocessor',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('drop', 'drop',\n",
       "                                                  ['Invoice ID', 'City',\n",
       "                                                   'gross margin percentage',\n",
       "                                                   'cogs', 'Payment',\n",
       "                                                   'Total']),\n",
       "                                                 ('date_one_hot',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['Date']),\n",
       "                                                 ('time_one_hot',\n",
       "                                                  OneHotEncoder(handle_unknown='ign...\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['Branch']),\n",
       "                                                 ('member_one_hot',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['Customer type']),\n",
       "                                                 ('gender_one_hot',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['Gender']),\n",
       "                                                 ('product_one_hot',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['Product line']),\n",
       "                                                 ('num_scaler',\n",
       "                                                  StandardScaler(),\n",
       "                                                  ['Quantity', 'Unit price',\n",
       "                                                   'Rating'])])),\n",
       "                ('regressor', LinearRegression())])"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(data_train1, train_target)\n",
    "\n",
    "pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcb20bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d8c835",
   "metadata": {},
   "source": [
    "## We can now check our Cross Validation score to see its sensitivity to unseen data and calculate its 95% confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "8637b449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV R^2 score mean: 0.8838268021396655\n",
      "CV NMSE scores: [-15.86684353 -16.92717764 -13.16940822 -15.78303808 -16.41242194]\n",
      "95% Confidence interval for R^2 mean score: (0.8719809435421048, 0.8956726607372262)\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_validate(pipeline, data_train1, train_target, cv=5, scoring=('r2', 'neg_mean_squared_error'))\n",
    "\n",
    "# finding mean\n",
    "\n",
    "r2_scores = cv_scores['test_r2']\n",
    "mean_r2 = np.mean(r2_scores)\n",
    "\n",
    "print (f\"CV R^2 score mean: {mean_r2}\")\n",
    "print (f\"CV NMSE scores: {cv_scores['test_neg_mean_squared_error']}\")\n",
    "\n",
    "\n",
    "std_r2 = np.std(r2_scores)\n",
    "\n",
    "# we used 5 folds\n",
    "n_folds = 5\n",
    "\n",
    "# finding standard error of mean\n",
    "sem_r2 = std_r2 / np.sqrt(n_folds)\n",
    "\n",
    "# calculating 95% interval\n",
    "confidence_interval_95 = scipy.stats.t.interval(0.95, n_folds - 1, loc=mean_r2, scale=sem_r2)\n",
    "\n",
    "print(f\"95% Confidence interval for R^2 mean score: {confidence_interval_95}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a07fb3e6",
   "metadata": {},
   "source": [
    "### We have acheived 95% confidence interval for R^2 of  (87.198%, 89.567%)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fadd34",
   "metadata": {},
   "source": [
    "## Now we will start out next model, but this time using the Lasso regularizer and using and Cross Validation scheme to find the best hyperparameters.\n",
    "- First we will try some basic parameters for Lasso's alpha value which effects the lasso's strength \n",
    "- Then, we will construct a similar pipeline, but using the regularizer using Grid Search CV\n",
    "- Lastly, we will construct another pipeline but using Random Search CV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11078225",
   "metadata": {},
   "source": [
    "## Constructing second model with lasso using grid search CV & random search CV plotting results\n",
    "- For this model, we will make predictions using the same previous preprocessor and also another prediction using only categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "d9adc1bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "alphas = [0.0001, 0.05, 0.01, 0.2, 0.1, 0.15, 0.16, 0.17, 0.2, 0.3, 0.4, 0.5]\n",
    "\n",
    "parameters = {'alpha': alphas}\n",
    "\n",
    "\n",
    "pipeline_grid = Pipeline([\n",
    "    # first handle date & time\n",
    "    ('date_encode', Date_Encoder()),\n",
    "    ('time_encode', Time_Encoder()),\n",
    "    # use preprocessor\n",
    "    ('preprocessor', preprocessor),\n",
    "    # add my models\n",
    "    ('Lasso_Grid', GridSearchCV(estimator = Lasso(random_state = 0), param_grid = parameters, cv = 5, scoring = 'r2'))])\n",
    "\n",
    "\n",
    "param_dist = {'alpha': stats.expon(scale = 0.005)}\n",
    "\n",
    "pipeline_random = Pipeline([\n",
    "    # first handle date & time\n",
    "    ('date_encode', Date_Encoder()),\n",
    "    ('time_encode', Time_Encoder()),\n",
    "    # use preprocessor\n",
    "    ('preprocessor', preprocessor),\n",
    "    # add my models\n",
    "    ('Lasso_Random', RandomizedSearchCV(estimator = Lasso(random_state = 0), param_distributions = param_dist, cv = 5, scoring = 'r2'))])\n",
    "\n",
    "#Training\n",
    "pipeline_grid.fit(data_train1, train_target)\n",
    "pipeline_random.fit(data_train1, train_target)\n",
    "\n",
    "\n",
    "final_model1 = pipeline_grid\n",
    "# pickle file\n",
    "with open ('final_model1.pkl', 'wb') as file:\n",
    "    pickle.dump(final_model1, file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bea014c",
   "metadata": {},
   "source": [
    "## Testing Lassos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "8a181206",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best found hyperparameters for Grid Search: {'alpha': 0.15}\n",
      " Highest R^2 score for Grid Search: 0.8870963838646044\n",
      "Best found hyperparameters for Random Search: {'alpha': 0.01805733710374144}\n",
      " Highest R^2 score for Random Search: 0.8849323150857729\n",
      "95% CI for R^2 of Grid Search): (0.8934297114240409, 0.8934346250290494)\n",
      "95% CI for R^2 of Random Search: (0.8898126951377474, 0.8898174949241537)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Printing scores\n",
    "print(f\"Best found hyperparameters for Grid Search: {pipeline_grid['Lasso_Grid'].best_params_}\")\n",
    "print(f\" Highest R^2 score for Grid Search: {pipeline_grid['Lasso_Grid'].best_score_}\")\n",
    "\n",
    "print(f\"Best found hyperparameters for Random Search: {pipeline_random['Lasso_Random'].best_params_}\")\n",
    "print(f\" Highest R^2 score for Random Search: {pipeline_random['Lasso_Random'].best_score_}\")\n",
    "\n",
    "\n",
    "prediction_grid = pipeline_grid.predict(data_test1)\n",
    "prediction_rand = pipeline_random.predict(data_test1)\n",
    "\n",
    "# 95% CONFIDENCE INTERVAL\n",
    "r2_score_grid = r2_score(test_target, prediction_grid)\n",
    "r2_score_rand = r2_score(test_target, prediction_rand)\n",
    "\n",
    "scores_grid = [r2_score(test_target, prediction_grid + np.random.normal(0, 0.01, len(test_target))) for _ in range(1000)]\n",
    "scores_rand = [r2_score(test_target, prediction_rand + np.random.normal(0, 0.01, len(test_target))) for _ in range(1000)]\n",
    "\n",
    "\n",
    "ci_grid = compute_confidence_interval(scores_grid)\n",
    "ci_rand= compute_confidence_interval(scores_rand)\n",
    "\n",
    "print(f\"95% CI for R^2 of Grid Search): {ci_grid}\")\n",
    "print(f\"95% CI for R^2 of Random Search: {ci_rand}\")\n",
    "\n",
    "\n",
    "\n",
    "#plotting GridCV\n",
    "plt.scatter(test_target, prediction_grid, color='red', label='Predictions')\n",
    "\n",
    "# perfect prediction\n",
    "plt.plot([min(test_target), max(test_target)], [min(test_target), max(test_target)],\n",
    "         color='blue', linestyle='--', label='Perfect Prediction')\n",
    "\n",
    "plt.title('Grid Search Lasso Model Predictions vs Actual Gross Income Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "#plostting RandCV\n",
    "plt.scatter(test_target, prediction_rand, color='red', label='Predictions')\n",
    "\n",
    "# perfect prediction\n",
    "plt.plot([min(test_target), max(test_target)], [min(test_target), max(test_target)],\n",
    "         color='blue', linestyle='--', label='Perfect Prediction')\n",
    "\n",
    "plt.title('Random Search Lasso Model Predictions vs Actual Gross Income Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32317281",
   "metadata": {},
   "source": [
    "### Grid search appears to have a slightly better interval."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e987744",
   "metadata": {},
   "source": [
    "## Training & testing model using only categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "8e475216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best found hyperparameters for dropped: {'alpha': 0.17}\n",
      " Highest R^2 score for dropped: -0.008692884893453568\n",
      "95% CI for R^2 (model with dropped features): (-0.02050285457346388, -0.020488265861529215)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preprocessor2 = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('drop', 'drop', ['Invoice ID', 'City', 'gross margin percentage', 'cogs', 'Payment',\n",
    "                          'Total', 'Gender', 'Product line', 'Date', 'Time',\n",
    "                          'Customer type', 'Quantity', 'Unit price']),\n",
    "        ('branch_one_hot', OneHotEncoder(), ['Branch']),\n",
    "        ('member_one_hot', OneHotEncoder(), ['Customer type']),\n",
    "        ('gender_one_hot', OneHotEncoder(), ['Gender']),\n",
    "        ('product_one_hot', OneHotEncoder(), ['Product line'])\n",
    "       \n",
    "       \n",
    "    ],\n",
    "    remainder='passthrough'  # keeps all other columns\n",
    ")\n",
    "\n",
    "pipeline_dropped = Pipeline([\n",
    "    # first handle date & time\n",
    "    ('date_encode', Date_Encoder()),\n",
    "    ('time_encode', Time_Encoder()),\n",
    "    # use preprocessor\n",
    "    ('preprocessor', preprocessor2),\n",
    "    # add my models\n",
    "    ('Lasso_Grid', GridSearchCV(estimator = Lasso(random_state = 0, max_iter = 5000), param_grid = parameters, cv = 5, scoring = 'r2'))])\n",
    "\n",
    "pipeline_dropped.fit(data_train1, train_target)\n",
    "\n",
    "prediction_dropped = pipeline_dropped.predict(data_test1)\n",
    "\n",
    "r2_score_dropped = r2_score(test_target, prediction_dropped)\n",
    "\n",
    "scores_dropped = [r2_score(test_target, prediction_dropped + \n",
    "                           np.random.normal(0, 0.01, len(test_target))) for _ in range(1000)]\n",
    "\n",
    "ci_dropped = compute_confidence_interval(scores_dropped)\n",
    "\n",
    "print(f\"Best found hyperparameters for dropped: {pipeline_dropped['Lasso_Grid'].best_params_}\")\n",
    "print(f\" Highest R^2 score for dropped: {pipeline_dropped['Lasso_Grid'].best_score_}\")\n",
    "\n",
    "\n",
    "print(f\"95% CI for R^2 (model with dropped features): {ci_dropped}\")\n",
    "\n",
    "\n",
    "# plotting dropped\n",
    "plt.scatter(test_target, prediction_dropped, color='red', label='Predictions')\n",
    "\n",
    "# perfect prediction\n",
    "plt.plot([min(test_target), max(test_target)], [min(test_target), max(test_target)],\n",
    "         color='blue', linestyle='--', label='Perfect Prediction')\n",
    "\n",
    "plt.title('Only Categorical Features Used Predictions vs Actual Gross Income Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "706ec1de",
   "metadata": {},
   "source": [
    "### Model with only categorical features fails to make valid predicitons."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e2c8de",
   "metadata": {},
   "source": [
    "## Predicting Unit Price "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d1a769",
   "metadata": {},
   "source": [
    "### Starting with checking correlation matrix of numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "edd3338c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.628454</td>\n",
       "      <td>-0.025594</td>\n",
       "      <td>0.628454</td>\n",
       "      <td>0.628454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001034</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.703549</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>0.703549</td>\n",
       "      <td>0.703549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.628454</td>\n",
       "      <td>0.703549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.057089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.025594</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>-0.057089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.057089</td>\n",
       "      <td>-0.057089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.628454</td>\n",
       "      <td>0.703549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.057089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.628454</td>\n",
       "      <td>0.703549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.057089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5\n",
       "0  1.000000  0.001034  0.628454 -0.025594  0.628454  0.628454\n",
       "1  0.001034  1.000000  0.703549 -0.017327  0.703549  0.703549\n",
       "2  0.628454  0.703549  1.000000 -0.057089  1.000000  1.000000\n",
       "3 -0.025594 -0.017327 -0.057089  1.000000 -0.057089 -0.057089\n",
       "4  0.628454  0.703549  1.000000 -0.057089  1.000000  1.000000\n",
       "5  0.628454  0.703549  1.000000 -0.057089  1.000000  1.000000"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix  ### 0: Unit price, 1: Quantity, 2: Total, 3: Rating, 4: gross income, 5: cogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24261ea7",
   "metadata": {},
   "source": [
    "### Unit Price is most correlated to Total, gross income, and cogs. But as we know, each of these categories relay the same information due to their 1.0 correlation score. Due to this, we only include one of these features for our model. We will also not be including rating due to its low correlation score.\n",
    "- After trying different numerical feature combinations, it appears that uaing Total and Quantity provide better predictions even though Quantity had a low correlation score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c17033d",
   "metadata": {},
   "source": [
    "### Making train/test sets and constructing pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "b9f8278b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making sets\n",
    "data_train2 = data_train.drop('Unit price', axis =1)\n",
    "data_test2 = data_test.drop('Unit price', axis =1)\n",
    "\n",
    "train_target2 = data_train['Unit price']\n",
    "test_target2 = data_test['Unit price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "17c79052",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor3 = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('drop', 'drop', ['Invoice ID', 'City', 'gross margin percentage', 'cogs', 'Payment', 'Rating']),\n",
    "        ('date_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Date']),\n",
    "        ('time_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Time']),\n",
    "        ('branch_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Branch']),\n",
    "        ('member_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Customer type']),\n",
    "        ('gender_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Gender']),\n",
    "        ('product_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Product line']),\n",
    "        # adding numerical scaling\n",
    "        ('num_scaler', StandardScaler(), ['Total','Quantity'])\n",
    "    ],\n",
    "    remainder='passthrough'  # keeps all other columns\n",
    ")\n",
    "\n",
    "\n",
    "up_pipeline = Pipeline([\n",
    "    # first handle date & time\n",
    "    ('date_encode', Date_Encoder()),\n",
    "    ('time_encode', Time_Encoder()),\n",
    "    # use preprocessor\n",
    "    ('preprocessor', preprocessor3),\n",
    "    # add my models\n",
    "    ('regressor', LinearRegression())\n",
    "    \n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e4f7f5",
   "metadata": {},
   "source": [
    "### Training and checking scores / testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "93c7ec34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV R^2 score mean: 0.7703474633622037\n",
      "CV NMSE scores: [-134.42592169 -179.15262799 -154.52109328 -161.94780941 -160.72322565]\n",
      "95% Confidence interval for R^2 mean score: (0.8564013165416742, 0.9112522877376568)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#training\n",
    "up_pipeline.fit(data_train2, train_target2)\n",
    "\n",
    "\n",
    "# predict\n",
    "up_prediction = up_pipeline.predict(data_test2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# 95% confidence interval\n",
    "cv_scores2 = cross_validate(up_pipeline, data_train2, train_target2, cv=5, scoring=('r2', 'neg_mean_squared_error'))\n",
    "\n",
    "# finding mean\n",
    "r2_scores2 = cv_scores2['test_r2']\n",
    "up_mean_r2 = np.mean(r2_scores2)\n",
    "\n",
    "print (f\"CV R^2 score mean: {up_mean_r2}\")\n",
    "print (f\"CV NMSE scores: {cv_scores2['test_neg_mean_squared_error']}\")\n",
    "\n",
    "std_r2 = np.std(r2_scores2)\n",
    "\n",
    "# we used 5 folds\n",
    "n_folds = 5\n",
    "\n",
    "# finding standard error of mean\n",
    "sem_r2 = std_r2 / np.sqrt(n_folds)\n",
    "\n",
    "# calculating 95% interval\n",
    "up_confidence_interval_95 = scipy.stats.t.interval(0.95, n_folds - 1, loc=mean_r2, scale=sem_r2)\n",
    "print(f\"95% Confidence interval for R^2 mean score: {up_confidence_interval_95}\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# make the plot\n",
    "plt.scatter(test_target2, up_prediction, color='red', label='Predictions')\n",
    "\n",
    "# perfect prediction\n",
    "plt.plot([min(test_target2), max(test_target2)], [min(test_target2), max(test_target2)],\n",
    "         color='blue', linestyle='--', label='Perfect Prediction')\n",
    "\n",
    "plt.title('Model Predictions vs Actual Unit Price Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8407dd0",
   "metadata": {},
   "source": [
    "### Looks like our model has an average  R^2 score of 77.03% with a 95% confidence interval of (85.64%, 91.12%)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5206aefb",
   "metadata": {},
   "source": [
    "## Start construction and training of Lasso models using Grid and Random search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "d6c209d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.802e+03, tolerance: 4.440e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.121e+03, tolerance: 4.440e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.278e+03, tolerance: 4.532e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.402e+03, tolerance: 4.338e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.310e+03, tolerance: 4.410e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "\n",
    "alphas = [ 0.09, 0.1, 0.3, 0.5, 0.8]\n",
    "\n",
    "parameters = {'alpha': alphas}\n",
    "\n",
    "\n",
    "up_pipeline_grid = Pipeline([\n",
    "    # first handle date & time\n",
    "    ('date_encode', Date_Encoder()),\n",
    "    ('time_encode', Time_Encoder()),\n",
    "    # use preprocessor\n",
    "    ('preprocessor', preprocessor3),\n",
    "    # add my models\n",
    "    ('Lasso_Grid', GridSearchCV(estimator = Lasso(random_state = 0), param_grid = parameters, cv = 5, scoring = 'r2'))])\n",
    "\n",
    "\n",
    "param_dist = {'alpha': stats.expon(scale = 0.3)}\n",
    "\n",
    "up_pipeline_random = Pipeline([\n",
    "    # first handle date & time\n",
    "    ('date_encode', Date_Encoder()),\n",
    "    ('time_encode', Time_Encoder()),\n",
    "    # use preprocessor\n",
    "    ('preprocessor', preprocessor3),\n",
    "    # add my models\n",
    "    ('Lasso_Random', RandomizedSearchCV(estimator = Lasso(random_state = 0), param_distributions = param_dist, cv = 5, scoring = 'r2'))])\n",
    "\n",
    "#Training\n",
    "up_pipeline_grid.fit(data_train2, train_target2)\n",
    "up_pipeline_random.fit(data_train2, train_target2)\n",
    "\n",
    "\n",
    "\n",
    "final_model2 = up_pipeline_random\n",
    "# pickle file\n",
    "with open ('final_model2.pkl', 'wb') as file:\n",
    "    pickle.dump(final_model2, file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e8819c",
   "metadata": {},
   "source": [
    "## Testing the two models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "5d6ba1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# predict\n",
    "prediction1 = up_pipeline_grid.predict(data_test2)\n",
    "prediction2 = up_pipeline_random.predict(data_test2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "0bca58a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.180e+03, tolerance: 3.502e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.642e+03, tolerance: 3.453e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.751e+03, tolerance: 3.464e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.927e+03, tolerance: 3.492e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.529e+03, tolerance: 3.435e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.062e+03, tolerance: 3.570e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.568e+03, tolerance: 3.528e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.739e+03, tolerance: 3.540e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.613e+03, tolerance: 3.532e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\andre\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.657e+03, tolerance: 3.460e+01\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "# Cross Val\n",
    "\n",
    "# 95% confidence interval\n",
    "cv_scores = cross_validate(up_pipeline_grid, data_train2, train_target2, cv=5, scoring=('r2', 'neg_mean_squared_error'))\n",
    "cv_scores2 = cross_validate(up_pipeline_random, data_train2, train_target2, cv=5, scoring=('r2', 'neg_mean_squared_error'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "da844f45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid CV R^2 score mean: 0.7772174114437322\n",
      "Grid CV NMSE scores: [-130.75171705 -169.40068859 -147.05751005 -159.44590412 -161.03808672]\n",
      "Random CV R^2 score mean: 0.7771832185895879\n",
      "Random CV NMSE scores: [-130.90570649 -169.49402685 -147.41902991 -158.95876899 -160.96082226]\n",
      "95% Confidence interval for R^2 mean score: (0.8605382777492169, 0.9071153265301141)\n",
      "95% Confidence interval for R^2 mean score: (0.8605382777492169, 0.9071153265301141)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# finding mean\n",
    "r2_scores = cv_scores['test_r2']\n",
    "up_mean_r = np.mean(r2_scores)\n",
    "r2_scores2 = cv_scores2['test_r2']\n",
    "up_mean_r2 = np.mean(r2_scores2)\n",
    "\n",
    "print (f\"Grid CV R^2 score mean: {up_mean_r}\")\n",
    "print (f\"Grid CV NMSE scores: {cv_scores['test_neg_mean_squared_error']}\")\n",
    "print (f\"Random CV R^2 score mean: {up_mean_r2}\")\n",
    "print (f\"Random CV NMSE scores: {cv_scores2['test_neg_mean_squared_error']}\")\n",
    "\n",
    "std_r = np.std(r2_scores)\n",
    "std_r2 = np.std(r2_scores2)\n",
    "\n",
    "\n",
    "# we used 5 folds\n",
    "n_folds = 5\n",
    "\n",
    "# finding standard error of mean\n",
    "sem_r2 = std_r / np.sqrt(n_folds)\n",
    "sem_r2_2 = std_r2 / np.sqrt(n_folds)\n",
    "# calculating 95% interval\n",
    "up_confidence_interval_95 = scipy.stats.t.interval(0.95, n_folds - 1, loc=mean_r2, scale=sem_r2)\n",
    "print(f\"95% Confidence interval for R^2 mean score: {up_confidence_interval_95}\")\n",
    "up_confidence_interval_95_2 = scipy.stats.t.interval(0.95, n_folds - 1, loc=mean_r2, scale=sem_r2)\n",
    "print(f\"95% Confidence interval for R^2 mean score: {up_confidence_interval_95_2}\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# make the plot for grid\n",
    "plt.scatter(test_target2, prediction1, color='red', label='Predictions')\n",
    "\n",
    "\n",
    "# perfect prediction\n",
    "plt.plot([min(test_target2), max(test_target2)], [min(test_target2), max(test_target2)],\n",
    "         color='blue', linestyle='--', label='Perfect Prediction')\n",
    "\n",
    "plt.title('Grid CV Model Predictions vs Actual Unit Price Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# make plot for rand\n",
    "plt.scatter(test_target2, prediction2, color='red', label='Predictions')\n",
    "\n",
    "# perfect prediction\n",
    "plt.plot([min(test_target2), max(test_target2)], [min(test_target2), max(test_target2)],\n",
    "         color='blue', linestyle='--', label='Perfect Prediction')\n",
    "\n",
    "plt.title('Random CV Model Predictions vs Actual Unit Price Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61231e0a",
   "metadata": {},
   "source": [
    "### Random search cv has a slightly higher R^2 value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aba3736",
   "metadata": {},
   "source": [
    "## Predicting day of the week of purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "c8eda4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use accuracy\n",
    "# preprocess, make pipeline, \n",
    "\n",
    "# classifier\n",
    "log_reg = LogisticRegression (penalty = None, random_state = 0, max_iter  = 300)\n",
    "\n",
    "# making training data\n",
    "data_train3 = data_train.drop('Date', axis = 1)\n",
    "encode = Date_Encoder()\n",
    "target_train3 = encode.transform(data_train['Date'])\n",
    "target_train3 = target_train3.values.ravel()\n",
    "\n",
    "# making testing data\n",
    "data_test3 = data_test.drop('Date', axis = 1)\n",
    "target_test3 = encode.transform(data_test['Date'])\n",
    "\n",
    "\n",
    "\n",
    "preprocessor4 = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('drop', 'drop', ['Invoice ID', 'City', 'gross margin percentage', 'cogs', 'Payment', 'Total']),\n",
    "        ('time_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Time']),\n",
    "        ('branch_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Branch']),\n",
    "        ('member_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Customer type']),\n",
    "        ('gender_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Gender']),\n",
    "        ('product_one_hot', OneHotEncoder(handle_unknown='ignore'), ['Product line']),\n",
    "        # adding numerical scaling\n",
    "        ('num_scaler', StandardScaler(), ['Quantity', 'Unit price', 'Rating']),\n",
    "       \n",
    "    ],\n",
    "    remainder='passthrough'  # keeps all other columns\n",
    ")\n",
    "\n",
    "\n",
    "first_pipe = Pipeline([\n",
    "    # first handle date & time\n",
    "    ('time_encode', Time_Encoder()),\n",
    "    # use preprocessor\n",
    "    ('preprocessor', preprocessor4),\n",
    "    # add my models\n",
    "    ('log_reg', log_reg)\n",
    "    \n",
    "])\n",
    "\n",
    "\n",
    "#training\n",
    "first_pipe.fit(data_train3, target_train3)\n",
    "\n",
    "\n",
    "final_model3 = first_pipe\n",
    "# pickle file\n",
    "with open ('final_model3.pkl', 'wb') as file:\n",
    "    pickle.dump(final_model3, file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a20a2c",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "e675ac70",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d954700",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c315c4d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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