JM2_Regression_Project_v0.8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "af5b9bb1",
   "metadata": {},
   "source": [
    "# Phase 0: Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7224bc89",
   "metadata": {},
   "source": [
    "Loading required python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "b889603a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from statsmodels.graphics.correlation import plot_corr\n",
    "from scipy.stats import pearsonr\n",
    "%matplotlib inline\n",
    "\n",
    "#models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor \n",
    "\n",
    "#metrics, procssing and model selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f58fe7",
   "metadata": {},
   "source": [
    "# Phase 1: Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da7d75e0",
   "metadata": {},
   "source": [
    "As kaggle already provided us with data in the form of csv files, all we have to do is load them into dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "7ab5516c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading csv's\n",
    "train=pd.read_csv('df - train_set.csv')\n",
    "test=pd.read_csv('df - test_set.csv')\n",
    "test_id=test['Index']\n",
    "test=test.drop('Index',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e80fec7a",
   "metadata": {},
   "source": [
    "# Phase 2: Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46418913",
   "metadata": {},
   "source": [
    "Kaggle tries to provide us with clean data but it's always a good idea to check for ourselves. We check for null and empty values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "28fa98f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Province            0\n",
       "Container           0\n",
       "Size_Grade          0\n",
       "Weight_Kg           0\n",
       "Commodities         0\n",
       "Date                0\n",
       "Low_Price           0\n",
       "High_Price          0\n",
       "Sales_Total         0\n",
       "Total_Qty_Sold      0\n",
       "Total_Kg_Sold       0\n",
       "Stock_On_Hand       0\n",
       "avg_price_per_kg    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "c23b8763",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data contains 0 empty values over all columns\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "for x in train.columns:\n",
    "    if '' in sorted(train[x].unique()):\n",
    "        i+=1\n",
    "print('The data contains '+str(i)+' empty values over all columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28245fc5",
   "metadata": {},
   "source": [
    "none of our columns contain any null or empty values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "62353cb2",
   "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>Province</th>\n",
       "      <th>Container</th>\n",
       "      <th>Size_Grade</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Commodities</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>EC120</td>\n",
       "      <td>1L</td>\n",
       "      <td>12.0</td>\n",
       "      <td>APPLE GRANNY SMITH</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>108.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3236.0</td>\n",
       "      <td>29</td>\n",
       "      <td>348.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>M4183</td>\n",
       "      <td>1L</td>\n",
       "      <td>18.3</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>150.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>51710.0</td>\n",
       "      <td>332</td>\n",
       "      <td>6075.6</td>\n",
       "      <td>822</td>\n",
       "      <td>8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GAUTENG</td>\n",
       "      <td>AT200</td>\n",
       "      <td>1L</td>\n",
       "      <td>20.0</td>\n",
       "      <td>AVOCADO PINKERTON</td>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4860.0</td>\n",
       "      <td>66</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>50</td>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRANSVAAL</td>\n",
       "      <td>BJ090</td>\n",
       "      <td>1L</td>\n",
       "      <td>9.0</td>\n",
       "      <td>TOMATOES-LONG LIFE</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>10</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WESTERN FREESTATE</td>\n",
       "      <td>PP100</td>\n",
       "      <td>1R</td>\n",
       "      <td>10.0</td>\n",
       "      <td>POTATO SIFRA (WASHED)</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41530.0</td>\n",
       "      <td>927</td>\n",
       "      <td>9270.0</td>\n",
       "      <td>393</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Province Container Size_Grade  Weight_Kg             Commodities  \\\n",
       "0               CAPE     EC120         1L       12.0      APPLE GRANNY SMITH   \n",
       "1               CAPE     M4183         1L       18.3  APPLE GOLDEN DELICIOUS   \n",
       "2            GAUTENG     AT200         1L       20.0       AVOCADO PINKERTON   \n",
       "3          TRANSVAAL     BJ090         1L        9.0      TOMATOES-LONG LIFE   \n",
       "4  WESTERN FREESTATE     PP100         1R       10.0   POTATO SIFRA (WASHED)   \n",
       "\n",
       "         Date  Low_Price  High_Price  Sales_Total  Total_Qty_Sold  \\\n",
       "0  2020-03-10      108.0       112.0       3236.0              29   \n",
       "1  2020-09-09      150.0       170.0      51710.0             332   \n",
       "2  2020-05-05       70.0        80.0       4860.0              66   \n",
       "3  2020-01-20       60.0        60.0        600.0              10   \n",
       "4  2020-07-14       40.0        45.0      41530.0             927   \n",
       "\n",
       "   Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  \n",
       "0          348.0              0              9.30  \n",
       "1         6075.6            822              8.51  \n",
       "2         1320.0             50              3.68  \n",
       "3           90.0              0              6.67  \n",
       "4         9270.0            393              4.48  "
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42c2f03",
   "metadata": {},
   "source": [
    "The table contains information about 'Commodoties that we are not interested in, we filter only for 'APPLE GOLDEN DELICIOUS'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "9023923f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province</th>\n",
       "      <th>Container</th>\n",
       "      <th>Size_Grade</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Commodities</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>M4183</td>\n",
       "      <td>1L</td>\n",
       "      <td>18.3</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>150.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>51710.0</td>\n",
       "      <td>332</td>\n",
       "      <td>6075.6</td>\n",
       "      <td>822</td>\n",
       "      <td>8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>JG110</td>\n",
       "      <td>2M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>320</td>\n",
       "      <td>3520.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>W.CAPE-BERGRIVER ETC</td>\n",
       "      <td>JE090</td>\n",
       "      <td>2S</td>\n",
       "      <td>9.0</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>18</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1506</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>M4183</td>\n",
       "      <td>1S</td>\n",
       "      <td>18.3</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>80.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>32020.0</td>\n",
       "      <td>388</td>\n",
       "      <td>7100.4</td>\n",
       "      <td>443</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>EASTERN CAPE</td>\n",
       "      <td>IA400</td>\n",
       "      <td>1S</td>\n",
       "      <td>400.0</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Province Container Size_Grade  Weight_Kg  \\\n",
       "1                   CAPE     M4183         1L       18.3   \n",
       "7                   CAPE     JG110         2M       11.0   \n",
       "24  W.CAPE-BERGRIVER ETC     JE090         2S        9.0   \n",
       "40                  CAPE     M4183         1S       18.3   \n",
       "69          EASTERN CAPE     IA400         1S      400.0   \n",
       "\n",
       "               Commodities        Date  Low_Price  High_Price  Sales_Total  \\\n",
       "1   APPLE GOLDEN DELICIOUS  2020-09-09      150.0       170.0      51710.0   \n",
       "7   APPLE GOLDEN DELICIOUS  2020-04-14       50.0        50.0      16000.0   \n",
       "24  APPLE GOLDEN DELICIOUS  2020-04-16       55.0        55.0        990.0   \n",
       "40  APPLE GOLDEN DELICIOUS  2020-05-04       80.0       120.0      32020.0   \n",
       "69  APPLE GOLDEN DELICIOUS  2020-09-28     1800.0      1800.0       1800.0   \n",
       "\n",
       "    Total_Qty_Sold  Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  \n",
       "1              332         6075.6            822              8.51  \n",
       "7              320         3520.0              0              4.55  \n",
       "24              18          162.0           1506              6.11  \n",
       "40             388         7100.4            443              4.51  \n",
       "69               1          400.0              2              4.50  "
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter for Golden Delicious Apples\n",
    "train=train[train['Commodities']=='APPLE GOLDEN DELICIOUS']\n",
    "test=test[test['Commodities']=='APPLE GOLDEN DELICIOUS']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435f1c55",
   "metadata": {},
   "source": [
    "We may as well remove the \"Commodities\" column as it has no more useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "53d8d1dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Province          1952 non-null   object \n",
      " 1   Container         1952 non-null   object \n",
      " 2   Size_Grade        1952 non-null   object \n",
      " 3   Weight_Kg         1952 non-null   float64\n",
      " 4   Date              1952 non-null   object \n",
      " 5   Low_Price         1952 non-null   float64\n",
      " 6   High_Price        1952 non-null   float64\n",
      " 7   Sales_Total       1952 non-null   float64\n",
      " 8   Total_Qty_Sold    1952 non-null   int64  \n",
      " 9   Total_Kg_Sold     1952 non-null   float64\n",
      " 10  Stock_On_Hand     1952 non-null   int64  \n",
      " 11  avg_price_per_kg  1952 non-null   float64\n",
      "dtypes: float64(6), int64(2), object(4)\n",
      "memory usage: 198.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#Remove Commodities\n",
    "train=train.drop([\"Commodities\"],axis=1)\n",
    "test=test.drop([\"Commodities\"],axis=1)\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eefc84d7",
   "metadata": {},
   "source": [
    "We note that the DataFrame consists of 12 columns.(11 predictors and 1 response)\n",
    "\n",
    "The predictor variables are broken down into the following types:\n",
    "\n",
    "    -object: Province, Container, Size_Grade and Date\n",
    "\n",
    "    -float: Weight_Kg, Low_Price, High_Price, Sales_Total and Total_Kg_Sold\n",
    "\n",
    "    -integer: Total_Qty_Sold and Stock_On_Hand\n",
    "    \n",
    "with the response variable(avg_price_per_kg) being a float."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "836d1774",
   "metadata": {},
   "source": [
    "# Phase 3: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca47bf3",
   "metadata": {},
   "source": [
    "## 3.1: Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "275bb4c3",
   "metadata": {},
   "source": [
    "#### 3.1.1: Numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "c161ac3c",
   "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>Weight_Kg</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.460912</td>\n",
       "      <td>174.307377</td>\n",
       "      <td>215.648053</td>\n",
       "      <td>20053.533811</td>\n",
       "      <td>174.510758</td>\n",
       "      <td>2960.176332</td>\n",
       "      <td>408.393955</td>\n",
       "      <td>6.778893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>99.655169</td>\n",
       "      <td>373.553578</td>\n",
       "      <td>433.546159</td>\n",
       "      <td>39005.069445</td>\n",
       "      <td>308.810797</td>\n",
       "      <td>6097.416527</td>\n",
       "      <td>724.450582</td>\n",
       "      <td>2.248744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1325.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>219.600000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>5495.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>853.500000</td>\n",
       "      <td>126.500000</td>\n",
       "      <td>6.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.300000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>21082.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3093.525000</td>\n",
       "      <td>468.000000</td>\n",
       "      <td>8.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>369464.000000</td>\n",
       "      <td>4237.000000</td>\n",
       "      <td>74000.000000</td>\n",
       "      <td>6400.000000</td>\n",
       "      <td>21.240000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Weight_Kg    Low_Price   High_Price    Sales_Total  Total_Qty_Sold  \\\n",
       "count  1952.000000  1952.000000  1952.000000    1952.000000     1952.000000   \n",
       "mean     40.460912   174.307377   215.648053   20053.533811      174.510758   \n",
       "std      99.655169   373.553578   433.546159   39005.069445      308.810797   \n",
       "min       3.000000     2.000000     5.000000       5.000000        1.000000   \n",
       "25%       9.000000    50.000000    60.000000    1325.000000       12.000000   \n",
       "50%      12.000000    80.000000   108.000000    5495.000000       64.000000   \n",
       "75%      18.300000   127.250000   160.000000   21082.500000      200.000000   \n",
       "max     400.000000  2300.000000  3300.000000  369464.000000     4237.000000   \n",
       "\n",
       "       Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  \n",
       "count    1952.000000    1952.000000       1952.000000  \n",
       "mean     2960.176332     408.393955          6.778893  \n",
       "std      6097.416527     724.450582          2.248744  \n",
       "min         3.000000       0.000000          0.250000  \n",
       "25%       219.600000       9.000000          5.460000  \n",
       "50%       853.500000     126.500000          6.670000  \n",
       "75%      3093.525000     468.000000          8.280000  \n",
       "max     74000.000000    6400.000000         21.240000  "
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56db8daf",
   "metadata": {},
   "source": [
    "We can see that the maximium values of our numeric data are many standard deviations(>3) away from the mean. This leads us to expect that they are outliers and will cause the distribution of the data to appear skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "4a483923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight_Kg           3.328254\n",
       "Low_Price           4.222672\n",
       "High_Price          3.828819\n",
       "Sales_Total         4.250317\n",
       "Total_Qty_Sold      4.445169\n",
       "Total_Kg_Sold       5.529586\n",
       "Stock_On_Hand       3.391815\n",
       "avg_price_per_kg    0.082452\n",
       "dtype: float64"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ebf49b",
   "metadata": {},
   "source": [
    "We can see that all of our numeric predictors are highly skewed(skew values greater than 1). While our response variable appears symmetrical(skew value close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "e28bbde0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight_Kg            9.109698\n",
       "Low_Price           17.073369\n",
       "High_Price          13.630768\n",
       "Sales_Total         24.200524\n",
       "Total_Qty_Sold      32.338123\n",
       "Total_Kg_Sold       45.153801\n",
       "Stock_On_Hand       14.671224\n",
       "avg_price_per_kg     1.042921\n",
       "dtype: float64"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09bcf45e",
   "metadata": {},
   "source": [
    "The kurtosis of our predictor values are much greater than 3. This indicates that there are strong outliers in the predictor variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2834fe67",
   "metadata": {},
   "source": [
    "These outliers are to be expected in data of this nature: For example:\n",
    "When looking at a column like Sales_Total, many places can have sales up to a certain point but after a certain amount only the biggest places can make that much in sales. This would skew our data toward the more 'acheivable' Sales_total values and cause the larger transactions to appear as outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05d5b36",
   "metadata": {},
   "source": [
    "We now convert the date column from object to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "479807b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Date (object) --> Date (datetime)\n",
    "train['Date'] = pd.to_datetime(train['Date'])\n",
    "test['Date'] = pd.to_datetime(test['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61deb223",
   "metadata": {},
   "source": [
    "#### 3.1.3: Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "f493dd32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot countplots of categorical data\n",
    "fig, ax =plt.subplots(1,3, figsize=(20, 9))\n",
    "sns.countplot(x=train['Size_Grade'], ax=ax[0])\n",
    "sns.countplot(x=train['Container'], ax=ax[1])\n",
    "sns.countplot(x=\"Province\", data=train, ax=ax[2])\n",
    "ax[2].tick_params(labelrotation=45)\n",
    "fig.tight_layout(pad=4.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "446cc484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['CAPE', 53.53],\n",
       " ['EASTERN CAPE', 10.55],\n",
       " ['NATAL', 5.69],\n",
       " ['ORANGE FREE STATE', 0.46],\n",
       " ['TRANSVAAL', 0.77],\n",
       " ['W.CAPE-BERGRIVER ETC', 22.54],\n",
       " ['WEST COAST', 6.45]]"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of Province as a percentage of all provinces\n",
    "dict_Prov=[]\n",
    "for x in sorted(train['Province'].unique()):\n",
    "    dict_Prov.append([x,round(len(train[train['Province']==x])*100/len(train['Province']),2)])\n",
    "dict_Prov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "0fe61fbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['AC030', 1.59],\n",
       " ['DT063', 0.31],\n",
       " ['EC120', 22.95],\n",
       " ['EF120', 3.79],\n",
       " ['EG140', 0.15],\n",
       " ['IA400', 7.12],\n",
       " ['JE090', 28.94],\n",
       " ['JG110', 4.35],\n",
       " ['M4183', 30.12],\n",
       " ['M6125', 0.51],\n",
       " ['M9125', 0.15]]"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of Container as a percentage of all containers\n",
    "dict_Cont=[]\n",
    "for x in sorted(train['Container'].unique()):\n",
    "    dict_Cont.append([x,round(len(train[train['Container']==x])*100/len(train['Container']),2)])\n",
    "dict_Cont"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "ac9fecba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['1L', 11.53],\n",
       " ['1M', 16.34],\n",
       " ['1S', 27.25],\n",
       " ['1U', 0.1],\n",
       " ['1X', 8.76],\n",
       " ['2L', 6.15],\n",
       " ['2M', 11.89],\n",
       " ['2S', 15.78],\n",
       " ['2U', 0.87],\n",
       " ['2X', 1.33]]"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of Size_Grade as a percentage of all Size_Grades\n",
    "dict_Sgr=[]\n",
    "for x in sorted(train['Size_Grade'].unique()):\n",
    "    dict_Sgr.append([x,round(len(train[train['Size_Grade']==x])*100/len(train['Size_Grade']),2)])\n",
    "dict_Sgr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad2865b",
   "metadata": {},
   "source": [
    "## 3.2: Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "34310807",
   "metadata": {},
   "outputs": [
    {
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       "        }</style><table id=\"T_7bb2c_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Weight_Kg</th>        <th class=\"col_heading level0 col1\" >Low_Price</th>        <th class=\"col_heading level0 col2\" >High_Price</th>        <th class=\"col_heading level0 col3\" >Sales_Total</th>        <th class=\"col_heading level0 col4\" >Total_Qty_Sold</th>        <th class=\"col_heading level0 col5\" >Total_Kg_Sold</th>        <th class=\"col_heading level0 col6\" >Stock_On_Hand</th>        <th class=\"col_heading level0 col7\" >avg_price_per_kg</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7bb2c_level0_row0\" class=\"row_heading level0 row0\" >Weight_Kg</th>\n",
       "                        <td id=\"T_7bb2c_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_7bb2c_row0_col1\" class=\"data row0 col1\" >0.863182</td>\n",
       "                        <td id=\"T_7bb2c_row0_col2\" class=\"data row0 col2\" >0.905852</td>\n",
       "                        <td id=\"T_7bb2c_row0_col3\" class=\"data row0 col3\" >0.180518</td>\n",
       "                        <td id=\"T_7bb2c_row0_col4\" class=\"data row0 col4\" >-0.133317</td>\n",
       "                        <td id=\"T_7bb2c_row0_col5\" class=\"data row0 col5\" >0.294117</td>\n",
       "                        <td id=\"T_7bb2c_row0_col6\" class=\"data row0 col6\" >-0.144105</td>\n",
       "                        <td id=\"T_7bb2c_row0_col7\" class=\"data row0 col7\" >-0.337886</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb2c_level0_row1\" class=\"row_heading level0 row1\" >Low_Price</th>\n",
       "                        <td id=\"T_7bb2c_row1_col0\" class=\"data row1 col0\" >0.863182</td>\n",
       "                        <td id=\"T_7bb2c_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_7bb2c_row1_col2\" class=\"data row1 col2\" >0.938140</td>\n",
       "                        <td id=\"T_7bb2c_row1_col3\" class=\"data row1 col3\" >0.183230</td>\n",
       "                        <td id=\"T_7bb2c_row1_col4\" class=\"data row1 col4\" >-0.122823</td>\n",
       "                        <td id=\"T_7bb2c_row1_col5\" class=\"data row1 col5\" >0.269744</td>\n",
       "                        <td id=\"T_7bb2c_row1_col6\" class=\"data row1 col6\" >-0.127677</td>\n",
       "                        <td id=\"T_7bb2c_row1_col7\" class=\"data row1 col7\" >-0.141740</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb2c_level0_row2\" class=\"row_heading level0 row2\" >High_Price</th>\n",
       "                        <td id=\"T_7bb2c_row2_col0\" class=\"data row2 col0\" >0.905852</td>\n",
       "                        <td id=\"T_7bb2c_row2_col1\" class=\"data row2 col1\" >0.938140</td>\n",
       "                        <td id=\"T_7bb2c_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_7bb2c_row2_col3\" class=\"data row2 col3\" >0.265672</td>\n",
       "                        <td id=\"T_7bb2c_row2_col4\" class=\"data row2 col4\" >-0.096456</td>\n",
       "                        <td id=\"T_7bb2c_row2_col5\" class=\"data row2 col5\" >0.372282</td>\n",
       "                        <td id=\"T_7bb2c_row2_col6\" class=\"data row2 col6\" >-0.094174</td>\n",
       "                        <td id=\"T_7bb2c_row2_col7\" class=\"data row2 col7\" >-0.164496</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb2c_level0_row3\" class=\"row_heading level0 row3\" >Sales_Total</th>\n",
       "                        <td id=\"T_7bb2c_row3_col0\" class=\"data row3 col0\" >0.180518</td>\n",
       "                        <td id=\"T_7bb2c_row3_col1\" class=\"data row3 col1\" >0.183230</td>\n",
       "                        <td id=\"T_7bb2c_row3_col2\" class=\"data row3 col2\" >0.265672</td>\n",
       "                        <td id=\"T_7bb2c_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_7bb2c_row3_col4\" class=\"data row3 col4\" >0.771865</td>\n",
       "                        <td id=\"T_7bb2c_row3_col5\" class=\"data row3 col5\" >0.962338</td>\n",
       "                        <td id=\"T_7bb2c_row3_col6\" class=\"data row3 col6\" >0.284693</td>\n",
       "                        <td id=\"T_7bb2c_row3_col7\" class=\"data row3 col7\" >0.108473</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb2c_level0_row4\" class=\"row_heading level0 row4\" >Total_Qty_Sold</th>\n",
       "                        <td id=\"T_7bb2c_row4_col0\" class=\"data row4 col0\" >-0.133317</td>\n",
       "                        <td id=\"T_7bb2c_row4_col1\" class=\"data row4 col1\" >-0.122823</td>\n",
       "                        <td id=\"T_7bb2c_row4_col2\" class=\"data row4 col2\" >-0.096456</td>\n",
       "                        <td id=\"T_7bb2c_row4_col3\" class=\"data row4 col3\" >0.771865</td>\n",
       "                        <td id=\"T_7bb2c_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_7bb2c_row4_col5\" class=\"data row4 col5\" >0.651440</td>\n",
       "                        <td id=\"T_7bb2c_row4_col6\" class=\"data row4 col6\" >0.390168</td>\n",
       "                        <td id=\"T_7bb2c_row4_col7\" class=\"data row4 col7\" >0.087536</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb2c_level0_row5\" class=\"row_heading level0 row5\" >Total_Kg_Sold</th>\n",
       "                        <td id=\"T_7bb2c_row5_col0\" class=\"data row5 col0\" >0.294117</td>\n",
       "                        <td id=\"T_7bb2c_row5_col1\" class=\"data row5 col1\" >0.269744</td>\n",
       "                        <td id=\"T_7bb2c_row5_col2\" class=\"data row5 col2\" >0.372282</td>\n",
       "                        <td id=\"T_7bb2c_row5_col3\" class=\"data row5 col3\" >0.962338</td>\n",
       "                        <td id=\"T_7bb2c_row5_col4\" class=\"data row5 col4\" >0.651440</td>\n",
       "                        <td id=\"T_7bb2c_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "                        <td id=\"T_7bb2c_row5_col6\" class=\"data row5 col6\" >0.229062</td>\n",
       "                        <td id=\"T_7bb2c_row5_col7\" class=\"data row5 col7\" >-0.001007</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb2c_level0_row6\" class=\"row_heading level0 row6\" >Stock_On_Hand</th>\n",
       "                        <td id=\"T_7bb2c_row6_col0\" class=\"data row6 col0\" >-0.144105</td>\n",
       "                        <td id=\"T_7bb2c_row6_col1\" class=\"data row6 col1\" >-0.127677</td>\n",
       "                        <td id=\"T_7bb2c_row6_col2\" class=\"data row6 col2\" >-0.094174</td>\n",
       "                        <td id=\"T_7bb2c_row6_col3\" class=\"data row6 col3\" >0.284693</td>\n",
       "                        <td id=\"T_7bb2c_row6_col4\" class=\"data row6 col4\" >0.390168</td>\n",
       "                        <td id=\"T_7bb2c_row6_col5\" class=\"data row6 col5\" >0.229062</td>\n",
       "                        <td id=\"T_7bb2c_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "                        <td id=\"T_7bb2c_row6_col7\" class=\"data row6 col7\" >0.105028</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7bb2c_level0_row7\" class=\"row_heading level0 row7\" >avg_price_per_kg</th>\n",
       "                        <td id=\"T_7bb2c_row7_col0\" class=\"data row7 col0\" >-0.337886</td>\n",
       "                        <td id=\"T_7bb2c_row7_col1\" class=\"data row7 col1\" >-0.141740</td>\n",
       "                        <td id=\"T_7bb2c_row7_col2\" class=\"data row7 col2\" >-0.164496</td>\n",
       "                        <td id=\"T_7bb2c_row7_col3\" class=\"data row7 col3\" >0.108473</td>\n",
       "                        <td id=\"T_7bb2c_row7_col4\" class=\"data row7 col4\" >0.087536</td>\n",
       "                        <td id=\"T_7bb2c_row7_col5\" class=\"data row7 col5\" >-0.001007</td>\n",
       "                        <td id=\"T_7bb2c_row7_col6\" class=\"data row7 col6\" >0.105028</td>\n",
       "                        <td id=\"T_7bb2c_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c2cff23c88>"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = train.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36d7dec",
   "metadata": {},
   "source": [
    "#### 3.2.1: Date and avg_price_per_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "654d6d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2020-9', '2020-4', '2020-5', '2020-3', '2019-12', '2020-8',\n",
       "       '2020-7', '2020-2', '2020-6', '2020-1', '2020-10'], dtype=object)"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seperating date,month and year\n",
    "training_day=pd.DataFrame([])\n",
    "training_day['avg_price_per_kg']=train['avg_price_per_kg']\n",
    "training_day['Date']=train['Date']\n",
    "training_day['Day']=[x.weekday() for x in train['Date']]\n",
    "training_day['Month']=[x.month for x in train['Date']]\n",
    "training_day['Year-Month']=[str(x.year)+'-'+str(x.month) for x in train['Date']]\n",
    "training_day['Year-Month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "e578ac2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplot of data_components and avg_price_per_kg\n",
    "fig, ax =plt.subplots(1,3, figsize=(20, 9))\n",
    "i=0\n",
    "for a in training_day.drop(['Date','avg_price_per_kg'],axis=1).columns:\n",
    "    sns.scatterplot(x=training_day[a],y=training_day['avg_price_per_kg'],ax=ax[i])\n",
    "    i+=1\n",
    "ax[2].tick_params(labelrotation=45)\n",
    "fig.tight_layout(pad=4.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "9980958a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(1,3, figsize=(20, 9))\n",
    "i=0\n",
    "for a in training_day.drop(['Date','avg_price_per_kg'],axis=1).columns:\n",
    "    avg_date_comp=training_day.groupby(a)['avg_price_per_kg'].mean()\n",
    "    sns.scatterplot(x=avg_date_comp.index,y=avg_date_comp,ax=ax[i])\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e76cd8",
   "metadata": {},
   "source": [
    "The relationship between the day of the week and the mean of the avg_price_per_kg is the most clear. Also,considering the data we have is not even for 1 complete year, the relationship between month and mean of avg_price_per_kg cannot be determined to any meaningful degree. Since each month only appears once and some of them are in different years.\n",
    "\n",
    "We will use the day of the week in our modelling since it's average is taken over many months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "c1775a44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Province          1952 non-null   object \n",
      " 1   Container         1952 non-null   object \n",
      " 2   Size_Grade        1952 non-null   object \n",
      " 3   Weight_Kg         1952 non-null   float64\n",
      " 4   Low_Price         1952 non-null   float64\n",
      " 5   High_Price        1952 non-null   float64\n",
      " 6   Sales_Total       1952 non-null   float64\n",
      " 7   Total_Qty_Sold    1952 non-null   int64  \n",
      " 8   Total_Kg_Sold     1952 non-null   float64\n",
      " 9   Stock_On_Hand     1952 non-null   int64  \n",
      " 10  avg_price_per_kg  1952 non-null   float64\n",
      " 11  Day               1952 non-null   int64  \n",
      "dtypes: float64(6), int64(3), object(3)\n",
      "memory usage: 262.8+ KB\n"
     ]
    }
   ],
   "source": [
    "train['Day']=training_day['Day']\n",
    "train=train.drop('Date',axis=1)\n",
    "\n",
    "test['Day']=[x.weekday() for x in test['Date']]\n",
    "test=test.drop('Date',axis=1)\n",
    "\n",
    "train_dummies=train.copy()\n",
    "test_dummies=test.copy()\n",
    "train_dummies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb350afc",
   "metadata": {},
   "source": [
    "#### 3.2.2 Categorical data  and avg_price_per_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "c0e3bdb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3RxdzmRMR8TzwpKQHJJ0M7AAcHBFP96oMZmaWepb8JQmYB9ii/H9TRPy+V/HNzGy2Xrb8A3hW0knAL534zcxGT8+Sf5sLS0VgZmajpOend3DiNzMbfT63j5lZAzn5m5k1kJO/mVkDOfmbmTWQk7+ZWQM5+ZuZNZCTv5lZAzn5m5k1kJO/mVkDOfmbmTWQk7+ZWQM5+ZuZNZCTv5lZAzn5m5k1UM+Sf7mAu5mZzQVqT/6S1oC8mLsrADOzuUOtyV/SVOB2SReDKwAzs7lFbclf0kLAkcDR5LV7vwHDrwAkTZM0U9LMWbNm1VVMM7NGqi35R8RTwDuAi4FjgfnbK4BhPH56RPRFRN/EiRPrKqaZWSPV2u0TEQ9GxJMR8XfgMGCBVgUgaUNJa9UZ38zM+tez2T4R8Q+yAnhO0r3ApcCTvYpvZmaz9XSef9kDuBN4FbBXRPyll/HNzCz1NPlLWhzYGdghIu7qZWwzM5ttQi+DRcQjknaNiGd6GdfMzF6q56d3cOI3Mxt9PrePmVkDOfmbmTWQk7+ZWQM5+ZuZNZCTv5lZAzn5m5k1kJO/mVkDOfmbmTWQk7+ZWQM5+ZuZNZCTv5lZAzn5m5k1kJO/mVkDOfmbmTWQk7+ZWQPVnvwlrSlpE0nzSBpfdzwzMxtarVfykrQX8Bngf8vfTEkXRMTjdcY1M7PB1dbylzQPsC/wzoh4EzADWAn4oKRF64prZmZDU0TU88SZ/K8ELo2ICySNA7YAdgHuB86JQYJLmgZMK4trAr+dw6IsBfx9Dh87EqMVdzRj+zU3I3bT4o5m7JHEXTkiJg50Z23JH0DS9sBRwKkRcVPp898X2Bk4cLDkX2EZZkZEX91x5pa4oxnbr7kZsZsWdzRj1xm37gHfm4BrgQMlbRkRL0TExcDywHo1xzYzswHUOuAbEc9I+iYQwIclrQX8C1gGeKjO2GZmNrBakz9ARDwi6VzgHuAw4BnggIh4uO7YxfQexZlb4o5mbL/mZsRuWtzRjF1b3Fr7/F8WLPv8IyJe7FlQMzN7mZ4mfzMzmzv49A4NI0lNjN0kZVp1I/k7NnyN/JJIWkfSipImjXZZekXS/JLG92J6bUfcpSQtDdnf18vYc4NeJWJJS0haHaCX3aqSVpD0akmT29b1NAFLWlXSayG/Y72ML2lrSe/qVbwq49Y+4Du3kbQLcDrwM2BZSZdGxAU9jL8R+b5HRNzSo5h7Am8GFpV0KnBfRPxvD+LuDXwMeEzSXcCXgD/0KjlJ2hxYDngqIq7uUcytgYnA8xHx3Yh4UZLqrPgkvQ74NvBnSc8CXwNujIhH6opZ4r4G+A5wNzCPpDsi4sRWAu7RcTw7AZ8H7pe0ekSs3av4knYETgbe27G+7s+7mrgR0Yg/QMBiwM3ATmXdFsBfgXf3qAxTyB/KZ8r/zXsQ89XAn4BNgKOBrwAfBtasOe6y5HEeGwCLAOeSle6WwLgevO43Af8or/VeshJ6XQ9i/hn4JHl8y5XAhNb3r6aYE8hkf1BZfl95n48EFq/xtY4HLgWOLctrkDP6zqj7s20rw5bA74AtyvJVwLo9ir058DSwXlleFFh0LMVtTLdPpMeAXwCPlnU3ATsBH5d0YJ3xJfWRLZTDI+IjwOXAi5IGPPy6IosDd0fEzyPiDOAS8guzh6Rla4z7AjAP8GJEPEEmpSeAPYCVob7ugTKrbFvguIg4mTylyPLAWyStU0fMYjcy+X08InYg34OLWy2ymrqAgqxcFwGIiDPJBs6qwPZQT9dTRLwA/JI8YSMR8TuygbGjpJOqjtepfHfGk+cOu6l04W4GHC3pCknrtm1Xh8fJin4dSQuSe0Bfl3R9zbEri9uI5C9p3rbFp4EPtRYi4nZgf2AvScvU+GWZwOwv6rLkaS/eA1wu6d01xSSya2keSYeU5RuAH5F7BCvXGHcW8D1gW0nLRcQ/gc8CSwDvL9vUsmtcEtMfyUQ0MSLuI3eTlyYrn7rMBF5VfpRExJ7AfOT7QNTQ3VVe61eBDSVtUtZdTp4La7864rZVJo8CR0iav8R5jGxMbdTqg69D+T2Pi4jry+9pPuBg4AsR8Q5yT+/sOse4IuJOYB/gP8g9zCuBt5GNy7MkjasjdqVxe7GLNJp/ZOvnbOCjwMSy7nrgqrZtJgAXt+6vOP6GtO2Kkq3ho4F3leWNyK6nDSuOuTGwWVl+G9kVsGvbNh8qr7myLhhyl/RI4K3kHsfG5EEqBwLLl22WAK4BFqvhvV6z/XZ5zXsDi5R1qwP3AdtUGHOpttsblO/WFh3b/BDYrsrXSVakhwNvLOuOBz7dWi7r/h/QV2HclenoOiuf753AfG3rvgZsUPXnW557Z+AK4CJK921Zv3jHdlcBkyuOvQ1wavmOb9z2mb+nn9iT5va4r+iWv6SdgdOAnwLbAccARMQ2ZGv4u5K2A94JrEvFA+CSdiW/qIsqz3JKRDwHXBgR55Va+lbydNfPVBRzZ+BC4BDgVEn7At8FHgC2kXRY2fRB4Ekq2vuTNBU4C1iF/LIeHxG/BH5M7o4fVLq+ticrgOeriNsWfxXgN5KOB4iI35L9wdsBO0haOiL+QH4e81UUczfgEkmXSDqKfI/PJFudW0hauGz6R2D+imKuBnyTPNPjk8CPyiSGi4DnyPf5XZI2JD+LxyqKuybZzXO1pC1a6yNiGnAL8FNJu0jaA9iKGiaTlN/qZ4BvkBM2prSV45HWXrukt5FjTk9UGHsX4IvA/5GnpzlS0vIR8StyHK213X7kJIMn5/q4ddTOc8MfeSrUW5g9uLsVcAHZCm7tARxHtpauA15bcfzlgRuYPRg1b/nfOrBufPm/D3ArsGIFMdcjB5JbrYOpwPRyezngLeRA5DXAH4D1K3qtq5M/xr6y/HpyMHChsrwNubdzA9karWwvp60MK5Mt0N8DJ7etPww4A7iabB3/DVi9gniTyb7XTYHdy3fpe+Vz3618p04FTiH3Ntao6HUeSNugKvAp4BFy8HMCmRB/TI4p7VNRzHElzsfJPak7gS07tnlf2eZHtO1hVvwZf4AcM2v9nq8m936mlnWLAgeV38A6FcZdkdx7a+1Jrw5c1v6ZkuMP+wO/qSp23XEr/4Dmpj9gudZ/suY8k6wAvsRLd4/nryH2fMB55fYq5UM7DTgHmKesPxy4o8Ivy2bA29uWXwf8N7Bkx3brAktX+FpfVZKS2tZdC+zcsd0S1DgjghxHeS3Z9/5hYLXy2a9QfiAfAtaqKNaKwEVty8uWBHgFsGQpx55k46KSmCXOPsB/Mrsx8U6y1f8QpasFmJfZFW8ls4zImXKTyu1DyApg6362W6DKuB3PfSxZwR5A7tV9sfyGHi3v9fjyflQ+k42s0BduW76AHMNrLc9XvmOVxq4zbqVv0NzwR9aOkzvesPHA7uX2guQA2Yfb7q/ji7o02cLem5zlcww5G+Jc4Jtlm7dRQSu0M27b7QnkgFDrB1lprI64rQqtNbXxIuBN5famwBI1xR3H7L2p84A3l9v3AS8Cm1T5Obe9znnIivXEtvuWI/cuDq34NU4EVmuLO4Oc5XEe8Muy/oPAfhXHHbBRVCqAu8jxh8nkQPp4Kp7GS9lDbls+obzH32hb91bgK/1tX2Xs1vet/D+D0udOdmWuOtbivqL6/Esf+/Xk7unPJG0oad7I6wjMKFPu/knOBli4NWshyjtZQfy1S8zFIuJv5J7G6WTSPS0ifk7uGj9R4n4rsh96JDG3lrR+ua0St2UcmTjmlXQQMF3SYlXMaJK0k/KAsZYXO/7/DXhUeYDZZ4EFRhqzI/6K8O+ZLK3+5QuAcZKWKet+D+zYesxIP+dyQNFHy8FEzwFvB15f+vuJiIfIFumWI4nTEfN1wA+AcyWdRR6stwc5ieEqYK+y6QJkg6OquOsA55QxpNa6f+eLiDif3Lu6Efg18Hj5nVU2s6iMI31P0lJtcU8kK7/264AvDSxYxtBeqCN2mT4MebwQ5DjOg5KmACcx+3s/duJWWUuP1l95Y5Yk+8dafewfIPtdd+rY9p1kV8trKi7DruSsnQvJH8NeZCttz/IB7V62O4js+16UEbZEyYOKXiQHT1v9guPa7h9P9v1+gZwKVlX30uZkN8PtwNfb1rfH/ky5/5dVxe14rx+hrdVd1r+ulOsf5Dz/8eSBZpV0cZGzWP6L7H5Yo6zbltzDO7ksHwB8n7Y9zxHEm0AmumnkHuvu5GDvxzu224ocw9lypDHL800kp4reTDZWprTd1961t0n5HKZW+fmW5+4jD068mmzQLdVx/3+TleIngduq/I4NFLvj+z2NbODcQkXjhb2OW+kHNtp/ZMtvv7bld5CDX+uV5S2q/LDa4sxHHjy1dVk+kOz2aE3n3K3E/QIV9fGXxPBRcl713mS/Z6sCmNC23VXkYFCV/c7bkwOpC5B9sOe33dfqFjmRTMSvrvi9XokcXG5V7p0VwKHA9p3lqSj2+8iZUyeW934xchbPKiVRXkQm4REfZcrsCQHntn2vFiCn8V4IHFPWrUiOI+1d4etcgty7WI1s3X+Bl1YA85DjCmcBu5V1osLuU3Jc6tBy+1SywdRZARxLdqdW3ZDbYKDYzO7WPBiYRYVdqWTjpWdxK3vDRuuPnFkytfwwDif7A1dou/9D5Gya+ctfpYe8A68hu1fOBT7Wtn43ckpa68exMjlVa/kKYq5AtgTnZ/bMpUPIaX2dc8wPprpBzk2Bbcvtxcr/pckK4MK27eYlB0ArmeHSTzm2LwloHTLptve7t5KmakhIrWS4GTmQe27rdZOV8RJ0DK6PIM4B5fa7gV9R5qyXz3wHMvEuXF7jEq3XPMK4i7Y91/xtn2+rAtilrGsNJs/X/l5X9B4vzOwxnFacCcDnyC6m1ve98lNXkMfctF73AoPEXrL8r2wuf1sZFulV3EoL3us/Mun/mhzsmkgOPF1ellds2+5C6jmAaydy93My2QXzWeANbfcfQra6F6kw5hTyuIVpJcG274a/g6wAVie7Iw6oMO6O5FjJ6/u5b1myAjiT7Pb4NBUOvJUYa1GmsHasfw1ZAXyyLK9LBRVs2/O3BtrGk0n5+rJ8JPAUOfNm2QrjrVm+029rW3dc+V5PLsvz0rZHW2Hca8g9qvXLulYSXhb4CDmWdgLZJTSR6gd3tyO7OzalozIpr/lzZJfIf5AV70IVxt6ZHK/Zup/7OmOfT0UzBEsO+QjwCV5+oFptcSPGcPInu3DubU+2Zf1a5LTKT5D9+28H7qfi5F+S4d2UflZyfvfnyS6B9mmk36Ki6V/lC3oPZRZN23q1Jamp5DjAX6muxb89ObNjm7K8FGWqYcd295F9wJWeXKv8QG4hp/a9rMsOWJucX34D2f+7TAUxty7Pu3DH+g+Se5h/ILt+WjO5XvZ+zEHM1p7MgWV5HHmU9IrkHu0vgTeU5HgXsHZF7+/a5X17J4M0VMiK7u/AW6r8fNuef9fyW23tXb2sciHHcP5BtRXfpPK72qr1vg+wXaWxy3fsLrKX4GRyjGxzXj7DqfLXHDG2k//bgSPK7Xk6/q9MDqxOJ/toq05GO5L9bq2B2wltcU8tCeHTZMv/L4ywZViS+8LkaXt3bK0r/8d1LO8IPFxhYhhHHrB1SVmeRO7N7Nix3V7kTISqB3d3Lj+QTTrWd7YMTyyvu4r+9kXIrpbflM/y4Lb7PkJWrK0ukC2pprKZQB4jcFvbuhspZ80sy+8t3+cfV5GAy/dqIfKYjMM67nsr8N625ZXI82Lt3t/7X9FnvRG593E2WdGvQTaqFi3370K2zqses1sNuKzcXp48MO8cMjm3ulqmVhW77bf6KV465XwG8HPaknxdrzlibCf/E1ofWD9v6qrMrggq2zUsz/cGco9jM3KX7D/Ll7SVhJchW2ZnlS9QZRUP2X31hnK7s3Xw6vJjPr6qBEzOPliDnL9+JdmtcwtwZD/b7k6FB7gwu8K7lJfP2Dq9vPetSnfd8qMZ0XvNS7vQPlOeczOyEjgJ2LPct8JI4vQTdwWyW2cVcv7+F8r/zw6w/YKd5R1h/CtoO9cS2aV4e0nEn2/7PNavMm55rsXbbi9GVq6t/1eQ43WTy/0bUI53qDJ2eW0/ICdOXAscUfLLBcw+Q8CGVcVm9rjKNHK20jJl+YTyO7ul7TNev8rX/JJy1PGkdf3R1sIid1WnlzensxX8OdpmfFRcho3akyt5wNiFJfmO69i2iq6AjcjKZAI5hfCDbfe1kt848ujWeaior50cW7iVcioGskV0CW0nxCvrazlPfdvzX0DbOAM5rnFT+Turbf2Iu/V46SypRcj+583L8rnknsU1ZCtwpYpf53+RU5VXJmcx/a7j/jeQLf/5q3jPyQrnqnL7al66d3MwWREtSVa+k+v4zMmB65nADm3rLitl25k8Kv8qsjus6jGkVuwpZfn9ZOOmfery+8hu2yoru1bczcvrvJLsy78cmFG2mU5Np8ho/xszB3lJWgt4SNJpkg6JiHvIGnsvsoVK5FWT9iX7qH9XcfxXlxOTvYrs3qDEPBz4J3mxkFXbHxMRz44w5hTyi/DPiHieHHDbrxywRVkH+WOdSrYWRnyQSzmY6WPkLultymsO/JNMPpL0SdV4aUZJG0naVNIEMgFt3Xb3byJii4jYAnitpA1KOWaNMOZSwB8kLVFWPU3uhq9cDrTajjw30o3k4P6/RhKvLW7rQMNNybGarwHvAu6RdErZZiOyErwnIp6p4j2PvJLbREnXkHsZK7adEvqCiPgjuUe1OB2vtcLPfE3yNBjHKq/6BlkRHUvu/RxJ9sXvT0Un4+sn9jHlhHFfI/PJuuWEfZAnP/wnOfBaddxPkBNFDiyxvwXsW7Z5ltkHddWn7tqlwhqzNb/7Q+TJwc4gd5u+w+yTs51AflkqvWIT2e92G9nfeh159sYNOrY5i9xNreQwb3KQ82eUVhE5yDqZHBS7hxxs3IGc134X1XX1LEEmoT3K8mpkK7s1xXMFcmbPadRwygZm73GsX5Y3ILsgDu7Ybq/yPViqwti7kl16re6ArcjW/iO8dJ77ghW/5vaDeK4pr2tlshX8fbL7aeea4l1Ojl+dRg607kPuQW5TvvM7VhW3n3IsRXbhvb/8dnYh96D/yOzTdCxKBWMqg8Q+pnyfdySnT3+ITMTfJLv7qh4vbMX9QIm7Z8f9rS63Wrp6XhKr7gAVv3Gnk7uhE8gpheeXxDeFHJjblurPlbMTeXTsVm3rjgf+h47Rd7K1UsU8/s4EvHpJwK0uiHXJc/F/lWwRVj0A1qrs1iUruw+U9a1uteXIYxiqnkE1nApvJ/IAszuoaFC7owxTyBknrX7Z/2D2uZgq7XroiNuekK8mK4BVyGm9lc+u6Yh3MTmL5wiy7/uH5XPYvYa467YSKtldeQp5jqIdyC6ezdq2HXG3aRexv9/2vVus5JJKxnaGiDuDcoAe2dC6gorOtjtkuXoRpII3r9WnPy/Z77ws2RXwp5IALyHPbb1AxXFbSbh1ytj52+47oSSJxWp6zZ0JuHWt1M7ZPfPVFH+n8tqPK8utg6emljJV2tdPdxXe+dQw+6GtLFPI1ufi5Oym66tKBEPE7awArqZtXKfmeN8Bbii352293io/Z7IL70Wy4fRmchrrBODLZMv7bWQf+P41vNahYr+1xK765HhDxd23VAD7lO0rzWGDlq1XgSp4E0X2+32qJIF72xLFGtR31shdyL2L1pSv9isW/YQKr5TUT+yBEvAUypWhqk7CHfG3L+/zq8ryweRMhMrOYNjPez2cCq/SFuEAZdmZPI5jgfKdW6XumO2vtdy+Ajiqh/F+BFzbtlzHdM5ty3f6k+Qe9MXk9OjWEc0Hk425EZ8baQSxF6nytQ8z7qV1vOZBy9XLYBW9kWuSJzY6vocxp5AHMLX6glvTSGdQYwu0xBgoAfcqGU0pld+7yZZ4pfP4+4k3qhVeR1n2BG7u1fesLW6rsvsw8NFexSu3r6CfqbwVx3sTeSGcJckuvBvIPvZ5S+Kt7Ij4uSX2aL7mAcvU64AVvZGHkKPllQ68DRGzswJ4O3lqh8ouijJE7J4l4H7iTyVnIPQk7mhXeB1l6WlrrC3ufOSxBpWPawwQr9cVTuvgvYXLcs8+29GKPZqvud/yjGbwEbyJa5EDND1L/iVuexL+L2pu9XfE7mkC7if+aL7XPa/w5oY/2o476FG8Xlc4O5MzapZoW9ervbpRiT2ar7nzr9WHOuZIWjDywiy9jjuV3DXeICLu7nHsUXnNo2U03+umkjQhZh8/0ot4u5OTJ/rIQwh6lpBGK/ZovuaXlGOsJv/R1LQkPJr8Xr/ySVo4Ip5sUuzRfM3/LoOTv5lZ84yZ0zuYmVl1nPzNzBrIyd/MrIGc/M3MGsjJ317xJC0r6RJJ90m6R9LVktaYg+c5WtKCw9juPElrz1lpzXrDs33sFU2SyAPyLoyIr5Z165OH09/U5XP9iTyX09+rLucA8Xo6596axS1/e6XbBniulfgBIuJ24GZJp0r6taS7ykWAkLS1pJ9KukzSvZK+qfRe8mpm10u6vmz7FUkzJd0t6cTW85fH95XbT0r6tKQ7JP1C0jJl/URJl0v6ZfnbrKz/hKTpkq4lLxFqVgsnf3uley15cZhOe5GXAF2PvErXqZKWK/dtABxNXip0VfIc818kr+y0TURsU7b7aET0kWch3UrSuv3EWQj4RUSsR14F7NCy/kzg9IjYmLx27Hltj9mIPJf+ft2/XLPhmTDaBTAbJZsD34q87OXDkm4gz7X+OHBLRPwFQNLt5AVlbu7nOfaRNI38HS1HVhZ3dmzzLHmREshKaPtyeztg7eyVAmBRSYuU21dGxNMjenVmQ3Dyt1e6u8mLaHQa7Bqp7desfYF+fieSViGvNbtxRDwi6QLy4uqdnms7d0v7c40DNulM8qUyeGqQsplVwt0+9kr3E2A+Sa3uFiRtTF6Xd19J48sF6rckTxs9mCfIc69DXlv2KeCx0o8/pctyXUteoLxVpvW7fLzZiLjlb69oERGS9gTOkHQc8Ax5+c+jgYXJawEH8MGI+KuktQZ5uunANZIeiohtJP2K3LO4n7zmbTfeC3xZ0p3k7/BG4PAun8Nsjnmqp5lZA7nbx8ysgZz8zcwayMnfzKyBnPzNzBrIyd/MrIGc/M3MGsjJ38ysgZz8zcwa6P8DItqCGLjHM9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the mean avg_price_per_kg against the Date\n",
    "avg_container=train_dummies.groupby('Container')['avg_price_per_kg'].mean()\n",
    "sns.barplot(x=avg_container.index,y=avg_container)\n",
    "plt.tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "def8207e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in avg_container.index:\n",
    "    if x in ['AC030','DT063','IA400']:\n",
    "        train_dummies=train_dummies.replace(x,'Lowest_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'Lowest_mean_cont')\n",
    "    if x in ['JE090','JG110']:\n",
    "        train_dummies=train_dummies.replace(x,'medium_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'medium_mean_cont')\n",
    "    if x in ['M6125','M9125']:\n",
    "        train_dummies=train_dummies.replace(x,'high_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'high_mean_cont')\n",
    "    if x in ['EC120','EF120','EG140','M4183']:\n",
    "        train_dummies=train_dummies.replace(x,'Highest_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'Highest_mean_cont')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "8db86349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_prov=train_dummies.groupby('Province')['avg_price_per_kg'].mean()\n",
    "sns.barplot(x=avg_prov.index,y=avg_prov)\n",
    "plt.tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "b05765bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in avg_prov.index:\n",
    "    if x in ['Natal']:\n",
    "        train_dummies=train_dummies.replace(x,'Lowest_Mean_Prov')\n",
    "        test_dummies=test_dummies.replace(x,'Lowest_Mean_Prov')\n",
    "    if x in ['Eastern Cape','Orange free State','West Coast']:\n",
    "        train_dummies=train_dummies.replace(x,'Medium_Mean_Prov')\n",
    "        test_dummies=test_dummies.replace(x,'Medium_Mean_Prov')\n",
    "    if x in ['Cape','W.CAPE-BERGRIVER ETC']:\n",
    "        train_dummies=train_dummies.replace(x,'High_Mean_Prov')\n",
    "        test_dummies=test_dummies.replace(x,'High_Mean_Prov')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "bdad3222",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_sgr=train_dummies.groupby('Size_Grade')['avg_price_per_kg'].mean()\n",
    "sns.barplot(x=avg_sgr.index,y=avg_sgr)\n",
    "plt.tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "ed6ecf02",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grouping by letter grade\n",
    "for x in avg_prov.index:\n",
    "    if 'L' in x:\n",
    "        train_dummies=train_dummies.replace(x,'L')\n",
    "        test_dummies=test_dummies.replace(x,'L')\n",
    "    if 'M' in x:\n",
    "        train_dummies=train_dummies.replace(x,'M')\n",
    "        test_dummies=test_dummies.replace(x,'M')\n",
    "    if 'S' in x:\n",
    "        train_dummies=train_dummies.replace(x,'S')\n",
    "        test_dummies=test_dummies.replace(x,'S')\n",
    "    if 'U' in x:\n",
    "        train_dummies=train_dummies.replace(x,'U')\n",
    "        test_dummies=test_dummies.replace(x,'U')\n",
    "    if 'X' in x:\n",
    "        train_dummies=train_dummies.replace(x,'X')\n",
    "        test_dummies=test_dummies.replace(x,'X')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e122c8",
   "metadata": {},
   "source": [
    "As we can see, the mean of the avg_price_per_kg has some variance depending on the goup it belongs to for each of the categoical columns, the affect is not extremely drastic in most cases. Here we have group each value in the categorical columns with values that have similar affect on the mean. Now we will convert those column to numerical values using dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "8ceced9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dummies = pd.get_dummies(train_dummies,drop_first=True)\n",
    "train_dummies.columns = [col.replace(\" \",\"_\") for col in train_dummies.columns]\n",
    "train_dummies.columns = [col.replace(\".\",\"_\") for col in train_dummies.columns]\n",
    "train_dummies.columns = [col.replace(\"-\",\"_\") for col in train_dummies.columns]\n",
    "test_dummies = pd.get_dummies(test_dummies,drop_first=True)\n",
    "test_dummies.columns = [col.replace(\" \",\"_\") for col in test_dummies.columns]\n",
    "test_dummies.columns = [col.replace(\".\",\"_\") for col in test_dummies.columns]\n",
    "test_dummies.columns = [col.replace(\"-\",\"_\") for col in test_dummies.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "a03cf7d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 24 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   Weight_Kg                   1952 non-null   float64\n",
      " 1   Low_Price                   1952 non-null   float64\n",
      " 2   High_Price                  1952 non-null   float64\n",
      " 3   Sales_Total                 1952 non-null   float64\n",
      " 4   Total_Qty_Sold              1952 non-null   int64  \n",
      " 5   Total_Kg_Sold               1952 non-null   float64\n",
      " 6   Stock_On_Hand               1952 non-null   int64  \n",
      " 7   avg_price_per_kg            1952 non-null   float64\n",
      " 8   Day                         1952 non-null   int64  \n",
      " 9   Province_High_Mean_Prov     1952 non-null   uint8  \n",
      " 10  Province_L                  1952 non-null   uint8  \n",
      " 11  Province_S                  1952 non-null   uint8  \n",
      " 12  Container_Lowest_mean_cont  1952 non-null   uint8  \n",
      " 13  Container_high_mean_cont    1952 non-null   uint8  \n",
      " 14  Container_medium_mean_cont  1952 non-null   uint8  \n",
      " 15  Size_Grade_1M               1952 non-null   uint8  \n",
      " 16  Size_Grade_1S               1952 non-null   uint8  \n",
      " 17  Size_Grade_1U               1952 non-null   uint8  \n",
      " 18  Size_Grade_1X               1952 non-null   uint8  \n",
      " 19  Size_Grade_2L               1952 non-null   uint8  \n",
      " 20  Size_Grade_2M               1952 non-null   uint8  \n",
      " 21  Size_Grade_2S               1952 non-null   uint8  \n",
      " 22  Size_Grade_2U               1952 non-null   uint8  \n",
      " 23  Size_Grade_2X               1952 non-null   uint8  \n",
      "dtypes: float64(6), int64(3), uint8(15)\n",
      "memory usage: 245.6 KB\n"
     ]
    }
   ],
   "source": [
    "train_dummies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a68c2b3",
   "metadata": {},
   "source": [
    "All of our data are now numeric."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7091e276",
   "metadata": {},
   "source": [
    "#### 3.2.3: Numerical values and avg_price_per_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "1c1f6aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Weight_Kg</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight_Kg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.863182</td>\n",
       "      <td>0.905852</td>\n",
       "      <td>0.180518</td>\n",
       "      <td>-0.133317</td>\n",
       "      <td>0.294117</td>\n",
       "      <td>-0.144105</td>\n",
       "      <td>-0.337886</td>\n",
       "      <td>-0.050023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low_Price</th>\n",
       "      <td>0.863182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>0.183230</td>\n",
       "      <td>-0.122823</td>\n",
       "      <td>0.269744</td>\n",
       "      <td>-0.127677</td>\n",
       "      <td>-0.141740</td>\n",
       "      <td>-0.035710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>0.905852</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.265672</td>\n",
       "      <td>-0.096456</td>\n",
       "      <td>0.372282</td>\n",
       "      <td>-0.094174</td>\n",
       "      <td>-0.164496</td>\n",
       "      <td>-0.040340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales_Total</th>\n",
       "      <td>0.180518</td>\n",
       "      <td>0.183230</td>\n",
       "      <td>0.265672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.771865</td>\n",
       "      <td>0.962338</td>\n",
       "      <td>0.284693</td>\n",
       "      <td>0.108473</td>\n",
       "      <td>-0.026877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <td>-0.133317</td>\n",
       "      <td>-0.122823</td>\n",
       "      <td>-0.096456</td>\n",
       "      <td>0.771865</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651440</td>\n",
       "      <td>0.390168</td>\n",
       "      <td>0.087536</td>\n",
       "      <td>-0.016428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <td>0.294117</td>\n",
       "      <td>0.269744</td>\n",
       "      <td>0.372282</td>\n",
       "      <td>0.962338</td>\n",
       "      <td>0.651440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.229062</td>\n",
       "      <td>-0.001007</td>\n",
       "      <td>-0.041020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <td>-0.144105</td>\n",
       "      <td>-0.127677</td>\n",
       "      <td>-0.094174</td>\n",
       "      <td>0.284693</td>\n",
       "      <td>0.390168</td>\n",
       "      <td>0.229062</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105028</td>\n",
       "      <td>-0.040417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_price_per_kg</th>\n",
       "      <td>-0.337886</td>\n",
       "      <td>-0.141740</td>\n",
       "      <td>-0.164496</td>\n",
       "      <td>0.108473</td>\n",
       "      <td>0.087536</td>\n",
       "      <td>-0.001007</td>\n",
       "      <td>0.105028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.065115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>-0.050023</td>\n",
       "      <td>-0.035710</td>\n",
       "      <td>-0.040340</td>\n",
       "      <td>-0.026877</td>\n",
       "      <td>-0.016428</td>\n",
       "      <td>-0.041020</td>\n",
       "      <td>-0.040417</td>\n",
       "      <td>0.065115</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Weight_Kg  Low_Price  High_Price  Sales_Total  \\\n",
       "Weight_Kg          1.000000   0.863182    0.905852     0.180518   \n",
       "Low_Price          0.863182   1.000000    0.938140     0.183230   \n",
       "High_Price         0.905852   0.938140    1.000000     0.265672   \n",
       "Sales_Total        0.180518   0.183230    0.265672     1.000000   \n",
       "Total_Qty_Sold    -0.133317  -0.122823   -0.096456     0.771865   \n",
       "Total_Kg_Sold      0.294117   0.269744    0.372282     0.962338   \n",
       "Stock_On_Hand     -0.144105  -0.127677   -0.094174     0.284693   \n",
       "avg_price_per_kg  -0.337886  -0.141740   -0.164496     0.108473   \n",
       "Day               -0.050023  -0.035710   -0.040340    -0.026877   \n",
       "\n",
       "                  Total_Qty_Sold  Total_Kg_Sold  Stock_On_Hand  \\\n",
       "Weight_Kg              -0.133317       0.294117      -0.144105   \n",
       "Low_Price              -0.122823       0.269744      -0.127677   \n",
       "High_Price             -0.096456       0.372282      -0.094174   \n",
       "Sales_Total             0.771865       0.962338       0.284693   \n",
       "Total_Qty_Sold          1.000000       0.651440       0.390168   \n",
       "Total_Kg_Sold           0.651440       1.000000       0.229062   \n",
       "Stock_On_Hand           0.390168       0.229062       1.000000   \n",
       "avg_price_per_kg        0.087536      -0.001007       0.105028   \n",
       "Day                    -0.016428      -0.041020      -0.040417   \n",
       "\n",
       "                  avg_price_per_kg       Day  \n",
       "Weight_Kg                -0.337886 -0.050023  \n",
       "Low_Price                -0.141740 -0.035710  \n",
       "High_Price               -0.164496 -0.040340  \n",
       "Sales_Total               0.108473 -0.026877  \n",
       "Total_Qty_Sold            0.087536 -0.016428  \n",
       "Total_Kg_Sold            -0.001007 -0.041020  \n",
       "Stock_On_Hand             0.105028 -0.040417  \n",
       "avg_price_per_kg          1.000000  0.065115  \n",
       "Day                       0.065115  1.000000  "
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ecfaa3",
   "metadata": {},
   "source": [
    "To perfrom linear regression we make the assumption that there is an underlying linear formula to the data we are modelling. So to optimize our model we select features so that they have a correlation with our data.\n",
    "\n",
    "In the following section we will take alot look at the importance of each variable one the response by looking at it's correlation with the response variable as well as it's p-value.\n",
    "\n",
    "We will also make sure we try and avoid multicollinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "bda58953",
   "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>Correlation_Coefficient</th>\n",
       "      <th>P_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Province_High_Mean_Prov</th>\n",
       "      <td>0.262051</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province_L</th>\n",
       "      <td>-0.365193</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2S</th>\n",
       "      <td>-0.352996</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight_Kg</th>\n",
       "      <td>-0.337886</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province_S</th>\n",
       "      <td>-0.165182</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>-0.164496</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2M</th>\n",
       "      <td>-0.153372</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low_Price</th>\n",
       "      <td>-0.141740</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Container_medium_mean_cont</th>\n",
       "      <td>-0.370769</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Container_Lowest_mean_cont</th>\n",
       "      <td>-0.393817</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_1M</th>\n",
       "      <td>0.175779</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_1X</th>\n",
       "      <td>0.251451</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales_Total</th>\n",
       "      <td>0.108473</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <td>0.105028</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2L</th>\n",
       "      <td>-0.089733</td>\n",
       "      <td>0.000072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <td>0.087536</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2U</th>\n",
       "      <td>-0.069457</td>\n",
       "      <td>0.002137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>0.065115</td>\n",
       "      <td>0.004001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2X</th>\n",
       "      <td>-0.051624</td>\n",
       "      <td>0.022556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Correlation_Coefficient   P_Value\n",
       "Province_High_Mean_Prov                    0.262051  0.000000\n",
       "Province_L                                -0.365193  0.000000\n",
       "Size_Grade_2S                             -0.352996  0.000000\n",
       "Weight_Kg                                 -0.337886  0.000000\n",
       "Province_S                                -0.165182  0.000000\n",
       "High_Price                                -0.164496  0.000000\n",
       "Size_Grade_2M                             -0.153372  0.000000\n",
       "Low_Price                                 -0.141740  0.000000\n",
       "Container_medium_mean_cont                -0.370769  0.000000\n",
       "Container_Lowest_mean_cont                -0.393817  0.000000\n",
       "Size_Grade_1M                              0.175779  0.000000\n",
       "Size_Grade_1X                              0.251451  0.000000\n",
       "Sales_Total                                0.108473  0.000002\n",
       "Stock_On_Hand                              0.105028  0.000003\n",
       "Size_Grade_2L                             -0.089733  0.000072\n",
       "Total_Qty_Sold                             0.087536  0.000108\n",
       "Size_Grade_2U                             -0.069457  0.002137\n",
       "Day                                        0.065115  0.004001\n",
       "Size_Grade_2X                             -0.051624  0.022556"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Corellation and P-Values against avg_price_per_kg\n",
    "corrs = train_dummies.corr()['avg_price_per_kg'].sort_values(ascending=False)\n",
    "# Build a dictionary of correlation coefficients and p-values\n",
    "dict_cp = {}\n",
    "\n",
    "column_titles = [col for col in corrs.index if col!= 'avg_price_per_kg']\n",
    "for col in column_titles:\n",
    "    p_val = round(pearsonr(train_dummies[col], train_dummies['avg_price_per_kg'])[1],6)\n",
    "    dict_cp[col] = {'Correlation_Coefficient':corrs[col],\n",
    "                    'P_Value':p_val}\n",
    "#choosing columns with p values<0.1    \n",
    "train_cp = pd.DataFrame(dict_cp).T\n",
    "train_cp_sorted = train_cp.sort_values('P_Value')\n",
    "train_cp_sorted[train_cp_sorted['P_Value']<0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "a2f0225e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = train_dummies['avg_price_per_kg']  \n",
    " \n",
    "#Choosing features based on P-Values\n",
    "X_names = list(train_cp[train_cp['P_Value'] < 0.1].index)\n",
    "X_data = train_dummies[X_names]\n",
    "\n",
    "X_data_test=test_dummies[X_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6119581",
   "metadata": {},
   "source": [
    "To deal with collinearity we plot a matrix of the columns that are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "946dc0d1",
   "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>High_Price</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>High_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Low_Price</th>\n",
       "      <td>0.938140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.863182</td>\n",
       "      <td>0.938140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>0.905852</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>0.905852</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight_Kg</th>\n",
       "      <td>0.905852</td>\n",
       "      <td>0.863182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            High_Price  Low_Price  Weight_Kg  High_Price\n",
       "Low_Price     0.938140   1.000000   0.863182    0.938140\n",
       "High_Price    1.000000   0.938140   0.905852    1.000000\n",
       "High_Price    1.000000   0.938140   0.905852    1.000000\n",
       "Weight_Kg     0.905852   0.863182   1.000000    0.905852"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation matrix and find rows and columns where correlation coefficients > 0.9 or <-0.9\n",
    "corr = X_data.corr()\n",
    "r, c = np.where(np.abs(corr) >0.9)\n",
    "\n",
    "# We are only interested in the off diagonal entries:\n",
    "off_diagonal = np.where(r != c)\n",
    "\n",
    "# Show the correlation matrix rows and columns where we have highly correlated off diagonal entries:\n",
    "corr.iloc[r[off_diagonal], c[off_diagonal]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97af32c7",
   "metadata": {},
   "source": [
    "Here we drop High_Price since it has a high correlation with the Weight_Kg and the Low_Price columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "d0422202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrics\n",
    "fig = plt.figure(figsize=(15,15));\n",
    "ax = fig.add_subplot(111);\n",
    "X_data=train_dummies.drop('High_Price',axis=1)\n",
    "X_data_test=test_dummies.drop('High_Price',axis=1)\n",
    "plot_corr(X_data.corr(), xnames = X_data.corr().columns, ax = ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "254ba6d9",
   "metadata": {},
   "source": [
    "It seems like most of the colinearity has been taken care of."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "126c49ae",
   "metadata": {},
   "source": [
    "# Phase 4: Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79a53875",
   "metadata": {},
   "source": [
    "## 4.1: Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36a5d8f",
   "metadata": {},
   "source": [
    "We seperate our model building (test and training) sets for linear and non-linear modelling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "cd0c9379",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the training and testing data for linear models\n",
    "\n",
    "X_lin=train_dummies[X_data.drop('avg_price_per_kg',axis=1).columns]\n",
    "y_lin=train_dummies['avg_price_per_kg']\n",
    "\n",
    "test_lin=test_dummies[X_data_test.columns]\n",
    "X_lin.head()\n",
    "# standardize features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X_lin)\n",
    "test_scaled = scaler.fit_transform(test_lin)\n",
    "# Train test split\n",
    "x_train_ln, x_test_ln, y_train_ln,y_test_ln = train_test_split(X_scaled,y_lin,test_size=0.2,random_state=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0be04e0",
   "metadata": {},
   "source": [
    "On the other hand we achieve our best model scores on the non-linear models when we use only the numeric columns while including the day of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "5f054899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Province          1952 non-null   object \n",
      " 1   Container         1952 non-null   object \n",
      " 2   Size_Grade        1952 non-null   object \n",
      " 3   Weight_Kg         1952 non-null   float64\n",
      " 4   Low_Price         1952 non-null   float64\n",
      " 5   High_Price        1952 non-null   float64\n",
      " 6   Sales_Total       1952 non-null   float64\n",
      " 7   Total_Qty_Sold    1952 non-null   int64  \n",
      " 8   Total_Kg_Sold     1952 non-null   float64\n",
      " 9   Stock_On_Hand     1952 non-null   int64  \n",
      " 10  avg_price_per_kg  1952 non-null   float64\n",
      " 11  Day               1952 non-null   int64  \n",
      "dtypes: float64(6), int64(3), object(3)\n",
      "memory usage: 262.8+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "3e8c4770",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the training and testing data for non-linear models\n",
    "for x in test:\n",
    "    if np.issubdtype(test[x].dtype, np.number)==False:\n",
    "        test=test.drop(x,axis=1)\n",
    "for y in train:\n",
    "    if np.issubdtype(train[y].dtype, np.number)==False:\n",
    "        train=train.drop(y,axis=1)\n",
    "X=train.drop('avg_price_per_kg',axis=1)\n",
    "y=train['avg_price_per_kg']\n",
    "\n",
    "x_train, x_test, y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41dfadc7",
   "metadata": {},
   "source": [
    "## 4.2: Modelling Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da917b2",
   "metadata": {},
   "source": [
    "## 4.2.1: Random Forest Model"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "ed281a34",
   "metadata": {},
   "source": [
    "The Random Forest Model utilise multiple randomly created decision trees by combining their output to generate a prediction\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce73bf9",
   "metadata": {},
   "source": [
    "The  combination of learning models will increase the accuracy of the result. The random forest algorithm allows easy  measurement of the relative importance of the features used on the prediction. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9414d0",
   "metadata": {},
   "source": [
    "Utilising the Random Forest Model:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdebb2b2",
   "metadata": {},
   "source": [
    "### 1.Training "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c45d7ba",
   "metadata": {},
   "source": [
    "Fitting the model to our data and applying hyperparamers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "18e45468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=200)"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our forest consists of 100 trees with a max depth of 5\n",
    "RF = RandomForestRegressor(n_estimators=200, max_depth=5)\n",
    "RF.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9bd0c6",
   "metadata": {},
   "source": [
    "### 2.Testing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0ec4c4",
   "metadata": {},
   "source": [
    "To understand the performance of this model, we will get the mean square error and R^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "d1aa64ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.7352261361450763\n",
      "R^2 0.8846120358291305\n"
     ]
    }
   ],
   "source": [
    "# Get predictions\n",
    "y_pred = RF.predict(x_test)\n",
    "\n",
    "# Compute RMSE\n",
    "print(\"RMSE:\",metrics.mean_squared_error(y_test,y_pred,squared=False))\n",
    "print(\"R^2\",metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d23429",
   "metadata": {},
   "source": [
    "Then we will evaluate it against the expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "b922e642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "f, ax = plt.subplots(figsize=(5,5))\n",
    "\n",
    "# Plot on axes\n",
    "ax.set_title('Actual vs Predicted')\n",
    "ax.set_xlabel('Actual')\n",
    "ax.set_ylabel('Predicted')\n",
    "ax.scatter(y_test,y_pred)\n",
    "ax.plot(y_test,y_test,'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff91d28b",
   "metadata": {},
   "source": [
    "### 3. Adjusting Hyperparameters "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e3c308",
   "metadata": {},
   "source": [
    "The standard hyperpameter values will not give the best model performance. For that reason, adjustments to the model hyperparameters must take place to get the best performing model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2393e9b",
   "metadata": {},
   "source": [
    "Creating different hyperparameters of the \"n_estimators\" to compare the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "cd5c7e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 trees in forest\n",
    "forest_1 = RandomForestRegressor(n_estimators=2, max_depth=5, random_state=23)\n",
    "\n",
    "# 20 trees in forest\n",
    "forest_2 = RandomForestRegressor(n_estimators=20, max_depth=5, random_state=23)\n",
    "\n",
    "# 100 trees in forest\n",
    "forest_3 = RandomForestRegressor(n_estimators=100, max_depth=5, random_state=23)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286f0c5e",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "6925aa5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=2, random_state=23)"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "c147b241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=20, random_state=23)"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "29d3d498",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, random_state=23)"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_3.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff6f4cc",
   "metadata": {},
   "source": [
    "Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "991d2914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "f, ax = plt.subplots(figsize=(15,5), nrows=1, ncols=3,sharey=True)\n",
    "\n",
    "# Create list of titles and predictions to use in for loop\n",
    "pred = [forest_1.predict(x_test), forest_2.predict(x_test), forest_3.predict(x_test)]\n",
    "title = ['trees = 2', 'trees = 20', 'trees = 100']\n",
    "\n",
    "# Loop through all axes to plot each model's results \n",
    "for i in range(3):\n",
    "    rmse = round(np.sqrt(metrics.mean_squared_error(pred[i],y_test)))\n",
    "    ax[i].set_title(title[i]+\"  (RMSE: \"+str(rmse)+ \")\")\n",
    "    ax[i].set_xlabel('Actual')\n",
    "    ax[i].set_ylabel('Predicted')\n",
    "    ax[i].plot(y_test,y_test,'r')\n",
    "    ax[i].scatter(y_test,pred[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ea603e",
   "metadata": {},
   "source": [
    "## 4.2.2: Boosting models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f23b6f",
   "metadata": {},
   "source": [
    "Boosting is an ensemble method in which many predictors are trained sequentially and each succesive predictor learns from the errors of it's predecessor. \n",
    "In simple terms 'Many weak learners are combined to form a strong learner.\n",
    "\n",
    "The following Boosting methods will make use of CARTS( Classification and Regression Trees) similar to the Random forrest.\n",
    "\n",
    "We will use the following models:\n",
    "        \n",
    "        -AdaBoost (Adaptive Boosting):\n",
    "            After training, each predictor is assigned a coefficient that depends on the training error.\n",
    "            This coefficient adds more weight to the instances wrongly predicted, so that the next predictor \n",
    "            pays more attention to those values.\n",
    "        \n",
    "        -GradientBoost(GB):\n",
    "            Gradient boosting also does sequential correction of predecessor errors. But, unlike AdaBoost, the weight of\n",
    "            training instances are not tweaked. \n",
    "            Instead each predictor is trained using the residual errors of the previous \n",
    "            labels. \n",
    "            GB's exhastive search procedure means that each cart is trained to find the best split points \n",
    "            and features.\n",
    "            \n",
    "        \n",
    "        -Stochastic GradientBoost(SGB):\n",
    "            This method imporves the generalizability GB method by adding further variance to the ensemble diversity. Each \n",
    "            predictor is trained on a random subset of the training data that's been sampled without replacement. \n",
    "            Features are sampled without replacement when choosing split points.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22179875",
   "metadata": {},
   "source": [
    "### 1. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c3ba92bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=5, max_features=0.75, n_estimators=200,\n",
       "                          random_state=1, subsample=0.9)"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision tree/ Base estimator\n",
    "dt = DecisionTreeRegressor(max_depth=5, random_state=1)\n",
    "\n",
    "# Instantiate Ada\n",
    "ada = AdaBoostRegressor(base_estimator=dt, n_estimators=200, random_state=1)\n",
    "ada.fit(x_train,y_train)\n",
    "# Instantiate GB\n",
    "gb = GradientBoostingRegressor(max_depth=5, \n",
    "            n_estimators=200,\n",
    "            random_state=1)\n",
    "gb.fit(x_train,y_train)\n",
    "\n",
    "# Instantiate SGBR\n",
    "sgb = GradientBoostingRegressor(max_depth=5, \n",
    "            subsample=0.9,\n",
    "            max_features=0.75,\n",
    "            n_estimators=200,                                \n",
    "            random_state=1)\n",
    "sgb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb52d77",
   "metadata": {},
   "source": [
    "### 2. Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "4ac98e1a",
   "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>Training MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "      <th>Train R^2</th>\n",
       "      <th>Test R^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.567357</td>\n",
       "      <td>0.757402</td>\n",
       "      <td>0.937433</td>\n",
       "      <td>0.877546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>0.120621</td>\n",
       "      <td>0.414874</td>\n",
       "      <td>0.997172</td>\n",
       "      <td>0.963259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGB</th>\n",
       "      <td>0.123121</td>\n",
       "      <td>0.406970</td>\n",
       "      <td>0.997054</td>\n",
       "      <td>0.964646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Training MSE  Test MSE  Train R^2  Test R^2\n",
       "Ada      0.567357  0.757402   0.937433  0.877546\n",
       "GB       0.120621  0.414874   0.997172  0.963259\n",
       "SGB      0.123121  0.406970   0.997054  0.964646"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionary of results\n",
    "train_ada = ada.predict(x_train)\n",
    "train_gb = gb.predict(x_train)\n",
    "train_sgb = sgb.predict(x_train)\n",
    "\n",
    "test_ada = ada.predict(x_test)\n",
    "test_gb = gb.predict(x_test)\n",
    "test_sgb = sgb.predict(x_test)\n",
    "\n",
    "results_dict = {'Training MSE':{\n",
    "                        \"Ada\": metrics.mean_squared_error(y_train, train_ada,squared=False),\n",
    "                        \"GB\": metrics.mean_squared_error(y_train, train_gb,squared=False),\n",
    "                        \"SGB\": metrics.mean_squared_error(y_train, train_sgb,squared=False),\n",
    "                },\n",
    "                'Test MSE':{\n",
    "                        \"Ada\": metrics.mean_squared_error(y_test, test_ada,squared=False),\n",
    "                        \"GB\": metrics.mean_squared_error(y_test, test_gb,squared=False),\n",
    "                        \"SGB\": metrics.mean_squared_error(y_test, test_sgb,squared=False)\n",
    "                },\n",
    "                'Train R^2':{\n",
    "                        \"Ada\":metrics.r2_score(y_train, train_ada),\n",
    "                        \"GB\":metrics.r2_score(y_train, train_gb),\n",
    "                        \"SGB\":metrics.r2_score(y_train, train_sgb)\n",
    "                    \n",
    "                },\n",
    "                'Test R^2':{\n",
    "                        \"Ada\":metrics.r2_score(y_test, test_ada),\n",
    "                        \"GB\":metrics.r2_score(y_test, test_gb),\n",
    "                        \"SGB\":metrics.r2_score(y_test, test_sgb)\n",
    "                }\n",
    "                }\n",
    "pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "7c1763c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "f, ax = plt.subplots(figsize=(15,5), nrows=1, ncols=3,sharey=True)\n",
    "\n",
    "# Create list of titles and predictions to use in for loop\n",
    "pred = [test_ada, test_gb, test_sgb]\n",
    "title = ['Ada', 'GB', 'SGB']\n",
    "\n",
    "# Loop through all axes to plot each model's results \n",
    "for i in range(3):\n",
    "    rmse = round(np.sqrt(metrics.mean_squared_error(y_test,pred[i])),2)\n",
    "    ax[i].set_title(title[i]+\"  (RMSE aprox: \"+str(rmse)+ \")\")\n",
    "    ax[i].set_xlabel('Actual')\n",
    "    ax[i].set_ylabel('Predicted')\n",
    "    ax[i].plot(y_test,y_test,'r')\n",
    "    ax[i].scatter(y_test,pred[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c722a044",
   "metadata": {},
   "source": [
    "## 4.3: Simple modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "b44890e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "7606d212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ridge Regression\n",
    "ridge = Ridge()\n",
    "ridge.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "aea6e705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.01)"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lasso Reg\n",
    "lasso = Lasso(alpha=0.01)\n",
    "lasso.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "eec200fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Support Vector model\n",
    "svr = SVR()\n",
    "svr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "7db74dc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gradient Boosting model\n",
    "params = {}\n",
    "gbr=GradientBoostingRegressor()\n",
    "gbr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "74d72301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=3)"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KNN model\n",
    "knn= KNeighborsRegressor(n_neighbors = 3)\n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "c5803322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "decision_tree = DecisionTreeRegressor()\n",
    "decision_tree.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "34423efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "random_forest = RandomForestRegressor(n_estimators=100)\n",
    "random_forest.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "42eb2dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compare models\n",
    "\n",
    "# Make training set predictions for each model\n",
    "train_lin = linreg.predict(x_train)\n",
    "train_ridge = ridge.predict(x_train)\n",
    "train_lasso = lasso.predict(x_train)\n",
    "train_Knn = knn.predict(x_train)\n",
    "train_svr = svr.predict(x_train)\n",
    "train_gbr = gbr.predict(x_train)\n",
    "train_DcT = decision_tree.predict(x_train)\n",
    "train_RnF = random_forest.predict(x_train)\n",
    "\n",
    "# Make test set predictions for each model\n",
    "test_lin = linreg.predict(x_test)\n",
    "test_ridge = ridge.predict(x_test)\n",
    "test_lasso = lasso.predict(x_test)\n",
    "test_Knn = knn.predict(x_test)\n",
    "test_svr = svr.predict(x_test)\n",
    "test_gbr = gbr.predict(x_test)\n",
    "test_DcT = decision_tree.predict(x_test)\n",
    "test_RnF = random_forest.predict(x_test)\n",
    "\n",
    "# Dictionary of results\n",
    "results_dict = {'Training MSE':{\n",
    "                        \"Least Squares\":metrics.mean_squared_error(y_train, train_lin,squared=False),\n",
    "                        \"Ridge\": metrics.mean_squared_error(y_train, train_ridge,squared=False),\n",
    "                        \"LASSO\": metrics.mean_squared_error(y_train, train_lasso,squared=False),\n",
    "                        \"SVR\": metrics.mean_squared_error(y_train, train_svr,squared=False),\n",
    "                        \"GBR\": metrics.mean_squared_error(y_train, train_gbr,squared=False),\n",
    "                        \"KNN\": metrics.mean_squared_error(y_train, train_Knn,squared=False),\n",
    "                        \"Decision Tree\": metrics.mean_squared_error(y_train, train_DcT,squared=False),\n",
    "                        \"Random Forest\": metrics.mean_squared_error(y_train, train_RnF,squared=False)\n",
    "                },\n",
    "                'Test MSE':{\n",
    "                        \"Least Squares\": metrics.mean_squared_error(y_test, test_lin,squared=False),\n",
    "                        \"Ridge\": metrics.mean_squared_error(y_test, test_ridge,squared=False),\n",
    "                        \"LASSO\": metrics.mean_squared_error(y_test, test_lasso,squared=False),\n",
    "                        \"SVR\": metrics.mean_squared_error(y_test, test_svr,squared=False),\n",
    "                        \"GBR\": metrics.mean_squared_error(y_test, test_gbr,squared=False),\n",
    "                        \"KNN\": metrics.mean_squared_error(y_test, test_Knn,squared=False),\n",
    "                        \"Decision Tree\": metrics.mean_squared_error(y_test, test_DcT,squared=False),\n",
    "                        \"Random Forest\": metrics.mean_squared_error(y_test, test_RnF,squared=False)\n",
    "                }\n",
    "                }\n",
    "results=pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "0051057a",
   "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>Training MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Least Squares</th>\n",
       "      <td>1.814342e+00</td>\n",
       "      <td>1.665826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>1.814342e+00</td>\n",
       "      <td>1.665822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASSO</th>\n",
       "      <td>1.814354e+00</td>\n",
       "      <td>1.664986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR</th>\n",
       "      <td>2.052712e+00</td>\n",
       "      <td>1.931900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBR</th>\n",
       "      <td>4.634587e-01</td>\n",
       "      <td>0.522869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>6.782407e-01</td>\n",
       "      <td>0.891125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>9.205880e-16</td>\n",
       "      <td>0.879725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>2.467146e-01</td>\n",
       "      <td>0.466119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Training MSE  Test MSE\n",
       "Least Squares  1.814342e+00  1.665826\n",
       "Ridge          1.814342e+00  1.665822\n",
       "LASSO          1.814354e+00  1.664986\n",
       "SVR            2.052712e+00  1.931900\n",
       "GBR            4.634587e-01  0.522869\n",
       "KNN            6.782407e-01  0.891125\n",
       "Decision Tree  9.205880e-16  0.879725\n",
       "Random Forest  2.467146e-01  0.466119"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d595b416",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create submission dataframe\n",
    "submission = pd.DataFrame(\n",
    "    {'Index': test_id,\n",
    "     'avg_price_per_kg': gb.predict(test)\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "ffe6cd49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save DataFrame to csv file for submission\n",
    "submission.to_csv(\"Answergb.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10a262e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0549a40",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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