multi_product_forecast.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Product Sales Forecast\n",
    "\n",
    "Sales forecasting is key not only to the sales department but to the whole organization. A good forecast will allow to align resources and focus on the right objectives. But above anything, an acurate forecast will get the company closer to business goals and provide stability to the organization.\n",
    "\n",
    "Traditionally, sales forecasting is based on personal judging with very little, or no support at all, of any other tools. Data Science can help make better forecasts, especially in the context of multi-product forecasts.\n",
    "\n",
    "In this project we explore the possibility of building a single model to get sales predictions for multiple products. Instead of fitting one model per product, we will explore if fitting one model for all products provides a good enough prediction for all products. In Machine Learning, more data is always better. The main benefit of this approach is that we will have more training data.\n",
    "\n",
    "Dataset source: http://archive.ics.uci.edu/ml/datasets/Sales_Transactions_Dataset_Weekly\n",
    "\n",
    "The dataset is taken from the UCI repository and contains weekly sales of 800+ products. Data is provided both normalised and non-normalised. The data is organised per product code. Each row contains sold quantities over 52 weeks for one product. Find below the list of attributes:\n",
    "* Product_Code\n",
    "* Weekly sales per product (52 weeks): W0, W1, .... W51\n",
    "* Normalised sales data: Normalized 0, Normalized 1, ... Normalized 51\n",
    "* MIN: minimum quantity sold per product\n",
    "* MAX: maximum quantity sold per product\n",
    "\n",
    "In this project, we learnt that using all time series to train a single model allow for capturing more patterns that repeat across multiple series. These patterns cannot be learnt by fitting one model per serries since, for some series, there are very little data. With the single-model approach, the common patterns are leveraged to make predictions for any product. The product is an input variable to oor model.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Library Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import Lasso, Ridge\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.exceptions import ConvergenceWarning, UserWarning\n",
    "import warnings\n",
    "\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 120)\n",
    "warnings.simplefilter(action='ignore', category=ConvergenceWarning)\n",
    "warnings.simplefilter(action='ignore', category=UserWarning, append=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Code</th>\n",
       "      <th>W0</th>\n",
       "      <th>W1</th>\n",
       "      <th>W2</th>\n",
       "      <th>W3</th>\n",
       "      <th>W4</th>\n",
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       "      <th>W9</th>\n",
       "      <th>W10</th>\n",
       "      <th>W11</th>\n",
       "      <th>W12</th>\n",
       "      <th>W13</th>\n",
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       "      <th>W16</th>\n",
       "      <th>W17</th>\n",
       "      <th>W18</th>\n",
       "      <th>W19</th>\n",
       "      <th>W20</th>\n",
       "      <th>W21</th>\n",
       "      <th>W22</th>\n",
       "      <th>W23</th>\n",
       "      <th>W24</th>\n",
       "      <th>W25</th>\n",
       "      <th>W26</th>\n",
       "      <th>W27</th>\n",
       "      <th>W28</th>\n",
       "      <th>W29</th>\n",
       "      <th>W30</th>\n",
       "      <th>W31</th>\n",
       "      <th>W32</th>\n",
       "      <th>W33</th>\n",
       "      <th>W34</th>\n",
       "      <th>W35</th>\n",
       "      <th>W36</th>\n",
       "      <th>W37</th>\n",
       "      <th>W38</th>\n",
       "      <th>W39</th>\n",
       "      <th>W40</th>\n",
       "      <th>W41</th>\n",
       "      <th>W42</th>\n",
       "      <th>W43</th>\n",
       "      <th>W44</th>\n",
       "      <th>W45</th>\n",
       "      <th>W46</th>\n",
       "      <th>W47</th>\n",
       "      <th>W48</th>\n",
       "      <th>W49</th>\n",
       "      <th>W50</th>\n",
       "      <th>W51</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>Normalized 0</th>\n",
       "      <th>Normalized 1</th>\n",
       "      <th>Normalized 2</th>\n",
       "      <th>Normalized 3</th>\n",
       "      <th>Normalized 4</th>\n",
       "      <th>Normalized 5</th>\n",
       "      <th>Normalized 6</th>\n",
       "      <th>Normalized 7</th>\n",
       "      <th>Normalized 8</th>\n",
       "      <th>Normalized 9</th>\n",
       "      <th>Normalized 10</th>\n",
       "      <th>Normalized 11</th>\n",
       "      <th>Normalized 12</th>\n",
       "      <th>Normalized 13</th>\n",
       "      <th>Normalized 14</th>\n",
       "      <th>Normalized 15</th>\n",
       "      <th>Normalized 16</th>\n",
       "      <th>Normalized 17</th>\n",
       "      <th>Normalized 18</th>\n",
       "      <th>Normalized 19</th>\n",
       "      <th>Normalized 20</th>\n",
       "      <th>Normalized 21</th>\n",
       "      <th>Normalized 22</th>\n",
       "      <th>Normalized 23</th>\n",
       "      <th>Normalized 24</th>\n",
       "      <th>Normalized 25</th>\n",
       "      <th>Normalized 26</th>\n",
       "      <th>Normalized 27</th>\n",
       "      <th>Normalized 28</th>\n",
       "      <th>Normalized 29</th>\n",
       "      <th>Normalized 30</th>\n",
       "      <th>Normalized 31</th>\n",
       "      <th>Normalized 32</th>\n",
       "      <th>Normalized 33</th>\n",
       "      <th>Normalized 34</th>\n",
       "      <th>Normalized 35</th>\n",
       "      <th>Normalized 36</th>\n",
       "      <th>Normalized 37</th>\n",
       "      <th>Normalized 38</th>\n",
       "      <th>Normalized 39</th>\n",
       "      <th>Normalized 40</th>\n",
       "      <th>Normalized 41</th>\n",
       "      <th>Normalized 42</th>\n",
       "      <th>Normalized 43</th>\n",
       "      <th>Normalized 44</th>\n",
       "      <th>Normalized 45</th>\n",
       "      <th>Normalized 46</th>\n",
       "      <th>Normalized 47</th>\n",
       "      <th>Normalized 48</th>\n",
       "      <th>Normalized 49</th>\n",
       "      <th>Normalized 50</th>\n",
       "      <th>Normalized 51</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P1</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P3</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P4</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Code  W0  W1  W2  W3  W4  W5  W6  W7  W8  W9  W10  W11  W12  W13  \\\n",
       "0           P1  11  12  10   8  13  12  14  21   6  14   11   14   16    9   \n",
       "1           P2   7   6   3   2   7   1   6   3   3   3    2    2    6    2   \n",
       "2           P3   7  11   8   9  10   8   7  13  12   6   14    9    4    7   \n",
       "3           P4  12   8  13   5   9   6   9  13  13  11    8    4    5    4   \n",
       "4           P5   8   5  13  11   6   7   9  14   9   9   11   18    8    4   \n",
       "\n",
       "   W14  W15  W16  W17  W18  W19  W20  W21  W22  W23  W24  W25  W26  W27  W28  \\\n",
       "0    9    9   14    9    3   12    5   11    7   12    5    9    7   10    5   \n",
       "1    0    6    2    7    7    9    4    7    2    4    5    3    5    8    5   \n",
       "2   12    8    7   11   10    7    7   13   11    8   10    8   14    5    3   \n",
       "3   15    7   11    9   15    4    6    7   11    7    9    6   10   10    2   \n",
       "4   13    8   10   15    6   13   11    6   10    9    8   12    8    9   13   \n",
       "\n",
       "   W29  W30  W31  W32  W33  W34  W35  W36  W37  W38  W39  W40  W41  W42  W43  \\\n",
       "0   11    7   10   12    6    5   14   10    9   12   17    7   11    4    7   \n",
       "1    5    3    1    3    2    3   10    5    2    7    3    2    5    2    4   \n",
       "2   13   11    9    7    8    7    9    6   12   12    9    3    5    6   14   \n",
       "3    6    7    2    5   12    5   19    8    6    8    8   12    6    9   10   \n",
       "4    3    5    3    5    5    9    7    4    8    8    5    5    8    7   11   \n",
       "\n",
       "   W44  W45  W46  W47  W48  W49  W50  W51  MIN  MAX  Normalized 0  \\\n",
       "0    8   10   12    3    7    6    5   10    3   21          0.44   \n",
       "1    5    1    1    4    5    1    6    0    0   10          0.70   \n",
       "2    5    5    7    8   14    8    8    7    3   14          0.36   \n",
       "3    3    4    6    8   14    8    7    8    2   19          0.59   \n",
       "4    7   12    6    6    5   11    8    9    3   18          0.33   \n",
       "\n",
       "   Normalized 1  Normalized 2  Normalized 3  Normalized 4  Normalized 5  \\\n",
       "0          0.50          0.39          0.28          0.56          0.50   \n",
       "1          0.60          0.30          0.20          0.70          0.10   \n",
       "2          0.73          0.45          0.55          0.64          0.45   \n",
       "3          0.35          0.65          0.18          0.41          0.24   \n",
       "4          0.13          0.67          0.53          0.20          0.27   \n",
       "\n",
       "   Normalized 6  Normalized 7  Normalized 8  Normalized 9  Normalized 10  \\\n",
       "0          0.61          1.00          0.17          0.61           0.44   \n",
       "1          0.60          0.30          0.30          0.30           0.20   \n",
       "2          0.36          0.91          0.82          0.27           1.00   \n",
       "3          0.41          0.65          0.65          0.53           0.35   \n",
       "4          0.40          0.73          0.40          0.40           0.53   \n",
       "\n",
       "   Normalized 11  Normalized 12  Normalized 13  Normalized 14  Normalized 15  \\\n",
       "0           0.61           0.72           0.33           0.33           0.33   \n",
       "1           0.20           0.60           0.20           0.00           0.60   \n",
       "2           0.55           0.09           0.36           0.82           0.45   \n",
       "3           0.12           0.18           0.12           0.76           0.29   \n",
       "4           1.00           0.33           0.07           0.67           0.33   \n",
       "\n",
       "   Normalized 16  Normalized 17  Normalized 18  Normalized 19  Normalized 20  \\\n",
       "0           0.61           0.33           0.00           0.50           0.11   \n",
       "1           0.20           0.70           0.70           0.90           0.40   \n",
       "2           0.36           0.73           0.64           0.36           0.36   \n",
       "3           0.53           0.41           0.76           0.12           0.24   \n",
       "4           0.47           0.80           0.20           0.67           0.53   \n",
       "\n",
       "   Normalized 21  Normalized 22  Normalized 23  Normalized 24  Normalized 25  \\\n",
       "0           0.44           0.22           0.50           0.11           0.33   \n",
       "1           0.70           0.20           0.40           0.50           0.30   \n",
       "2           0.91           0.73           0.45           0.64           0.45   \n",
       "3           0.29           0.53           0.29           0.41           0.24   \n",
       "4           0.20           0.47           0.40           0.33           0.60   \n",
       "\n",
       "   Normalized 26  Normalized 27  Normalized 28  Normalized 29  Normalized 30  \\\n",
       "0           0.22           0.39           0.11           0.44           0.22   \n",
       "1           0.50           0.80           0.50           0.50           0.30   \n",
       "2           1.00           0.18           0.00           0.91           0.73   \n",
       "3           0.47           0.47           0.00           0.24           0.29   \n",
       "4           0.33           0.40           0.67           0.00           0.13   \n",
       "\n",
       "   Normalized 31  Normalized 32  Normalized 33  Normalized 34  Normalized 35  \\\n",
       "0           0.39           0.50           0.17           0.11           0.61   \n",
       "1           0.10           0.30           0.20           0.30           1.00   \n",
       "2           0.55           0.36           0.45           0.36           0.55   \n",
       "3           0.00           0.18           0.59           0.18           1.00   \n",
       "4           0.00           0.13           0.13           0.40           0.27   \n",
       "\n",
       "   Normalized 36  Normalized 37  Normalized 38  Normalized 39  Normalized 40  \\\n",
       "0           0.39           0.33           0.50           0.78           0.22   \n",
       "1           0.50           0.20           0.70           0.30           0.20   \n",
       "2           0.27           0.82           0.82           0.55           0.00   \n",
       "3           0.35           0.24           0.35           0.35           0.59   \n",
       "4           0.07           0.33           0.33           0.13           0.13   \n",
       "\n",
       "   Normalized 41  Normalized 42  Normalized 43  Normalized 44  Normalized 45  \\\n",
       "0           0.44           0.06           0.22           0.28           0.39   \n",
       "1           0.50           0.20           0.40           0.50           0.10   \n",
       "2           0.18           0.27           1.00           0.18           0.18   \n",
       "3           0.24           0.41           0.47           0.06           0.12   \n",
       "4           0.33           0.27           0.53           0.27           0.60   \n",
       "\n",
       "   Normalized 46  Normalized 47  Normalized 48  Normalized 49  Normalized 50  \\\n",
       "0           0.50           0.00           0.22           0.17           0.11   \n",
       "1           0.10           0.40           0.50           0.10           0.60   \n",
       "2           0.36           0.45           1.00           0.45           0.45   \n",
       "3           0.24           0.35           0.71           0.35           0.29   \n",
       "4           0.20           0.20           0.13           0.53           0.33   \n",
       "\n",
       "   Normalized 51  \n",
       "0           0.39  \n",
       "1           0.00  \n",
       "2           0.36  \n",
       "3           0.35  \n",
       "4           0.40  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/raw/Sales_Transactions_Dataset_Weekly.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Code</th>\n",
       "      <th>W0</th>\n",
       "      <th>W1</th>\n",
       "      <th>W2</th>\n",
       "      <th>W3</th>\n",
       "      <th>W4</th>\n",
       "      <th>W5</th>\n",
       "      <th>W6</th>\n",
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       "      <th>W10</th>\n",
       "      <th>W11</th>\n",
       "      <th>W12</th>\n",
       "      <th>W13</th>\n",
       "      <th>W14</th>\n",
       "      <th>W15</th>\n",
       "      <th>W16</th>\n",
       "      <th>W17</th>\n",
       "      <th>W18</th>\n",
       "      <th>W19</th>\n",
       "      <th>W20</th>\n",
       "      <th>W21</th>\n",
       "      <th>W22</th>\n",
       "      <th>W23</th>\n",
       "      <th>W24</th>\n",
       "      <th>W25</th>\n",
       "      <th>W26</th>\n",
       "      <th>W27</th>\n",
       "      <th>W28</th>\n",
       "      <th>W29</th>\n",
       "      <th>W30</th>\n",
       "      <th>W31</th>\n",
       "      <th>W32</th>\n",
       "      <th>W33</th>\n",
       "      <th>W34</th>\n",
       "      <th>W35</th>\n",
       "      <th>W36</th>\n",
       "      <th>W37</th>\n",
       "      <th>W38</th>\n",
       "      <th>W39</th>\n",
       "      <th>W40</th>\n",
       "      <th>W41</th>\n",
       "      <th>W42</th>\n",
       "      <th>W43</th>\n",
       "      <th>W44</th>\n",
       "      <th>W45</th>\n",
       "      <th>W46</th>\n",
       "      <th>W47</th>\n",
       "      <th>W48</th>\n",
       "      <th>W49</th>\n",
       "      <th>W50</th>\n",
       "      <th>W51</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>Normalized 0</th>\n",
       "      <th>Normalized 1</th>\n",
       "      <th>Normalized 2</th>\n",
       "      <th>Normalized 3</th>\n",
       "      <th>Normalized 4</th>\n",
       "      <th>Normalized 5</th>\n",
       "      <th>Normalized 6</th>\n",
       "      <th>Normalized 7</th>\n",
       "      <th>Normalized 8</th>\n",
       "      <th>Normalized 9</th>\n",
       "      <th>Normalized 10</th>\n",
       "      <th>Normalized 11</th>\n",
       "      <th>Normalized 12</th>\n",
       "      <th>Normalized 13</th>\n",
       "      <th>Normalized 14</th>\n",
       "      <th>Normalized 15</th>\n",
       "      <th>Normalized 16</th>\n",
       "      <th>Normalized 17</th>\n",
       "      <th>Normalized 18</th>\n",
       "      <th>Normalized 19</th>\n",
       "      <th>Normalized 20</th>\n",
       "      <th>Normalized 21</th>\n",
       "      <th>Normalized 22</th>\n",
       "      <th>Normalized 23</th>\n",
       "      <th>Normalized 24</th>\n",
       "      <th>Normalized 25</th>\n",
       "      <th>Normalized 26</th>\n",
       "      <th>Normalized 27</th>\n",
       "      <th>Normalized 28</th>\n",
       "      <th>Normalized 29</th>\n",
       "      <th>Normalized 30</th>\n",
       "      <th>Normalized 31</th>\n",
       "      <th>Normalized 32</th>\n",
       "      <th>Normalized 33</th>\n",
       "      <th>Normalized 34</th>\n",
       "      <th>Normalized 35</th>\n",
       "      <th>Normalized 36</th>\n",
       "      <th>Normalized 37</th>\n",
       "      <th>Normalized 38</th>\n",
       "      <th>Normalized 39</th>\n",
       "      <th>Normalized 40</th>\n",
       "      <th>Normalized 41</th>\n",
       "      <th>Normalized 42</th>\n",
       "      <th>Normalized 43</th>\n",
       "      <th>Normalized 44</th>\n",
       "      <th>Normalized 45</th>\n",
       "      <th>Normalized 46</th>\n",
       "      <th>Normalized 47</th>\n",
       "      <th>Normalized 48</th>\n",
       "      <th>Normalized 49</th>\n",
       "      <th>Normalized 50</th>\n",
       "      <th>Normalized 51</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>P815</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>P816</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>P817</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>P818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>P819</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Product_Code  W0  W1  W2  W3  W4  W5  W6  W7  W8  W9  W10  W11  W12  W13  \\\n",
       "806         P815   0   0   1   0   0   2   1   0   0   1    0    0    1    0   \n",
       "807         P816   0   1   0   0   1   2   2   6   0   1    0    2    2    0   \n",
       "808         P817   1   0   0   0   1   1   2   1   1   0    0    1    0    0   \n",
       "809         P818   0   0   0   1   0   0   0   0   1   0    0    0    0    0   \n",
       "810         P819   0   1   0   0   0   0   0   0   0   0    0    0    0    0   \n",
       "\n",
       "     W14  W15  W16  W17  W18  W19  W20  W21  W22  W23  W24  W25  W26  W27  \\\n",
       "806    1    1    3    0    0    0    0    0    0    1    0    0    0    0   \n",
       "807    5    1    1    1    2    3    1    0    2    1    7    3    3    3   \n",
       "808    0    0    0    0    0    0    1    0    0    0    1    0    0    1   \n",
       "809    0    0    0    0    0    1    0    0    1    0    2    1    1    0   \n",
       "810    0    0    0    0    3    0    0    0    2    0    0    0    1    0   \n",
       "\n",
       "     W28  W29  W30  W31  W32  W33  W34  W35  W36  W37  W38  W39  W40  W41  \\\n",
       "806    2    0    0    0    0    0    0    0    0    0    1    2    1    0   \n",
       "807    2    2    3    1    1    4    5    4    3    2    4    6    6    7   \n",
       "808    0    0    0    0    0    1    0    0    0    0    0    0    0    1   \n",
       "809    1    1    0    1    1    1    0    0    0    0    0    0    0    0   \n",
       "810    0    0    0    0    1    0    0    3    1    1    1    0    1    0   \n",
       "\n",
       "     W42  W43  W44  W45  W46  W47  W48  W49  W50  W51  MIN  MAX  Normalized 0  \\\n",
       "806    0    1    1    0    0    1    0    0    2    0    0    3          0.00   \n",
       "807    3    3    4    2    4    5    5    5    6    5    0    7          0.00   \n",
       "808    2    0    0    2    2    0    0    0    4    3    0    4          0.25   \n",
       "809    0    0    0    1    1    0    0    0    2    0    0    2          0.00   \n",
       "810    0    0    0    0    0    0    0    0    0    1    0    3          0.00   \n",
       "\n",
       "     Normalized 1  Normalized 2  Normalized 3  Normalized 4  Normalized 5  \\\n",
       "806          0.00          0.33           0.0          0.00          0.67   \n",
       "807          0.14          0.00           0.0          0.14          0.29   \n",
       "808          0.00          0.00           0.0          0.25          0.25   \n",
       "809          0.00          0.00           0.5          0.00          0.00   \n",
       "810          0.33          0.00           0.0          0.00          0.00   \n",
       "\n",
       "     Normalized 6  Normalized 7  Normalized 8  Normalized 9  Normalized 10  \\\n",
       "806          0.33          0.00          0.00          0.33            0.0   \n",
       "807          0.29          0.86          0.00          0.14            0.0   \n",
       "808          0.50          0.25          0.25          0.00            0.0   \n",
       "809          0.00          0.00          0.50          0.00            0.0   \n",
       "810          0.00          0.00          0.00          0.00            0.0   \n",
       "\n",
       "     Normalized 11  Normalized 12  Normalized 13  Normalized 14  \\\n",
       "806           0.00           0.33            0.0           0.33   \n",
       "807           0.29           0.29            0.0           0.71   \n",
       "808           0.25           0.00            0.0           0.00   \n",
       "809           0.00           0.00            0.0           0.00   \n",
       "810           0.00           0.00            0.0           0.00   \n",
       "\n",
       "     Normalized 15  Normalized 16  Normalized 17  Normalized 18  \\\n",
       "806           0.33           1.00           0.00           0.00   \n",
       "807           0.14           0.14           0.14           0.29   \n",
       "808           0.00           0.00           0.00           0.00   \n",
       "809           0.00           0.00           0.00           0.00   \n",
       "810           0.00           0.00           0.00           1.00   \n",
       "\n",
       "     Normalized 19  Normalized 20  Normalized 21  Normalized 22  \\\n",
       "806           0.00           0.00            0.0           0.00   \n",
       "807           0.43           0.14            0.0           0.29   \n",
       "808           0.00           0.25            0.0           0.00   \n",
       "809           0.50           0.00            0.0           0.50   \n",
       "810           0.00           0.00            0.0           0.67   \n",
       "\n",
       "     Normalized 23  Normalized 24  Normalized 25  Normalized 26  \\\n",
       "806           0.33           0.00           0.00           0.00   \n",
       "807           0.14           1.00           0.43           0.43   \n",
       "808           0.00           0.25           0.00           0.00   \n",
       "809           0.00           1.00           0.50           0.50   \n",
       "810           0.00           0.00           0.00           0.33   \n",
       "\n",
       "     Normalized 27  Normalized 28  Normalized 29  Normalized 30  \\\n",
       "806           0.00           0.67           0.00           0.00   \n",
       "807           0.43           0.29           0.29           0.43   \n",
       "808           0.25           0.00           0.00           0.00   \n",
       "809           0.00           0.50           0.50           0.00   \n",
       "810           0.00           0.00           0.00           0.00   \n",
       "\n",
       "     Normalized 31  Normalized 32  Normalized 33  Normalized 34  \\\n",
       "806           0.00           0.00           0.00           0.00   \n",
       "807           0.14           0.14           0.57           0.71   \n",
       "808           0.00           0.00           0.25           0.00   \n",
       "809           0.50           0.50           0.50           0.00   \n",
       "810           0.00           0.33           0.00           0.00   \n",
       "\n",
       "     Normalized 35  Normalized 36  Normalized 37  Normalized 38  \\\n",
       "806           0.00           0.00           0.00           0.33   \n",
       "807           0.57           0.43           0.29           0.57   \n",
       "808           0.00           0.00           0.00           0.00   \n",
       "809           0.00           0.00           0.00           0.00   \n",
       "810           1.00           0.33           0.33           0.33   \n",
       "\n",
       "     Normalized 39  Normalized 40  Normalized 41  Normalized 42  \\\n",
       "806           0.67           0.33           0.00           0.00   \n",
       "807           0.86           0.86           1.00           0.43   \n",
       "808           0.00           0.00           0.25           0.50   \n",
       "809           0.00           0.00           0.00           0.00   \n",
       "810           0.00           0.33           0.00           0.00   \n",
       "\n",
       "     Normalized 43  Normalized 44  Normalized 45  Normalized 46  \\\n",
       "806           0.33           0.33           0.00           0.00   \n",
       "807           0.43           0.57           0.29           0.57   \n",
       "808           0.00           0.00           0.50           0.50   \n",
       "809           0.00           0.00           0.50           0.50   \n",
       "810           0.00           0.00           0.00           0.00   \n",
       "\n",
       "     Normalized 47  Normalized 48  Normalized 49  Normalized 50  Normalized 51  \n",
       "806           0.33           0.00           0.00           0.67           0.00  \n",
       "807           0.71           0.71           0.71           0.86           0.71  \n",
       "808           0.00           0.00           0.00           1.00           0.75  \n",
       "809           0.00           0.00           0.00           1.00           0.00  \n",
       "810           0.00           0.00           0.00           0.00           0.33  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(811, 107)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Code      object\n",
       "W0                 int64\n",
       "W1                 int64\n",
       "W2                 int64\n",
       "W3                 int64\n",
       "                  ...   \n",
       "Normalized 47    float64\n",
       "Normalized 48    float64\n",
       "Normalized 49    float64\n",
       "Normalized 50    float64\n",
       "Normalized 51    float64\n",
       "Length: 107, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We have 811 rows (products) and 107 attributes. Product_Code is the product identifier and is a string. The rest of the features are numerical. At first sight, data seem to be correct. There are a lot of products with no sales information (value 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1800 with 110 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(30,25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Low quantities are the dominant value across all weeks. Let's check summary statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>W0</th>\n",
       "      <th>W1</th>\n",
       "      <th>W2</th>\n",
       "      <th>W3</th>\n",
       "      <th>W4</th>\n",
       "      <th>W5</th>\n",
       "      <th>W6</th>\n",
       "      <th>W7</th>\n",
       "      <th>W8</th>\n",
       "      <th>W9</th>\n",
       "      <th>W10</th>\n",
       "      <th>W11</th>\n",
       "      <th>W12</th>\n",
       "      <th>W13</th>\n",
       "      <th>W14</th>\n",
       "      <th>W15</th>\n",
       "      <th>W16</th>\n",
       "      <th>W17</th>\n",
       "      <th>W18</th>\n",
       "      <th>W19</th>\n",
       "      <th>W20</th>\n",
       "      <th>W21</th>\n",
       "      <th>W22</th>\n",
       "      <th>W23</th>\n",
       "      <th>W24</th>\n",
       "      <th>W25</th>\n",
       "      <th>W26</th>\n",
       "      <th>W27</th>\n",
       "      <th>W28</th>\n",
       "      <th>W29</th>\n",
       "      <th>W30</th>\n",
       "      <th>W31</th>\n",
       "      <th>W32</th>\n",
       "      <th>W33</th>\n",
       "      <th>W34</th>\n",
       "      <th>W35</th>\n",
       "      <th>W36</th>\n",
       "      <th>W37</th>\n",
       "      <th>W38</th>\n",
       "      <th>W39</th>\n",
       "      <th>W40</th>\n",
       "      <th>W41</th>\n",
       "      <th>W42</th>\n",
       "      <th>W43</th>\n",
       "      <th>W44</th>\n",
       "      <th>W45</th>\n",
       "      <th>W46</th>\n",
       "      <th>W47</th>\n",
       "      <th>W48</th>\n",
       "      <th>W49</th>\n",
       "      <th>W50</th>\n",
       "      <th>W51</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>Normalized 0</th>\n",
       "      <th>Normalized 1</th>\n",
       "      <th>Normalized 2</th>\n",
       "      <th>Normalized 3</th>\n",
       "      <th>Normalized 4</th>\n",
       "      <th>Normalized 5</th>\n",
       "      <th>Normalized 6</th>\n",
       "      <th>Normalized 7</th>\n",
       "      <th>Normalized 8</th>\n",
       "      <th>Normalized 9</th>\n",
       "      <th>Normalized 10</th>\n",
       "      <th>Normalized 11</th>\n",
       "      <th>Normalized 12</th>\n",
       "      <th>Normalized 13</th>\n",
       "      <th>Normalized 14</th>\n",
       "      <th>Normalized 15</th>\n",
       "      <th>Normalized 16</th>\n",
       "      <th>Normalized 17</th>\n",
       "      <th>Normalized 18</th>\n",
       "      <th>Normalized 19</th>\n",
       "      <th>Normalized 20</th>\n",
       "      <th>Normalized 21</th>\n",
       "      <th>Normalized 22</th>\n",
       "      <th>Normalized 23</th>\n",
       "      <th>Normalized 24</th>\n",
       "      <th>Normalized 25</th>\n",
       "      <th>Normalized 26</th>\n",
       "      <th>Normalized 27</th>\n",
       "      <th>Normalized 28</th>\n",
       "      <th>Normalized 29</th>\n",
       "      <th>Normalized 30</th>\n",
       "      <th>Normalized 31</th>\n",
       "      <th>Normalized 32</th>\n",
       "      <th>Normalized 33</th>\n",
       "      <th>Normalized 34</th>\n",
       "      <th>Normalized 35</th>\n",
       "      <th>Normalized 36</th>\n",
       "      <th>Normalized 37</th>\n",
       "      <th>Normalized 38</th>\n",
       "      <th>Normalized 39</th>\n",
       "      <th>Normalized 40</th>\n",
       "      <th>Normalized 41</th>\n",
       "      <th>Normalized 42</th>\n",
       "      <th>Normalized 43</th>\n",
       "      <th>Normalized 44</th>\n",
       "      <th>Normalized 45</th>\n",
       "      <th>Normalized 46</th>\n",
       "      <th>Normalized 47</th>\n",
       "      <th>Normalized 48</th>\n",
       "      <th>Normalized 49</th>\n",
       "      <th>Normalized 50</th>\n",
       "      <th>Normalized 51</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.00000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.00000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "      <td>811.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.902589</td>\n",
       "      <td>9.129470</td>\n",
       "      <td>9.389642</td>\n",
       "      <td>9.717633</td>\n",
       "      <td>9.574599</td>\n",
       "      <td>9.466091</td>\n",
       "      <td>9.720099</td>\n",
       "      <td>9.585697</td>\n",
       "      <td>9.784217</td>\n",
       "      <td>9.681874</td>\n",
       "      <td>9.790382</td>\n",
       "      <td>9.678175</td>\n",
       "      <td>9.827374</td>\n",
       "      <td>9.686806</td>\n",
       "      <td>9.907522</td>\n",
       "      <td>10.045623</td>\n",
       "      <td>10.033292</td>\n",
       "      <td>9.905055</td>\n",
       "      <td>10.007398</td>\n",
       "      <td>9.644883</td>\n",
       "      <td>9.849568</td>\n",
       "      <td>9.710234</td>\n",
       "      <td>9.902589</td>\n",
       "      <td>9.861899</td>\n",
       "      <td>10.166461</td>\n",
       "      <td>8.892725</td>\n",
       "      <td>6.950678</td>\n",
       "      <td>7.193588</td>\n",
       "      <td>7.383477</td>\n",
       "      <td>7.339088</td>\n",
       "      <td>7.607891</td>\n",
       "      <td>7.610358</td>\n",
       "      <td>7.759556</td>\n",
       "      <td>7.906289</td>\n",
       "      <td>7.992602</td>\n",
       "      <td>7.997534</td>\n",
       "      <td>8.014797</td>\n",
       "      <td>8.073983</td>\n",
       "      <td>8.251541</td>\n",
       "      <td>7.965475</td>\n",
       "      <td>8.182491</td>\n",
       "      <td>8.240444</td>\n",
       "      <td>8.394575</td>\n",
       "      <td>8.318126</td>\n",
       "      <td>8.434032</td>\n",
       "      <td>8.556104</td>\n",
       "      <td>8.720099</td>\n",
       "      <td>8.670777</td>\n",
       "      <td>8.674476</td>\n",
       "      <td>8.895191</td>\n",
       "      <td>8.861899</td>\n",
       "      <td>8.889026</td>\n",
       "      <td>3.780518</td>\n",
       "      <td>16.308261</td>\n",
       "      <td>0.289396</td>\n",
       "      <td>0.299100</td>\n",
       "      <td>0.306732</td>\n",
       "      <td>0.319852</td>\n",
       "      <td>0.326905</td>\n",
       "      <td>0.319420</td>\n",
       "      <td>0.332848</td>\n",
       "      <td>0.326572</td>\n",
       "      <td>0.32434</td>\n",
       "      <td>0.326843</td>\n",
       "      <td>0.331134</td>\n",
       "      <td>0.331455</td>\n",
       "      <td>0.338878</td>\n",
       "      <td>0.331591</td>\n",
       "      <td>0.355771</td>\n",
       "      <td>0.344821</td>\n",
       "      <td>0.335919</td>\n",
       "      <td>0.345253</td>\n",
       "      <td>0.354994</td>\n",
       "      <td>0.330210</td>\n",
       "      <td>0.340271</td>\n",
       "      <td>0.353551</td>\n",
       "      <td>0.366017</td>\n",
       "      <td>0.370629</td>\n",
       "      <td>0.417694</td>\n",
       "      <td>0.389618</td>\n",
       "      <td>0.207596</td>\n",
       "      <td>0.223046</td>\n",
       "      <td>0.229605</td>\n",
       "      <td>0.224365</td>\n",
       "      <td>0.240493</td>\n",
       "      <td>0.250395</td>\n",
       "      <td>0.248323</td>\n",
       "      <td>0.259371</td>\n",
       "      <td>0.262256</td>\n",
       "      <td>0.268903</td>\n",
       "      <td>0.270173</td>\n",
       "      <td>0.273144</td>\n",
       "      <td>0.277497</td>\n",
       "      <td>0.264994</td>\n",
       "      <td>0.286375</td>\n",
       "      <td>0.279396</td>\n",
       "      <td>0.299149</td>\n",
       "      <td>0.287571</td>\n",
       "      <td>0.304846</td>\n",
       "      <td>0.316017</td>\n",
       "      <td>0.334760</td>\n",
       "      <td>0.314636</td>\n",
       "      <td>0.33815</td>\n",
       "      <td>0.358903</td>\n",
       "      <td>0.373009</td>\n",
       "      <td>0.427941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.067163</td>\n",
       "      <td>12.564766</td>\n",
       "      <td>13.045073</td>\n",
       "      <td>13.553294</td>\n",
       "      <td>13.095765</td>\n",
       "      <td>12.823195</td>\n",
       "      <td>13.347375</td>\n",
       "      <td>13.049138</td>\n",
       "      <td>13.550237</td>\n",
       "      <td>13.137916</td>\n",
       "      <td>13.577685</td>\n",
       "      <td>13.007548</td>\n",
       "      <td>13.366525</td>\n",
       "      <td>13.214865</td>\n",
       "      <td>13.389975</td>\n",
       "      <td>13.743655</td>\n",
       "      <td>13.890316</td>\n",
       "      <td>13.364486</td>\n",
       "      <td>13.560015</td>\n",
       "      <td>13.129259</td>\n",
       "      <td>13.473781</td>\n",
       "      <td>12.827964</td>\n",
       "      <td>13.003290</td>\n",
       "      <td>12.617572</td>\n",
       "      <td>12.298777</td>\n",
       "      <td>10.016879</td>\n",
       "      <td>9.449151</td>\n",
       "      <td>9.729464</td>\n",
       "      <td>10.187970</td>\n",
       "      <td>10.161884</td>\n",
       "      <td>10.600332</td>\n",
       "      <td>10.390931</td>\n",
       "      <td>10.714137</td>\n",
       "      <td>10.838140</td>\n",
       "      <td>10.824638</td>\n",
       "      <td>10.773653</td>\n",
       "      <td>10.950947</td>\n",
       "      <td>10.854683</td>\n",
       "      <td>11.272094</td>\n",
       "      <td>10.758691</td>\n",
       "      <td>11.078674</td>\n",
       "      <td>11.141299</td>\n",
       "      <td>11.348777</td>\n",
       "      <td>11.250455</td>\n",
       "      <td>11.223499</td>\n",
       "      <td>11.382041</td>\n",
       "      <td>11.621684</td>\n",
       "      <td>11.435870</td>\n",
       "      <td>11.222996</td>\n",
       "      <td>10.941375</td>\n",
       "      <td>10.492710</td>\n",
       "      <td>9.558011</td>\n",
       "      <td>6.403214</td>\n",
       "      <td>17.150637</td>\n",
       "      <td>0.266307</td>\n",
       "      <td>0.281343</td>\n",
       "      <td>0.284234</td>\n",
       "      <td>0.296498</td>\n",
       "      <td>0.297291</td>\n",
       "      <td>0.292765</td>\n",
       "      <td>0.301855</td>\n",
       "      <td>0.298986</td>\n",
       "      <td>0.29320</td>\n",
       "      <td>0.292093</td>\n",
       "      <td>0.301031</td>\n",
       "      <td>0.304610</td>\n",
       "      <td>0.299536</td>\n",
       "      <td>0.289895</td>\n",
       "      <td>0.300587</td>\n",
       "      <td>0.312811</td>\n",
       "      <td>0.301809</td>\n",
       "      <td>0.302416</td>\n",
       "      <td>0.299286</td>\n",
       "      <td>0.297747</td>\n",
       "      <td>0.299420</td>\n",
       "      <td>0.294376</td>\n",
       "      <td>0.304491</td>\n",
       "      <td>0.303188</td>\n",
       "      <td>0.315159</td>\n",
       "      <td>0.323586</td>\n",
       "      <td>0.214272</td>\n",
       "      <td>0.226805</td>\n",
       "      <td>0.229479</td>\n",
       "      <td>0.229580</td>\n",
       "      <td>0.235536</td>\n",
       "      <td>0.248798</td>\n",
       "      <td>0.237675</td>\n",
       "      <td>0.247164</td>\n",
       "      <td>0.248657</td>\n",
       "      <td>0.249708</td>\n",
       "      <td>0.257877</td>\n",
       "      <td>0.248350</td>\n",
       "      <td>0.253397</td>\n",
       "      <td>0.246666</td>\n",
       "      <td>0.262363</td>\n",
       "      <td>0.253084</td>\n",
       "      <td>0.266993</td>\n",
       "      <td>0.256630</td>\n",
       "      <td>0.263396</td>\n",
       "      <td>0.262226</td>\n",
       "      <td>0.275203</td>\n",
       "      <td>0.266029</td>\n",
       "      <td>0.27569</td>\n",
       "      <td>0.286665</td>\n",
       "      <td>0.295197</td>\n",
       "      <td>0.342360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.10500</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.32000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.33000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.535000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.53500</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.635000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.365000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.415000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               W0          W1          W2          W3          W4          W5  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  811.000000  811.000000   \n",
       "mean     8.902589    9.129470    9.389642    9.717633    9.574599    9.466091   \n",
       "std     12.067163   12.564766   13.045073   13.553294   13.095765   12.823195   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      3.000000    3.000000    3.000000    4.000000    4.000000    3.000000   \n",
       "75%     12.000000   12.000000   12.000000   13.000000   13.000000   12.500000   \n",
       "max     54.000000   53.000000   56.000000   59.000000   61.000000   52.000000   \n",
       "\n",
       "               W6          W7          W8          W9         W10         W11  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  811.000000  811.000000   \n",
       "mean     9.720099    9.585697    9.784217    9.681874    9.790382    9.678175   \n",
       "std     13.347375   13.049138   13.550237   13.137916   13.577685   13.007548   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      4.000000    4.000000    4.000000    4.000000    4.000000    4.000000   \n",
       "75%     13.000000   12.500000   13.000000   13.000000   13.000000   13.000000   \n",
       "max     56.000000   62.000000   63.000000   52.000000   56.000000   57.000000   \n",
       "\n",
       "              W12         W13         W14         W15         W16         W17  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  811.000000  811.000000   \n",
       "mean     9.827374    9.686806    9.907522   10.045623   10.033292    9.905055   \n",
       "std     13.366525   13.214865   13.389975   13.743655   13.890316   13.364486   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      3.000000    4.000000    4.000000    4.000000    4.000000    4.000000   \n",
       "75%     13.000000   13.000000   13.000000   14.000000   13.000000   13.000000   \n",
       "max     61.000000   55.000000   57.000000   59.000000   62.000000   67.000000   \n",
       "\n",
       "              W18         W19         W20         W21         W22         W23  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  811.000000  811.000000   \n",
       "mean    10.007398    9.644883    9.849568    9.710234    9.902589    9.861899   \n",
       "std     13.560015   13.129259   13.473781   12.827964   13.003290   12.617572   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      4.000000    4.000000    4.000000    4.000000    4.000000    4.000000   \n",
       "75%     13.000000   13.000000   13.000000   13.000000   13.000000   14.000000   \n",
       "max     57.000000   56.000000   64.000000   58.000000   51.000000   72.000000   \n",
       "\n",
       "              W24         W25         W26         W27         W28         W29  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  811.000000  811.000000   \n",
       "mean    10.166461    8.892725    6.950678    7.193588    7.383477    7.339088   \n",
       "std     12.298777   10.016879    9.449151    9.729464   10.187970   10.161884   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000    1.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      5.000000    5.000000    3.000000    3.000000    3.000000    3.000000   \n",
       "75%     16.000000   15.000000    9.000000   10.000000    9.000000   10.000000   \n",
       "max     64.000000   64.000000   46.000000   47.000000   44.000000   42.000000   \n",
       "\n",
       "              W30         W31         W32         W33         W34         W35  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  811.000000  811.000000   \n",
       "mean     7.607891    7.610358    7.759556    7.906289    7.992602    7.997534   \n",
       "std     10.600332   10.390931   10.714137   10.838140   10.824638   10.773653   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      3.000000    3.000000    3.000000    3.000000    3.000000    3.000000   \n",
       "75%     10.000000   10.000000   10.000000   10.000000   10.500000   10.000000   \n",
       "max     48.000000   47.000000   49.000000   46.000000   46.000000   46.000000   \n",
       "\n",
       "              W36         W37         W38         W39         W40         W41  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  811.000000  811.000000   \n",
       "mean     8.014797    8.073983    8.251541    7.965475    8.182491    8.240444   \n",
       "std     10.950947   10.854683   11.272094   10.758691   11.078674   11.141299   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      3.000000    3.000000    3.000000    3.000000    4.000000    3.000000   \n",
       "75%     10.000000   11.000000   11.000000   10.000000   10.000000   11.000000   \n",
       "max     55.000000   47.000000   52.000000   47.000000   48.000000   50.000000   \n",
       "\n",
       "              W42         W43         W44         W45         W46         W47  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  811.000000  811.000000   \n",
       "mean     8.394575    8.318126    8.434032    8.556104    8.720099    8.670777   \n",
       "std     11.348777   11.250455   11.223499   11.382041   11.621684   11.435870   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    1.000000    1.000000    0.000000   \n",
       "50%      4.000000    4.000000    4.000000    4.000000    4.000000    4.000000   \n",
       "75%     10.000000   11.000000   11.000000   11.000000   11.000000   12.000000   \n",
       "max     52.000000   50.000000   46.000000   46.000000   55.000000   49.000000   \n",
       "\n",
       "              W48         W49         W50         W51         MIN         MAX  \\\n",
       "count  811.000000  811.000000  811.000000  811.000000  811.000000  811.000000   \n",
       "mean     8.674476    8.895191    8.861899    8.889026    3.780518   16.308261   \n",
       "std     11.222996   10.941375   10.492710    9.558011    6.403214   17.150637   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    1.000000   \n",
       "25%      1.000000    1.000000    1.000000    1.000000    0.000000    3.000000   \n",
       "50%      4.000000    4.000000    5.000000    5.000000    0.000000    9.000000   \n",
       "75%     12.000000   12.000000   13.000000   14.000000    4.000000   21.000000   \n",
       "max     50.000000   52.000000   57.000000   73.000000   24.000000   73.000000   \n",
       "\n",
       "       Normalized 0  Normalized 1  Normalized 2  Normalized 3  Normalized 4  \\\n",
       "count    811.000000    811.000000    811.000000    811.000000    811.000000   \n",
       "mean       0.289396      0.299100      0.306732      0.319852      0.326905   \n",
       "std        0.266307      0.281343      0.284234      0.296498      0.297291   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.250000      0.280000      0.290000      0.290000      0.310000   \n",
       "75%        0.500000      0.500000      0.500000      0.535000      0.550000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       Normalized 5  Normalized 6  Normalized 7  Normalized 8  Normalized 9  \\\n",
       "count    811.000000    811.000000    811.000000     811.00000    811.000000   \n",
       "mean       0.319420      0.332848      0.326572       0.32434      0.326843   \n",
       "std        0.292765      0.301855      0.298986       0.29320      0.292093   \n",
       "min        0.000000      0.000000      0.000000       0.00000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000       0.00000      0.000000   \n",
       "50%        0.300000      0.310000      0.330000       0.32000      0.330000   \n",
       "75%        0.520000      0.530000      0.540000       0.53500      0.530000   \n",
       "max        1.000000      1.000000      1.000000       1.00000      1.000000   \n",
       "\n",
       "       Normalized 10  Normalized 11  Normalized 12  Normalized 13  \\\n",
       "count     811.000000     811.000000     811.000000     811.000000   \n",
       "mean        0.331134       0.331455       0.338878       0.331591   \n",
       "std         0.301031       0.304610       0.299536       0.289895   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.310000       0.310000       0.330000       0.330000   \n",
       "75%         0.540000       0.550000       0.560000       0.530000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "       Normalized 14  Normalized 15  Normalized 16  Normalized 17  \\\n",
       "count     811.000000     811.000000     811.000000     811.000000   \n",
       "mean        0.355771       0.344821       0.335919       0.345253   \n",
       "std         0.300587       0.312811       0.301809       0.302416   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.360000       0.330000       0.330000       0.330000   \n",
       "75%         0.560000       0.580000       0.560000       0.560000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "       Normalized 18  Normalized 19  Normalized 20  Normalized 21  \\\n",
       "count     811.000000     811.000000     811.000000     811.000000   \n",
       "mean        0.354994       0.330210       0.340271       0.353551   \n",
       "std         0.299286       0.297747       0.299420       0.294376   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.350000       0.310000       0.330000       0.360000   \n",
       "75%         0.560000       0.540000       0.560000       0.550000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "       Normalized 22  Normalized 23  Normalized 24  Normalized 25  \\\n",
       "count     811.000000     811.000000     811.000000     811.000000   \n",
       "mean        0.366017       0.370629       0.417694       0.389618   \n",
       "std         0.304491       0.303188       0.315159       0.323586   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.130000       0.060000   \n",
       "50%         0.360000       0.380000       0.440000       0.350000   \n",
       "75%         0.580000       0.580000       0.635000       0.600000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "       Normalized 26  Normalized 27  Normalized 28  Normalized 29  \\\n",
       "count     811.000000     811.000000     811.000000     811.000000   \n",
       "mean        0.207596       0.223046       0.229605       0.224365   \n",
       "std         0.214272       0.226805       0.229479       0.229580   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.180000       0.200000       0.210000       0.190000   \n",
       "75%         0.330000       0.365000       0.360000       0.380000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "       Normalized 30  Normalized 31  Normalized 32  Normalized 33  \\\n",
       "count     811.000000     811.000000     811.000000     811.000000   \n",
       "mean        0.240493       0.250395       0.248323       0.259371   \n",
       "std         0.235536       0.248798       0.237675       0.247164   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.210000       0.220000       0.220000       0.230000   \n",
       "75%         0.390000       0.400000       0.400000       0.415000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "       Normalized 34  Normalized 35  Normalized 36  Normalized 37  \\\n",
       "count     811.000000     811.000000     811.000000     811.000000   \n",
       "mean        0.262256       0.268903       0.270173       0.273144   \n",
       "std         0.248657       0.249708       0.257877       0.248350   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.250000       0.250000       0.240000       0.250000   \n",
       "75%         0.430000       0.450000       0.420000       0.440000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "       Normalized 38  Normalized 39  Normalized 40  Normalized 41  \\\n",
       "count     811.000000     811.000000     811.000000     811.000000   \n",
       "mean        0.277497       0.264994       0.286375       0.279396   \n",
       "std         0.253397       0.246666       0.262363       0.253084   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.250000       0.250000       0.250000       0.260000   \n",
       "75%         0.450000       0.430000       0.470000       0.440000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "       Normalized 42  Normalized 43  Normalized 44  Normalized 45  \\\n",
       "count     811.000000     811.000000     811.000000     811.000000   \n",
       "mean        0.299149       0.287571       0.304846       0.316017   \n",
       "std         0.266993       0.256630       0.263396       0.262226   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.020000   \n",
       "50%         0.280000       0.270000       0.300000       0.310000   \n",
       "75%         0.490000       0.450000       0.500000       0.500000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "       Normalized 46  Normalized 47  Normalized 48  Normalized 49  \\\n",
       "count     811.000000     811.000000      811.00000     811.000000   \n",
       "mean        0.334760       0.314636        0.33815       0.358903   \n",
       "std         0.275203       0.266029        0.27569       0.286665   \n",
       "min         0.000000       0.000000        0.00000       0.000000   \n",
       "25%         0.085000       0.000000        0.10500       0.100000   \n",
       "50%         0.330000       0.310000        0.33000       0.330000   \n",
       "75%         0.500000       0.500000        0.50000       0.550000   \n",
       "max         1.000000       1.000000        1.00000       1.000000   \n",
       "\n",
       "       Normalized 50  Normalized 51  \n",
       "count     811.000000     811.000000  \n",
       "mean        0.373009       0.427941  \n",
       "std         0.295197       0.342360  \n",
       "min         0.000000       0.000000  \n",
       "25%         0.110000       0.090000  \n",
       "50%         0.350000       0.430000  \n",
       "75%         0.560000       0.670000  \n",
       "max         1.000000       1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values and data in general makes perfect sense."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "As per exploratory analysis, data is clean. We only need to structure it in way it is easier for modeling (i.e. tidy dataset). In this case we have values of the variable week as column names. We need to melt the dataset, renaming the column to \"Week\" and values to \"Sales\". For our analysis, we only need weekly sales. Therefore, first, we remove the following columns: MIN, MAX and Normalized N (all normalized data columns) and convert \"Product_Code\" to a numerical feature by extracting the numerical value from the original feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(811, 53)\n"
     ]
    },
    {
     "data": {
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       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P3</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P4</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Code  W0  W1  W2  W3  W4  W5  W6  W7  W8  W9  W10  W11  W12  W13  \\\n",
       "0           P1  11  12  10   8  13  12  14  21   6  14   11   14   16    9   \n",
       "1           P2   7   6   3   2   7   1   6   3   3   3    2    2    6    2   \n",
       "2           P3   7  11   8   9  10   8   7  13  12   6   14    9    4    7   \n",
       "3           P4  12   8  13   5   9   6   9  13  13  11    8    4    5    4   \n",
       "4           P5   8   5  13  11   6   7   9  14   9   9   11   18    8    4   \n",
       "\n",
       "   W14  W15  W16  W17  W18  W19  W20  W21  W22  W23  W24  W25  W26  W27  W28  \\\n",
       "0    9    9   14    9    3   12    5   11    7   12    5    9    7   10    5   \n",
       "1    0    6    2    7    7    9    4    7    2    4    5    3    5    8    5   \n",
       "2   12    8    7   11   10    7    7   13   11    8   10    8   14    5    3   \n",
       "3   15    7   11    9   15    4    6    7   11    7    9    6   10   10    2   \n",
       "4   13    8   10   15    6   13   11    6   10    9    8   12    8    9   13   \n",
       "\n",
       "   W29  W30  W31  W32  W33  W34  W35  W36  W37  W38  W39  W40  W41  W42  W43  \\\n",
       "0   11    7   10   12    6    5   14   10    9   12   17    7   11    4    7   \n",
       "1    5    3    1    3    2    3   10    5    2    7    3    2    5    2    4   \n",
       "2   13   11    9    7    8    7    9    6   12   12    9    3    5    6   14   \n",
       "3    6    7    2    5   12    5   19    8    6    8    8   12    6    9   10   \n",
       "4    3    5    3    5    5    9    7    4    8    8    5    5    8    7   11   \n",
       "\n",
       "   W44  W45  W46  W47  W48  W49  W50  W51  \n",
       "0    8   10   12    3    7    6    5   10  \n",
       "1    5    1    1    4    5    1    6    0  \n",
       "2    5    5    7    8   14    8    8    7  \n",
       "3    3    4    6    8   14    8    7    8  \n",
       "4    7   12    6    6    5   11    8    9  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['Normalized ' + str(n) for n in range(52)]\n",
    "columns += ['MIN', 'MAX']\n",
    "df = df.drop(columns, axis='columns')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>Product_Code</th>\n",
       "      <th>W0</th>\n",
       "      <th>W1</th>\n",
       "      <th>W2</th>\n",
       "      <th>W3</th>\n",
       "      <th>W4</th>\n",
       "      <th>W5</th>\n",
       "      <th>W6</th>\n",
       "      <th>W7</th>\n",
       "      <th>W8</th>\n",
       "      <th>W9</th>\n",
       "      <th>W10</th>\n",
       "      <th>W11</th>\n",
       "      <th>W12</th>\n",
       "      <th>W13</th>\n",
       "      <th>W14</th>\n",
       "      <th>W15</th>\n",
       "      <th>W16</th>\n",
       "      <th>W17</th>\n",
       "      <th>W18</th>\n",
       "      <th>W19</th>\n",
       "      <th>W20</th>\n",
       "      <th>W21</th>\n",
       "      <th>W22</th>\n",
       "      <th>W23</th>\n",
       "      <th>W24</th>\n",
       "      <th>W25</th>\n",
       "      <th>W26</th>\n",
       "      <th>W27</th>\n",
       "      <th>W28</th>\n",
       "      <th>W29</th>\n",
       "      <th>W30</th>\n",
       "      <th>W31</th>\n",
       "      <th>W32</th>\n",
       "      <th>W33</th>\n",
       "      <th>W34</th>\n",
       "      <th>W35</th>\n",
       "      <th>W36</th>\n",
       "      <th>W37</th>\n",
       "      <th>W38</th>\n",
       "      <th>W39</th>\n",
       "      <th>W40</th>\n",
       "      <th>W41</th>\n",
       "      <th>W42</th>\n",
       "      <th>W43</th>\n",
       "      <th>W44</th>\n",
       "      <th>W45</th>\n",
       "      <th>W46</th>\n",
       "      <th>W47</th>\n",
       "      <th>W48</th>\n",
       "      <th>W49</th>\n",
       "      <th>W50</th>\n",
       "      <th>W51</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Code  W0  W1  W2  W3  W4  W5  W6  W7  W8  W9  W10  W11  W12  W13  \\\n",
       "0            1  11  12  10   8  13  12  14  21   6  14   11   14   16    9   \n",
       "1            2   7   6   3   2   7   1   6   3   3   3    2    2    6    2   \n",
       "2            3   7  11   8   9  10   8   7  13  12   6   14    9    4    7   \n",
       "3            4  12   8  13   5   9   6   9  13  13  11    8    4    5    4   \n",
       "4            5   8   5  13  11   6   7   9  14   9   9   11   18    8    4   \n",
       "\n",
       "   W14  W15  W16  W17  W18  W19  W20  W21  W22  W23  W24  W25  W26  W27  W28  \\\n",
       "0    9    9   14    9    3   12    5   11    7   12    5    9    7   10    5   \n",
       "1    0    6    2    7    7    9    4    7    2    4    5    3    5    8    5   \n",
       "2   12    8    7   11   10    7    7   13   11    8   10    8   14    5    3   \n",
       "3   15    7   11    9   15    4    6    7   11    7    9    6   10   10    2   \n",
       "4   13    8   10   15    6   13   11    6   10    9    8   12    8    9   13   \n",
       "\n",
       "   W29  W30  W31  W32  W33  W34  W35  W36  W37  W38  W39  W40  W41  W42  W43  \\\n",
       "0   11    7   10   12    6    5   14   10    9   12   17    7   11    4    7   \n",
       "1    5    3    1    3    2    3   10    5    2    7    3    2    5    2    4   \n",
       "2   13   11    9    7    8    7    9    6   12   12    9    3    5    6   14   \n",
       "3    6    7    2    5   12    5   19    8    6    8    8   12    6    9   10   \n",
       "4    3    5    3    5    5    9    7    4    8    8    5    5    8    7   11   \n",
       "\n",
       "   W44  W45  W46  W47  W48  W49  W50  W51  \n",
       "0    8   10   12    3    7    6    5   10  \n",
       "1    5    1    1    4    5    1    6    0  \n",
       "2    5    5    7    8   14    8    8    7  \n",
       "3    3    4    6    8   14    8    7    8  \n",
       "4    7   12    6    6    5   11    8    9  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Product_Code = df.Product_Code.str.replace('P', '')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Code    int64\n",
       "W0              int64\n",
       "W1              int64\n",
       "W2              int64\n",
       "W3              int64\n",
       "W4              int64\n",
       "W5              int64\n",
       "W6              int64\n",
       "W7              int64\n",
       "W8              int64\n",
       "W9              int64\n",
       "W10             int64\n",
       "W11             int64\n",
       "W12             int64\n",
       "W13             int64\n",
       "W14             int64\n",
       "W15             int64\n",
       "W16             int64\n",
       "W17             int64\n",
       "W18             int64\n",
       "W19             int64\n",
       "W20             int64\n",
       "W21             int64\n",
       "W22             int64\n",
       "W23             int64\n",
       "W24             int64\n",
       "W25             int64\n",
       "W26             int64\n",
       "W27             int64\n",
       "W28             int64\n",
       "W29             int64\n",
       "W30             int64\n",
       "W31             int64\n",
       "W32             int64\n",
       "W33             int64\n",
       "W34             int64\n",
       "W35             int64\n",
       "W36             int64\n",
       "W37             int64\n",
       "W38             int64\n",
       "W39             int64\n",
       "W40             int64\n",
       "W41             int64\n",
       "W42             int64\n",
       "W43             int64\n",
       "W44             int64\n",
       "W45             int64\n",
       "W46             int64\n",
       "W47             int64\n",
       "W48             int64\n",
       "W49             int64\n",
       "W50             int64\n",
       "W51             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Product_Code = df.Product_Code.astype(int)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>Product_Code</th>\n",
       "      <th>Week</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>W0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>W0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>W0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>W0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>W0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Code Week  Sales\n",
       "0             1   W0     11\n",
       "1             2   W0      7\n",
       "2             3   W0      7\n",
       "3             4   W0     12\n",
       "4             5   W0      8"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molten_df = df.melt(id_vars='Product_Code', var_name='Week', value_name='Sales')\n",
    "molten_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>Product_Code</th>\n",
       "      <th>Week</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Code Week  Sales\n",
       "0             1    0     11\n",
       "1             2    0      7\n",
       "2             3    0      7\n",
       "3             4    0     12\n",
       "4             5    0      8\n",
       "5             6    0      3\n",
       "6             7    0      4\n",
       "7             8    0      8\n",
       "8             9    0     14\n",
       "9            10    0     22"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molten_df.Week = molten_df.Week.str.replace('W', '')\n",
    "molten_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "molten_df.Sales.hist(figsize=(10, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per the histogram above, we see that a lot of items sell very little quantities. So we should expect the error to look \"high\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "molten_df.to_csv('../data/processed/clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "In this section we will create lags and difference features:\n",
    "* Last_Week_Sales: quantities sold the week before\n",
    "* Last_Week_Diff: difference of quantities sold between this and previous weeks\n",
    "* Week-2_Sales: quantities sold two weeks ago\n",
    "* Week-2_Diff: difference of quantities sold between this and two weeks ago\n",
    "* Week-3_Sales: quantities sold three weeks ago\n",
    "* Week-3_Diff: difference of quantities sold between this and three weeks ago\n",
    "* Week-4_Sales: quantities sold 4 weeks ago\n",
    "* Week-4_Diff: difference of quantities sold between this and four weeks ago"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Product_Code</th>\n",
       "      <th>Week</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Last_Week_Sales</th>\n",
       "      <th>Last_Week_Diff</th>\n",
       "      <th>Week-2_Sales</th>\n",
       "      <th>Week-2_Diff</th>\n",
       "      <th>Week-3_Sales</th>\n",
       "      <th>Week-3_Diff</th>\n",
       "      <th>Week-4_Sales</th>\n",
       "      <th>Week-4_Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4055</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4056</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4057</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4058</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4059</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Product_Code  Week  Sales  Last_Week_Sales  Last_Week_Diff  \\\n",
       "4055             1     5     12             13.0             5.0   \n",
       "4056             2     5      1              7.0             5.0   \n",
       "4057             3     5      8             10.0             1.0   \n",
       "4058             4     5      6              9.0             4.0   \n",
       "4059             5     5      7              6.0            -5.0   \n",
       "\n",
       "      Week-2_Sales  Week-2_Diff  Week-3_Sales  Week-3_Diff  Week-4_Sales  \\\n",
       "4055           8.0         -2.0          10.0         -2.0          12.0   \n",
       "4056           2.0         -1.0           3.0         -3.0           6.0   \n",
       "4057           9.0          1.0           8.0         -3.0          11.0   \n",
       "4058           5.0         -8.0          13.0          5.0           8.0   \n",
       "4059          11.0         -2.0          13.0          8.0           5.0   \n",
       "\n",
       "      Week-4_Diff  \n",
       "4055          1.0  \n",
       "4056         -1.0  \n",
       "4057          4.0  \n",
       "4058         -4.0  \n",
       "4059         -3.0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/processed/clean.csv')\n",
    "df['Last_Week_Sales'] = df.groupby(['Product_Code'])['Sales'].shift()\n",
    "df['Last_Week_Diff'] = df.groupby(['Product_Code'])['Last_Week_Sales'].diff()\n",
    "df['Week-2_Sales'] = df.groupby(['Product_Code'])['Sales'].shift(2)\n",
    "df['Week-2_Diff'] = df.groupby(['Product_Code'])['Week-2_Sales'].diff()\n",
    "df['Week-3_Sales'] = df.groupby(['Product_Code'])['Sales'].shift(3)\n",
    "df['Week-3_Diff'] = df.groupby(['Product_Code'])['Week-3_Sales'].diff()\n",
    "df['Week-4_Sales'] = df.groupby(['Product_Code'])['Sales'].shift(4)\n",
    "df['Week-4_Diff'] = df.groupby(['Product_Code'])['Week-4_Sales'].diff()\n",
    "df = df.dropna()\n",
    "df = df.drop(['Unnamed: 0'], axis = 'columns')\n",
    "df = df.sort_values(['Week', 'Product_Code'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('../data/processed/abt.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation Strategy\n",
    "The model is intended to be used for predicting sales for one week (one-step forecast). Validation stratetgy should take this in consideration. Our approach will simulate this real-world use case by training the model in all the weeks up to the one we want to predict, and evaluate our score in the new week. In order to get a better estimate of model peformance we will repeat this procedure several times and take the mean of all scores as the final score.\n",
    "\n",
    "The following helper function creates an iterator yielding indices for training and validation data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cv_split(data, n_splits):\n",
    "    '''Generator function returning indices for training and validation sets according to a sliding window for CV\n",
    "       \n",
    "       Arguments:\n",
    "           data: training data\n",
    "           n_splits: number of splits\n",
    "       \n",
    "       Return:\n",
    "           tuple of training and validation indices\n",
    "    '''\n",
    "    stop_week = max(data.Week)\n",
    "    start_week = stop_week - n_splits\n",
    "    for week in range(start_week, stop_week):\n",
    "        yield (data[data.Week < week].index, data[data.Week == week].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (30007, 10), y_train: (30007,), X_test: (8110, 10), y_test: (8110,)\n"
     ]
    }
   ],
   "source": [
    "train = df[df.Week < 42]\n",
    "test = df[df.Week >= 42]\n",
    "y_train, y_test = train.Sales, test.Sales\n",
    "X_train, X_test = train.drop(['Sales'], axis='columns'), test.drop(['Sales'], axis='columns')\n",
    "print(f'X_train: {X_train.shape}, y_train: {y_train.shape}, X_test: {X_test.shape}, y_test: {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: (25141,)\n",
      "VALIDATION: (811,)\n",
      "TRAIN: (25952,)\n",
      "VALIDATION: (811,)\n",
      "TRAIN: (26763,)\n",
      "VALIDATION: (811,)\n",
      "TRAIN: (27574,)\n",
      "VALIDATION: (811,)\n",
      "TRAIN: (28385,)\n",
      "VALIDATION: (811,)\n"
     ]
    }
   ],
   "source": [
    "# Sample sliding window CV\n",
    "cvs = cv_split(X_train, 5)\n",
    "for train_idx, val_idx in cvs:\n",
    "    print(f'TRAIN: {train_idx.shape}')\n",
    "    print(f'VALIDATION: {val_idx.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline Performance\n",
    "A baseline model provides a benchmark for model development phase. Baseline performance is the score we need to beat. \n",
    "\n",
    "We use sales of the previous week as our baseline model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Product_Code</th>\n",
       "      <th>Week</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Last_Week_Sales</th>\n",
       "      <th>Last_Week_Diff</th>\n",
       "      <th>Week-2_Sales</th>\n",
       "      <th>Week-2_Diff</th>\n",
       "      <th>Week-3_Sales</th>\n",
       "      <th>Week-3_Diff</th>\n",
       "      <th>Week-4_Sales</th>\n",
       "      <th>Week-4_Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Code  Week  Sales  Last_Week_Sales  Last_Week_Diff  Week-2_Sales  \\\n",
       "0             1     5     12             13.0             5.0           8.0   \n",
       "1             2     5      1              7.0             5.0           2.0   \n",
       "2             3     5      8             10.0             1.0           9.0   \n",
       "3             4     5      6              9.0             4.0           5.0   \n",
       "4             5     5      7              6.0            -5.0          11.0   \n",
       "\n",
       "   Week-2_Diff  Week-3_Sales  Week-3_Diff  Week-4_Sales  Week-4_Diff  \n",
       "0         -2.0          10.0         -2.0          12.0          1.0  \n",
       "1         -1.0           3.0         -3.0           6.0         -1.0  \n",
       "2          1.0           8.0         -3.0          11.0          4.0  \n",
       "3         -8.0          13.0          5.0           8.0         -4.0  \n",
       "4         -2.0          13.0          8.0           5.0         -3.0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/processed/abt.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (30007, 10), y_train: (30007,), X_test: (8110, 10), y_test: (8110,)\n"
     ]
    }
   ],
   "source": [
    "train = df[df.Week < 42]\n",
    "test = df[df.Week >= 42]\n",
    "y_train, y_test = train.Sales, test.Sales\n",
    "X_train, X_test = train.drop(['Sales'], axis='columns'), test.drop(['Sales'], axis='columns')\n",
    "print(f'X_train: {X_train.shape}, y_train: {y_train.shape}, X_test: {X_test.shape}, y_test: {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Week 31 - Error 20.614057\n",
      "Week 32 - Error 16.943280\n",
      "Week 33 - Error 19.520345\n",
      "Week 34 - Error 18.954377\n",
      "Week 35 - Error 17.166461\n",
      "Week 36 - Error 17.815043\n",
      "Week 37 - Error 20.335388\n",
      "Week 38 - Error 17.437731\n",
      "Week 39 - Error 17.405672\n",
      "Week 40 - Error 17.489519\n",
      "Mean Error for baseline model = 18.368187\n"
     ]
    }
   ],
   "source": [
    "cvs = cv_split(X_train, 10)\n",
    "scores = []\n",
    "for train_ind, val_ind in cvs:\n",
    "    train = X_train.iloc[train_ind, :]\n",
    "    val = X_train.iloc[val_ind, :]\n",
    "    y_hat = val['Last_Week_Sales'].values\n",
    "    y = y_train.iloc[val_ind].values\n",
    "    error = mean_squared_error(y, y_hat)\n",
    "    print(f'Week {val.iloc[0, 1]:d} - Error {error:.6f}')\n",
    "    scores.append(error)\n",
    "print(f'Mean Error for baseline model = {np.mean(scores):.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Product_Code</th>\n",
       "      <th>Week</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Last_Week_Sales</th>\n",
       "      <th>Last_Week_Diff</th>\n",
       "      <th>Week-2_Sales</th>\n",
       "      <th>Week-2_Diff</th>\n",
       "      <th>Week-3_Sales</th>\n",
       "      <th>Week-3_Diff</th>\n",
       "      <th>Week-4_Sales</th>\n",
       "      <th>Week-4_Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Code  Week  Sales  Last_Week_Sales  Last_Week_Diff  Week-2_Sales  \\\n",
       "0             1     5     12             13.0             5.0           8.0   \n",
       "1             2     5      1              7.0             5.0           2.0   \n",
       "2             3     5      8             10.0             1.0           9.0   \n",
       "3             4     5      6              9.0             4.0           5.0   \n",
       "4             5     5      7              6.0            -5.0          11.0   \n",
       "\n",
       "   Week-2_Diff  Week-3_Sales  Week-3_Diff  Week-4_Sales  Week-4_Diff  \n",
       "0         -2.0          10.0         -2.0          12.0          1.0  \n",
       "1         -1.0           3.0         -3.0           6.0         -1.0  \n",
       "2          1.0           8.0         -3.0          11.0          4.0  \n",
       "3         -8.0          13.0          5.0           8.0         -4.0  \n",
       "4         -2.0          13.0          8.0           5.0         -3.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/processed/abt.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Product_Code  Week  Sales  Last_Week_Sales  Last_Week_Diff  \\\n",
      "30007             1    42      4             11.0             4.0   \n",
      "30008             2    42      2              5.0             3.0   \n",
      "30009             3    42      6              5.0             2.0   \n",
      "30010             4    42      9              6.0            -6.0   \n",
      "30011             5    42      7              8.0             3.0   \n",
      "\n",
      "       Week-2_Sales  Week-2_Diff  Week-3_Sales  Week-3_Diff  Week-4_Sales  \\\n",
      "30007           7.0        -10.0          17.0          5.0          12.0   \n",
      "30008           2.0         -1.0           3.0         -4.0           7.0   \n",
      "30009           3.0         -6.0           9.0         -3.0          12.0   \n",
      "30010          12.0          4.0           8.0          0.0           8.0   \n",
      "30011           5.0          0.0           5.0         -3.0           8.0   \n",
      "\n",
      "       Week-4_Diff  \n",
      "30007          3.0  \n",
      "30008          5.0  \n",
      "30009          0.0  \n",
      "30010          2.0  \n",
      "30011          0.0  \n",
      "X_train: (30007, 10), y_train: (30007,), X_test: (8110, 10), y_test: (8110,)\n"
     ]
    }
   ],
   "source": [
    "train = df[df.Week < 42]\n",
    "test = df[df.Week >= 42]\n",
    "y_train, y_test = train.Sales, test.Sales\n",
    "X_train, X_test = train.drop(['Sales'], axis='columns'), test.drop(['Sales'], axis='columns')\n",
    "print(f'X_train: {X_train.shape}, y_train: {y_train.shape}, X_test: {X_test.shape}, y_test: {y_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fisrt we create the hyperparamaters grid. We use Python dictionaries to store hyperparameters for each algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_params = dict(lasso__alpha=[0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10])\n",
    "ridge_params = dict(ridge__alpha=[0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10])\n",
    "rf_params = dict(randomforestregressor__n_estimators=[100, 250, 500], randomforestregressor__max_features=['auto', 'sqrt', 0.33])\n",
    "xgb_params=dict(\n",
    "    xgbregressor__learning_rate=[0.0001, 0.001, 0.01, 0.1, 0.5],\n",
    "    xgbregressor__gamma=[1., 10., 100., 1000.],\n",
    "    xgbregressor__max_depth=[1, 3, 5],\n",
    "    xgbregressor__min_child_weight=[10, 100, 1000],\n",
    "    xgbregressor__n_estimators=[100, 500, 1000]\n",
    ")\n",
    "parameters = dict(\n",
    "    lasso=lasso_params,\n",
    "    ridge=ridge_params,\n",
    "    rf=rf_params,\n",
    "    xgb=xgb_params\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create model pipelines. We use dictionaries to store all model pipelines. Keys for the pipeline dictionary must be consistent with keys of the parameters grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelines = dict(\n",
    "    lasso=make_pipeline(StandardScaler(), Lasso(random_state=1)),\n",
    "    ridge=make_pipeline(StandardScaler(), Ridge(random_state=1)),\n",
    "    rf=make_pipeline(StandardScaler(), RandomForestRegressor(random_state=1)),\n",
    "    xgb=make_pipeline(StandardScaler(), xgb.XGBRegressor(objective='reg:squarederror',random_state=1))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitted_models = {}\n",
    "for name, pipeline in pipelines.items():\n",
    "    cvs = cv_split(X_train, 12)\n",
    "    model = GridSearchCV(pipeline, parameters[name], cv=cvs, scoring='neg_mean_squared_error')\n",
    "    model.fit(X_train, y_train)\n",
    "    fitted_models[name] = model\n",
    "    print(name, 'model has been fitted.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try a better model: Xgboost. The following cell took 2+ days (wall time) to complete in my machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "cvs = cv_split(X_train, 12)\n",
    "model = GridSearchCV(pipelines['xgb'], parameters['xgb'], cv=cvs, scoring='neg_mean_squared_error')\n",
    "model.fit(X_train, y_train)\n",
    "fitted_models['xgb'] = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check CV scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV scores (MSE):\n",
      "----------\n",
      "lasso: 12.043432\n",
      "ridge: 12.150550\n",
      "rf   : 11.224835\n",
      "xgb  : 11.058038\n"
     ]
    }
   ],
   "source": [
    "print('CV scores (MSE):')\n",
    "print('-' * 10)\n",
    "for name, model in fitted_models.items():\n",
    "    print(f'{name:5}: {abs(model.best_score_):.6f}' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('standardscaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('xgbregressor',\n",
       "                 XGBRegressor(base_score=0.5, booster='gbtree',\n",
       "                              colsample_bylevel=1, colsample_bynode=1,\n",
       "                              colsample_bytree=1, gamma=10.0,\n",
       "                              importance_type='gain', learning_rate=0.01,\n",
       "                              max_delta_step=0, max_depth=5,\n",
       "                              min_child_weight=100, missing=None,\n",
       "                              n_estimators=1000, n_jobs=1, nthread=None,\n",
       "                              objective='reg:squarederror', random_state=1,\n",
       "                              reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "                              seed=None, silent=None, subsample=1,\n",
       "                              verbosity=1))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our best model\n",
    "fitted_models['xgb'].best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The winning parameters are:\n",
    "* `n_estimators`= 1000\n",
    "* `learning_rate` = 0.01\n",
    "* `min_child_weight` = 100\n",
    "* `gamma` = 10\n",
    "* `max_depth` = 5\n",
    "\n",
    "let's save the winning model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../models/xgb_grid_search.pkl', 'wb') as f:\n",
    "    pickle.dump(fitted_models['xgb'].best_estimator_, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All CV scores are much better than our baseline, so we are on the right track.\n",
    "\n",
    "We had left 10 weeks worth of data for testing performance of teh final model. Check performance on hold out data set (MSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST scores:\n",
      "------------\n",
      "lasso: 12.158350\n",
      "ridge: 12.172450\n",
      "rf   : 11.612914\n",
      "xgb  : 11.348226\n"
     ]
    }
   ],
   "source": [
    "y = df.Sales\n",
    "X = df.drop(['Sales'], axis='columns')\n",
    "print('TEST scores:')\n",
    "print('-' * 12)\n",
    "for name, model in fitted_models.items():\n",
    "    cvs = cv_split(X, 10)\n",
    "    scores = cross_val_score(model.best_estimator_, X, y, cv=cvs, scoring='neg_mean_squared_error')\n",
    "    print(f'{name:5}: {np.mean(abs(scores)):.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let´s check model performance graphically for our winning model XGBoost. By building a scatter plot of the predictions for week 52 vs actual values we can check graphically how good our model's predictions are. The tighter the plot is along the main diagonal, the better our predictions are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted_models['xgb'].best_estimator_.fit(X[X.Week < 51], df[df.Week  < 51].Sales)\n",
    "y_hat = fitted_models['xgb'].best_estimator_.predict(X[X.Week == 51].values)\n",
    "y = pd.DataFrame(df[df.Week == 51].Sales)\n",
    "y['Prediction'] = np.round(y_hat)\n",
    "y.plot.scatter(x='Prediction', y='Sales', figsize=(10, 6), title='Prediction vs Sales', ylim=(0,40), xlim=(0,40))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model is working pretty well for products selling under 25 units. For products selling around 30 units, our model is underpredicting. This might be further investigated.\n",
    "\n",
    "Now, to get new predictions we need the product identifier and week number as inputs and compute the new features (i.e. lag and difference features).\n",
    "\n",
    "The model can be further optimized to produce better predictions. Find below some ideas worth trying in order to improve it:\n",
    "* Investigate products with sales over 25 units\n",
    "* Try more features (lags, differences) not only at the product level, but at the global\n",
    "* Try a neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}