SentimentAnalysis_and_Forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1= pd.read_excel(\"data_2017-2020_02072020.xlsx\", sheet_name= \"working\")# merged all 3 sheets in excel of stackline data in one sheet and named it \"working\"; reading sheet named \"working\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36936 entries, 0 to 36935\n",
      "Data columns (total 33 columns):\n",
      "PK                        36936 non-null object\n",
      "RetailerName              36936 non-null object\n",
      "WeekId                    36936 non-null int64\n",
      "WeekEnding                36936 non-null datetime64[ns]\n",
      "StacklineSku              36936 non-null object\n",
      "RetailerSku               36936 non-null object\n",
      "Title                     36936 non-null object\n",
      "Brand                     36936 non-null object\n",
      "Category                  36936 non-null object\n",
      "SubCategory               36936 non-null object\n",
      "ModelNumber               28077 non-null object\n",
      "RetailPrice               36936 non-null float64\n",
      "RetailSales               36936 non-null float64\n",
      "WholesaleSales            36936 non-null float64\n",
      "UnitsSold                 36936 non-null int64\n",
      "UnitsOnHand               36936 non-null int64\n",
      "RetailerMargin            36936 non-null float64\n",
      "WeeksOnHand               36930 non-null float64\n",
      "InventoryStatus           0 non-null float64\n",
      "IsActive                  36936 non-null int64\n",
      "InStock                   36936 non-null float64\n",
      "BrandCOGS                 23635 non-null float64\n",
      "UnitsReturned             36936 non-null int64\n",
      "Returns - Retail Value    36936 non-null float64\n",
      "Return Rate %             36936 non-null float64\n",
      "MAP Price                 0 non-null float64\n",
      "MAP Price Violations      23635 non-null float64\n",
      "AvailabilityStatus        36936 non-null object\n",
      "AvailabilityStatusCode    36936 non-null object\n",
      "BuyBox(Win%)              4757 non-null float64\n",
      "Review Star               5106 non-null float64\n",
      "IsVerified                5106 non-null object\n",
      "ReviewText                5106 non-null object\n",
      "dtypes: datetime64[ns](1), float64(14), int64(5), object(13)\n",
      "memory usage: 9.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()#column information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PK', 'RetailerName', 'WeekId', 'WeekEnding', 'StacklineSku',\n",
       "       'RetailerSku', 'Title', 'Brand', 'Category', 'SubCategory',\n",
       "       'ModelNumber', 'RetailPrice', 'RetailSales', 'WholesaleSales',\n",
       "       'UnitsSold', 'UnitsOnHand', 'RetailerMargin', 'WeeksOnHand',\n",
       "       'InventoryStatus', 'IsActive', 'InStock', 'BrandCOGS', 'UnitsReturned',\n",
       "       'Returns - Retail Value', 'Return Rate %', 'MAP Price',\n",
       "       'MAP Price Violations', 'AvailabilityStatus', 'AvailabilityStatusCode',\n",
       "       'BuyBox(Win%)', 'Review Star', 'IsVerified', 'ReviewText'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_col= ['PK', 'WeekEnding', 'StacklineSku',\n",
    "       'RetailerSku', 'Category', 'SubCategory',\n",
    "       'RetailPrice', 'WholesaleSales',\n",
    "       'UnitsSold', 'UnitsOnHand', 'WeeksOnHand',\n",
    "       'IsActive', 'InStock', 'BrandCOGS', 'Return Rate %', 'AvailabilityStatus', 'AvailabilityStatusCode',\n",
    "       'BuyBox(Win%)', 'Review Star', 'IsVerified', 'ReviewText']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_working= df1[['PK', 'WeekEnding', 'StacklineSku',\n",
    "       'RetailerSku', 'Category', 'SubCategory',\n",
    "       'RetailPrice', 'WholesaleSales',\n",
    "       'UnitsSold', 'UnitsOnHand', 'WeeksOnHand',\n",
    "       'IsActive', 'InStock', 'BrandCOGS', 'Return Rate %', 'AvailabilityStatus', 'AvailabilityStatusCode',\n",
    "       'BuyBox(Win%)', 'Review Star', 'IsVerified', 'ReviewText']]#keeping relevant columns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'blue'>  Sentiment Analysis** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x167bed73a20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_working.isnull(), cbar=False)#EDA of null values for each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x167bf2c88d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\t\n",
    "df_working['Review Star'].value_counts().plot(kind='bar')#EDA of customer review rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer #importing Sentiment Analysis Package \n",
    "analyser = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_analyzer_scores(sentence):#making a function to extract sentiment score\n",
    "    score = analyser.polarity_scores(sentence)\n",
    "    return score['compound']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_score= []\n",
    "sentiment=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,len(df_working)):#storing sentiment of each review\n",
    "    try:\n",
    "        text=df_working.loc[i,'ReviewText']\n",
    "        #print(text)\n",
    "        value= sentiment_analyzer_scores(text)\n",
    "        sentiment_score.append(value)\n",
    "        #print (sentiment_score)\n",
    "        if value > 0.5:\n",
    "            sentiment.append(\"Positive\")\n",
    "        elif value < -0.5:\n",
    "            sentiment.append(\"Negative\")\n",
    "        else: \n",
    "            sentiment.append(\"Neutral\")\n",
    "            \n",
    "    except:\n",
    "        sentiment_score.append(\"No Review\")\n",
    "        sentiment.append(\"No\")\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.8481,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.784,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.7115,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " -0.6956,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.7902,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.6249,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.8442,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.0,\n",
       " 'No Review',\n",
       " 0.7878,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.5978,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.4215,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.935,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.9161,\n",
       " 'No Review',\n",
       " 0.9011,\n",
       " 0.5719,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.5719,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.4215,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.4019,\n",
       " -0.4767,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.6696,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.8248,\n",
       " 0.7464,\n",
       " 0.1679,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.5551,\n",
       " 'No Review',\n",
       " 0.8311,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " -0.1159,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.9022,\n",
       " 0.0,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.807,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.8908,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.9337,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.6249,\n",
       " -0.3089,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.8689,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.6428,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " -0.4767,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " -0.2755,\n",
       " 0.3612,\n",
       " 0.8717,\n",
       " 0.3612,\n",
       " 'No Review',\n",
       " 0.3848,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.7634,\n",
       " -0.2057,\n",
       " 0.6486,\n",
       " 0.7946,\n",
       " -0.1541,\n",
       " -0.0572,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.8309,\n",
       " 0.6467,\n",
       " 'No Review',\n",
       " 0.0,\n",
       " 0.6369,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.3612,\n",
       " 0.6249,\n",
       " 'No Review',\n",
       " 0.296,\n",
       " 0.3612,\n",
       " 0.6369,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.238,\n",
       " 'No Review',\n",
       " 0.9876,\n",
       " 'No Review',\n",
       " 0.9313,\n",
       " 0.9632,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.6249,\n",
       " 0.6369,\n",
       " -0.1027,\n",
       " 0.7717,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.765,\n",
       " 'No Review',\n",
       " 0.8774,\n",
       " 'No Review',\n",
       " 0.7003,\n",
       " 'No Review',\n",
       " 0.8883,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.8053,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.4973,\n",
       " 'No Review',\n",
       " 0.0,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.8779,\n",
       " 0.1027,\n",
       " 0.4767,\n",
       " 'No Review',\n",
       " 0.0,\n",
       " 0.3612,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.0516,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.4588,\n",
       " 'No Review',\n",
       " 0.638,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.6739,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " -0.7777,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.4588,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.6892,\n",
       " 0.0772,\n",
       " 'No Review',\n",
       " 0.8481,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.6486,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " -0.2755,\n",
       " 0.3612,\n",
       " 0.049,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.5106,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.6249,\n",
       " 'No Review',\n",
       " 0.5093,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 0.4404,\n",
       " 0.8074,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " -0.296,\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " ...]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Negative',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Negative',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'Positive',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'Neutral',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " 'No Review',\n",
       " ...]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shive\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\shive\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df_working[\"sentiment_score\"]= sentiment_score #adding new column of sentiment score in current data frame\n",
    "df_working[\"sentiment_category\"]= sentiment #adding new column of positive, negative and neutral sentiment in current dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x167bae492e8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_working['sentiment_category'].value_counts().plot(kind='bar')#bar graph of sentiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36936 entries, 0 to 36935\n",
      "Data columns (total 23 columns):\n",
      "PK                        36936 non-null object\n",
      "WeekEnding                36936 non-null datetime64[ns]\n",
      "StacklineSku              36936 non-null object\n",
      "RetailerSku               36936 non-null object\n",
      "Category                  36936 non-null object\n",
      "SubCategory               36936 non-null object\n",
      "RetailPrice               36936 non-null float64\n",
      "WholesaleSales            36936 non-null float64\n",
      "UnitsSold                 36936 non-null int64\n",
      "UnitsOnHand               36936 non-null int64\n",
      "WeeksOnHand               36930 non-null float64\n",
      "IsActive                  36936 non-null int64\n",
      "InStock                   36936 non-null float64\n",
      "BrandCOGS                 23635 non-null float64\n",
      "Return Rate %             36936 non-null float64\n",
      "AvailabilityStatus        36936 non-null object\n",
      "AvailabilityStatusCode    36936 non-null object\n",
      "BuyBox(Win%)              4757 non-null float64\n",
      "Review Star               5106 non-null float64\n",
      "IsVerified                5106 non-null object\n",
      "ReviewText                5106 non-null object\n",
      "sentiment_score           36936 non-null object\n",
      "sentiment_category        36936 non-null object\n",
      "dtypes: datetime64[ns](1), float64(8), int64(3), object(11)\n",
      "memory usage: 6.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_working.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_working.to_csv(\"sentiment_score.csv\") #saving updated dataframe into csv file which includes sentiment score and sentiment category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'blue'>  LSTM Time Series** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ts= df1[[\"WeekId\",\"WeekEnding\", \"RetailerSku\", \"UnitsSold\"]]#creating new dataframe with relevant column for LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ************************* HIDDEN FOR CONFIDENTIALITY *************************\n",
    "df_ts= df_ts[df_ts[\"RetailerSku\"]== \"******\"] #doing LSTM for a random ASIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ts= df_ts.sort_values(by= [\"WeekId\"]) #sorting values as per weekid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ts[\"WeekId\"]= df_ts[\"WeekId\"].astype(str) #dropping 2020 value as we are considering full 3 year data to include trend and seasonality\n",
    "ind_drop = df_ts[df_ts['WeekId'].apply(lambda x: x.startswith('2020'))].index \n",
    "df_ts = df_ts.drop(ind_drop)\n",
    "\n",
    "df_ts[\"WeekId\"]= df_ts[\"WeekId\"].astype(int)\n",
    "df_ts= df_ts.reset_index()\n",
    "df_ts['WeekEnding'] = pd.to_datetime(df_ts['WeekEnding'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y= df_ts[\"UnitsSold\"] # defining y variable\n",
    "x= df_ts[\"WeekEnding\"] # defining x variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shive\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) #plotting time series graph of selected ASIN\n",
    "plt.plot(x, y)\n",
    "plt.title(\"Historical Sales Trend of Champion ASIN\")\n",
    "plt.xlabel(\"Time Period\")\n",
    "plt.ylabel(\"Units Sold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "numpy.random.seed(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_value = y.values\n",
    "y_value = y_value.astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 54.,  50.,  53.,  41.,  53.,  37.,  50.,  77.,  65.,  63.,  39.,\n",
       "        84.,  72.,  72.,  57.,  75.,  73.,  54.,  81.,  63.,  62.,  62.,\n",
       "        57.,  53.,  60.,  33.,  54.,  75.,  55.,  60.,  50.,  60.,  66.,\n",
       "        54.,  66.,  64.,  65.,  60.,  76.,  67.,  55.,  62.,  73.,  51.,\n",
       "        65.,  56.,  52.,  78.,  71.,  40.,  50.,  35.,  50.,  80.,  66.,\n",
       "        62.,  48.,  68.,  63.,  64.,  71.,  60.,  36.,  18.,  11.,   3.,\n",
       "         6.,   1.,   0.,   0.,   0.,   1.,   3.,  18.,  35.,  69.,  53.,\n",
       "        67.,  63.,  58.,  63.,  50.,  42.,  35.,  60.,  53.,  58.,  56.,\n",
       "        46.,  51.,  55.,  15.,   9.,  24.,  63.,  11.,  34.,  49.,  56.,\n",
       "        87.,  90.,  71.,  47.,  45.,  85.,  80.,  68., 110.,  88.,  77.,\n",
       "        85.,  82.,  62.,  49.,  80.,  94.,  90., 102., 114.,  95., 110.,\n",
       "       117., 156.,  97., 121., 109., 261., 191., 114., 145.,  87., 112.,\n",
       "        93.,  79.,  92., 104.,  97.,  95.,  96., 134., 114., 114., 111.,\n",
       "       130., 149., 111.,  80., 103., 129., 143., 138., 114., 162.,  43.,\n",
       "        17.,   9.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 54.],\n",
       "       [ 50.],\n",
       "       [ 53.],\n",
       "       [ 41.],\n",
       "       [ 53.],\n",
       "       [ 37.],\n",
       "       [ 50.],\n",
       "       [ 77.],\n",
       "       [ 65.],\n",
       "       [ 63.],\n",
       "       [ 39.],\n",
       "       [ 84.],\n",
       "       [ 72.],\n",
       "       [ 72.],\n",
       "       [ 57.],\n",
       "       [ 75.],\n",
       "       [ 73.],\n",
       "       [ 54.],\n",
       "       [ 81.],\n",
       "       [ 63.],\n",
       "       [ 62.],\n",
       "       [ 62.],\n",
       "       [ 57.],\n",
       "       [ 53.],\n",
       "       [ 60.],\n",
       "       [ 33.],\n",
       "       [ 54.],\n",
       "       [ 75.],\n",
       "       [ 55.],\n",
       "       [ 60.],\n",
       "       [ 50.],\n",
       "       [ 60.],\n",
       "       [ 66.],\n",
       "       [ 54.],\n",
       "       [ 66.],\n",
       "       [ 64.],\n",
       "       [ 65.],\n",
       "       [ 60.],\n",
       "       [ 76.],\n",
       "       [ 67.],\n",
       "       [ 55.],\n",
       "       [ 62.],\n",
       "       [ 73.],\n",
       "       [ 51.],\n",
       "       [ 65.],\n",
       "       [ 56.],\n",
       "       [ 52.],\n",
       "       [ 78.],\n",
       "       [ 71.],\n",
       "       [ 40.],\n",
       "       [ 50.],\n",
       "       [ 35.],\n",
       "       [ 50.],\n",
       "       [ 80.],\n",
       "       [ 66.],\n",
       "       [ 62.],\n",
       "       [ 48.],\n",
       "       [ 68.],\n",
       "       [ 63.],\n",
       "       [ 64.],\n",
       "       [ 71.],\n",
       "       [ 60.],\n",
       "       [ 36.],\n",
       "       [ 18.],\n",
       "       [ 11.],\n",
       "       [  3.],\n",
       "       [  6.],\n",
       "       [  1.],\n",
       "       [  0.],\n",
       "       [  0.],\n",
       "       [  0.],\n",
       "       [  1.],\n",
       "       [  3.],\n",
       "       [ 18.],\n",
       "       [ 35.],\n",
       "       [ 69.],\n",
       "       [ 53.],\n",
       "       [ 67.],\n",
       "       [ 63.],\n",
       "       [ 58.],\n",
       "       [ 63.],\n",
       "       [ 50.],\n",
       "       [ 42.],\n",
       "       [ 35.],\n",
       "       [ 60.],\n",
       "       [ 53.],\n",
       "       [ 58.],\n",
       "       [ 56.],\n",
       "       [ 46.],\n",
       "       [ 51.],\n",
       "       [ 55.],\n",
       "       [ 15.],\n",
       "       [  9.],\n",
       "       [ 24.],\n",
       "       [ 63.],\n",
       "       [ 11.],\n",
       "       [ 34.],\n",
       "       [ 49.],\n",
       "       [ 56.],\n",
       "       [ 87.],\n",
       "       [ 90.],\n",
       "       [ 71.],\n",
       "       [ 47.],\n",
       "       [ 45.],\n",
       "       [ 85.],\n",
       "       [ 80.],\n",
       "       [ 68.],\n",
       "       [110.],\n",
       "       [ 88.],\n",
       "       [ 77.],\n",
       "       [ 85.],\n",
       "       [ 82.],\n",
       "       [ 62.],\n",
       "       [ 49.],\n",
       "       [ 80.],\n",
       "       [ 94.],\n",
       "       [ 90.],\n",
       "       [102.],\n",
       "       [114.],\n",
       "       [ 95.],\n",
       "       [110.],\n",
       "       [117.],\n",
       "       [156.],\n",
       "       [ 97.],\n",
       "       [121.],\n",
       "       [109.],\n",
       "       [261.],\n",
       "       [191.],\n",
       "       [114.],\n",
       "       [145.],\n",
       "       [ 87.],\n",
       "       [112.],\n",
       "       [ 93.],\n",
       "       [ 79.],\n",
       "       [ 92.],\n",
       "       [104.],\n",
       "       [ 97.],\n",
       "       [ 95.],\n",
       "       [ 96.],\n",
       "       [134.],\n",
       "       [114.],\n",
       "       [114.],\n",
       "       [111.],\n",
       "       [130.],\n",
       "       [149.],\n",
       "       [111.],\n",
       "       [ 80.],\n",
       "       [103.],\n",
       "       [129.],\n",
       "       [143.],\n",
       "       [138.],\n",
       "       [114.],\n",
       "       [162.],\n",
       "       [ 43.],\n",
       "       [ 17.],\n",
       "       [  9.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize the dataset\n",
    "y_value= y_value.reshape((-1,1))\n",
    "y_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1)) #Min max scaling\n",
    "y_value = scaler.fit_transform(y_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.20689655],\n",
       "       [0.19157088],\n",
       "       [0.20306513],\n",
       "       [0.15708812],\n",
       "       [0.20306513],\n",
       "       [0.14176245],\n",
       "       [0.19157088],\n",
       "       [0.29501916],\n",
       "       [0.24904215],\n",
       "       [0.24137931],\n",
       "       [0.14942529],\n",
       "       [0.32183908],\n",
       "       [0.27586207],\n",
       "       [0.27586207],\n",
       "       [0.2183908 ],\n",
       "       [0.28735632],\n",
       "       [0.27969349],\n",
       "       [0.20689655],\n",
       "       [0.31034483],\n",
       "       [0.24137931],\n",
       "       [0.23754789],\n",
       "       [0.23754789],\n",
       "       [0.2183908 ],\n",
       "       [0.20306513],\n",
       "       [0.22988506],\n",
       "       [0.12643678],\n",
       "       [0.20689655],\n",
       "       [0.28735632],\n",
       "       [0.21072797],\n",
       "       [0.22988506],\n",
       "       [0.19157088],\n",
       "       [0.22988506],\n",
       "       [0.25287356],\n",
       "       [0.20689655],\n",
       "       [0.25287356],\n",
       "       [0.24521073],\n",
       "       [0.24904215],\n",
       "       [0.22988506],\n",
       "       [0.29118774],\n",
       "       [0.25670498],\n",
       "       [0.21072797],\n",
       "       [0.23754789],\n",
       "       [0.27969349],\n",
       "       [0.1954023 ],\n",
       "       [0.24904215],\n",
       "       [0.21455939],\n",
       "       [0.19923372],\n",
       "       [0.29885057],\n",
       "       [0.27203065],\n",
       "       [0.1532567 ],\n",
       "       [0.19157088],\n",
       "       [0.13409962],\n",
       "       [0.19157088],\n",
       "       [0.30651341],\n",
       "       [0.25287356],\n",
       "       [0.23754789],\n",
       "       [0.18390805],\n",
       "       [0.2605364 ],\n",
       "       [0.24137931],\n",
       "       [0.24521073],\n",
       "       [0.27203065],\n",
       "       [0.22988506],\n",
       "       [0.13793103],\n",
       "       [0.06896552],\n",
       "       [0.04214559],\n",
       "       [0.01149425],\n",
       "       [0.02298851],\n",
       "       [0.00383142],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.        ],\n",
       "       [0.00383142],\n",
       "       [0.01149425],\n",
       "       [0.06896552],\n",
       "       [0.13409962],\n",
       "       [0.26436782],\n",
       "       [0.20306513],\n",
       "       [0.25670498],\n",
       "       [0.24137931],\n",
       "       [0.22222222],\n",
       "       [0.24137931],\n",
       "       [0.19157088],\n",
       "       [0.16091954],\n",
       "       [0.13409962],\n",
       "       [0.22988506],\n",
       "       [0.20306513],\n",
       "       [0.22222222],\n",
       "       [0.21455939],\n",
       "       [0.17624521],\n",
       "       [0.1954023 ],\n",
       "       [0.21072797],\n",
       "       [0.05747126],\n",
       "       [0.03448276],\n",
       "       [0.09195402],\n",
       "       [0.24137931],\n",
       "       [0.04214559],\n",
       "       [0.1302682 ],\n",
       "       [0.18773946],\n",
       "       [0.21455939],\n",
       "       [0.33333333],\n",
       "       [0.34482759],\n",
       "       [0.27203065],\n",
       "       [0.18007663],\n",
       "       [0.17241379],\n",
       "       [0.3256705 ],\n",
       "       [0.30651341],\n",
       "       [0.2605364 ],\n",
       "       [0.42145594],\n",
       "       [0.33716475],\n",
       "       [0.29501916],\n",
       "       [0.3256705 ],\n",
       "       [0.31417625],\n",
       "       [0.23754789],\n",
       "       [0.18773946],\n",
       "       [0.30651341],\n",
       "       [0.36015326],\n",
       "       [0.34482759],\n",
       "       [0.3908046 ],\n",
       "       [0.43678161],\n",
       "       [0.36398467],\n",
       "       [0.42145594],\n",
       "       [0.44827586],\n",
       "       [0.59770115],\n",
       "       [0.37164751],\n",
       "       [0.46360153],\n",
       "       [0.41762452],\n",
       "       [1.        ],\n",
       "       [0.73180077],\n",
       "       [0.43678161],\n",
       "       [0.55555556],\n",
       "       [0.33333333],\n",
       "       [0.42911877],\n",
       "       [0.35632184],\n",
       "       [0.30268199],\n",
       "       [0.35249042],\n",
       "       [0.39846743],\n",
       "       [0.37164751],\n",
       "       [0.36398467],\n",
       "       [0.36781609],\n",
       "       [0.51340996],\n",
       "       [0.43678161],\n",
       "       [0.43678161],\n",
       "       [0.42528736],\n",
       "       [0.49808429],\n",
       "       [0.57088123],\n",
       "       [0.42528736],\n",
       "       [0.30651341],\n",
       "       [0.39463602],\n",
       "       [0.49425287],\n",
       "       [0.54789272],\n",
       "       [0.52873563],\n",
       "       [0.43678161],\n",
       "       [0.62068966],\n",
       "       [0.16475096],\n",
       "       [0.0651341 ],\n",
       "       [0.03448276]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104 52\n"
     ]
    }
   ],
   "source": [
    "# split into train and test sets\n",
    "train_size = int(len(y_value) * 0.67)\n",
    "test_size = len(y_value) - train_size\n",
    "train, test = y_value[0:train_size,:], y_value[train_size:len(y_value),:]\n",
    "print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.20689655],\n",
       "       [0.19157088],\n",
       "       [0.20306513],\n",
       "       [0.15708812],\n",
       "       [0.20306513]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(y_value, look_back=1): #defining function to transform dataset in LSTM format\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(y_value)-look_back-1):\n",
    "        a = y_value[i:(i+look_back), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(y_value[i + look_back, 0])\n",
    "    return numpy.array(dataX), numpy.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape into X=t and Y=t+1\n",
    "look_back = 1\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.20689655],\n",
       "       [0.19157088],\n",
       "       [0.20306513],\n",
       "       [0.15708812],\n",
       "       [0.20306513]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testX.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 1s - loss: 0.0249\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.0072\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.0054\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0051\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.0055\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0048\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0051\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0051\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0050\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0049\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0048\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0045\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0045\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0047\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0044\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0042\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0036\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0041\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0036\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0039\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0036\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0036\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0040\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0036\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0037\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0035\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0037\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1a3fb2aad68>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit the LSTM network\n",
    "from keras.layers import Dropout\n",
    "model = Sequential()\n",
    "model.add(LSTM(4, input_shape=(1, look_back)))\n",
    "model.add(Dropout(0.05)) # dropout regularization\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions\n",
    "trainPredict = model.predict(trainX)\n",
    "testPredict = model.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.28640103],\n",
       "       [0.27320498],\n",
       "       [0.24154387],\n",
       "       [0.35213944],\n",
       "       [0.29431546]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testPredict[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[74.75067 ],\n",
       "       [71.3065  ],\n",
       "       [63.04295 ],\n",
       "       [91.908394],\n",
       "       [76.81634 ]], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testPredict_inverted = scaler.inverse_transform(testPredict) #inverse transforming test predicted value\n",
    "testPredict_inverted[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(testPredict_inverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53.433086],\n",
       "       [50.698658],\n",
       "       [52.7489  ],\n",
       "       [44.57198 ],\n",
       "       [52.7489  ]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainPredict_inverted = scaler.inverse_transform(trainPredict) #inverse transforming train predicted value\n",
    "trainPredict_inverted[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trainPredict_inverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainY_inverted= scaler.inverse_transform([trainY])\n",
    "testY_inverted= scaler.inverse_transform([testY])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 80.,  68., 110.,  88.,  77.,  85.,  82.,  62.,  49.,  80.,  94.,\n",
       "         90., 102., 114.,  95., 110., 117., 156.,  97., 121., 109., 261.,\n",
       "        191., 114., 145.,  87., 112.,  93.,  79.,  92., 104.,  97.,  95.,\n",
       "         96., 134., 114., 114., 111., 130., 149., 111.,  80., 103., 129.,\n",
       "        143., 138., 114., 162.,  43.,  17.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testY_inverted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 15.32 RMSE\n",
      "Test Score: 38.13 RMSE\n"
     ]
    }
   ],
   "source": [
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(trainY_inverted[0], trainPredict_inverted[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(testY_inverted[0], testPredict_inverted[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot = numpy.empty_like(y_value)\n",
    "trainPredictPlot[:, :] = numpy.nan\n",
    "trainPredictPlot[look_back:len(trainPredict_inverted)+look_back, :] = trainPredict_inverted\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = numpy.empty_like(y_value)\n",
    "testPredictPlot[:, :] = numpy.nan\n",
    "testPredictPlot[len(trainPredict_inverted)+(look_back*2)+1:len(y_value)-1, :] = testPredict_inverted\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(y_value), label= \"Original Data\")\n",
    "#plt.plot(trainPredictPlot, label= \"Train Predicted data\")\n",
    "plt.plot(testPredictPlot, label= \"Test Predicted data\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot = numpy.empty_like(y_value)\n",
    "trainPredictPlot[:, :] = numpy.nan\n",
    "trainPredictPlot[look_back:len(trainPredict_inverted)+look_back, :] = trainPredict_inverted\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = numpy.empty_like(y_value)\n",
    "testPredictPlot[:, :] = numpy.nan\n",
    "testPredictPlot[len(trainPredict_inverted)+(look_back*2)+1:len(y_value)-1, :] = testPredict_inverted\n",
    "# plot baseline and predictions\n",
    "fig= plt.figure(figsize=(15,8))\n",
    "ax = fig.add_subplot(111)\n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(5)\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "plt.plot(x, scaler.inverse_transform(y_value), label= \"Original Data\", color= \"grey\", linewidth=4)\n",
    "#plt.plot(trainPredictPlot, label= \"Train Predicted data\")\n",
    "plt.plot(x, testPredictPlot, label= \"Test Predicted data\", color= \"Dodgerblue\", linewidth= 4)\n",
    "plt.title(\"Prediction of Units Sold using LSTM\")\n",
    "plt.xlabel(\"Time Period\")\n",
    "plt.ylabel(\"Units Sold\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_pred=pd.DataFrame(testPredict_inverted[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list = [] #making new dataframe of predicted units sold value to save it as a csv file\n",
    "for i in range(len(testPredict_inverted)):\n",
    "    \n",
    "    x = testPredict_inverted[i][0]\n",
    "    new_list.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list= pd.DataFrame(np.array(new_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.750671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.306503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.042950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91.908394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76.816338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>69.239784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74.750671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72.684280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58.918308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50.016060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71.306503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>80.944084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>78.192879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>86.436028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>94.635170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>81.631416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>91.908394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>96.675461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>122.681656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>83.005440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>99.388863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>91.225639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>184.759796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>144.869553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>94.635170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>115.461357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>76.127884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>93.272644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>80.256554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>70.617584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>79.568832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>87.806213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>83.005440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>81.631416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>82.318542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>108.143631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>94.635170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>94.635170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>92.590721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>105.461136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>118.098869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>92.590721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>71.306503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>87.121284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>104.788902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>114.137718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>110.815033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>94.635170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>126.574394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>45.930016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "0    74.750671\n",
       "1    71.306503\n",
       "2    63.042950\n",
       "3    91.908394\n",
       "4    76.816338\n",
       "5    69.239784\n",
       "6    74.750671\n",
       "7    72.684280\n",
       "8    58.918308\n",
       "9    50.016060\n",
       "10   71.306503\n",
       "11   80.944084\n",
       "12   78.192879\n",
       "13   86.436028\n",
       "14   94.635170\n",
       "15   81.631416\n",
       "16   91.908394\n",
       "17   96.675461\n",
       "18  122.681656\n",
       "19   83.005440\n",
       "20   99.388863\n",
       "21   91.225639\n",
       "22  184.759796\n",
       "23  144.869553\n",
       "24   94.635170\n",
       "25  115.461357\n",
       "26   76.127884\n",
       "27   93.272644\n",
       "28   80.256554\n",
       "29   70.617584\n",
       "30   79.568832\n",
       "31   87.806213\n",
       "32   83.005440\n",
       "33   81.631416\n",
       "34   82.318542\n",
       "35  108.143631\n",
       "36   94.635170\n",
       "37   94.635170\n",
       "38   92.590721\n",
       "39  105.461136\n",
       "40  118.098869\n",
       "41   92.590721\n",
       "42   71.306503\n",
       "43   87.121284\n",
       "44  104.788902\n",
       "45  114.137718\n",
       "46  110.815033\n",
       "47   94.635170\n",
       "48  126.574394\n",
       "49   45.930016"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 74.75067 ],\n",
       "       [ 71.3065  ],\n",
       "       [ 63.04295 ],\n",
       "       [ 91.908394],\n",
       "       [ 76.81634 ],\n",
       "       [ 69.239784],\n",
       "       [ 74.75067 ],\n",
       "       [ 72.68428 ],\n",
       "       [ 58.91831 ],\n",
       "       [ 50.01606 ],\n",
       "       [ 71.3065  ],\n",
       "       [ 80.944084],\n",
       "       [ 78.19288 ],\n",
       "       [ 86.43603 ],\n",
       "       [ 94.63517 ],\n",
       "       [ 81.63142 ],\n",
       "       [ 91.908394],\n",
       "       [ 96.67546 ],\n",
       "       [122.681656],\n",
       "       [ 83.00544 ],\n",
       "       [ 99.38886 ],\n",
       "       [ 91.22564 ],\n",
       "       [184.7598  ],\n",
       "       [144.86955 ],\n",
       "       [ 94.63517 ],\n",
       "       [115.46136 ],\n",
       "       [ 76.127884],\n",
       "       [ 93.272644],\n",
       "       [ 80.25655 ],\n",
       "       [ 70.617584],\n",
       "       [ 79.56883 ],\n",
       "       [ 87.80621 ],\n",
       "       [ 83.00544 ],\n",
       "       [ 81.63142 ],\n",
       "       [ 82.31854 ],\n",
       "       [108.14363 ],\n",
       "       [ 94.63517 ],\n",
       "       [ 94.63517 ],\n",
       "       [ 92.59072 ],\n",
       "       [105.461136],\n",
       "       [118.09887 ],\n",
       "       [ 92.59072 ],\n",
       "       [ 71.3065  ],\n",
       "       [ 87.121284],\n",
       "       [104.7889  ],\n",
       "       [114.13772 ],\n",
       "       [110.81503 ],\n",
       "       [ 94.63517 ],\n",
       "       [126.574394],\n",
       "       [ 45.930016]], dtype=float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testPredict_inverted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list.to_csv('lstm_pred.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'blue'>  **Clustering 1** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cluster= df1[[\"WeekId\",\"WeekEnding\", \"RetailerSku\", \"UnitsSold\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cluster1 = df_cluster.groupby(['RetailerSku']).size().reset_index(name='count').sort_values(by = ['count'], ascending = False)#groupby of units sold at sku level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_active_cluster= df_cluster1[df_cluster1[\"count\"]==161] #filtering active ASIN which is having values for total 161 weeks\n",
    "\n",
    "#filtering active ASIN for 3 years\n",
    "#merge with actual sales dataframe\n",
    "df_new = pd.merge(df_cluster,df_active_cluster,on='RetailerSku',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10626 entries, 0 to 10625\n",
      "Data columns (total 5 columns):\n",
      "WeekId         10626 non-null int64\n",
      "WeekEnding     10626 non-null datetime64[ns]\n",
      "RetailerSku    10626 non-null object\n",
      "UnitsSold      10626 non-null int64\n",
      "count          10626 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(3), object(1)\n",
      "memory usage: 498.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new[\"WeekId\"]= df_new[\"WeekId\"].astype(str)\n",
    "df_active_2019 = df_new[df_new['WeekId'].apply(lambda x: x.startswith('2019'))]#filtering 2019 active ASIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2019 = df_active_2019.groupby(['WeekId', \"RetailerSku\"], as_index= False)[\"UnitsSold\"].sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_structured = df_2019.pivot_table('UnitsSold', [\"RetailerSku\"], 'WeekId') #transforming WeekIds into columns to see sales trend of each ASIN across weekends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans #Kmeans clustering for active 2019 ASINs\n",
    "km_x= df_structured.iloc[:, 1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0, ..., 55, 26,  2],\n",
       "       [24, 26, 66, ..., 73, 91,  2],\n",
       "       [17, 24, 19, ..., 20, 19,  2],\n",
       "       ...,\n",
       "       [15,  5,  1, ..., 13,  6,  2],\n",
       "       [13,  8, 17, ...,  0,  0,  2],\n",
       "       [31, 38, 34, ..., 67, 29,  2]], dtype=int64)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting elbow graph\n",
    "wcss=[]\n",
    "for i in range(1,11):\n",
    "    kmeans= KMeans(n_clusters=i, init=\"k-means++\", max_iter=300, n_init=10, random_state=0)\n",
    "    kmeans.fit(km_x)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.xticks(np.arange(1,11,1))\n",
    "plt.title(\"Elbow Method Graph\")\n",
    "plt.xlabel(\"# of clusters\")\n",
    "plt.ylabel(\"wcss\")    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying k-means with number of cluster-3\n",
    "kmeans=KMeans(n_clusters=3, init=\"k-means++\", n_init=10, max_iter=300, random_state=0)\n",
    "km_y_pred= kmeans.fit_predict(km_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 0, 1, 0, 0, 0, 1, 2, 0, 2, 2, 2, 2,\n",
       "       0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km_y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_structured[\"Cluster\"]= km_y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA #since there are 52 dimension, hence using PCA to visualize the graph in reduced dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>pca1</th>\n",
       "      <th>pca2</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2087.228488</td>\n",
       "      <td>-465.193173</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1971.942282</td>\n",
       "      <td>-409.980119</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2050.296129</td>\n",
       "      <td>-333.201082</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1893.824256</td>\n",
       "      <td>-300.653079</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>553.849586</td>\n",
       "      <td>273.657079</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pca1        pca2  Cluster\n",
       "0 -2087.228488 -465.193173        2\n",
       "1 -1971.942282 -409.980119        2\n",
       "2 -2050.296129 -333.201082        2\n",
       "3 -1893.824256 -300.653079        2\n",
       "4   553.849586  273.657079        0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_dim = PCA(n_components=2).fit_transform(km_x)#fitting PCA with n components =2\n",
    "result = pd.DataFrame(reduced_dim,columns=['pca1','pca2'])\n",
    "result[\"Cluster\"]= km_y_pred\n",
    "result.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"pca1\", y=\"pca2\", hue=result[\"Cluster\"], palette=['green',\"orange\", 'red'], data=result) #plotting clustering graph using PCA axis\n",
    "plt.title('K-means Clustering with reduced dimensions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1)) #feature scaling the data\n",
    "km_x_scaled = scaler.fit_transform(km_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        8.90111669e-03, 5.50031733e-03, 1.00000000e+00],\n",
       "       [5.38116592e-03, 6.13062957e-03, 1.58197507e-02, ...,\n",
       "        1.18142094e-02, 1.92511106e-02, 1.00000000e+00],\n",
       "       [3.81165919e-03, 5.65904268e-03, 4.55417066e-03, ...,\n",
       "        3.23676970e-03, 4.01946266e-03, 1.00000000e+00],\n",
       "       ...,\n",
       "       [3.36322870e-03, 1.17896722e-03, 2.39693193e-04, ...,\n",
       "        2.10390031e-03, 1.26930400e-03, 1.00000000e+00],\n",
       "       [2.91479821e-03, 1.88634756e-03, 4.07478428e-03, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       [6.95067265e-03, 8.96015091e-03, 8.14956855e-03, ...,\n",
       "        1.08431785e-02, 6.13496933e-03, 1.00000000e+00]])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km_x_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wcss=[]\n",
    "for i in range(1,11): #plotting elbow graph for scaled data\n",
    "    kmeans= KMeans(n_clusters=i, init=\"k-means++\", max_iter=300, n_init=10, random_state=0)\n",
    "    kmeans.fit(km_x_scaled)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "fig= plt.figure(figsize=(15,8))\n",
    "ax = fig.add_subplot(111)\n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(5)\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.xticks(np.arange(1,11,1))\n",
    "plt.title(\"Elbow Method Graph\")\n",
    "plt.xlabel(\"# of clusters\")\n",
    "plt.ylabel(\"wcss\")    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying k-means with number of cluster-3 \n",
    "kmeans=KMeans(n_clusters=3, init=\"k-means++\", n_init=10, max_iter=300, random_state=0)\n",
    "km_y_pred_scaled= kmeans.fit_predict(km_x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 2, 0, 0, 0, 2, 1, 0, 1, 1, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km_y_pred_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>pca1</th>\n",
       "      <th>pca2</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.487508</td>\n",
       "      <td>0.160318</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.459223</td>\n",
       "      <td>0.162589</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.470475</td>\n",
       "      <td>0.156263</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.437871</td>\n",
       "      <td>0.159500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.286444</td>\n",
       "      <td>-0.711785</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pca1      pca2  Cluster\n",
       "0 -0.487508  0.160318        1\n",
       "1 -0.459223  0.162589        1\n",
       "2 -0.470475  0.156263        1\n",
       "3 -0.437871  0.159500        1\n",
       "4  0.286444 -0.711785        0"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_dim = PCA(n_components=2).fit_transform(km_x_scaled)\n",
    "result = pd.DataFrame(reduced_dim,columns=['pca1','pca2'])\n",
    "result[\"Cluster\"]= km_y_pred_scaled\n",
    "result.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure(figsize=(15,8)) #cluster graph of scaled data\n",
    "ax = fig.add_subplot(111)\n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(5)\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "sns.scatterplot(x=\"pca1\", y=\"pca2\",s=100, hue=result[\"Cluster\"], palette=['green',\"orange\", 'red'], data=result)\n",
    "plt.title('K-means Clustering with reduced dimensions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_structured[\"Cluster\"]= km_y_pred_scaled #adding column for cluster mapping of each ASIN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'blue'>  **Clustering-2 (after adding category)** </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cluster_new= df1[[\"WeekId\",\"WeekEnding\", \"RetailerSku\", \"UnitsSold\", \"Category\"]]\n",
    "\n",
    "aggr_cat= {\"Category\":\"last\"}\n",
    "df_sku_cat = df_cluster_new.groupby([\"RetailerSku\"], as_index= False).agg(aggr_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merge = pd.merge(df_structured,df_sku_cat,on='RetailerSku',how='inner')\n",
    "\n",
    "df_merge= df_merge.drop([\"Cluster\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "category= pd.get_dummies(df_merge[\"Category\"], drop_first= True)\n",
    "df_merge.drop([\"Category\"], axis=1, inplace= True)\n",
    "\n",
    "df_with_dummy= pd.concat([df_merge, category], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "km_x= df_with_dummy.iloc[:, 1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting elbow graph\n",
    "wcss=[]\n",
    "for i in range(1,11):\n",
    "    kmeans= KMeans(n_clusters=i, init=\"k-means++\", max_iter=300, n_init=10, random_state=0)\n",
    "    kmeans.fit(km_x)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.xticks(np.arange(1,11,1))\n",
    "plt.title(\"Elbow Method Graph\")\n",
    "plt.xlabel(\"# of clusters\")\n",
    "plt.ylabel(\"wcss\")    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying k-means with number of cluster-2\n",
    "kmeans=KMeans(n_clusters=3, init=\"k-means++\", n_init=10, max_iter=300, random_state=0)\n",
    "km_y_pred= kmeans.fit_predict(km_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_dummy[\"Cluster\"]= km_y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>pca1</th>\n",
       "      <th>pca2</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2107.671689</td>\n",
       "      <td>-467.704030</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1992.086490</td>\n",
       "      <td>-412.858574</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2068.763383</td>\n",
       "      <td>-335.123025</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1911.248849</td>\n",
       "      <td>-302.932009</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>544.996456</td>\n",
       "      <td>264.277929</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pca1        pca2  Cluster\n",
       "0 -2107.671689 -467.704030        2\n",
       "1 -1992.086490 -412.858574        2\n",
       "2 -2068.763383 -335.123025        2\n",
       "3 -1911.248849 -302.932009        2\n",
       "4   544.996456  264.277929        0"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_dim = PCA(n_components=2).fit_transform(km_x)\n",
    "result = pd.DataFrame(reduced_dim,columns=['pca1','pca2'])\n",
    "result[\"Cluster\"]= km_y_pred\n",
    "result.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"pca1\", y=\"pca2\", hue=result[\"Cluster\"], palette=['green',\"orange\",\"red\"], data=result)\n",
    "plt.title('K-means Clustering with reduced dimensions')\n",
    "plt.show()"
   ]
  },
  {
   "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": 2
}