Final_CodeX_energy_drinks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2ff3baf3",
   "metadata": {},
   "source": [
    "## ----- Data Cleaning and preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "95ea99ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries \n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d266388",
   "metadata": {},
   "source": [
    "### There are 3 datasets which are needed and we have to combine them for further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f364c90",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv(r\"C:\\Users\\91701\\Downloads\\C6 Input Files\\C6 Input Files\\Dataset\\dim_repondents.csv\")\n",
    "df2=pd.read_csv(r\"C:\\Users\\91701\\Downloads\\C6 Input Files\\C6 Input Files\\Dataset\\fact_survey_responses.csv\")\n",
    "df3=pd.read_csv(r\"C:\\Users\\91701\\Downloads\\C6 Input Files\\C6 Input Files\\Dataset\\dim_cities.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c2cb008e",
   "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>Respondent_ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120031</td>\n",
       "      <td>Aniruddh Issac</td>\n",
       "      <td>15-18</td>\n",
       "      <td>Female</td>\n",
       "      <td>CT117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120032</td>\n",
       "      <td>Trisha Rout</td>\n",
       "      <td>19-30</td>\n",
       "      <td>Male</td>\n",
       "      <td>CT118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120033</td>\n",
       "      <td>Yuvraj  Virk</td>\n",
       "      <td>15-18</td>\n",
       "      <td>Male</td>\n",
       "      <td>CT116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent_ID            Name    Age  Gender City_ID\n",
       "0         120031  Aniruddh Issac  15-18  Female   CT117\n",
       "1         120032     Trisha Rout  19-30    Male   CT118\n",
       "2         120033    Yuvraj  Virk  15-18    Male   CT116"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50c1c1df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Respondent_ID  10000 non-null  int64 \n",
      " 1   Name           10000 non-null  object\n",
      " 2   Age            10000 non-null  object\n",
      " 3   Gender         10000 non-null  object\n",
      " 4   City_ID        10000 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 390.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()\n",
    "# no null values and this dataset has 10k rows and 5 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a63835e1",
   "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>Response_ID</th>\n",
       "      <th>Respondent_ID</th>\n",
       "      <th>Consume_frequency</th>\n",
       "      <th>Consume_time</th>\n",
       "      <th>Consume_reason</th>\n",
       "      <th>Heard_before</th>\n",
       "      <th>Brand_perception</th>\n",
       "      <th>General_perception</th>\n",
       "      <th>Tried_before</th>\n",
       "      <th>Taste_experience</th>\n",
       "      <th>...</th>\n",
       "      <th>Improvements_desired</th>\n",
       "      <th>Ingredients_expected</th>\n",
       "      <th>Health_concerns</th>\n",
       "      <th>Interest_in_natural_or_organic</th>\n",
       "      <th>Marketing_channels</th>\n",
       "      <th>Packaging_preference</th>\n",
       "      <th>Limited_edition_packaging</th>\n",
       "      <th>Price_range</th>\n",
       "      <th>Purchase_location</th>\n",
       "      <th>Typical_consumption_situations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103001</td>\n",
       "      <td>120031</td>\n",
       "      <td>2-3 times a week</td>\n",
       "      <td>To stay awake during work/study</td>\n",
       "      <td>Increased energy and focus</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Not sure</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>Reduced sugar content</td>\n",
       "      <td>Guarana</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>TV commercials</td>\n",
       "      <td>Compact and portable cans</td>\n",
       "      <td>Yes</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Supermarkets</td>\n",
       "      <td>Studying/working late</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103002</td>\n",
       "      <td>120032</td>\n",
       "      <td>2-3 times a month</td>\n",
       "      <td>Throughout the day</td>\n",
       "      <td>To boost performance</td>\n",
       "      <td>No</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Not sure</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>More natural ingredients</td>\n",
       "      <td>Caffeine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Not Sure</td>\n",
       "      <td>Print media</td>\n",
       "      <td>Compact and portable cans</td>\n",
       "      <td>No</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Supermarkets</td>\n",
       "      <td>Sports/exercise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103003</td>\n",
       "      <td>120033</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>Before exercise</td>\n",
       "      <td>Increased energy and focus</td>\n",
       "      <td>No</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Not sure</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>More natural ingredients</td>\n",
       "      <td>Caffeine</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Online ads</td>\n",
       "      <td>Innovative bottle design</td>\n",
       "      <td>Not Sure</td>\n",
       "      <td>100-150</td>\n",
       "      <td>Supermarkets</td>\n",
       "      <td>Studying/working late</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Response_ID  Respondent_ID  Consume_frequency  \\\n",
       "0       103001         120031   2-3 times a week   \n",
       "1       103002         120032  2-3 times a month   \n",
       "2       103003         120033             Rarely   \n",
       "\n",
       "                      Consume_time              Consume_reason Heard_before  \\\n",
       "0  To stay awake during work/study  Increased energy and focus          Yes   \n",
       "1               Throughout the day        To boost performance           No   \n",
       "2                  Before exercise  Increased energy and focus           No   \n",
       "\n",
       "  Brand_perception General_perception Tried_before  Taste_experience  ...  \\\n",
       "0          Neutral           Not sure           No                 5  ...   \n",
       "1          Neutral           Not sure           No                 5  ...   \n",
       "2          Neutral           Not sure           No                 2  ...   \n",
       "\n",
       "       Improvements_desired Ingredients_expected Health_concerns  \\\n",
       "0     Reduced sugar content              Guarana              No   \n",
       "1  More natural ingredients             Caffeine             Yes   \n",
       "2  More natural ingredients             Caffeine              No   \n",
       "\n",
       "  Interest_in_natural_or_organic Marketing_channels  \\\n",
       "0                            Yes     TV commercials   \n",
       "1                       Not Sure        Print media   \n",
       "2                            Yes         Online ads   \n",
       "\n",
       "        Packaging_preference Limited_edition_packaging Price_range  \\\n",
       "0  Compact and portable cans                       Yes       50-99   \n",
       "1  Compact and portable cans                        No       50-99   \n",
       "2   Innovative bottle design                  Not Sure     100-150   \n",
       "\n",
       "  Purchase_location Typical_consumption_situations  \n",
       "0      Supermarkets          Studying/working late  \n",
       "1      Supermarkets                Sports/exercise  \n",
       "2      Supermarkets          Studying/working late  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3b27faf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 23 columns):\n",
      " #   Column                          Non-Null Count  Dtype \n",
      "---  ------                          --------------  ----- \n",
      " 0   Response_ID                     10000 non-null  int64 \n",
      " 1   Respondent_ID                   10000 non-null  int64 \n",
      " 2   Consume_frequency               10000 non-null  object\n",
      " 3   Consume_time                    10000 non-null  object\n",
      " 4   Consume_reason                  10000 non-null  object\n",
      " 5   Heard_before                    10000 non-null  object\n",
      " 6   Brand_perception                10000 non-null  object\n",
      " 7   General_perception              10000 non-null  object\n",
      " 8   Tried_before                    10000 non-null  object\n",
      " 9   Taste_experience                10000 non-null  int64 \n",
      " 10  Reasons_preventing_trying       10000 non-null  object\n",
      " 11  Current_brands                  10000 non-null  object\n",
      " 12  Reasons_for_choosing_brands     10000 non-null  object\n",
      " 13  Improvements_desired            10000 non-null  object\n",
      " 14  Ingredients_expected            10000 non-null  object\n",
      " 15  Health_concerns                 10000 non-null  object\n",
      " 16  Interest_in_natural_or_organic  10000 non-null  object\n",
      " 17  Marketing_channels              10000 non-null  object\n",
      " 18  Packaging_preference            10000 non-null  object\n",
      " 19  Limited_edition_packaging       10000 non-null  object\n",
      " 20  Price_range                     10000 non-null  object\n",
      " 21  Purchase_location               10000 non-null  object\n",
      " 22  Typical_consumption_situations  10000 non-null  object\n",
      "dtypes: int64(3), object(20)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()\n",
    "# # no null values and this dataset has 10k rows and 23 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9eb7cdc7",
   "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>City_ID</th>\n",
       "      <th>City</th>\n",
       "      <th>Tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CT111</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Tier 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CT112</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Tier 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CT113</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Tier 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  City_ID       City    Tier\n",
       "0   CT111      Delhi  Tier 1\n",
       "1   CT112     Mumbai  Tier 1\n",
       "2   CT113  Bangalore  Tier 1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "943984b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   City_ID  10 non-null     object\n",
      " 1   City     10 non-null     object\n",
      " 2   Tier     10 non-null     object\n",
      "dtypes: object(3)\n",
      "memory usage: 368.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df3.info()\n",
    "# no null values and this dataset has 10 rows and 3 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31bc0adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets merge df1 and df2 on the basis of common column \"Respondent_ID\"\n",
    "df4=pd.merge(df1, df2, on = 'Respondent_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96ab2ee2",
   "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>Respondent_ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City_ID</th>\n",
       "      <th>Response_ID</th>\n",
       "      <th>Consume_frequency</th>\n",
       "      <th>Consume_time</th>\n",
       "      <th>Consume_reason</th>\n",
       "      <th>Heard_before</th>\n",
       "      <th>...</th>\n",
       "      <th>Improvements_desired</th>\n",
       "      <th>Ingredients_expected</th>\n",
       "      <th>Health_concerns</th>\n",
       "      <th>Interest_in_natural_or_organic</th>\n",
       "      <th>Marketing_channels</th>\n",
       "      <th>Packaging_preference</th>\n",
       "      <th>Limited_edition_packaging</th>\n",
       "      <th>Price_range</th>\n",
       "      <th>Purchase_location</th>\n",
       "      <th>Typical_consumption_situations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120031</td>\n",
       "      <td>Aniruddh Issac</td>\n",
       "      <td>15-18</td>\n",
       "      <td>Female</td>\n",
       "      <td>CT117</td>\n",
       "      <td>103001</td>\n",
       "      <td>2-3 times a week</td>\n",
       "      <td>To stay awake during work/study</td>\n",
       "      <td>Increased energy and focus</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Reduced sugar content</td>\n",
       "      <td>Guarana</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>TV commercials</td>\n",
       "      <td>Compact and portable cans</td>\n",
       "      <td>Yes</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Supermarkets</td>\n",
       "      <td>Studying/working late</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120032</td>\n",
       "      <td>Trisha Rout</td>\n",
       "      <td>19-30</td>\n",
       "      <td>Male</td>\n",
       "      <td>CT118</td>\n",
       "      <td>103002</td>\n",
       "      <td>2-3 times a month</td>\n",
       "      <td>Throughout the day</td>\n",
       "      <td>To boost performance</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>More natural ingredients</td>\n",
       "      <td>Caffeine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Not Sure</td>\n",
       "      <td>Print media</td>\n",
       "      <td>Compact and portable cans</td>\n",
       "      <td>No</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Supermarkets</td>\n",
       "      <td>Sports/exercise</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent_ID            Name    Age  Gender City_ID  Response_ID  \\\n",
       "0         120031  Aniruddh Issac  15-18  Female   CT117       103001   \n",
       "1         120032     Trisha Rout  19-30    Male   CT118       103002   \n",
       "\n",
       "   Consume_frequency                     Consume_time  \\\n",
       "0   2-3 times a week  To stay awake during work/study   \n",
       "1  2-3 times a month               Throughout the day   \n",
       "\n",
       "               Consume_reason Heard_before  ...      Improvements_desired  \\\n",
       "0  Increased energy and focus          Yes  ...     Reduced sugar content   \n",
       "1        To boost performance           No  ...  More natural ingredients   \n",
       "\n",
       "  Ingredients_expected Health_concerns  Interest_in_natural_or_organic  \\\n",
       "0              Guarana              No                             Yes   \n",
       "1             Caffeine             Yes                        Not Sure   \n",
       "\n",
       "  Marketing_channels       Packaging_preference Limited_edition_packaging  \\\n",
       "0     TV commercials  Compact and portable cans                       Yes   \n",
       "1        Print media  Compact and portable cans                        No   \n",
       "\n",
       "  Price_range Purchase_location Typical_consumption_situations  \n",
       "0       50-99      Supermarkets          Studying/working late  \n",
       "1       50-99      Supermarkets                Sports/exercise  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ae1995f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets merge df3 and df4 on the basis of common column \"City_ID\"\n",
    "df=pd.merge(df4, df3, on = 'City_ID')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc3bc626",
   "metadata": {},
   "source": [
    "### this is our final dataset for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "681319a3",
   "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>Respondent_ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City_ID</th>\n",
       "      <th>Response_ID</th>\n",
       "      <th>Consume_frequency</th>\n",
       "      <th>Consume_time</th>\n",
       "      <th>Consume_reason</th>\n",
       "      <th>Heard_before</th>\n",
       "      <th>...</th>\n",
       "      <th>Health_concerns</th>\n",
       "      <th>Interest_in_natural_or_organic</th>\n",
       "      <th>Marketing_channels</th>\n",
       "      <th>Packaging_preference</th>\n",
       "      <th>Limited_edition_packaging</th>\n",
       "      <th>Price_range</th>\n",
       "      <th>Purchase_location</th>\n",
       "      <th>Typical_consumption_situations</th>\n",
       "      <th>City</th>\n",
       "      <th>Tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120031</td>\n",
       "      <td>Aniruddh Issac</td>\n",
       "      <td>15-18</td>\n",
       "      <td>Female</td>\n",
       "      <td>CT117</td>\n",
       "      <td>103001</td>\n",
       "      <td>2-3 times a week</td>\n",
       "      <td>To stay awake during work/study</td>\n",
       "      <td>Increased energy and focus</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>TV commercials</td>\n",
       "      <td>Compact and portable cans</td>\n",
       "      <td>Yes</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Supermarkets</td>\n",
       "      <td>Studying/working late</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>Tier 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120055</td>\n",
       "      <td>Umang Sarkar</td>\n",
       "      <td>31-45</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>CT117</td>\n",
       "      <td>103025</td>\n",
       "      <td>Once a week</td>\n",
       "      <td>Before exercise</td>\n",
       "      <td>To enhance sports performance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Outdoor billboards</td>\n",
       "      <td>Eco-friendly design</td>\n",
       "      <td>No</td>\n",
       "      <td>Above 150</td>\n",
       "      <td>Gyms and fitness centers</td>\n",
       "      <td>Studying/working late</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>Tier 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent_ID            Name    Age      Gender City_ID  Response_ID  \\\n",
       "0         120031  Aniruddh Issac  15-18      Female   CT117       103001   \n",
       "1         120055    Umang Sarkar  31-45  Non-binary   CT117       103025   \n",
       "\n",
       "  Consume_frequency                     Consume_time  \\\n",
       "0  2-3 times a week  To stay awake during work/study   \n",
       "1       Once a week                  Before exercise   \n",
       "\n",
       "                  Consume_reason Heard_before  ... Health_concerns  \\\n",
       "0     Increased energy and focus          Yes  ...              No   \n",
       "1  To enhance sports performance          Yes  ...              No   \n",
       "\n",
       "  Interest_in_natural_or_organic  Marketing_channels  \\\n",
       "0                            Yes      TV commercials   \n",
       "1                             No  Outdoor billboards   \n",
       "\n",
       "        Packaging_preference Limited_edition_packaging Price_range  \\\n",
       "0  Compact and portable cans                       Yes       50-99   \n",
       "1        Eco-friendly design                        No   Above 150   \n",
       "\n",
       "          Purchase_location Typical_consumption_situations       City    Tier  \n",
       "0              Supermarkets          Studying/working late  Ahmedabad  Tier 2  \n",
       "1  Gyms and fitness centers          Studying/working late  Ahmedabad  Tier 2  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ff09e613",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets remove the column which are not needed\n",
    "df.drop([\"Name\",\"Respondent_ID\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd79b059",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10000 entries, 0 to 9999\n",
      "Data columns (total 27 columns):\n",
      " #   Column                          Non-Null Count  Dtype \n",
      "---  ------                          --------------  ----- \n",
      " 0   Age                             10000 non-null  object\n",
      " 1   Gender                          10000 non-null  object\n",
      " 2   City_ID                         10000 non-null  object\n",
      " 3   Response_ID                     10000 non-null  int64 \n",
      " 4   Consume_frequency               10000 non-null  object\n",
      " 5   Consume_time                    10000 non-null  object\n",
      " 6   Consume_reason                  10000 non-null  object\n",
      " 7   Heard_before                    10000 non-null  object\n",
      " 8   Brand_perception                10000 non-null  object\n",
      " 9   General_perception              10000 non-null  object\n",
      " 10  Tried_before                    10000 non-null  object\n",
      " 11  Taste_experience                10000 non-null  int64 \n",
      " 12  Reasons_preventing_trying       10000 non-null  object\n",
      " 13  Current_brands                  10000 non-null  object\n",
      " 14  Reasons_for_choosing_brands     10000 non-null  object\n",
      " 15  Improvements_desired            10000 non-null  object\n",
      " 16  Ingredients_expected            10000 non-null  object\n",
      " 17  Health_concerns                 10000 non-null  object\n",
      " 18  Interest_in_natural_or_organic  10000 non-null  object\n",
      " 19  Marketing_channels              10000 non-null  object\n",
      " 20  Packaging_preference            10000 non-null  object\n",
      " 21  Limited_edition_packaging       10000 non-null  object\n",
      " 22  Price_range                     10000 non-null  object\n",
      " 23  Purchase_location               10000 non-null  object\n",
      " 24  Typical_consumption_situations  10000 non-null  object\n",
      " 25  City                            10000 non-null  object\n",
      " 26  Tier                            10000 non-null  object\n",
      "dtypes: int64(2), object(25)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "# this dataset is having 10k rows and 27 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fea3899",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11eb5208",
   "metadata": {},
   "source": [
    "### 1. Demographic Insights "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153ae854",
   "metadata": {},
   "source": [
    "a. Who prefers energy drink more? (male/female/non-binary?)\n",
    "\n",
    "b. Which age group prefers energy drink more?\n",
    "\n",
    "c. Which type of marketing reaches the most Youth (15-30)?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd73e0e",
   "metadata": {},
   "source": [
    "1-a. Who prefers energy drink more? (male/female/non-binary?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2a411f5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pallete=['red','green','cyan','blue','yellow','salmon','orange']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1230b901-c0f7-43ae-a6da-98ac36009408",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gen=(df[\"Gender\"].value_counts()/100).sort_values(ascending=True).to_frame().reset_index().rename(columns={'index':'gender','Gender':'Percentage'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25428279-4e64-4244-918e-c1659a553749",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "57e9d432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '5.07'), Text(0, 0, '34.55'), Text(0, 0, '60.38')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4))\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('percentage_in_total')\n",
    "fig=(df[\"Gender\"].value_counts()/100).sort_values(ascending=True).plot.bar(color=pallete)\n",
    "# plt.bar_label()\n",
    "plt.bar_label(fig.containers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5f1c2d69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male          60.38\n",
       "Female        34.55\n",
       "Non-binary     5.07\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Gender\"].value_counts()/100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b94881d",
   "metadata": {},
   "source": [
    "### conclusion -Males prefer energy drink most with higest percentage which is 60.38% while women taking place with around 35% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c49c3aef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "813b831e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7a35bba4",
   "metadata": {},
   "source": [
    "1-b. Which age group prefers energy drink more?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "801e210b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'percentage_in_total')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.ylabel('Age_group')\n",
    "plt.xlabel('percentage_in_total')\n",
    "fig=(df[\"Age\"].value_counts()/100).sort_values(ascending=True).plot.barh(color=pallete)\n",
    "plt.bar_label(fig.containers[0])\n",
    "plt.xlabel('percentage_in_total')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ffc73d",
   "metadata": {},
   "source": [
    "### conclusion- Age group 19-30 prefers energy drink most taking lead place with around 55% while around 39% customers belong to 15-18 and 31-45 age group so you can not ignore this fact as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "903438f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19-30    55.0\n",
       "31-45    24.0\n",
       "15-18    15.0\n",
       "46-65     4.0\n",
       "65+       2.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df[\"Age\"].value_counts()/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0972fdbc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08e97088",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f35c06b7",
   "metadata": {},
   "source": [
    "1-c. Which type of marketing reaches the most Youth (15-30)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a584daa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7008, 27)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Age\"]==\"15-18\") | (df[\"Age\"]==\"19-30\")].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "018d41ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Online ads            48.0\n",
       "TV commercials        25.0\n",
       "Outdoor billboards    10.0\n",
       "Other                 10.0\n",
       "Print media            6.0\n",
       "Name: Marketing_channels, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df[(df[\"Age\"]==\"15-18\") | (df[\"Age\"]==\"19-30\")][\"Marketing_channels\"].value_counts()/70.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90347fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '6.4'),\n",
       " Text(0, 0, '10.0'),\n",
       " Text(0, 0, '10.0'),\n",
       " Text(0, 0, '25.5'),\n",
       " Text(0, 0, '48.1')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "fig=(df[(df[\"Age\"]==\"15-18\") | (df[\"Age\"]==\"19-30\")][\"Marketing_channels\"].value_counts()/70.08).sort_values(ascending=True).plot.barh(color=pallete)\n",
    "plt.bar_label(fig.containers[0],fmt='%.1f')\n",
    "#plt.xlabel('Marketing_channels')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a0cfc9",
   "metadata": {},
   "source": [
    "### conclusion- For age group 15-30 ,Online ads have taken first place with 48% while though TV commercials also around 25% young people are watching ads after that Outdoor billboards taking place with  10% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98581752",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7986a759",
   "metadata": {},
   "source": [
    "## Consumer Preferences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274624be",
   "metadata": {},
   "source": [
    "a. What are the preferred ingredients of energy drinks among respondents?\n",
    "\n",
    "b. What packaging preferences do respondents have for energy drinks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18c7dc30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "407d0866",
   "metadata": {},
   "source": [
    "2-a. What are the preferred ingredients of energy drinks among respondents?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "915db8f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '15.5'),\n",
       " Text(0, 0, '20.2'),\n",
       " Text(0, 0, '25.3'),\n",
       " Text(0, 0, '39.0')]"
      ]
     },
     "execution_count": 24,
     "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": [
    "fig=(df[\"Ingredients_expected\"].value_counts()/100).sort_values(ascending=True).plot.barh(color=pallete)\n",
    "plt.bar_label(fig.containers[0],fmt='%.1f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cad4a6c1",
   "metadata": {},
   "source": [
    "### conclusion-- Among 10k respondents 39% are expecting caffeine ,25% Vitamines while Sugar and Gurana haing 3rd and 4th place with 20% and 16% ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a255f848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Caffeine    38.96\n",
       "Vitamins    25.34\n",
       "Sugar       20.17\n",
       "Guarana     15.53\n",
       "Name: Ingredients_expected, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[\"Ingredients_expected\"].value_counts()/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "939ff0d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "86862434",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I tried to see the relationship between age group and ingredients expected but there is no \n",
    "# correlation between them \n",
    "# df[(df[\"Age\"]==\"19-30\")][\"Ingredients_expected\"].value_counts()/55.20 \n",
    "#do the same for other age groups as well"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af21977e",
   "metadata": {},
   "source": [
    "2-b. What packaging preferences do respondents have for energy drinks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "addbad47",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=(df[\"Packaging_preference\"].value_counts()/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "61ea3e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '4.8'),\n",
       " Text(0, 0, '9.8'),\n",
       " Text(0, 0, '15.0'),\n",
       " Text(0, 0, '30.5'),\n",
       " Text(0, 0, '39.8')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "fig=data.sort_values(ascending=True).plot.barh(color=pallete)\n",
    "plt.bar_label(fig.containers[0],fmt='%.1f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92807c80",
   "metadata": {},
   "source": [
    "### conclusion- Among 10k respondents 40% people are using 'Compact and portable cans' and 30% 'Innovative bottle design'  while 'Collectible packaging 'and 'Eco-friendly design' are taking 3rd and 4th place with 15% and 10%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f565a8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0754481c",
   "metadata": {},
   "source": [
    "## 3. Competition Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54e509e3",
   "metadata": {},
   "source": [
    "a. Who are the current market leaders?\n",
    "\n",
    "b. What are the primary reasons consumers prefer those brands over ours?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b14ddb0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5ba76aea",
   "metadata": {},
   "source": [
    "3-a. Who are the current market leaders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b2298e73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '4.8'),\n",
       " Text(0, 0, '9.8'),\n",
       " Text(0, 0, '9.8'),\n",
       " Text(0, 0, '10.6'),\n",
       " Text(0, 0, '18.5'),\n",
       " Text(0, 0, '21.1'),\n",
       " Text(0, 0, '25.4')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=(df[\"Current_brands\"].value_counts()/100).sort_values(ascending=True).plot.barh(color=pallete,figsize=(8,4))\n",
    "plt.bar_label(fig.containers[0],fmt='%.1f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d0346d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cola-Coka    25.38\n",
       "Bepsi        21.12\n",
       "Gangster     18.54\n",
       "Blue Bull    10.58\n",
       "CodeX         9.80\n",
       "Sky 9         9.79\n",
       "Others        4.79\n",
       "Name: Current_brands, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Current_brands\"].value_counts()/100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65d75a19",
   "metadata": {},
   "source": [
    "### Conclusion--Cola-Coka is on the top with 25.38% and Bepsi on the second place with 21% ,Gangster ,'Blue Bull',\"Codex\" are taking 3rd, 4th,5th place with 19%,11% and 10% respectively"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe6fa4e",
   "metadata": {},
   "source": [
    "## CodeX and 'Sky 9' is having almost same percentage of customers which is 9.8% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "296d1bc6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9d4df96",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "467d988d",
   "metadata": {},
   "source": [
    "3-b. What are the primary reasons consumers prefer those brands over ours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ec66d77e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=round(df[df[\"Current_brands\"]!=\"CodeX\"][\"Reasons_for_choosing_brands\"].value_counts()/90.20,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4c9ffc00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand reputation           26.5\n",
       "Taste/flavor preference    20.3\n",
       "Availability               19.0\n",
       "Effectiveness              17.4\n",
       "Other                      16.8\n",
       "Name: Reasons_for_choosing_brands, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8f7765df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '16.8'),\n",
       " Text(0, 0, '17.4'),\n",
       " Text(0, 0, '19.0'),\n",
       " Text(0, 0, '20.3'),\n",
       " Text(0, 0, '26.5')]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=x.sort_values(ascending=True).plot.barh(color=pallete,figsize=(9,3))\n",
    "plt.bar_label(fig.containers[0],fmt='%.1f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb3c0b5",
   "metadata": {},
   "source": [
    "## Around 27% are choosing brand just because of 'Brand Reputation'  and 20% are preferring beacause of 'taste/flavour'  and 19% are taking because of 'availability' and 17% because of enery drink \"effectiveness\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d50bac6",
   "metadata": {},
   "source": [
    "conclusion- As the data is showing that all these above reasons for choosing other brands over  CODEX are contributing almost with the same importance . so we must focus on each category of reason for chosing other brands equally ,like on the first place brand reputation is on the first place with 27 percentage of people, the reason is\n",
    "1-Brands save decision making time\n",
    "2-Brands provids safety\n",
    "3-Brand adds value\n",
    "4-Strong Brands give consumers a reason to share their experience. and so on \n",
    "so it becomes very important to creat a brand "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5c371119",
   "metadata": {},
   "outputs": [],
   "source": [
    "#round(df[df[\"Current_brands\"]==\"CodeX\"][\"Reasons_for_choosing_brands\"].value_counts()/9.81)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bb6ab8d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8e6d974c",
   "metadata": {},
   "source": [
    "## 4.Marketing Channels and Brand Awareness\n",
    "\n",
    "a. Which marketing channel can be used to reach more customers?\n",
    "\n",
    "b. How effective are different marketing strategies and channels in reaching our customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cbb0e2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6a9bb89d",
   "metadata": {},
   "source": [
    "4-a. Which marketing channel can be used to reach more customers?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edbac5e1",
   "metadata": {},
   "source": [
    "## 1-overall marketing channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9dfdd889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Marketing_channels'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "df[\"Marketing_channels\"].value_counts().plot(kind=\"pie\",autopct='%1.1f%%',shadow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb08c28b",
   "metadata": {},
   "source": [
    "## 2-for young population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "16cec605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAEYCAYAAAATRII7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABA90lEQVR4nO3deXxcZfXH8c+ZyZ62aZuu6TYtbSGthZZSoEBpqQhKAAFR/IGA4i4ioKhxQQdFjMqmZSkqsgmI7EgUEOwClK50CTTpnu477TT7Nuf3x53StEmamWYmdzJz3q/XvJJM7r3znS45ufc+z3lEVTHGGGPc5nE7gDHGGANWkIwxxsQJK0jGGGPighUkY4wxccEKkjHGmLhgBckYY0xcsIJkTJwQkcEi8rKIrBGRdSLyRxFJC2O/chHpE/p8XuyTtv7axnSUFSRj4oCICPAC8JKqjgJGA92A30RyHFU9IwbxjOkUVpCMiQ/TgVpVfQRAVZuAm4HrRCRLRL4sIi+IyGuhM6jft3YQEakMfZwmIrNF5DkRKRORJ0NFDxGZKCJzRGSJiLwuIgNbOc5FIrJARJaKyJsi0j/0fK6IvCEiH4rIX4GDx8wWkWIRWS4iH4jIFbH4QzKJzQqSMfFhLLCk+ROqegDYBIwMPTUeuAIYB1whIkPaOeYE4CZgDDACOFNEUoEZwOWqOhH4G62fhb0DnK6qE4B/AD8KPf9L4B1VHQu8CAwNPf9pYJuqnqSqnwBeC+M9G3OYFLcDGGPC9paqBgBEZCUwDNh8lO0XquqW0PbLAB+wH/gE8N/QCZMX2N7KvoOBZ0JnT2nAhtDzZwOXAahqsYjsCz1fAtwlIr8DXlXVt4/tLZpkZmdIxsSHlcDE5k+ISA+cM5C1oafqmn27ifZ/oWxtewE+VNXxocc4VT2vlX1nAPep6jjgm0DG0V5IVVcDJ+MUpttF5BftZDOmBStIxsSHt4AsEbkGQES8wF3Ao6paHcXXWQX0FZHJoddJFZGxrWyXA2wNfX5ts+fnAleG9v0M0Cv0eR5Qrap/B/6AU5yMiYgVJGPigDpt9y8FPi8ia4DVQC3w0yi/Tj1wOfA7EVkOLANaG5nnB54VkSXAnmbP3wacLSIf4ly62xR6fhywMHRp8JfA7dHMbZKD2PITxhhj4oGdIRljjIkLVpCMMcbEBStIxhhj4oIVJGOMMXHBCpIxxpi4YAXJGGNMXLCCZIwxJi5YLztjOsBXWNwD6A5kA1mhj80fzZ9Lx2nnUwNUhx41wAFgH/BR6LGvvKgg2KlvxJg4YBNjjTkKX2Gx4DQaHQkc1+xx8OseMXhZxWmCuh6n1c/q0MdVwOryooKqGLymMa6zgmRMiK+wOA+YApwGjMIpOsNxzmziyVYOFanVQCkwv7yoYL+boYzpKCtIJmn5CovzcQrQWaHHcHcTdUgQWA7MAWYDc8uLCvYddQ9j4owVJJMUfIXFqTgdqA8WoDOBPq6Giq0gzlIQszlUoD5yM5Ax7bGCZBJWqAidB3wB+CzOkgrJSnEK1H+Bf5QXFSx2OY8xLVhBMgnFV1icAnwSZ6nvSwit12NaWA08BTxVXlSwxu0wxoAVJJMAfIXFXuAcnDOhy4BcdxN1OYuBJ3HOnHa4HcYkLytIpsvyFRafCHwL+BzQz+U4iSAIzMI5c3q+vKgg4HIek2SsIJkuJTQv6NPA94FzXY6TyGqBx4E77ZKe6SxWkEyX4CssTgeuBm4GxrgcJ5kEgZeA35UXFSx0OYtJcFaQTFzzFRb3Bb4TethlOXfNAX5fXlTwb7eDmMRkBcnEpdCk1ZtxzooyXI5jDlcC3Ak8XV5U0OB2GJM4rCCZuOIrLB4M/Ba4ChCX45ij2wzcDTxYXlRQ53YY0/VZQTJxwVdYnA38GLgFyHQ5jonMeuAH5UUFL7kdxHRtVpCMq3yFxR7gWuB2IM/lOKZj3gRuLC8qWOl2ENM1WUEyrvEVFk8F7gEmuJ3FRE0j8ADwS+s+biJlBcl0Ol9h8UjgDzitfUxi2gP8DPirLTZowmUFyXSa0FyiXwE3AWnupjGdZCnOZby33Q5i4p8VJNMpfIXFJ+H0SxvrdhbjiseAG8qLCircDmLilxUkE1OhQQu3AL/GzoqS3TrgqvKiggVuBzHxyQqSiRlfYfEwnH5oZ7udxcSNRuA24A67t2SOZAXJxISvsPgaYAbQw+0sJi7NBb5UXlSw2e0gJn5YQTJR5Sss7g08BFzudhYT9/YB3ywvKnjW7SAmPlhBMlHjKyz+FPAoNsHVROYRnAEPVW4HMe7yuB3AJAZfYfGPgdewYmQi9xVgqa+w2CZIJzk7QzId4isszgD+AnzJ7Symy6sCrigvKih2O4hxh50hmWPmKyweiLNGjhUjEw3ZwMu+wuJvux3EuMPOkMwx8RUWjwdeBQa5HMUkpj8APy4vKrAfUEnECpKJmK+w+HzgOaCb21lMQnsWuKa8qKDW7SCmc9glOxMRX2HxdThnRlaMTKx9HnjLV1jcx+0gpnNYQTJh8xUW3wY8DKS4ncUkjTOA90Id4k2Cs0t2Jiy+wuI/ATe4ncMkrT3AxeVFBe+5HcTEjp0hmXb5Cov/gBUj464+wJuhRR1NgrKCZI7KV1h8O063bmPclgW86issPtPtICY2rCCZNvkKi3+Os+qnMfGiG/AfX2HxaW4HMdFn95BMq3yFxbfgzAUxJh4FgHPLiwoWux3ERI8VJNOCr7D4BuBPbucwph17gSnlRQWlbgcx0WEFyRzGV1j8DWAmIG5nMSYMW4Azy4sKNrkdxHScFSTzsdCieo9ixch0LauAs8qLCva4HcR0jBUkA4CvsPg84N+A1+0sxhyDRcD08qKCSreDmGNnBcngKyweASwGermdxZgOeBVn8qz9UOuibNh3kvMVFmcDL2HFyHR9FwI/dzuEOXZ2hpTkfIXF/8RpYmmOkQab2P7YzaR0z6Xf5b+kpnwZ+2c/gmoQT2omuQU3kdrr8IV067atYu/r94UOoPQ860qyRp9BU3WA3S/8hmBdJT2nXE3W6MkA7Hr+1/Q+7zukdM/t7LfX1QSBC8qLCl53O4iJnJ0hJbHQsuNWjDqoYvErpOYO+fjrj954gD4X3kLeV2aQPWYqgXnPtNgnte8wBl57L3lfmUG/L/yKva/fjwabqFo5h24TPsOAa+7mwOKXAaheu4C0/iOsGIXHAzzlKyz2uR3ERM4KUpIKDWK4w+0cXV3jgT3UrF9Et5POO/SkCMH6agCCdVV4u/VusZ8nNQPxOONHtLGegwMbxZuCNtShTY2Ix4MGm6hY/DI9TvtczN9LAukNPOcrLM5wO4iJjC0jkIRCgxj+gf1C0mH73vozPaddh4YKEEDup29g17N+JCUNT3oWA66+q9V967atYu+//0jjgV30ufD7iMdL9pip7HnlD1Quf42eU79MxfvFZI+djifVfrZGaCJwP/BVt4OY8NkPpCRjgxiip3rtQjzZPUkfcPhSPQcWv0y/z/sZfP1jZI87l33/+2ur+6fnHU/e1x5g4DX3EJj/LNpYjyc9m36f9zPw2ntJGzCSmrULyTr+TPb+50/sfvEO6rZaU4IIXOcrLP662yFM+KwgJZ+HgXFuh0gEdVtXUrNmAVsevI7dr/ye2o0r2PWsn4ZdG0jPOx6A7Pwp7RaR1D5DkNRM6ndvPOz5wLtPk3PGF6haOYf0wWPJLfg++995KmbvJ0HN8BUWn+J2CBMeK0hJxFdYfBVwhds5EkWvqV9m8PWPMfjbf6PvxT8iY9iJ9P3crQTrqmn4aCsANRuWHTbg4aCG/TvQYBMAjYFdNH60hZScfoe+/9FWmir2kjH0RLSxDkRADt5vMhFIB573FRbbiJAuwO4hJQlfYXE/4I9u50h04vGS++nvsvvFO0AET0Y3ci+4CYDqNQuo37GGnlO+RN2Wleye/xx4vYh46P2pb+PNyvn4OPvnPkHPs68GIDt/KrtfuJ0D858jZ8pVbrytrm4oMAO40u0g5uhsHlKS8BUWPwtc7nYOY1xUUF5U8G+3Q5i2WUFKAr7C4s8Bz7mdwxiXbQLGWr+7+GX3kBKcr7C4N87wV2OS3VDgN26HMG2zgpT47gX6ux3CmDjxXVv+PH7ZJbsE5issvgAodjuHMXGmBJhYXlTQ4HYQczg7Q0pQvsLiHsBDbucwJg6NA37kdgjTkhWkxPUHYLDbIYyJU7f6CouPdzuEOZxdsktAvsLiCcASbClyY45mLjDNFvSLH3aGlJjuwIqRMe05G7CZxnHEzpASjK+w+Gxgjts5jOki1gL55UUFjW4HMXaGlIh+63YAY7qQkcC1bocwDjtDSiC+wuKLgFfczmFMF7MRGF1eVGCda11mZ0gJwldY7MFmoRtzLIZhC/nFBStIieNKbJ0jY47Vz2zJc/dZQUoAvsLiVOA2t3MY04UNAr7pdohkZwUpMXwdGOF2CGO6uJ/4Couz3A6RzKwgdXG+wuJM4Fa3cxiTAPoD33U7RDKzgtT1/R8wwO0QxiSIH/kKi7u7HSJZhV2QRCRbRDyhz0eLyMUikhq7aCZM33Y7gDEJJBe4zu0QySqSM6S5QIaIDALeAK4GHo1FKBMeX2HxJOAUt3MYk2C+7naAZBVJQRJVrQYuAx5Q1c8DY2MTy4TpO24HMCYBjfUVFp/pdohkFFFBEpHJOM0IDy765o1+JBOO0NLkV7idw5gE9Q23AySjSArSTcBPgBdV9UMRGQHMikkqE46vAJluhzAmQX3eV1jc0+0QycZ62XVBvsJiAVbjNIY0xsTG98qLCma4HSKZpLS3gYj8C2izaqnqxVFNZMLxKawYGRNr3wCsIHWidgsScGfMU5hI2WAGY2LvE77C4jPKiwrmuR0kWbRbkFT148XeRCQTGKqqq2KayrTJV1g8BLjQ7RzGJIlvAFaQOkkkE2MvApYBr4W+Hi8itvZO5/sCNrrRmM7yBRvc0HkiGWXnB04F9gOo6jJgeNQTmfZ81u0AxiSRTOByt0Mki0gKUoOqBo54zobodSJfYXEucIbbOYxJMnaJvJNEUpA+FJErAa+IjBKRGdi11c52IXa5zpjOdq6vsDjd7RDJIJKCdANOq6A64GngAM5kWdN5bIi9MZ0vG5jqdohkEM6wbwBCfex+FnqYThb6De18t3MYk6QKcJpKmxgKuyCJyGjgFsDXfD9VnR79WKYVn8T5Tc0Y0/kKgBvdDpHowi5IwLPATOCvQFNs4pijsNF1xrjnOF9h8fHlRQU2BzOGIilIjar6YMySmDaFetdd5HYOY5JcAWAFKYYiGdTwLxH5jogMFJHeBx8xS2aamwQMdDuEMUmuwO0AiS6SgnQt8EOcod5LQo/FsQhlWrjA7QDGGKb4Cot7uB0ikUUyys66MrjndLcDGGNIxem0/7zbQRJVJPeQEJEzaDnK7vEoZzItneJ2AGMMAGdiBSlmIhn2/QRwHE6D1YOj7BSwghRDvsLi4UCu2zmMMQCc7HaARBbJGdIpwBi1JWY72yS3AxhjPjbeV1gs5UUF9nMwBiIZ1PABMCBWQUybrCAZEz9ygBFuh0hUkZwh9QFWishCnH52gC1h3gns/pEx8WUCsM7tEIkokoLkj1UI07rQhNiJbucwxhxmAvCc2yESUSTDvue0v5WJsuOB7m6HMMYcxgY2xEgkS5ifLiKLRKRSROpFpElEDsQynLH7R8bEoQluB0hUkQxquA/4P2ANzrK+XwPuj0Uo8zG7f2RM/OnvKyzOcztEIoqkIKGqawGvqjap6iPAp2MTy4SMdzuAMaZVdpYUA5EMaqgWkTRgmYj8HthOhAXNRMyGlxoTnyYAxW6HSDSRFJSrQ9t/F6gChgCfi0UoA77C4lTALgsYE5+GuR0gEUUyym5j6NNa4LbYxDHNDMHOQI2JV9YkIAYi6WV3Js5cpGEc3lzVLivFhs/tAMaYNtn6ZDEQyT2kh4GbcdZBsiXMYyybmiFVZLodwxjTOitIMSDh9koVkQWqelqM85iD/Dm3qvLTRry7KsnYv1dzKrdqn4ZyHcA6zUtfp3ndNmr/3G2a2y+Ix+t2XGOSTCOQXl5UEHQ7SCJp9wxJRA7OSp4lIn8AXuDwXnbvxyhbsusvQkYqTUN7UTW0l1Qxkm1MZcVhG6kSDCI7a0jfu0+7V2ynd+3GYH/WaV7Kes3L3qADem7Rvv1qSM9y6X0Yk4hScPp77nI7SCIJ55LdXUd83XyypgLToxfHNBPWTVMRPF60fzdq+3eTWoawm1M9q1psF1T215G6+wDZgV3aq3aT9mvcoAO8a4ODMjfowJyN2q/Pfrr3ivq7MCZxDcAKUlS1W5BU9ZzOCGJa6B/Ng3mEnpk09MxkP/1lP+PY0GIbVWoa8O6qJHP/HucSYeMGHcg6zctYpwO7bQr2z91O736Kx0b/GePcR1rR7lYmbJGMsrsD+L2q7g993Qv4gar+PEbZkl2nDysVITONpmG9qRzWWyoZzVbOYflh26jSFMSzo5r0vR9p94rt5NYdvES4Tgd226ADe27VPv3rSMvo7PzGdDIb+h1lkYyy+4yq/vTgF6q6T0QuAKwgxUZvtwO0RgSvl+CA7tQM6C41DGMXp3tKW2wXVPmojtQ9AbIDO51LhE3rdWDK2uCgjA06oOcm7df3AN1yXHgLxkSLjbSLskgKkldE0lW1DkBEMoH02MQyQJrbATrCI9o7k/remdQzQPZxEutbbKNKVQMpuyrIDOzRnKot2rdxgw6UdTowY10wr9tG7d9nFz372CVCE6fsDCnKIilITwJvicgjoa+/AjwW/UgmJOGHcouQnUbj8FwqyJUKjmcLsPSwbVRpbMKzo5qMvR9p96ptmlsXGvqeul4HdtugA3pt1b79G0jp0gXcdEnZbgdINJG0DvqdiCwHzg099WtVfT02sQyR/bKQsERISSGY14PqvB5SjY+dnMHKw7ZRRRXZU0va3v1kB3Zq77pN2q9pXXBgyjodlHnwEmElWT1cehsmMdn/0SiL6A9UVV8DXmvteyLynqpOjkoqA/aPPWwiiKB9sqjrk0UdefIRE1jb4hxTlYp6UnZXkBXYrT2rtmjfpvU6QNZpXsb6YF73jdqv72565oKIO+/EdDH2fzTKovkHaqOqosWf4wHsh2KUidA9ncbu6Rygjxwgn00ttlGlvgnP7ioy9u7VHlXbtE99ufZnnealrdO8buU6oNc2ze3fSEqqC2/BxBf7NxBl0SxI4fUgMuFI+PtH8UqEtBSCg3KoHpQj1YxgB2fxwWHbqKJBZLdzibDbgR3au3aj9g+uDw5MXat5meU6sNdm7du3isxuLr0N0znsDCnK7A80PtnfSxwTQbxo32zq+mZTxyDZy0TWtHaJ8EAdqbsryAq8ERy2Y92GK7Q25/ju7qQ20VYrbHU7Q6KJ5g8+u8QUPVaQEoAIPTJo6JFBgPJ+m+ecPuv+1B51vtyy46/cXZWddxoidsmna9vpdoBEE835HVdH8VjJzi7ZJZj5GRmpd13mHdSjonzEaYvvOOuM+T/fm7u3ZDahziemS2p0O0CiiaR1UAUt7xMFgMU4LYQ+aLmXOUZ2hpRgtqSmDG7MlaFr83h71DamZNTtH3BSycwBTZ60qnUjLp6zNW/KcPWkDHU7p4lIg9sBEk0kZ0j3Aj8EBgGDgVuAp4B/AH+LerLkVtf+Jqar+Mjj2dsoMhTg7ku9oxRqDn7PG6zPHr32uanT5t40ePTqZ+anNFaXuJfURMjOkKIskoJ0sao+pKoVqnpAVf8MnK+qzwC2bEE0+QMV2G9fCePdrIyPW6vv7SEDlg+XhUduI6hn8La5p5/9zg/HjV8+oySzZvd8VG3xt/hm/0ejLJKCVC0iXxART+jxBaA29D0b8h19e90OYKJjVlZWZfOvZ1zsGadwoK3te+8rGzd5gf/00xf+akvP/WvmoFoV+5TmGNS7HSDRRFKQrsIZuLALZ3TJ1cCXQk1WvxuDbMluj9sBTHQsS087bD5SRZb0fu8EWdrW9gdl1ewaevKye6dOefdHDQN2zJ+NBnfELqU5Bvb3EWWR9LJbD1zUxrffiU4c04wVpASxx+sdceRzD13gOfn0sqa9Hshtb//UxuqeY8qemHbCqqfqNw05953yYZ/uF/SmjY5NWhMBm4cUZZGMsusLfB3wNd9PVa+LfiyDFaSEsD41ZZOGBjQ0V5Mu3d8aL3M+tUynhnssjzal+Ta9fpZv0+vs6jvh/dUjLw/Wp/c8JbqJTQSsIEVZJMOLXwbeBt4EmmITxzRjBSkBzMnK3Ay0Opz70U95Tpu+vGm7VyNf6K3f7qUn99u9lIpuQ9aWHn/V9spug09FxPX1yarrKnlqzp1s31cOCFdNvYURA8Z+/P03lz3DorVvARAMNrFj/yaKrnmeoAb5yxu/pKaukgsnfYWThp8FwEOv3coVU26kZ3YfF95Nu6wgRVkkBSlLVX8csyTmSFaQEsDczMw2f3lrSJGMf50may+Zr8e88mj3ys0jT11SNLIurcfuVaOu+HBPn5NORMS11Yafm3cfY4ZM4mvn+WlsaqC+8fAZDOeOv4Jzx18BQEn5PGaVPE92Rg9ml7zAWfkXMn74FB74z085afhZlJTPY3CfkfFajMAKUtRFMqjh1dCS5aZzWEFKAGXpaUctDv+Y6pnc6GFjR18nvf5A3xM//Mu0qW9/P2Popv/OlWBjeUePGamaukrWbS9h8gnOj4kUbypZ6W33l128bhYTR04HwOtJob6xjsZgAx7x0BRsYlbJC3zqpCs6JfsxqLh+5vQ2R0qaYxNJQboRpyjViMgBEakQEfsLiR0b9t3FNUBDpcjIo20T9EjK01M9UftN2xuszxq5/qWzp829aegJq55ckNJQtTxax27P3ooddMvI4e+zf0/Rc9/kyTl3UtdQ0+q29Q21lG5exPjhUwA4ZeR0SjbOY8arP+L8CVfy9ocvc+roc0lLjdtVbezsKAbCLkiq2l1VPaqaqao9Ql/bCpyxs9vtAKZjlqenr0Wk3Z+o/zpNJtelsDqary2oJ2/7vNPOfvdHJ01Yeu/KzOqd81CN6b3fJm1i8541TBlzMYWXP0R6Sgb/XfaPVrct2fgeI/qPJTvD+RGSmd6Nb3/mDn78uQcZ0ncUJRvfY8KIqTw15y7++oaf9Ts+jGX0Y2EFKQbaLUgickLo48mtPWIfMWmtcTuA6ZjZ2Znh/VIhIn87z7M/Vjl6BdaMmbzwV2dMXuDf3mvfqjmoVsTkdbL70jO7L77++QCMH3E2m/e0/s94SbPLdUf6z5InOP/kq1i89n+MGPAJrj7nx/x7yeOxiNwRVpBiIJwzpO+HPt7VyuPOGOUysAGodjuEOXbzMjPC7to+6yTPqVXpxLRBcWbtnsETlv9p6pR3fhgcuP3d2WjT9mgev0dWb3p168vO/ZsBWLV1KQN6DmuxXU1dJWu3r+BE3xktvrcrsIX9VXsYnTee+sZaPOJBEBoa4669oxWkGBDV8Lr+iEiGqta295yJIn/OIsDmmXRRE31D1teLtJgU25ZJq4JLf/hCcEIsMzUXFE/D5sHTF27wXZAb9KafEI1jbtmzlifn3EVTsIE+PQbypWk/Ysm62QBMGePMq5+/6jVWbl7Edefe2mL/h//7Ky469Tr65QymomYff379F9TUV1FwypeZMOLsaESMlu9eP3P6/W6HSDSRFKT3VfXk9p4zUeTPeQT4stsxTOQOeCRw5tDBPRCJaOHKv/yxcWlONZ1WlA7a3efEpatHfqGxLr3nKZFmTlKXXj9z+ktuh0g07c5DEpEBOEtOZIrIBA6tDNsDyIphNgNxdyfXhGd+RsZ6nP8vEbn3Ek/aL5/q/CbfffesmNB3zwoqs/PWlx7/pa0V3YdOCmdARhJb6XaARBTOxNjzcX5LHwzc3ez5A8BPY5DJHGKLHnZRs7KzAsey34fDPGN35QQX9AtwWrQzhaNb1bYRk97//Yi61O67V4/6wvzdfcd/AvHE7cxUl1Rgg45iIpJLdp9T1edjnMc0588ZDGx2O4aJ3HlD8hZuT0k59Vj2Hb5D1xY90jRCIpsnGBNNnpTa8mEXLNo0ZPpg9aQOdztPnHj7+pnT4+qGVqKI5B/8uyLysIj8B0BExojIV2OUywD4A1twlok3XcxOr9d3rPtuGCAjN/fhvSjGOWbeYGPGcRtemTJt7k2+/LInFqbWVy5zO1MceN/tAIkqkoL0CPA6kBf6ejVwU7QDmRbsPlIXsyXFuy0o0q8jx7jrMu8QjaMVSQVk4I75p06Z9+PxJy+9uzSravu7qCbrEt5LIt1BRJpEZJmIfCAiz4pIq/ffRWReGMe6qa39o0lE/CJyS+jzX4nIubF+zUgKUh9V/ScQBFDnH6N1/Y49u4/Uxbydmbmpo8fYnitD1+QxPxp5oq1nYF3+6YtuP3Pygl/s6v3RyjmoJlsLsWM5Q6pR1fGq+gmclWa/1fybIpICoKotJ2e1dBOdPKBMVX+hqm/G+nUiKUhVIpJLaLlyETkdu5zUGezyQBczJyszKnPz7rnUO0qh9WZwcSCz9qO88Svun3r2Oz+QQVvnzpFg0xa3M3WCAFDawWO8DYwUkWki8raIvEJo1J6IVIY+ThOR2SLynIiUiciT4vgezlWqWSIy68gDi0i5iPw2dDa2ONRR53URWSci32q23Q9FZJGIrBCR25o9/zMRWS0i7wDHN3v+URG5PPT5L0L7fiAif5YoThOIpCB9H3gFOE5E3gUeB26IVhDTpv+5HcBE5oP0tF7ROM7eHjJg+XBZGI1jxVJKU13349c8M3Xa3BsHjlz73DxvY20iD4mef/3M6cc8Lj90JvQZoCT01MnAjara2grAE3DOhsYAI4AzVfVPwDbgHFU9p42X2aSq43EK36PA5cDpwG2hDOcBo4BTgfHARBE5W0QmAl8MPXcBMKmN49+nqpNCZ3uZwIVhvPWwRFKQ9gFTgTOAbwJjAdcXBEt4/sAa6PjyBKZzNEFTwOM5aofvSMy42DNOnSkWcU9Q79Ats86Y+s4Pxpy44oHlGbV7F6Da+ZOqYuudY9wvU0SWAYuBTcDDoecXquqGNvZZqKpb1PkzXIazWnc4Xgl9LAEWqGqFqu4G6kSkJ3Be6LEU5wrMCTgFagrwoqpWq3MZ9pUWR3acIyILRKQEmI5TC6IikoL0HNBfVT9U1Q+AycDfohXEHFXMr92a6FiZlrYekexoHa8iS3rPy5el0TpeZ+nz0YcnnTH/F6edtuj2TT0C6+eiGreXHiP07jHud/Ae0nhVvUFV60PPVx1ln+YN/JoIf0HVg/sFjzhGMHQMAX7bLM9IVX34yIO0RpzJ0g8Al6vqOOAvQNQmUEdSkL4FvCQiA0IL9c3AOa0zsWcFqYuYnZW5M9rHfOgCz8RgF12wMbt6h++UpXedfda8H9f037loNhrsysuqNAIL3A6BMzG3ewf2fx24TkS6AYjIIHFGhc4FLhGRTBHpDlzUyr4Hi8+e0P6XdyBHC2EvYa6qi0I31N4AaoFzQ6eBJvbewhlMYj3G4ty7WRnhzTSPQG2adHtzgiw5b6lOjfaxO0taQ1XvsaWPTssv+3td+bDz39k05FMDg97U49zOFaGl18+cHg8d+P8MvCYi245yH6lNqvqGiOQD74XGI1QCX1LV90XkGWA5sAtY1Mq++0XkLzijf3e0tk1HtNupQUT+RWhkXcgYYDvOPSVU9eJoBjJt8OcsA05yO4Y5uknDBq+u9Xhau0HdIamNWvv4nU37vMrAaB/bDQq6s/+kJWuO+5ynIa17V2nQfNv1M6f73Q6RyMI5Q7I1j+LDm1hBimvVIlW1IjH5rb8hRTJeOV3WXvqeJkRBEpABOxedMmDnIgLdfavKjr9qT1X2wFMRSXU721E853aARBdWLzsR8QJvHsvpoYkSf86ngf+4HcO0bU5mxorvDuh3YqyO7wlq49//0LQ1JUjLVe8SQG16r+2rRn9x1d7eYycgkuN2niOUXT9zer7bIRJdWIMaVLUJCEr8/SNJJnNxZnibODUrK2tfLI8f9EjK01M9CbtSaUbdvoEnlTw47ex3fuAdvGX2HAk2xVNjYWss3QkiGWVXCZSEGqz+6eAjVsHMEfyBao59DoTpBAsz02M+L+9fp8nkuhRWx/p13JTSVNdt9Npnp06be2Pe6DXPvOdtrImH9llWkDpB2KPsgBdCD+Oef+BMRDNxaFtKypCYv4iIPHyeJ/CdfyfafNOWBPUO3jp38uCtc9nbK79k1egvVtVm5J6KSGcvy7Hu+pnTu9xcsK4o7PWQTBzw5+TgDLWMy5U8NweCXPNSDTsrFRH4xsmp3Hh6Orf+r5aXVzXiEeiXLTx6SSZ53Vv/mXKgThlzfyWXnJDCfRdkUteofPYf1Ww5oHxnUhrfmZQGwDf+VcO3Tknj5IHeznyLbdrt9eyePnRw3856vUfubizJrmNcZ71evKjK7L+x7PgrywM5x50SzQnI7fj99TOn/7iTXiuphf2bhoiMCjX6Wyki6w8+YhnOHMEfCNB2Ow/XpXjgrvMyWHl9N+Z/NZv7FzWwcncTPzwznRXf7sayb3XjwtEp/GpOXZvHuPV/dZw97FCReX1dI2cNTWHFt7N5YoWzGsPyHU00BYmbYgTwdmZmW+1fYuL+Cz1J2Wk/u2bnsInL7pk65d0fNwzYsWA2Goz6RORW2Oi6ThLpekgP4sxWPgenuerfYxHKHNXjbgdoy8Duno+LRPd0Ib+vh60HlB7ph+bzVtW3Pbt3ybYmdlYFOe+4Q1eSUz1Q3aA0NMHBk/lbZ9Xx6+nx1UZxdlZmp06YXDzaM35/VvJ2gk9trOo5puzxadPm3tRr+IZ/veNpqo/VkuIbr585PaqTP03bIilImar6Fs5lvo2q6gcKYhPLHMXrOLOo41r5/iBLtzdx2mCnQP3srVqG3FPBkyUN/OqclsUkqMoP3qjlzvMOvxr5qeNSKN8f5PSHq/jeaWm8sqqBkwd62rzk55YVGek9Ovs1773EG5eXbjuTR5vShm987axpb988auyHDy9JqwssjvJL2GCGThTJ/+o6cW4mrhGR74rIpUC3GOUybfEHGoGn3Y5xNJX1yuf+Wc29n874+OzoN5/MYPPN3blqXCr3LWw5ev2BRQ1cMCqFwT0O/yeZ4hGe+lwWS7/Zjc+PSeHe+fX8YHI633+9lsv/Wc0rq9xfVFVB93o8nd4GZ+UwGbMrJy56q8WF/rvfn3jWez895ZTFRWu6VWx+h0MNTDvCLtd1okgK0o04qxR+D5gIXA1cG4tQpl1xe9muockpRleNS+Wy/JaT7q86MZXnS1uufP3elkbuW1iP794KbnmjjseXN1D45uHr3D2wqJ5rTkpl/pYmctKFZy7P5K733J+atSY1tdytiZx3XebN1dAqzsbRo3LzqFOXFJ115ns/299nz/LZqB7r/LDNEJ+r9iaqiJqrhj6tBL4SmzgmLP7A+/hzPgA+4XaU5lSVr75SS34fL9+ffOiy3Jq9TYzKdS7dvVzWyAl9Wv4e9ORlh1ZkfnRZPYu3NVF07qErUvtqlFfXNPL6l7L4V2jEngjUNLg/SnROVuY2YLgbr71hgIzc3Jd3h+7mTDdeP56l1wf6nfjBn/s1edKq1w8vmLtl0LRh6kmJpMvFQ9fPnO7+P7Ak0m5BCi2v2yZrruqaJ4DfuR2iuXc3N/HEigbG9fMwfmYlAHd8Mp2Hlzawak8Qj8Cwnh5mFjiFZvG2JmYuruevF2e2e+xfzanjZ1PS8Yhw/sgU7l9UzbgHG/jWxLSYvqdwvJ2V4eqItzsv8w7940NNDQIR9YH72fbtzKmqpLfXyyvDRwCwv6mJH2zbytaGBgalpnJ33iByvC1HM25raOAXO7azo9E5231o8GAGpabxw23bWFNXx9Ru3bi5rzMKfubePYxMS+fc7h1ZMeHYeYP1WaPWvXj2yHUvBbcNPGPBuhGfzWxMzW6vxVMdTldt04nC6fa9G+fU9WmctUAOGySlqnNils60zZ+Th7OSbCSTm00MnDZs8Mpqj2eMmxl+/Xjj3OO3cnYk+yyuribL46Fw+7aPC9Kdu3aR4/Xy9dxc/rJ3LweCTfygb78W+167aSPfzO3DGdnZVAWDeIBN9fX8ff8+fj1gIF/dvIl78wZRq8ovd2zngcGxnzMciX09R31YNvrKAzWZfU/F6dV5pMeunzn9y52dK9mFcw9pAPBTnMtDfwQ+BexR1TlWjFzkD2wjzgc3JIN6qKsWidqS5cfqnku8xytEtCrrKVlZ5HgP/xHwv8pKLslxboddkpPDWxWVLfZbW1dHE3BGtjMvNdvjIdPjIUWE2qASVKVRFY8IM/bs5rt9Om2+cNh67V8zdvLC2yafvtC/vee+1XNQPfKNWls0F7RbkFS1SVVfU9VrgdOBtcBsEfluzNOZ9hRx+FpVppO9n5G+FhHXrxt+1EP6LxshCzt6nL1NjfRNcU66+3i97G1qOQClvL6e7h4P39u6hcvKN/CHXbtoUuW49HR6p3j53MZypnXrxqb6eoLAmIz4HZ2eVbNn8MnL/zh1yrs/bBq4fd5sNLgdmHf9zOlJO8fLTWGNshORdBG5DGci7PU4vz28GMtgJgz+wEriuHNDMpiVlbXX7QwHzbjIc6LCgWgdT0RancTchLKkpoYf9u3HP4f52NJQz0uBAAA/6defF33D+UrvXGbs2c33+vRh5t493LxtK8/u3x+taFGX2liTk7/qyWnT5t7Y54SyJ+Lq3mwyabcgicjjwHvAycBtqjpJVX+tqgnbBr+LucPtAMlsQWZG3PQvqsySXvPypUNNQHO9KewODVTY3dhIb2/LW5QDUlI5IT2dIWlppIjwyW7dWVl3+BD9tyoqGJORQXVQ2VzfwD15g3ijooKaYHyPUPdocGPejvmvup0jWYVzhvQlYBTOPKR5InIg9KgQkaj9NmaOkT+wEPif2zGS1abUlMFuZ2juoQs8E4Ow51j3P6dbt4/Pdl4KBJjereXc909kZFARDPJRqHDNr67muLRDw/wbVHli3z6+2juX2mAQCZ1mNaE0xH8z5zvzy0rju2omsHDuIXlUtXvo0aPZo7uqdnq7FNMqO0tywX6PZ1+DSFyt3lqbJt3+O0E+DGfbW7Zt5f82bqS8vp5z1q3l+f37+XpuLvOqq/j0+nW8V13F13JzAfigtoZbd2wHwCvCD/v247rNm/nshg2AcnnPnh8f9+l9+/hsTg8yPR6OT0+nNhjksxs2MDYjgx6tDCGPIzuAR90Okcxs+YlE4c9ZCExyO0YyKc7OWlLYr89Et3McKaVR6564s2mvV8lzO0sXU5hfVmr3j1wUXx0qTUf81u0AyWZ2VmbLMdFxoDFF0l+eLOvcztHFbANmuB0i2VlBShwvASvdDpFMlmakZ7W/lTv+OcUzucFDuds5upBb88tKO3UJEdOSFaRE4Q8ocJvbMZLJLq/Xlf514Qh6JOXpaZ5tbufoIlZg947ighWkROIP/BOw7hmdYGNKymYV6eN2jqN59VSZXJfCardzdAE/spF18cEKUuK5AUjK5a0705yszC1uZ2iXiDx8nifgdow490Z+WenrbocwDitIicYfKAEecDtGopublen+QkxhmH2SZ1JVOiVu54hTQeAWt0OYQ6wgJaZb6QLLnHdlK9PSerudIVz3XeSxM+bWPZpfVmrFOo5YQUpE/kAA+InbMRJVIzRWeNzv8B2uJaM84/dnY81CD1eN84ubiSNWkBLXI0CHuz+blj5IT1uLSPurCsaRey7xxm/LbXf8Jr+s1EYhxhkrSInKGQZ+Pc51chNFs7KydrudIVKlQ2XMzhwWuJ0jTiwmzlZbNg4rSInMH1gM/M3tGIlmXmZGa6syxL27LvP2UfsFpQ74cn5Zqd1Xi0NWkBLfT4C4WbMnEaxPTR3odoZjUT5Ajtvcl/fczuGy2/LLSsNqPms6nzVXTQb+nMuA592OkQgqRSomDxucjchRf5nb8vAWKpZVkNIjhVG/GQVAYGGAXS/tom57Hcf94jgyh7d+G6piRQXbn9oOQeh1di/6XugsAb555mZqt9TSfXx3Blw+AIBdr+wiY1AGPSaG13h/wEe6+Y8PNQ0QSA37TSeORcBkOzuKX3aGlAz8gRew1ihRsSAzY217xQig11m98P3Ad9hz6YPTGXrDULJGt90CT4PKtie24fu+j5F3jCSwIEDt1lpqN9fiSfMw6vZR1Gyooam6iYb9DdSsqwm7GAHs6C1DVg9KyrMku1TXBVhBSh7fAza4HaKrm5WVGdailNnHZ+PNPnztn4y8DNIHprexh6NmfQ3p/dNJ65eGJ8VDzmk5VCytAC8E64NoUNFGBQ/semEX/S7tF/F7uPtS7wnqDHtOJv78slJrPhznrCAlC3+gArgaayvUIYszMo5eUTqoYV8Dqb0PXU1L6ZVCw74GMvIySOmewrpfrqPH+B7U76xHVcn0RT76fF936bdshCyKZu44txD4g9shTPusICUTf+Bd4Fdux+jKtqd4fW699sCrBjLy1yPp85k+7HphF/0v68+uV3ax6f5NfDT7o4iONeMiz4kKydDnrhK41i7VdQ1WkJLP7cBbbofoirZ7vduDIgNi+RqpvVJp+Kjh468b9zWS2uvw8QcH3j9Ahi+DYF2Q+t31DL1+KAcWHyBYF/6I7sos6fXuGFkWrdxxSnGKUZnbQUx44qogiUiuiCwLPXaIyNbQ5yoi5x+x7U0i8qBbWaNNRP4qImPa2Wa2iJzSoRfyB4LAVcCODh0nCc3NytwU69fIHJ5J3c466nfXE2wMElgQoPuE7h9/XxuVvW/spe8FfQnWHypAH99bisCfP+OZGIQ9UQsff4ryy0pfcDuECV9cFSRV3auq41V1PDATuCf0+TeBLx6x+ReBpzs3YceISEpb31PVr6lq59x09Qd24hSlZJ8kGZE5WZm14W67+cHNrL99PXU76ii7uYyP5nzEgSUHKLu5jJp1NZTfU075neWAc9+o/G7nc/EKeV/Ko/zOctb8ZA09JvUgY9Chrj9739pLzzN74kn3kDEkA61X1vx8DZm+zBaDKNpTmybd/nuyJOqcnNeAn7sdwkQmbuchiYgfqFTVO0WkN1AGDFbVehHxAXOBYXrEGxCRa3BayiuwQlWvDm3/N6APsBv4iqpuEpFHgRpgAtAPuA64BpgMLFDVL4eOWQk8CFwAbAd+CvweGArcpKqviIgXKAKmAenA/ar6kIhMA34N7ANOAPJx2pZ8Gqcg/EVVZ4jIbOAWVV0cOvObBGQCz6nqL0M5Zofe21LgYeCU0Pv8m6reE/Efsj+nEPhtxPslqbOHDlq6z+ud4HaOaEpp1Lon7mza61Xy3M4SReuBU/LLSve5HcREJq7OkNqiqh/hjJT5TOipLwL/bKUYjcX5rWi6qp4E3Bj61gzgMVU9EXgS+FOz3XrhFKCbgVeAe4CxwDgRGR/aJhv4n6qOBSpw7sN8CriUQ4MEvgoEVHUSTjH5uogcXOL6ZOBGVR0NfAPwAeOb5TnSz1T1FOBEYKqInHjE98cDg1T1E6o6DqeRauT8gSLgL8e0b5IJQnCfx9NlOnyHqzFF0l+aLOvczhFF1cClVoy6pi5RkEKe5tBlu7Yu100HnlXVPfBxIQOn4DwV+vwJ4Kxm+/wrVNhKgJ2qWqKqQeBDnMIBUI9zCYDQdnNUtSH0+cFtzgOuEZFlwAIgFxgV+t5CVT04B+hc4CFVbTwiY3NfEJH3cc6ExgJH3ltaD4wQkRki8mkgrLkxbfgO8J8O7J8UytJS1yPSvf0tu55np3gmN3gTZo7aV/PLSle4HcIcm65UkF4GPikiJwNZqrokSsetC30MNvv84NcH7/k0NDsb+3i7UOE6uI0ANxy8B6aqw1X1jdD3qsINEzqrugX4ZOgMqhg4bOkAVd0HnATMBr4F/DXc47fgDzQCXwBbL+doZmdlJewgkKBHUp6a5kmE93dXflnpP9wOYY5dlylIqloJzMK5F9TWYIb/AZ8XkVyA0L0ngHkcOru6Cng7BhFfB74tIqmh1x4tItmtbPdf4JsHBzg0y3hQD5wCFhCR/hy6TPkxEekDeFT1eZxLlCd3KLk/UAkUABs7dJwE9k5mRnzebI2S4klyel0Kq9zO0QHPAz9yO4TpmC5TkEKexjkzaLUgqeqHwG+AOSKyHLg79K0bgK+IyAqcbgU3trZ/B/0VWAm8LyIfAA9x6OzpyO02AStCGa884j0sx7lUV4ZzmfHdVo4xCJgdujz4d6KxOqw/sAOn+O3v8LES0Nq01L5uZ4gpEfnr+Z6OXPp10/+Aq/LLSm3UaBcXt6PsjEv8OVOBN4A0t6PEi1qRmknDBqdylGH7ieKRuxtLsusY53aOCLwPTMsvK61wO4jpuK52hmRizR+YA3wZZzi5ARZnpK9JhmIEcN9Fnq7UYmc18BkrRonDCpJpyR94GviB2zHixayszKQZQrxklGf8/uwuMcBlPTA9v6x0l9tBTPRYQTKt8wfuwRkSnvRnSgsyM5JqMbt7LvFmtL+VqzbhFKOtbgcx0WUFybTNH3gQuJYkX7JiS0rKULczdKbSoTJmZ0/mu52jDduAT+aXldqI0ARkBckcnT/wBM48pXq3o7hhr8ezp0lksNs5Otudl3n7avz1OlwPTM0vK10byU4iMlhEXhaRNSKyTkT+KCLtDtoRkZ8e5Xt+EbklkhzhaOu4IpInIs+FPp8mIq+GPv+yiNwX+vxREbk82playRKz17GCZNrnLIF+MU7fv6TyTlZmonQwiMjG/nLcpr5xtdT5+8AZx1CMBHgBeElVRwGjgW4400Pa02ZBipajNVxuTlW3qWrMi82Rws0XLVaQTHj8gddxGsIm1Yim2VmZybbU98fuvMw7TOPjzPi/OGdGO49h3+lArao+AqCqTTh9K68TkazmZxgAIvJq6AykCMgMLX/zZOh7PxOR1SLyDnB8s33Gi8h8EVkhIi+KSK92np8tIveKyGJanxN5koi8Fzqj+3poH19ofmN7zhWRxaGcF4b2zRCRR0SkRESWisg5zY75toi8H3qcEXp+Wuj5V4CV4rhPRFaJyJs4jagPvvciEVkZeo93hvdX0jYrSCZ8/sBc4JNAZMuTdmHL0tNb67aRFHb2lsGrBrl+L+lJoCC/rLTyGPcfCxzWZkxVD+AMjGizWa6qFgI1oTZgV4nIRJxuL+Nxuv5Parb548CPQ62+SoBftvM8QJqqnqKqd7Xy8ifiFNLJwC9EJJJO7D7gVJzOKzNFJAO43nlLOg74P+Cx0PO7gE+p6snAFRzedLp5Q+hLcQrwGJzVEA4WrtzQ98aG3uPtEeRslRUkExl/YBEwFWcZjoS315t4Hb4jcc+l3hPU6aDthruAq/PLShva3TL2pgAvqmp1qKC9AiAiOUBPVZ0T2u4x4Oy2nm92vGeO8lovq2pNqEn0LJwCE65/qmpQVdfg3HM7AaeZ9N8BVLUMp0XYaCAV+IuIlADPcngT5+YNoc8GnlbVJlXdhtMZAyAA1AIPi8hlROHfiRUkEzl/4AOctZjc/u05ptalpmxUkZ5u53DTvu7Sb9kIWdTJL6vAD/LLSm/JLyvt6LSDlcDE5k+ISA+ctczWAo0c/nOws4a8H63h8pHvOZI/g0j2vRnYidOO7RQO787SbkPo0IoFpwLPARdyaEWEY2YFyRwbf2AbzpnSTLejxMqcrMwtbmeIBzMu8pyozm/DnaEauDK/rPTudrcMz1tAljgLdxJaSPMu4FFVrQbKgfEi4hGRIRx+NtJwsFkyzoKgl4hIpjjLkFwEoKoBYJ+ITAltdzXO8jStPh9m5s+G7vvk4iz4GckvBJ8PvZfjgBHAKpxm0leF3v9onGK8CsgBtodWLbgaaGvJ4bnAFSLiFZGBwMF7UN2AHFX9N05xOymCnK1KinYoJkb8gXrg2/hzFuCsqBvvEyojMjczM6nnXx1UmSW93hkjc6as1Kkxfqky4PP5ZaXh3LwPi6qqiFwKPCAit+L8Ev5vDo2gexfYgHMmVcrhy7D8GacJ8vuh+0jPAMtx7r00LxLX4tyvycK5TPaVdp5vzwqcS3V9gF+r6jZxVr0OxyacxUx7AN9S1VoReQB4MHRprhH4sqrWhZ5/PlSsX6Pts6IXce5prQwd/+Doy+7Ay6H7UQJ8P8yMbbLmqiY6/Dkn4wyvHeZ2lGg5fdjgD6s8nrFu54gHGfVa+ejdTTUeJVZdz58GvtGBwQsmAdglOxMd/sD7ONfq33Q7SjTUQ32VSFIPaGiuNk26vTFBSmNw6DrgO/llpVdaMTJWkEz0+AN7ceYq/c7tKB21PCN9LSLpbueIJ49/0nNak7AtiodcjzPZ9cEoHtN0YVaQTHT5A034A4U48xO6bCfmWVmZe9zOEG8aUyT9pcmyPkqHewmYmF9W2hU6i5tOYgXJxIY/8BLOvIa/u5zkmMzPzGhrxFFSe3aK5/QGLx1pp1QFfC+/rPTS/LLS/VGKZRKEFSQTO/7AXvyBq3FmjW92O04kylNTI5kdnzSCHkl5appnxzHu/h9gbH5Z6YxoZjKJwwqSiT1/4N84LVxm0gXWVwp4JNDgtGAxrSieJKfXprIqgl12Af+XX1Z6gS0bYY7GCpLpHP5ABf7At3Em1UXUsbmzvZeZuRanS7RpjYj89XzPgTC3fgTIzy8r/UcsI5nEYAXJdC5/YA5O88g/EKcL/83KyrThx+2YO84zqTKdFUfZZC3Oqq7X5ZeVJk0zXtMxVpBM5/MHavAHfoTTMfm/bsc50vsZ6ZluZ+gK7rvI09rl11rgDmBcflnprE6OZLo469Rg3OfPOQf4LXCa21EATvQN2a0isepIkFAe+lPjkl5VTMRZXfZx4Bf5ZaVdagCLiR9WkEz88Od8FmclT9fa9WxOSdl6wZC8QW69fldzwmYt/dXfm9YDP8kvKy1xO4/p2uySnYkf/sDLOPeXrsXpwtzp5mZlbHLjdbuoN8uGyNfyy0ovtGJkosHOkEx88uekAd8AfgYM6KyX/Ub/vnPey8qMdVfrrm428IuSa0vedjuISSxWkEx88+dkAdcB3wHyY/1yZw4dtPyA19vhdV0SUCPOMgQzrBCZWLGCZLoOZ/DD9cBnicFaXk3QNN43pA5n/Rrj2I6zLtCfS64tiWZjVWNasIJkuh5/Th7O5bxvAAOjddgV6Wmrr8obMDpax+vi5gL3Ay+WXFvS4HYYkxysIJmuy5+TgtNV/Ds4Sz13yJ965bz9l545U9rfMmFVAU8AD5RcW2KDFEyns4JkEoM/ZwxwFXAJTpfxiF2R1/+dlenpZ0UzVhdQDbyOs9rvKyXXloTbEsiYqLOCZBKPP2cUzn2mS4DJhDm94ZRhg9fUeTyjYpgsXgSAV3GK0Gsl15ZUu5zHGMAKkkl0/px+wMU4BepcIKO1zapEKk8fNjgLkUSdm7cLeBmnCL1l94VMPLKCZJKHPycbZ4n1zwBnAscDAjA7M3P5DQP6JtJw713Ae8A84B1gfsm1JUF3IxlzdFaQTPLy5+TiXNI747e9e/V9Kqf7FUB3l1MdiyCwEqf4zAPeLbm2JK6X+DCmNVaQjAkZ99g4AUYBJwMTgfHAcGAIkOZessMcAFY1eyzCOfsJuJrKmCiwgmRMO8Y9Ns6DM9/JF3oMO+LjEKCjS1YEgUqgAqfobAU2hh7loY+rS64tOdblw42Je1aQjImCUNHKBLJCH5t/fvBjBs4w6woOFZ+Dj+qSa0vsP6NJalaQjDHGxIVEHeJqjDGmi7GCZIwxJi5YQTLGGBMXrCAZY4yJC1aQjOmCRGSwiLwsImtEZJ2I/FFE0kRkvIhc0Gw7v4jc4mZWY8JlBcmYLkZEBKcn3UuqOgoYDXQDfoMzmfeCtveO+LW80TqWMe2xgmRM1zMdqFXVRwBUtQm4Gfga8HvgChFZJiJXhLYfIyKzRWS9iHzv4EFE5EsisjC07UMHi4+IVIrIXSKyHKe1kjGdwgqSMV3PWGBJ8ydU9QBOR4fbgWdUdbyqPhP69gnA+cCpwC9FJFVE8oErgDNVdTzQhLOeFEA2sEBVT1LVd2L9Zow5KMXtAMaYmCtW1TqgTkR2Af2BT+L061vkXAEkE6dDODjF6Xk3gprkZgXJmK5nJXB58ydEpAcwFGhsZfu6Zp834fy/F+AxVf1JK9vXhi4DGtOp7JKdMV3PW0CWiFwDHw88uAt4FNhJeEtovAVcLiL9QsfoLSLDYhPXmPBYQTKmi1GnAeWlwOdFZA2wGqgFfgrMwhnE0HxQQ2vHWAn8HHhDRFYA/8XpaG6Ma6y5qjHGmLhgZ0jGGGPighUkY4wxccEKkjHGmLhgBckYY0xcsIJkjDEmLlhBMsYYExesIBljjIkLVpCMMcbEhf8HiPfRm8oEpF0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "young =(df[df[\"Age\"]==\"19-30\"][\"Marketing_channels\"].value_counts()/55.20)\n",
    "\n",
    "# Create a figure and axis\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot the pie chart\n",
    "young.plot(kind='pie', ax=ax, autopct='%1.1f%%')\n",
    "\n",
    "# Set aspect ratio to make the pie circular\n",
    "ax.axis('equal')\n",
    "\n",
    "# Set title\n",
    "\n",
    "\n",
    "# Show the chart\n",
    "plt.tight_layout()\n",
    "#ax.set_title('for 19-30 Age group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e5a8eb",
   "metadata": {},
   "source": [
    "# other people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "30292fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other =(df[df[\"Age\"]!=\"19-30\"][\"Marketing_channels\"].value_counts()/44.80)\n",
    "\n",
    "# Create a figure and axis\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot the pie chart\n",
    "other.plot(kind='pie', ax=ax, autopct='%1.1f%%')\n",
    "\n",
    "# Set aspect ratio to make the pie circular\n",
    "ax.axis('equal')\n",
    "\n",
    "# Set title\n",
    "\n",
    "\n",
    "# Show the chart\n",
    "plt.tight_layout()\n",
    "#ax.set_title('for 19-30 Age group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "61480a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19-30    55.20\n",
       "31-45    23.76\n",
       "15-18    14.88\n",
       "46-65     4.26\n",
       "65+       1.90\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"].value_counts()/100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd9f2ef",
   "metadata": {},
   "source": [
    "48% people are preferring online ads in the age of 19-30 because youth is active on social media but on the other hand you can not ignore the fact that our remaining audience are seeing the production throught \"TV commercials\" as well in a good number.so you have to keep it in mind that while planning marketing don't ignore the \"TV commercials\" ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "390a9bb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "94052ce7",
   "metadata": {},
   "source": [
    "4-b. How effective are different marketing strategies and channels in reaching our customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "261eed4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#round(df[df[\"Tried_before\"]==\"Yes\"][\"Marketing_channels\"].value_counts()/48.81)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bf30b2d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Online ads            42.0\n",
       "TV commercials        27.0\n",
       "Outdoor billboards    12.0\n",
       "Other                 12.0\n",
       "Print media            7.0\n",
       "Name: Marketing_channels, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df[df[\"Current_brands\"]==\"CodeX\"][\"Marketing_channels\"].value_counts()/9.80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "97f151b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '7.4'),\n",
       " Text(0, 0, '11.8'),\n",
       " Text(0, 0, '12.1'),\n",
       " Text(0, 0, '26.6'),\n",
       " Text(0, 0, '41.9')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig=(df[df[\"Current_brands\"]==\"CodeX\"][\"Marketing_channels\"].value_counts()/9.80).sort_values(ascending=True).plot.barh(color=pallete,figsize=(3,3))\n",
    "plt.bar_label(fig.containers[0],fmt='%.1f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8514f42e",
   "metadata": {},
   "source": [
    "out of 980 custumers who has codex as their favourite brand,around 42% of them got to know through\n",
    "online ads and around 27% through tv commercials"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e6e2c5",
   "metadata": {},
   "source": [
    "## 5-Brand Penetration\n",
    "a. What do people think about our brand? (overall rating)\n",
    "\n",
    "b. Which cities do we need to focus more on?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12f38195",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3edd87d8",
   "metadata": {},
   "source": [
    "a. What do people think about our brand? (overall rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e1030ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Brand_perception'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((df[\"Brand_perception\"].value_counts()/100)).plot(kind=\"pie\",autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "153450b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neutral     59.74\n",
       "Positive    22.57\n",
       "Negative    17.69\n",
       "Name: Brand_perception, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Brand_perception\"].value_counts()/100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0939cf2",
   "metadata": {},
   "source": [
    "around 60% people are neutral about the brand and only 23% are feeling positive towards the brand\n",
    "so overall 82% customers are having no hate about the brand but in future we can work on it as 60% people are finding it neutral jbki brand name must be so unique and impressive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18e7d19b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20e0438a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "971a43e1",
   "metadata": {},
   "source": [
    "b. Which cities do we need to focus more on?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f94d19a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df.groupby(\"City\")[\"Heard_before\"].value_counts()\n",
    "m=data.rename(\"count\").to_frame().reset_index()\n",
    "df11=m.groupby('City')[\"count\"].sum()\n",
    "t=pd.merge(m,df11,on='City',how='left')\n",
    "t[\"per\"]=np.ceil((t[\"count_x\"]/t['count_y'])*100)\n",
    "t.drop(columns=[\"count_x\",\"count_y\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7ac82eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "fig=sns.barplot(data=t, y=\"City\", x=\"per\", hue=\"Heard_before\",palette=[\"blue\", \"cyan\"])\n",
    "for container in fig.containers:\n",
    "    fig.bar_label(container)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99c67ca6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79954b1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61bb641e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "072737b6",
   "metadata": {},
   "source": [
    "## 6. Purchase Behavior:\n",
    "a. Where do respondents prefer to purchase energy drinks?\n",
    "\n",
    "b. What are the typical consumption situations for energy drinks among \n",
    "respondents?\n",
    "\n",
    "c. What factors influence respondents' purchase decisions, such as price range and \n",
    "limited edition packaging?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1f1ae82",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e0731f7f",
   "metadata": {},
   "source": [
    "6-a. Where do respondents prefer to purchase energy drinks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0e96cb84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Supermarkets                45.0\n",
       "Online retailers            26.0\n",
       "Gyms and fitness centers    15.0\n",
       "Local stores                 8.0\n",
       "Other                        7.0\n",
       "Name: Purchase_location, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df[\"Purchase_location\"].value_counts()/100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9061d596",
   "metadata": {},
   "source": [
    " #### supermarkets are on the top place having 45% customers while 26% customers are purchasing through  'Online Retailers'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "31884743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '6.8'),\n",
       " Text(0, 0, '8.1'),\n",
       " Text(0, 0, '14.6'),\n",
       " Text(0, 0, '25.5'),\n",
       " Text(0, 0, '44.9')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=round((df[\"Purchase_location\"].value_counts()/100).sort_values(ascending=True),1).plot(kind=\"barh\",color=pallete,figsize=(8,4))\n",
    "plt.bar_label(fig.containers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "981ce5a7",
   "metadata": {},
   "source": [
    "6b. What are the typical consumption situations for energy drinks among respondents?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b1b3271f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "To stay awake during work/study    34.0\n",
       "Before exercise                    31.0\n",
       "For mental alertness               20.0\n",
       "Throughout the day                 14.0\n",
       "Name: Consume_time, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df[\"Consume_time\"].value_counts()/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5b88d7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '14.5'), Text(0, 0, '20'), Text(0, 0, '31.5'), Text(0, 0, '34.1')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=round((df[\"Consume_time\"].value_counts()/100).sort_values(ascending=True),1).plot(kind=\"barh\",color=pallete,figsize=(8,4))\n",
    "plt.bar_label(fig.containers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cb726aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fddca834",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cbd42da1",
   "metadata": {},
   "source": [
    "6c. What factors influence respondents' purchase decisions, such as price range and limited edition packaging?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "38f87648",
   "metadata": {},
   "outputs": [],
   "source": [
    "# trying to catch the behaviour of purchasing on the basis of 1-gender ,2-age-group,3-price_range,\n",
    "#4-city_wise\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3651fb97",
   "metadata": {},
   "source": [
    "### definately AGE is a factor for variation in price range .people above age 30  around 69% are buying  energy drink of 100+ rs.while people with less age go for price range 50-99 .\n",
    "#### we can launch different energy drinks for different buckets of price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ec1cb2d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "single=(df[(df[\"Age\"]==\"15-18\") | (df[\"Age\"]==\"19-30\")][\"Price_range\"].value_counts()/70.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "37fc104b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50-99        52.111872\n",
       "100-150      24.443493\n",
       "Above 150    13.356164\n",
       "Below 50     10.088470\n",
       "Name: Price_range, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ac5951a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '10'), Text(0, 0, '13'), Text(0, 0, '24'), Text(0, 0, '52')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=round(single.sort_values(ascending=True)).plot(kind=\"barh\",color=pallete,figsize=(8,4))\n",
    "plt.bar_label(fig.containers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b145d2e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ee4430f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a2f5a084",
   "metadata": {},
   "outputs": [],
   "source": [
    "mingle=round(df[(df[\"Age\"]!=\"15-18\") & (df[\"Age\"]!=\"19-30\")][\"Price_range\"].value_counts()/29.92)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "96d57140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '10'), Text(0, 0, '21'), Text(0, 0, '21'), Text(0, 0, '48')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=round(mingle.sort_values(ascending=True)).plot(kind=\"barh\",color=pallete,figsize=(8,4))\n",
    "plt.bar_label(fig.containers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afe25989",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "51fe709d",
   "metadata": {},
   "source": [
    "### 7. Product Development\n",
    "a. Which area of business should we focus more on our product development? \n",
    "(Branding/taste/availability)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf686a6",
   "metadata": {},
   "source": [
    "ans=all of three (show it on the basis of data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "183ad9fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand reputation           27.0\n",
       "Taste/flavor preference    20.0\n",
       "Availability               19.0\n",
       "Effectiveness              17.0\n",
       "Other                      17.0\n",
       "Name: Reasons_for_choosing_brands, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df[\"Reasons_for_choosing_brands\"].value_counts()/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d997cf0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.22"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Taste_experience\"]>=3].shape[0]/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f0511d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '16.79'),\n",
       " Text(0, 0, '17.48'),\n",
       " Text(0, 0, '19.1'),\n",
       " Text(0, 0, '20.11'),\n",
       " Text(0, 0, '26.52')]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=(df[\"Reasons_for_choosing_brands\"].value_counts()/100).sort_values(ascending=True).plot(kind=\"barh\",color=pallete,figsize=(10,4))\n",
    "plt.bar_label(fig.containers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b88ae682",
   "metadata": {},
   "source": [
    "each factor has importance but we should start with brand reputation and then availabilty because\n",
    "taste is good in future we can focus on it as well "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3e9565a",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}