drinks_promotion.ipynb

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    "id": "bA5ajAmk7XH6"
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   "source": [
    "# Where should a drinks company run promotions?\n",
    "\n",
    "## 📖 Background\n",
    "Your company owns a chain of stores across Russia that sell a variety of alcoholic drinks. The company recently ran a wine promotion in Saint Petersburg that was very successful. Due to the cost to the business, it isn’t possible to run the promotion in all regions. The marketing team would like to target 10 other regions that have similar buying habits to Saint Petersburg where they would expect the promotion to be similarly successful.\n",
    "\n",
    "### The data\n",
    "The marketing team has sourced you with historical sales volumes per capita for several different drinks types.\n",
    "\n",
    "- \"year\" - year (1998-2016)\n",
    "- \"region\" - name of a federal subject of Russia. It could be oblast, republic, krai, autonomous okrug, federal city and a single autonomous oblast\n",
    "- \"wine\" - sale of wine in litres by year per capita\n",
    "- \"beer\" - sale of beer in litres by year per capita\n",
    "- \"vodka\" - sale of vodka in litres by year per capita\n",
    "- \"champagne\" - sale of champagne in litres by year per capita\n",
    "- \"brandy\" - sale of brandy in litres by year per capita"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "offshore-publication",
   "metadata": {},
   "source": [
    "# Competition - Drinks Promotions\n",
    "## James Morgan (jhmmorgan)\n",
    "*2021-10-20*\n",
    "\n",
    "## Assumptions and restrictions\n",
    "Before we begin, it's worth noting that we'll be making a number of assumptions within the analysis and have a number of restrictions.\n",
    " - We don't know when the wine marketing promotion ran.\n",
    "     - We know that it was run in Saint Petersburg recently, however our data goes up to 2016 and it's unclear if the promotion ran after 2016, or if 2016 is considered \"recent\".\n",
    "     - We'll therefore assume the marketing promotion ran after 2016.\n",
    " - We don't know the demographics of each region, or how much residents spent on alcohol across all brands.\n",
    "     - We only know how much was spent, per capita for our brand.\n",
    " - We don't know why there is missing data\n",
    "     - For example, did our company not sell achohol to those regions, or was there no interest in our brand?\n",
    " - We're unaware of the compeition.\n",
    "\n",
    "## 1. Set Up\n",
    "### 1.1 Import Libraries"
   ]
  },
  {
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   "execution_count": 1,
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   "metadata": {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.decomposition import NMF\n",
    "from sklearn.preprocessing import Normalizer, MaxAbsScaler\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cross-bones",
   "metadata": {},
   "source": [
    "### 1.2 Default classes\n",
    "Used for better printing outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "portable-animation",
   "metadata": {
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   "source": [
    "class color:\n",
    "   PURPLE = '\\033[95m'\n",
    "   CYAN = '\\033[96m'\n",
    "   DARKCYAN = '\\033[36m'\n",
    "   BLUE = '\\033[94m'\n",
    "   GREEN = '\\033[92m'\n",
    "   YELLOW = '\\033[93m'\n",
    "   RED = '\\033[91m'\n",
    "   BOLD = '\\033[1m'\n",
    "   UNDERLINE = '\\033[4m'\n",
    "   HEADER = '\\033[1m' + '\\033[4m'\n",
    "   END = '\\033[0m'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorporate-houston",
   "metadata": {},
   "source": [
    "### 1.3 Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "divine-gothic",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>wine</th>\n",
       "      <th>beer</th>\n",
       "      <th>vodka</th>\n",
       "      <th>champagne</th>\n",
       "      <th>brandy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998</td>\n",
       "      <td>Republic of Adygea</td>\n",
       "      <td>1.9</td>\n",
       "      <td>8.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>Altai Krai</td>\n",
       "      <td>3.3</td>\n",
       "      <td>19.2</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998</td>\n",
       "      <td>Amur Oblast</td>\n",
       "      <td>2.1</td>\n",
       "      <td>21.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1998</td>\n",
       "      <td>Arkhangelsk Oblast</td>\n",
       "      <td>4.3</td>\n",
       "      <td>10.6</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>Astrakhan Oblast</td>\n",
       "      <td>2.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year              region  wine  beer  vodka  champagne  brandy\n",
       "0  1998  Republic of Adygea   1.9   8.8    3.4        0.3     0.1\n",
       "1  1998          Altai Krai   3.3  19.2   11.3        1.1     0.1\n",
       "2  1998         Amur Oblast   2.1  21.2   17.3        0.7     0.4\n",
       "3  1998  Arkhangelsk Oblast   4.3  10.6   11.7        0.4     0.3\n",
       "4  1998    Astrakhan Oblast   2.9  18.0    9.5        0.8     0.2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(r'./data/russian_alcohol_consumption.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "about-lafayette",
   "metadata": {},
   "source": [
    "## 2. EDA (Exploratory Data Analysis)\n",
    "### 2.1 Data Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "stock-consultancy",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[4mData Types for all features in the data\u001b[0m\n",
      "      Column    Dtype     Sample1    Sample2          Sample3\n",
      "0       year    int64        1998       1998             2003\n",
      "1     region   object  Altai Krai  Perm Krai  Khabarovsk Krai\n",
      "2       wine  float64         3.3        3.2              3.8\n",
      "3       beer  float64        19.2       19.0             47.4\n",
      "4      vodka  float64        11.3       16.7             15.2\n",
      "5  champagne  float64         1.1        0.9              0.7\n",
      "6     brandy  float64         0.1        0.2              0.4\n"
     ]
    }
   ],
   "source": [
    "# Data Type Check\n",
    "df_check = pd.DataFrame({\"Column\": df.columns, \"Dtype\": df.dtypes.astype(\"str\").tolist(), \n",
    "                         \"Sample1\": df.loc[1].tolist(),\n",
    "                         \"Sample2\": df.loc[50].tolist(), \n",
    "                         \"Sample3\": df.loc[500].tolist()})\n",
    "print(color.HEADER + \"Data Types for all features in the data\" + color.END)\n",
    "print(df_check.to_string())"
   ]
  },
  {
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   "id": "anticipated-stationery",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[4mWhich features contain null values?\u001b[0m\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>wine</th>\n",
       "      <td>63</td>\n",
       "      <td>3.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beer</th>\n",
       "      <td>58</td>\n",
       "      <td>3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vodka</th>\n",
       "      <td>61</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>champagne</th>\n",
       "      <td>63</td>\n",
       "      <td>3.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brandy</th>\n",
       "      <td>66</td>\n",
       "      <td>4.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           No.     %\n",
       "wine        63  3.90\n",
       "beer        58  3.59\n",
       "vodka       61  3.78\n",
       "champagne   63  3.90\n",
       "brandy      66  4.09"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(color.HEADER + \"Which features contain null values?\" + color.END)\n",
    "df_missing = pd.concat([df.isnull().sum()[df.isnull().sum()>0],round((df.isnull().sum()[df.isnull().sum()>0]/len(df))*100,2)], axis=1)\n",
    "df_missing.columns = [\"No.\", \"%\"]\n",
    "df_missing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elementary-dominican",
   "metadata": {},
   "source": [
    "##### Observations:\n",
    "There doesn't appear to be anything unusual with the data types.\n",
    "However, we do have several missing features in all of the alcoholic features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "protected-shannon",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[4mWhich regions have missing values?\u001b[0m\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>wine</th>\n",
       "      <th>beer</th>\n",
       "      <th>vodka</th>\n",
       "      <th>champagne</th>\n",
       "      <th>brandy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chechen Republic</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Crimea</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Ingushetia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sevastopol</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        year  region  wine  beer  vodka  champagne  brandy\n",
       "region                                                                    \n",
       "Chechen Republic           0       0    19    16     19         19      19\n",
       "Republic of Crimea         0       0    16    16     16         16      16\n",
       "Republic of Ingushetia     0       0    12    10     10         12      15\n",
       "Sevastopol                 0       0    16    16     16         16      16"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(color.HEADER + \"Which regions have missing values?\" + color.END)\n",
    "df_missing_grouped = df.groupby([\"region\"]).apply(lambda x: x.isnull().sum())\n",
    "df_missing_grouped = df_missing_grouped[df_missing_grouped.sum(axis=1)>0]\n",
    "df_missing_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opening-theorem",
   "metadata": {},
   "source": [
    "We need to decide what to do with these missing values.\n",
    "First, let's see if any of the data that is missing is from the last few years.  The thinking is that some regions might only have started selling our brand in recent years, whereas others may not be popular in recent years at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "accessory-agenda",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_missing_regions = df_missing_grouped.index.values\n",
    "filter1 = df.region.isin(df_missing_regions)\n",
    "df_filtered = df[(filter1)].sort_values(by=[\"region\", \"year\"])\n",
    "df_filtered.fillna(0, inplace = True)\n",
    "df_filtered[\"total\"] = df_filtered.wine + \\\n",
    "                       df_filtered.beer + \\\n",
    "                       df_filtered.vodka + \\\n",
    "                       df_filtered.champagne + \\\n",
    "                       df_filtered.brandy\n",
    "df_filtered[\"year\"] = df_filtered.year.astype(str)\n",
    "\n",
    "df_filtered_wine = pd.pivot_table(df_filtered[[\"year\", \"region\", \"wine\"]], values=\"wine\", index=\"region\", columns=[\"year\"])\n",
    "df_filtered_beer = pd.pivot_table(df_filtered[[\"year\", \"region\", \"beer\"]], values=\"beer\", index=\"region\", columns=[\"year\"])\n",
    "df_filtered_vodka = pd.pivot_table(df_filtered[[\"year\", \"region\", \"vodka\"]], values=\"vodka\", index=\"region\", columns=[\"year\"])\n",
    "df_filtered_champagne = pd.pivot_table(df_filtered[[\"year\", \"region\", \"champagne\"]], values=\"champagne\", index=\"region\", columns=[\"year\"])\n",
    "df_filtered_brandy = pd.pivot_table(df_filtered[[\"year\", \"region\", \"brandy\"]], values=\"brandy\", index=\"region\", columns=[\"year\"])\n",
    "df_filtered_total = pd.pivot_table(df_filtered[[\"year\", \"region\", \"total\"]], values=\"total\", index=\"region\", columns=[\"year\"])\n",
    "\n",
    "plt.figure(figsize = (20,12))\n",
    "plt.suptitle(\"Heatmap of alcohol sales for regions with missing data\")\n",
    "plt.subplot(3, 2, 1)\n",
    "plt.title(\"All alcohol\")\n",
    "sns.heatmap(df_filtered_total, vmin = 0, vmax = 50, cmap = \"Reds\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(3, 2, 2)\n",
    "plt.title(\"Wine\")\n",
    "sns.heatmap(df_filtered_wine, vmin = 0, vmax = 50, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(3, 2, 3)\n",
    "plt.title(\"Beer\")\n",
    "sns.heatmap(df_filtered_beer, vmin = 0, vmax = 50, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "          \n",
    "plt.subplot(3, 2, 4)\n",
    "plt.title(\"Vodka\")\n",
    "sns.heatmap(df_filtered_vodka, vmin = 0, vmax = 50, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(3, 2, 5)\n",
    "plt.title(\"Champagne\")\n",
    "sns.heatmap(df_filtered_champagne, vmin = 0, vmax = 50, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(3, 2, 6)\n",
    "plt.title(\"Brandy\")\n",
    "sns.heatmap(df_filtered_brandy, vmin = 0, vmax = 50, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mature-eugene",
   "metadata": {},
   "source": [
    "Interesting!\n",
    "\n",
    "**Chechen Republic** has barely any sales data. We'll be unable to predict the impact of a marketing campaign and they certainly won't be in the top 10, so we're safe to remove them from the data rather than fill in the missing data.\n",
    "\n",
    "**Republic of Ingushetia** has had some sales from 2001, but these decreased over several years before disappearing completely.  Similar to Chechen Republic, we're safe to remove their data completely.\n",
    "Of course, we could change the sales to zero as an alternative.\n",
    "\n",
    "**Republic of Crimea** and **Sevastopol** however are a different story.  They have no historic sales, however they have been selling for the last three years, which is a good indication of ongoing sales in the future.\n",
    "\n",
    "We have a number of options.\n",
    " - We could change all of their historic data to 0;\n",
    " - We could change their historic data to the mean of their current sales;\n",
    " - We could change their historic data to match their first year of sales (2014).\n",
    "\n",
    "I feel Option 2 - using the mean - is a good reflection. Let's implement our changes and check no missing values remain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "powered-symphony",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_cleaned = df[~df.region.isin([\"Chechen Republic\", \"Republic of Ingushetia\"])]#.set_index([\"region\", \"year\"])\n",
    "\n",
    "wine_series = round(df_cleaned.groupby(\"region\").wine.mean(),0)\n",
    "beer_series = round(df_cleaned.groupby(\"region\").beer.mean(),0)\n",
    "vodka_series = round(df_cleaned.groupby(\"region\").vodka.mean(),0)\n",
    "champagne_series = round(df_cleaned.groupby(\"region\").champagne.mean(),0)\n",
    "brandy_series = round(df_cleaned.groupby(\"region\").brandy.mean(),0)\n",
    "df_cleaned = df_cleaned.fillna({\"wine\": df_cleaned[\"region\"].apply(lambda x: wine_series[x]),\n",
    "                                \"beer\": df_cleaned[\"region\"].apply(lambda x: beer_series[x]),\n",
    "                                \"vodka\": df_cleaned[\"region\"].apply(lambda x: vodka_series[x]),\n",
    "                                \"champagne\": df_cleaned[\"region\"].apply(lambda x: champagne_series[x]),\n",
    "                                \"brandy\": df_cleaned[\"region\"].apply(lambda x: brandy_series[x]),})\n",
    "df_cleaned[\"total\"] = df_cleaned.wine + df_cleaned.beer + df_cleaned.vodka + df_cleaned.champagne + df_cleaned.brandy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "directed-suffering",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[91mNo. of missing values after amendments: 0\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "print(color.BOLD + color.RED + f\"No. of missing values after amendments: {df_cleaned.isna().sum().sum()}\" + color.END)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "green-jamaica",
   "metadata": {},
   "source": [
    "### 2.2 Looking at Saint Petersburg sales\n",
    "\n",
    "Let's now look at the Saint Petersburg sales data to understand the full impact of their marketing campaign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "precious-train",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_filtered = df_cleaned[df_cleaned.region == \"Saint Petersburg\"]\n",
    "\n",
    "df_melt = pd.melt(df_filtered, \n",
    "                  value_vars = [\"wine\", \"beer\", \"vodka\", \"champagne\", \"brandy\"],\n",
    "                  var_name = \"alcohol\",\n",
    "                  id_vars = [\"region\", \"year\", \"total\"])\n",
    "\n",
    "\n",
    "plt.figure(figsize = (20,6))\n",
    "plt.suptitle(\"Sale of alcohol over the years for Saint Petersburgh\", fontsize=16)\n",
    "plt.title(\"(Overlayed on the total amount of alcohol sold)\", fontsize=14)\n",
    "plt.fill_between(df_melt['year'], df_melt['total'], alpha=0.2)\n",
    "sns.lineplot(data = df_melt, x = \"year\", y = \"value\", hue = \"alcohol\")\n",
    "plt.xlim([1998, 2016])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "static-prompt",
   "metadata": {},
   "source": [
    "Hmm. This doesn't show us a lot.\n",
    "We can see that **beer** sales are much stronger, but are in decline.  The detail on the other types of alcohol are lost at this scale.\n",
    "\n",
    "How do **Saint Petersburg** sales ranking compared to other regions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "metric-rouge",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mIn 2009, Saint Petersburg ranked: \u001b[91mNo.1\u001b[0m\u001b[1m for alcohol sales.\u001b[0m\n",
      "\u001b[1mIn 2013, Saint Petersburg ranked: \u001b[91mNo.67\u001b[0m\u001b[1m for alcohol sales.\u001b[0m\n",
      "\u001b[1mIn 2016, Saint Petersburg ranked: \u001b[91mNo.72\u001b[0m\u001b[1m for alcohol sales.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "rank_saint_petersburg_09 = pd.DataFrame(df_cleaned[df_cleaned.year==2009])[[\"year\", \"region\", \"total\"]].sort_values(\"total\", ascending = False)\n",
    "rank_saint_petersburg_09[\"row\"] = np.arange(rank_saint_petersburg_09.shape[0])+1\n",
    "rsp_09 = rank_saint_petersburg_09.loc[rank_saint_petersburg_09.region == \"Saint Petersburg\", \"row\"].values[0]\n",
    "\n",
    "rank_saint_petersburg_13 = pd.DataFrame(df_cleaned[df_cleaned.year==2013])[[\"year\", \"region\", \"total\"]].sort_values(\"total\", ascending = False)\n",
    "rank_saint_petersburg_13[\"row\"] = np.arange(rank_saint_petersburg_13.shape[0])+1\n",
    "rsp_13 = rank_saint_petersburg_13.loc[rank_saint_petersburg_13.region == \"Saint Petersburg\", \"row\"].values[0]\n",
    "\n",
    "rank_saint_petersburg_16 = pd.DataFrame(df_cleaned[df_cleaned.year==2016])[[\"year\", \"region\", \"total\"]].sort_values(\"total\", ascending = False)\n",
    "rank_saint_petersburg_16[\"row\"] = np.arange(rank_saint_petersburg_16.shape[0])+1\n",
    "rsp_16 = rank_saint_petersburg_16.loc[rank_saint_petersburg_16.region == \"Saint Petersburg\", \"row\"].values[0]\n",
    "\n",
    "print(color.BOLD + \"In 2009, Saint Petersburg ranked: \" + color.RED + f\"No.{rsp_09}\" + color.END + color.BOLD + \" for alcohol sales.\" + color.END)\n",
    "print(color.BOLD + \"In 2013, Saint Petersburg ranked: \" + color.RED + f\"No.{rsp_13}\" + color.END + color.BOLD + \" for alcohol sales.\" + color.END)\n",
    "print(color.BOLD + \"In 2016, Saint Petersburg ranked: \" + color.RED + f\"No.{rsp_16}\" + color.END + color.BOLD + \" for alcohol sales.\" + color.END)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "arctic-claim",
   "metadata": {},
   "source": [
    "There are 83 regions included in the data.  They were the number 1 region for sales in 2009 but by 2016, they've dropped down to 72nd place.\n",
    "\n",
    "Let's explore the % of sales for each type of alcohol compared to the total volumn sold per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "varying-tomorrow",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_eda = df_cleaned[df_cleaned.region == \"Saint Petersburg\"].sort_values(by=[\"region\", \"year\"])\n",
    "\n",
    "df_eda[\"wine\"] = (df_eda.wine / df_eda.total) * 100\n",
    "df_eda[\"beer\"] = (df_eda.beer / df_eda.total) * 100\n",
    "df_eda[\"vodka\"] = (df_eda.vodka / df_eda.total) * 100\n",
    "df_eda[\"champagne\"] = (df_eda.champagne / df_eda.total) * 100\n",
    "df_eda[\"brandy\"] = (df_eda.brandy / df_eda.total) * 100\n",
    "df_eda[\"year\"] = df_eda.year.astype(str)\n",
    "\n",
    "df_eda_wine = pd.pivot_table(df_eda[[\"year\", \"region\", \"wine\"]], values=\"wine\", index=\"region\", columns=[\"year\"])\n",
    "df_eda_beer = pd.pivot_table(df_eda[[\"year\", \"region\", \"beer\"]], values=\"beer\", index=\"region\", columns=[\"year\"])\n",
    "df_eda_vodka = pd.pivot_table(df_eda[[\"year\", \"region\", \"vodka\"]], values=\"vodka\", index=\"region\", columns=[\"year\"])\n",
    "df_eda_champagne = pd.pivot_table(df_eda[[\"year\", \"region\", \"champagne\"]], values=\"champagne\", index=\"region\", columns=[\"year\"])\n",
    "df_eda_brandy = pd.pivot_table(df_eda[[\"year\", \"region\", \"brandy\"]], values=\"brandy\", index=\"region\", columns=[\"year\"])\n",
    "df_eda_total = pd.pivot_table(df_eda[[\"year\", \"region\", \"total\"]], values=\"total\", index=\"region\", columns=[\"year\"])\n",
    "\n",
    "plt.figure(figsize = (20,9))\n",
    "\n",
    "plt.suptitle(\"Heatmap of alcohol sales, as a % of the total for year in Saint Petersburg\")\n",
    "plt.subplot(3, 2, 1)\n",
    "plt.title(\"All alcohol\")\n",
    "sns.heatmap(df_eda_total, vmin = 0, cmap = \"Reds\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(3, 2, 2)\n",
    "plt.title(\"Wine\")\n",
    "sns.heatmap(df_eda_wine, vmin = 0, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(3, 2, 3)\n",
    "plt.title(\"Beer\")\n",
    "sns.heatmap(df_eda_beer, vmin = 0, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "          \n",
    "plt.subplot(3, 2, 4)\n",
    "plt.title(\"Vodka\")\n",
    "sns.heatmap(df_eda_vodka, vmin = 0, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(3, 2, 5)\n",
    "plt.title(\"Champagne\")\n",
    "sns.heatmap(df_eda_champagne, vmin = 0, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(3, 2, 6)\n",
    "plt.title(\"Brandy\")\n",
    "sns.heatmap(df_eda_brandy, vmin = 0, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "entitled-blend",
   "metadata": {},
   "source": [
    "This is interesting! \n",
    "\n",
    "Beer sales from **Saint Petersburg** are certainly greater than other alcohols, making up the most of their sales, however this has decreased in recent years, with more residents over the last three to four years buying wine, with a slight increase in champagne and brandy too.\n",
    "\n",
    "Of course, what we don't know is when the wine promotion ran from.  If it ran from 2014 to 2016, then we can certainly see the impact.  If it ran from 2017 onwards (which we have no data for), then Saint Petersburg was targeted well, due to their growing interest in wine in the few years prior.\n",
    "\n",
    "**I'm going to make the assumption that this task is up to date, i.e. the wine promotion ran from 2017 onwards and we're therefore looking for other regions who;**\n",
    " - Have a low volumns of sales,\n",
    " - Out of the sales, they spend a little on wine\n",
    " - The spending on wine has started to increase in recent years\n",
    "\n",
    "This is because, whilst the % of wine sales in Saint Petersburg has increased around 2014 to 2016, there's not a noticeable increase where I'd consider a campaign to be a huge success that you want to replicate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "careful-shipping",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3. Clustering similar regions to Saint Petersburg\n",
    "### 3.1 K-Means\n",
    "\n",
    "To start, we're going to use K-Means clustering to identify regions that are similar to Saint Petersburg.\n",
    "\n",
    "To do this, we'll need to pre-process the data to focus on each region over the years. We'll also focus on the % of sales for each regions total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "precise-injury",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "regions = df_cleaned.region\n",
    "df_groupings = pd.DataFrame(regions).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "parental-munich",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[4mSaint Petersburg's pre-processed data.\u001b[0m\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"21\" halign=\"left\">value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <th colspan=\"6\" halign=\"left\">wine</th>\n",
       "      <th colspan=\"4\" halign=\"left\">beer</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"4\" halign=\"left\">champagne</th>\n",
       "      <th colspan=\"6\" halign=\"left\">brandy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>...</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Saint Petersburg</th>\n",
       "      <td>0.067199</td>\n",
       "      <td>0.081716</td>\n",
       "      <td>0.110032</td>\n",
       "      <td>0.132472</td>\n",
       "      <td>0.14808</td>\n",
       "      <td>0.141434</td>\n",
       "      <td>0.807655</td>\n",
       "      <td>0.78856</td>\n",
       "      <td>0.71521</td>\n",
       "      <td>0.675283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042071</td>\n",
       "      <td>0.04685</td>\n",
       "      <td>0.042048</td>\n",
       "      <td>0.037849</td>\n",
       "      <td>0.011005</td>\n",
       "      <td>0.013279</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.021002</td>\n",
       "      <td>0.023766</td>\n",
       "      <td>0.023904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     value                                                   \\\n",
       "variable              wine                                                    \n",
       "year                  2011      2012      2013      2014     2015      2016   \n",
       "region                                                                        \n",
       "Saint Petersburg  0.067199  0.081716  0.110032  0.132472  0.14808  0.141434   \n",
       "\n",
       "                                                        ...            \\\n",
       "variable              beer                              ... champagne   \n",
       "year                  2011     2012     2013      2014  ...      2013   \n",
       "region                                                  ...             \n",
       "Saint Petersburg  0.807655  0.78856  0.71521  0.675283  ...  0.042071   \n",
       "\n",
       "                                                                             \\\n",
       "variable                                         brandy                       \n",
       "year                 2014      2015      2016      2011      2012      2013   \n",
       "region                                                                        \n",
       "Saint Petersburg  0.04685  0.042048  0.037849  0.011005  0.013279  0.016181   \n",
       "\n",
       "                                                \n",
       "variable                                        \n",
       "year                  2014      2015      2016  \n",
       "region                                          \n",
       "Saint Petersburg  0.021002  0.023766  0.023904  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prepro = df_cleaned.copy()\n",
    "df_prepro[\"region\"] = df_prepro.region.astype(\"category\")\n",
    "df_prepro = df_prepro[df_prepro.year > 2010]\n",
    "\n",
    "df_prepro[\"wine\"] = (df_prepro.wine / df_prepro.total)\n",
    "df_prepro[\"beer\"] = (df_prepro.beer / df_prepro.total)\n",
    "df_prepro[\"vodka\"] = (df_prepro.vodka / df_prepro.total)\n",
    "df_prepro[\"champagne\"] = (df_prepro.champagne / df_prepro.total)\n",
    "df_prepro[\"brandy\"] = (df_prepro.brandy / df_prepro.total)\n",
    "\n",
    "\n",
    "df_prepro = df_prepro.melt(id_vars = [\"region\", \"year\"], value_vars = [\"wine\", \"beer\", \"vodka\", \"champagne\", \"brandy\"]).pivot(values = [\"value\"], index = \"region\", columns = [\"variable\", \"year\"])\n",
    "print(color.HEADER + \"Saint Petersburg's pre-processed data.\" + color.END)\n",
    "df_prepro[df_prepro.index == \"Saint Petersburg\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "geological-place",
   "metadata": {},
   "source": [
    "We'll now run a **K-Means** model over this data, with a range of clusters, to see which one fits best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "relative-pursuit",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks = range(1, 11)\n",
    "inertias = []\n",
    "models = []\n",
    "models.append(np.nan)\n",
    "\n",
    "for k in ks:\n",
    "    # Create a KMeans instance with k clusters: model\n",
    "    model = KMeans(n_clusters = k, random_state = 42)\n",
    "    models.append(model)\n",
    "    # Fit model to samples\n",
    "    model.fit(df_prepro)\n",
    "    \n",
    "    # Append the inertia to the list of inertias\n",
    "    inertias.append(model.inertia_)\n",
    "    \n",
    "# Plot ks vs inertias\n",
    "plt.figure(figsize = (20,4))\n",
    "plt.plot(ks, inertias, '-o')\n",
    "plt.title(\"Elbow plot showing the inertia for each K CLuster\")\n",
    "plt.xlabel('number of clusters, k')\n",
    "plt.ylabel('inertia')\n",
    "plt.xticks(ks)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "numeric-colon",
   "metadata": {},
   "source": [
    "We now need to decide how many clusters are best.\n",
    "\n",
    "We're looking for 10 similar regions to Saint Petersburg.  Based on the number of regions, clusting our data into 6 or 7 groups would be ideal.  Lets try 6 clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "racial-sigma",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mK Means of 6 results in Saint Petersburg being in a group of: 11\u001b[0m\n"
     ]
    },
    {
     "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>region</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Altai Republic</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Komi Republic</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Kostroma Oblast</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Leningrad Oblast</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Republic of Adygea</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Republic of Mordovia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Vologda Oblast</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Yaroslavl Oblast</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Zabaykalsky Krai</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  region  label\n",
       "1         Altai Republic      4\n",
       "22         Komi Republic      4\n",
       "23       Kostroma Oblast      4\n",
       "28      Leningrad Oblast      4\n",
       "46    Republic of Adygea      4\n",
       "49    Republic of Crimea      4\n",
       "54  Republic of Mordovia      4\n",
       "59      Saint Petersburg      4\n",
       "78        Vologda Oblast      4\n",
       "81      Yaroslavl Oblast      4\n",
       "82      Zabaykalsky Krai      4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_labels = pd.DataFrame(df_prepro.index)\n",
    "region_labels[\"label\"] = models[6].labels_\n",
    "label_SP = region_labels.loc[region_labels.region == \"Saint Petersburg\", \"label\"].values[0]\n",
    "print(color.BOLD + f\"K Means of 6 results in Saint Petersburg being in a group of: {len(region_labels[region_labels.label == label_SP])}\" + color.END)\n",
    "region_labels = region_labels[region_labels.label == label_SP]\n",
    "region_labels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "serious-archive",
   "metadata": {},
   "source": [
    "Perfect! This is just the number we were looking for.  I wonder how these regions compare to Saint Petersburg?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "metallic-jefferson",
   "metadata": {},
   "source": [
    "**CHANGE THE BELOW PLOT TO SHOW EXAMPLES OF THE K MEANS EFFECTIVENESS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "amber-travel",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_eda2 = df_cleaned[df_cleaned.region.isin(region_labels.region)].sort_values(by=[\"region\", \"year\"])\n",
    "df_eda2[\"wine\"] = (df_eda2.wine / df_eda2.total) * 100\n",
    "df_eda2[\"year\"] = df_eda2.year.astype(str)\n",
    "df_eda2 = df_eda2[[\"year\", \"region\", \"wine\", \"total\"]].reset_index(drop=True)\n",
    "\n",
    "\n",
    "r1 = df_eda2[df_eda2.region==region_labels.reset_index(drop=True).region[1]]\n",
    "df_eda_total_r1 = pd.pivot_table(r1[[\"year\", \"region\", \"total\"]], values=\"total\", index=\"region\", columns=[\"year\"])\n",
    "df_eda_wine_r1 = pd.pivot_table(r1[[\"year\", \"region\", \"wine\"]], values=\"wine\", index=\"region\", columns=[\"year\"])\n",
    "\n",
    "r2 = df_eda2[df_eda2.region==region_labels.reset_index(drop=True).region[6]]\n",
    "df_eda_total_r2 = pd.pivot_table(r2[[\"year\", \"region\", \"total\"]], values=\"total\", index=\"region\", columns=[\"year\"])\n",
    "df_eda_wine_r2 = pd.pivot_table(r2[[\"year\", \"region\", \"wine\"]], values=\"wine\", index=\"region\", columns=[\"year\"])\n",
    "\n",
    "r3 = df_eda2[df_eda2.region==region_labels.reset_index(drop=True).region[2]]\n",
    "df_eda_total_r3 = pd.pivot_table(r3[[\"year\", \"region\", \"total\"]], values=\"total\", index=\"region\", columns=[\"year\"])\n",
    "df_eda_wine_r3 = pd.pivot_table(r3[[\"year\", \"region\", \"wine\"]], values=\"wine\", index=\"region\", columns=[\"year\"])\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize = (20,4))\n",
    "\n",
    "plt.suptitle(f\"Heatmap of alcohol sales, as a % of the total for year in {df_eda_total_r1.index[0]}\")\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"All alcohol\")\n",
    "sns.heatmap(df_eda_total_r1, vmin = 0, cmap = \"Reds\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Wine\")\n",
    "sns.heatmap(df_eda_wine_r1, vmin = 0, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize = (20,4))\n",
    "\n",
    "plt.suptitle(f\"Heatmap of alcohol sales, as a % of the total for year in {df_eda_total_r2.index[0]}\")\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"All alcohol\")\n",
    "sns.heatmap(df_eda_total_r2, vmin = 0, cmap = \"Reds\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Wine\")\n",
    "sns.heatmap(df_eda_wine_r2, vmin = 0, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize = (20,4))\n",
    "\n",
    "plt.suptitle(f\"Heatmap of alcohol sales, as a % of the total for year in {df_eda_total_r2.index[0]}\")\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"All alcohol\")\n",
    "sns.heatmap(df_eda_total_r3, vmin = 0, cmap = \"Reds\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Wine\")\n",
    "sns.heatmap(df_eda_wine_r3, vmin = 0, cmap = \"Blues\", linecolor = \"grey\", linewidth = 0.1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intimate-cornwall",
   "metadata": {},
   "source": [
    "This looks good.  We've picked three regions from the K-Means model and visually they look similar\n",
    " - There were previously higher sales\n",
    " - The recent wine sales has started to increase in recent years\n",
    " - The total % of wine sales are relatively low in comparision to the regions total sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "simplified-zimbabwe",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_groupings[\"kmeans\"] = df_groupings.region.isin(region_labels.region)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "competent-commodity",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 3.2 RFM\n",
    "\n",
    "I'm unsure how effective RFM (recency, frequency, monetary) clustering will be.  This is because we're not looking at individual customer orders. However, it might still give us some interesting insight:\n",
    " - We'll focus on the latest year (2016) - only.\n",
    " - The recency will always be '1' because each region left in our dataframe has purchased alcohol in 2016.\n",
    " - We'll set the frequency to be the percentage of sales that was wine per region\n",
    " - We'll set the monetry to the sale of wine in litres by year per captia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "legal-seven",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[4mRFM table for each region that as the same score as Saint Petersburg\u001b[0m\n"
     ]
    },
    {
     "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>monetary</th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "      <th>RFM_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bryansk Oblast</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.146138</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lipetsk Oblast</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.119231</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oryol Oblast</th>\n",
       "      <td>6.4</td>\n",
       "      <td>0.113475</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ryazan Oblast</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.116451</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Petersburg</th>\n",
       "      <td>7.1</td>\n",
       "      <td>0.141434</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zabaykalsky Krai</th>\n",
       "      <td>6.4</td>\n",
       "      <td>0.141593</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  monetary  frequency  recency  r_quartile f_quartile  \\\n",
       "region                                                                  \n",
       "Bryansk Oblast         7.0   0.146138        1           1          1   \n",
       "Lipetsk Oblast         6.2   0.119231        1           1          1   \n",
       "Oryol Oblast           6.4   0.113475        1           1          1   \n",
       "Ryazan Oblast          6.3   0.116451        1           1          1   \n",
       "Saint Petersburg       7.1   0.141434        1           1          1   \n",
       "Zabaykalsky Krai       6.4   0.141593        1           1          1   \n",
       "\n",
       "                 m_quartile RFM_Score  \n",
       "region                                 \n",
       "Bryansk Oblast            2       112  \n",
       "Lipetsk Oblast            2       112  \n",
       "Oryol Oblast              2       112  \n",
       "Ryazan Oblast             2       112  \n",
       "Saint Petersburg          2       112  \n",
       "Zabaykalsky Krai          2       112  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_RFM = df_cleaned.copy()\n",
    "df_RFM[\"region\"] = df_RFM.region.astype(\"category\")\n",
    "df_RFM = df_RFM[df_RFM.year == 2016]\n",
    "df_RFM[\"wine_pct\"] = (df_RFM.wine / df_RFM.total)\n",
    "\n",
    "\n",
    "\n",
    "df_RFM = df_RFM.melt(id_vars = [\"region\"], \n",
    "                     value_vars = [\"wine\", \"wine_pct\"]) \\\n",
    "              .pivot(values = [\"value\"], index = \"region\", columns = [\"variable\"])\n",
    "df_RFM.columns = [\"monetary\", \"frequency\"]\n",
    "df_RFM[\"recency\"] = 1\n",
    "\n",
    "df_RFM['r_quartile'] = 1\n",
    "df_RFM['f_quartile'] = pd.qcut(df_RFM['frequency'], 4, ['4','3','2','1'])\n",
    "df_RFM['m_quartile'] = pd.qcut(df_RFM['monetary'], 4, ['4','3','2','1'])\n",
    "\n",
    "\n",
    "df_RFM['RFM_Score'] = df_RFM.r_quartile.astype(str)+ df_RFM.f_quartile.astype(str) + df_RFM.m_quartile.astype(str)\n",
    "\n",
    "rfm_score = df_RFM.loc[df_RFM.index == \"Saint Petersburg\"].RFM_Score[0]\n",
    "df_RFM = df_RFM[df_RFM.RFM_Score == rfm_score]\n",
    "print(color.HEADER + \"RFM table for each region that as the same score as Saint Petersburg\" + color.END)\n",
    "df_RFM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "north-joint",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_groupings[\"rfm\"] = df_groupings.region.isin(df_RFM.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stable-monitor",
   "metadata": {},
   "source": [
    "### 3.3 NMF (Non-Negative Matrix Factorization)\n",
    "\n",
    "For our third and final model, we'll use an NMF to see which regions appear most like Saint Petersburg.\n",
    "\n",
    "We'll prepare our data before transforming it into a CSR_Matrix.  We'll then use a pipeline to scale the data and normalize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "answering-chance",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[4mDataframe prior to scaling and normalization\u001b[0m\n"
     ]
    },
    {
     "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>wine</th>\n",
       "      <th>beer</th>\n",
       "      <th>vodka</th>\n",
       "      <th>champagne</th>\n",
       "      <th>brandy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Republic of Adygea</th>\n",
       "      <td>3.6</td>\n",
       "      <td>27.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Altai Krai</th>\n",
       "      <td>4.8</td>\n",
       "      <td>37.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amur Oblast</th>\n",
       "      <td>6.2</td>\n",
       "      <td>46.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkhangelsk Oblast</th>\n",
       "      <td>8.4</td>\n",
       "      <td>51.6</td>\n",
       "      <td>12.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Astrakhan Oblast</th>\n",
       "      <td>4.5</td>\n",
       "      <td>38.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuvash Republic</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chukotka Autonomous Okrug</th>\n",
       "      <td>3.9</td>\n",
       "      <td>34.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sakha (Yakutia) Republic</th>\n",
       "      <td>4.3</td>\n",
       "      <td>56.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yamalo-Nenets Autonomous Okrug</th>\n",
       "      <td>4.5</td>\n",
       "      <td>75.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yaroslavl Oblast</th>\n",
       "      <td>10.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                wine  beer  vodka  champagne  brandy\n",
       "region                                                              \n",
       "Republic of Adygea               3.6  27.3    3.6        1.0     0.5\n",
       "Altai Krai                       4.8  37.3    4.5        0.7     0.3\n",
       "Amur Oblast                      6.2  46.9    6.0        1.3     0.5\n",
       "Arkhangelsk Oblast               8.4  51.6   12.2        1.8     0.9\n",
       "Astrakhan Oblast                 4.5  38.3    4.5        0.9     0.6\n",
       "...                              ...   ...    ...        ...     ...\n",
       "Chuvash Republic                 5.0  42.3    7.7        0.7     0.4\n",
       "Chukotka Autonomous Okrug        3.9  34.0   11.6        1.8     1.1\n",
       "Sakha (Yakutia) Republic         4.3  56.1    8.2        1.8     0.5\n",
       "Yamalo-Nenets Autonomous Okrug   4.5  75.8    8.2        1.7     1.3\n",
       "Yaroslavl Oblast                10.2  38.0    8.9        1.4     1.0\n",
       "\n",
       "[83 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_CSR = df_cleaned\n",
    "df_CSR = df_CSR[df_CSR.year == 2016].set_index(\"region\")\n",
    "df_CSR = df_CSR[[\"wine\", \"beer\", \"vodka\", \"champagne\", \"brandy\"]]\n",
    "print(color.HEADER + \"Dataframe prior to scaling and normalization\" + color.END)\n",
    "df_CSR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "antique-victim",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = csr_matrix(df_CSR)\n",
    "\n",
    "scaler = MaxAbsScaler()\n",
    "\n",
    "nmf = NMF(n_components = 20)\n",
    "\n",
    "normalizer = Normalizer()\n",
    "\n",
    "pipeline = make_pipeline(scaler, nmf, normalizer)\n",
    "\n",
    "norm_features = pipeline.fit_transform(dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dense-capital",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[4mWhich regions are most like Saint Petersburg?\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "region\n",
       "Saint Petersburg            1.000000\n",
       "Volgograd Oblast            0.932750\n",
       "Amur Oblast                 0.927541\n",
       "Rostov Oblast               0.927244\n",
       "Tula Oblast                 0.924484\n",
       "Chelyabinsk Oblast          0.914635\n",
       "Stavropol Krai              0.911600\n",
       "Republic of Tatarstan       0.908741\n",
       "Primorsky Krai              0.902622\n",
       "Jewish Autonomous Oblast    0.899825\n",
       "Leningrad Oblast            0.899408\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame: df\n",
    "dataframe = pd.DataFrame(norm_features, index = df_prepro.index)\n",
    "region = dataframe.loc[\"Saint Petersburg\"]\n",
    "\n",
    "similarities = dataframe.dot(region)\n",
    "df_NMF_rank = pd.DataFrame(similarities).reset_index()\n",
    "df_NMF_rank = df_NMF_rank.set_index(\"region\")[0].nlargest(n=11)\n",
    "print(color.HEADER + \"Which regions are most like Saint Petersburg?\" + color.END)\n",
    "df_NMF_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "above-cover",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_groupings[\"nmf\"] = df_groupings.region.isin(df_NMF_rank.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recorded-vision",
   "metadata": {},
   "source": [
    "## 4.0 Analysis of outcomes\n",
    "Great.  We've now completed our three models.  Let's score each region:\n",
    " - Each region gets a score of 1 for each model it appears in (maximum of 3)\n",
    " - We'll sort the scores to give kmeans the most importance, followed by RFM and then NMF\n",
    "     - In other words, a region that only appears in kmeans will be ranked higher than a region that only appears in rfm or nmf.\n",
    " - We'll filter our data to only show the top 10 regions that are closest to Saint Petersburg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "thorough-developer",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>region</th>\n",
       "      <th>kmeans</th>\n",
       "      <th>rfm</th>\n",
       "      <th>nmf</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zabaykalsky Krai</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Leningrad Oblast</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Republic of Adygea</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vologda Oblast</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kostroma Oblast</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Republic of Mordovia</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Altai Republic</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Komi Republic</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yaroslavl Oblast</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  region  kmeans    rfm    nmf  score\n",
       "0       Saint Petersburg    True   True   True      3\n",
       "1       Zabaykalsky Krai    True   True  False      2\n",
       "2       Leningrad Oblast    True  False   True      2\n",
       "3     Republic of Adygea    True  False  False      1\n",
       "4         Vologda Oblast    True  False  False      1\n",
       "5        Kostroma Oblast    True  False  False      1\n",
       "6     Republic of Crimea    True  False  False      1\n",
       "7   Republic of Mordovia    True  False  False      1\n",
       "8         Altai Republic    True  False  False      1\n",
       "9          Komi Republic    True  False  False      1\n",
       "10      Yaroslavl Oblast    True  False  False      1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_groupings[\"score\"] = df_groupings.kmeans.astype(int) + df_groupings.rfm.astype(int) + df_groupings.nmf.astype(int)\n",
    "final = df_groupings[df_groupings.score > 0].sort_values([\"score\", \"kmeans\", \"rfm\", \"nmf\"], ascending = False).reset_index(drop=True)\n",
    "final.iloc[:11]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "static-current",
   "metadata": {},
   "source": [
    "And there we have it, our 10 recommended regions.\n",
    "\n",
    "## 5.0 Next steps and the future\n",
    "In order to validate our analysis, we'll need our assumptions confirmed.\n",
    "If we were to obtain more detail, it would be great to analysis the regions against other features, such as their demographics and the competition.\n",
    "\n",
    "James Morgan"
   ]
  }
 ],
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