data visualisation clusters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## importing the needed libraries \n",
    "\n",
    "# for the clustering models\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import make_blobs\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "from sklearn import metrics\n",
    "\n",
    "# to interpret clustering results \n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.colors import ListedColormap\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, PredefinedSplit # gridsearch and train / test / validation split \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import StandardScaler  \n",
    "from sklearn.decomposition import PCA # to perfom PCA\n",
    "from sklearn.preprocessing import OneHotEncoder # dummy coding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the csv dataset\n",
    "df_original = pd.read_csv(\"xxx\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting the needed columns to a seperate dataset \n",
    "df = df_original[['Age', 'Gender', 'Education_classes', 'Net_income_household', 'PoliticalSpectrum_Left']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2563 entries, 0 to 2562\n",
      "Data columns (total 5 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   Age                     2563 non-null   int64 \n",
      " 1   Gender                  2563 non-null   object\n",
      " 2   Education_classes       2563 non-null   object\n",
      " 3   Net_income_household    2563 non-null   object\n",
      " 4   PoliticalSpectrum_Left  2563 non-null   object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 100.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking nulls and what type each column has \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1250x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing mplcursors\n",
    "\n",
    "# Setting the figure size and dpi\n",
    "fig = plt.figure(figsize=(25, 10), dpi=50)\n",
    "\n",
    "# Defining the grid layout\n",
    "grid = gridspec.GridSpec(1, 3)\n",
    "\n",
    "# Defining the plot titles\n",
    "plot_titles = [\n",
    "    'Age Distribution',\n",
    "    'Gender Distribution',\n",
    "    'Education Classes Distribution',\n",
    "]\n",
    "\n",
    "# Defining the column names for each plot\n",
    "plot_columns = [\n",
    "    'Age',\n",
    "    'Gender',\n",
    "    'Education_classes',\n",
    "]\n",
    "\n",
    "# Iterating over the grid and creating the subplots\n",
    "for i in range(grid.nrows * grid.ncols):\n",
    "    ax = fig.add_subplot(grid[i])\n",
    "    \n",
    "    # Creating the plot based on the column name\n",
    "    if i == 0:\n",
    "        sns.histplot(data=df, x=plot_columns[i], ax=ax)\n",
    "    else:\n",
    "        sns.countplot(data=df, x=plot_columns[i], ax=ax)\n",
    "    \n",
    "    # Setting the plot title and adjusting font size\n",
    "    ax.set_title(plot_titles[i], fontsize=18)\n",
    "    \n",
    "    # Removing axis labels\n",
    "    ax.set(xlabel='', ylabel='')\n",
    "    \n",
    "    # Setting tick label font size\n",
    "    ax.tick_params(axis='both', labelsize=16)\n",
    "    \n",
    "    # Enabling tooltips using mplcursors\n",
    "    #mplcursors.cursor(ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following conclusions can be drawn from the above graphs:\n",
    "* It is evident that the majority of survey respondents fall within the age range of 60-70. This age group constitutes a significant portion of the participants.\n",
    "* The gender distribution among the respondents appears to be relatively balanced, indicating a diverse representation in the survey.\n",
    "* When examining the educational backgrounds of the participants, it is notable that the medium education class holds the highest number of respondents, approximately 1500 individuals. Additionally, the high and low education classes consist of 600 and 500 respondents respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the figure size and dpi\n",
    "fig = plt.figure(figsize=(30, 15), dpi=50)\n",
    "\n",
    "# Defining the grid layout\n",
    "grid = gridspec.GridSpec(2, 1)\n",
    "\n",
    "# Adding subplots to the figure\n",
    "ax4 = fig.add_subplot(grid[0, :])\n",
    "ax5 = fig.add_subplot(grid[1, :])\n",
    "\n",
    "# Creating a countplot for the 'PoliticalSpectrum_Left' column\n",
    "sns.countplot(data=df, x='PoliticalSpectrum_Left', ax=ax4)\n",
    "ax4.set_title('Political Spectrum (Left) distribution', fontsize=18)\n",
    "ax4.tick_params(axis='both', labelsize=16)\n",
    "\n",
    "# Creating a countplot for the 'Net_income_household' column\n",
    "sns.countplot(data=df, x='Net_income_household', ax=ax5)\n",
    "ax5.set_title('Net Income Household distribution', fontsize=18)\n",
    "ax5.tick_params(axis='both', labelsize=16)\n",
    "\n",
    "# Removing axis labels\n",
    "for ax in [ax4, ax5]:\n",
    "    ax.set(xlabel='', ylabel='')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following conclusions can be drawn from the above graphs:\n",
    "* A clear pattern emerges regarding the political spectrum preferences of the respondents. The majority, comprising around 700 individuals, position themselves in the center of the political spectrum. Additionally, approximately 500 respondents lean towards the center-right, while roughly 380 respondents identify with the center-left. Moreover, about 300 respondents express a preference towards the left, whereas approximately 100 respondents align themselves with the right.\n",
    "* It is evident that the majority of the respondents fall within the income range of 1000 - 4999 EUR. However, the remaining income categories show a significantly lower number of respondents, with less than 200 individuals in each category.\n",
    "\n",
    "To improve the data and achieve better balance, the following data transformations will be applied:\n",
    "\n",
    "1) Converting the categories 'no idea', 'unknown', 'no answer', and 'votes blank or not' in the political spectrum column into 'other answer'.\n",
    "2) Grouping together categories 'centre right' and 'right' in the political spectrum column into 'right'\n",
    "3) Grouping together categories 'centre left' and 'left' in the political spectrum column into 'left'\n",
    "4) Grouping the net income categories of 0-999 and 1000-1999 together.\n",
    "5) Grouping all income categories above 5000 together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Centre                713\n",
       "Centre Right          516\n",
       "Centre Left           373\n",
       "Left                  322\n",
       "Unknown               209\n",
       "Votes blank or not    196\n",
       "Right                 108\n",
       "No idea               105\n",
       "No answer              21\n",
       "Name: PoliticalSpectrum_Left, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the value counts before the replacements\n",
    "df['PoliticalSpectrum_Left'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Centre    713\n",
       "Left      695\n",
       "Right     624\n",
       "Other     531\n",
       "Name: PoliticalSpectrum_Left, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Combining \"centre right\" & \"right\", \"centre left\" & \"left\", and converting \"unknown\", \"no idea\", \"no answer\", \"votes blank or not\" into \"other answer\"\n",
    "# Creating a dictionary to map the desired replacements\n",
    "dict_left = {\n",
    "    \"Centre Right\": \"Right\",\n",
    "    \"Right\": \"Right\",\n",
    "    \"Centre Left\": \"Left\",\n",
    "    \"Unknown\": \"Other\",\n",
    "    \"Votes blank or not\": \"Other\",\n",
    "    \"No idea\": \"Other\",\n",
    "    \"No answer\": \"Other\"\n",
    "}\n",
    "\n",
    "# Replacing the values in the 'PoliticalSpectrum_Left' column using the dictionary\n",
    "df.replace({'PoliticalSpectrum_Left': dict_left}, inplace=True)\n",
    "\n",
    "# Displaying the value counts after the replacements\n",
    "df['PoliticalSpectrum_Left'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000 - 2999 Euros       690\n",
       "3000 - 3999 Euros       505\n",
       "1000 - 1999 euro        496\n",
       "4000 - 4999 Euros       387\n",
       "5000 - 5999 Euros       162\n",
       "0 - 999 Euros           137\n",
       "Euro 6000 - 6999         71\n",
       "7000 - 7999 Euros        41\n",
       "10,000 Euros or more     31\n",
       "8000 - 8999 Euros        26\n",
       "9000 - 9999 Euros        17\n",
       "Name: Net_income_household, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the value counts before the replacements\n",
    "df['Net_income_household'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000 - 2999 Euros     690\n",
       "0 - 1999 Euro         633\n",
       "3000 - 3999 Euros     505\n",
       "4000 - 4999 Euros     387\n",
       "5000 Euros or more    348\n",
       "Name: Net_income_household, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Combining \"0 - 999 Euros\" & \"1000 - 1999 euro\", and converting all income levels above 5000 into one category\n",
    "# Creating a dictionary to map the desired replacements\n",
    "dict_income = {\n",
    "    \"0 - 999 Euros\": \"0 - 1999 Euro\",\n",
    "    \"1000 - 1999 euro\": \"0 - 1999 Euro\",\n",
    "    \"Euro 6000 - 6999\": \"5000 Euros or more\",\n",
    "    \"7000 - 7999 Euros\": \"5000 Euros or more\",\n",
    "    \"8000 - 8999 Euros\": \"5000 Euros or more\",\n",
    "    \"9000 - 9999 Euros\": \"5000 Euros or more\",\n",
    "    \"10,000 Euros or more\": \"5000 Euros or more\",\n",
    "    \"5000 - 5999 Euros\": \"5000 Euros or more\"\n",
    "}\n",
    "\n",
    "# Replacing the values in the 'Net_income_household' column using the dictionary\n",
    "df.replace({'Net_income_household': dict_income}, inplace=True)\n",
    "\n",
    "# Displaying the value counts after the replacements\n",
    "df['Net_income_household'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Defining the grid layout with 2 rows and 1 column\n",
    "grid = gridspec.GridSpec(2, 1)\n",
    "\n",
    "# Setting the figure size and dpi\n",
    "fig = plt.figure(figsize=(30, 15), dpi=50)\n",
    "\n",
    "# Creating subplots in the specified grid positions\n",
    "ax1 = fig.add_subplot(grid[0, 0])\n",
    "ax2 = fig.add_subplot(grid[1, 0])\n",
    "\n",
    "# Creating a countplot for 'PoliticalSpectrum_Left' column\n",
    "sns.countplot(data=df, x='PoliticalSpectrum_Left', ax=ax1)\n",
    "ax1.set_title('PoliticalSpectrum_Left', fontsize=18)\n",
    "ax1.tick_params(axis='both', labelsize=16)\n",
    "\n",
    "# Creating a countplot for 'Net_income_household' column\n",
    "sns.countplot(data=df, x='Net_income_household', ax=ax2)\n",
    "ax2.set_title('Net_income_household', fontsize=18)\n",
    "ax2.tick_params(axis='both', labelsize=16)\n",
    "\n",
    "# Removing axis labels for both subplots\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.set(xlabel='', ylabel='')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the political spectrum column value counts range between 531 and 713 instead of the original range od 21 to 713 and the net income column between 348 and 690."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education_classes</th>\n",
       "      <th>Net_income_household</th>\n",
       "      <th>PoliticalSpectrum_Left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Education_classes  Net_income_household  \\\n",
       "0   77       1                  0                     2   \n",
       "1   68       1                  1                     1   \n",
       "2   56       1                  0                     1   \n",
       "3   55       1                  1                     2   \n",
       "4   67       1                  1                     1   \n",
       "\n",
       "   PoliticalSpectrum_Left  \n",
       "0                       3  \n",
       "1                       3  \n",
       "2                       0  \n",
       "3                       1  \n",
       "4                       3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dummy coding the categorical features by creating dictionaries for mapping\n",
    "dict_edu = {'Low': 0, 'Medium': 1, 'High': 2}\n",
    "dict_gender = {'Female': 0, 'Male': 1}\n",
    "dict_inc = {'0 - 1999 Euro': 0, '2000 - 2999 Euros': 1, '3000 - 3999 Euros': 2, '4000 - 4999 Euros': 3, '5000 Euros or more': 4}\n",
    "dict_politics = {'Centre': 2, 'Left': 1, 'Right': 3, 'Other': 0}\n",
    "\n",
    "# Replacing the categorical values with the encoded values using the dictionaries\n",
    "df.replace({\"Gender\": dict_gender, 'Education_classes': dict_edu, 'Net_income_household': dict_inc, \"PoliticalSpectrum_Left\": dict_politics}, inplace=True)\n",
    "\n",
    "# Displaying the first few rows of the modified dataframe\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2563 entries, 0 to 2562\n",
      "Data columns (total 5 columns):\n",
      " #   Column                  Non-Null Count  Dtype\n",
      "---  ------                  --------------  -----\n",
      " 0   Age                     2563 non-null   int64\n",
      " 1   Gender                  2563 non-null   int64\n",
      " 2   Education_classes       2563 non-null   int64\n",
      " 3   Net_income_household    2563 non-null   int64\n",
      " 4   PoliticalSpectrum_Left  2563 non-null   int64\n",
      "dtypes: int64(5)\n",
      "memory usage: 100.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA \n",
    "Principal Component Analysis (PCA) is a dimensionality reduction technique that aims to summarize the information contained in a dataset by transforming it into a new set of uncorrelated variables, called principal components. These components are ordered by the amount of variance they explain in the original data so principal component 1 captures the most variance.\n",
    "\n",
    "By applying PCA to a dataset it will attempt to find linear combinations of the original columns (features) that explain most of the variance in the dataset. The result will be a set of new features, which are the principal components, and these new features will be uncorrelated with each other. \n",
    "\n",
    "Performing PCA on the dataset before feeding it into the k-means algorithm can improve clustering results by reducing noise, decreasing computation time, and mitigating the curse of dimensionality, while retaining the most important information in the data through principal components that capture the majority of the variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2563, 5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Standardizing the dataset by feature scaling\n",
    "# Defining standard scaler instance\n",
    "scaler = StandardScaler() \n",
    "# Fitting the StandardScaler to df\n",
    "scaler.fit(df)\n",
    "# Creating the scaled df \n",
    "df_scaled = pd.DataFrame(scaler.transform(df), columns=df.columns)\n",
    "\n",
    "df_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## PCA\n",
    "pca = PCA()\n",
    "# Finding pca components\n",
    "pca_comps = pca.fit_transform(df_scaled)\n",
    "# Finding how much each principal component explains the variance in the dataset\n",
    "var_exp = pca.explained_variance_ratio_ \n",
    "# Finding the cumulative variance\n",
    "cum_var_exp = np.cumsum(var_exp)\n",
    "\n",
    "# Plotting the explained variance\n",
    "fig = plt.figure(figsize=(10, 5), dpi=100)\n",
    "plt.bar(range(1,len(var_exp)+1), var_exp, alpha=0.5, align='center', label='individual explained variance')\n",
    "plt.step(range(1,len(var_exp)+1), cum_var_exp, where='mid', label='cumulative explained variance')\n",
    "plt.ylabel('explained variance ratio')\n",
    "plt.xlabel('principal component index')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of components to explain 90% varaince is 5\n"
     ]
    }
   ],
   "source": [
    "var_exp_thres = 0.9 # I'd like to explain 90% variance\n",
    "# calling pca instance \n",
    "pca = PCA(var_exp_thres) \n",
    "pca_comps = pca.fit_transform(df_scaled)\n",
    "print('Number of components to explain {:0.0f}% varaince is {}'.format(var_exp_thres*100, pca.n_components_))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-means \n",
    "\n",
    "Now as the optimal number of PC is determined (5) the reduced-dimensional data is fed into the k-means algorithm. K-means clustering partitions the data into k clusters, where k is a predefined number. The algorithm iteratively assigns data points to the nearest cluster centroid and updates the centroids until convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\elzad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=11.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the best number of clusters k is: 4\n"
     ]
    }
   ],
   "source": [
    "## The elbow method to find the best k\n",
    "# Creating the clustering model and visualizer\n",
    "kmeans = KMeans(random_state=1234)\n",
    "visualizer = KElbowVisualizer(kmeans, k=(1, 10))\n",
    "visualizer.fit(pca_comps)\n",
    "visualizer.show()\n",
    "print('the best number of clusters k is:', visualizer.elbow_value_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performing the final k-means on 4 clusters\n",
    "kmeans_best_cluster = KMeans(n_clusters=visualizer.elbow_value_, random_state=1234)\n",
    "kmeans_best_cluster.fit(pca_comps)\n",
    "# Saving the cluster lables\n",
    "cluster_labels_PCs = kmeans_best_cluster.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualizing the clusters\n",
    "\n",
    "# Getting a colormap from seaborn\n",
    "cmap = ListedColormap(sns.color_palette('tab10', 200).as_hex())\n",
    "\n",
    "# Setting up the graph\n",
    "fig = plt.figure(figsize=(20, 7.5), dpi=100)\n",
    "\n",
    "axes = fig.add_subplot(1, 3, 3, projection='3d')\n",
    "sc = axes.scatter(pca_comps[:,0],pca_comps[:,1],pca_comps[:,2], c = cluster_labels_PCs, marker='o', cmap=cmap, alpha=1)\n",
    "axes.set_xlabel('PC1')\n",
    "axes.set_ylabel('PC2')\n",
    "axes.set_zlabel('PC3')\n",
    "axes.set_title('clusters from top 3 PCs')\n",
    "axes.legend(*sc.legend_elements(), bbox_to_anchor=(1.05, 1), loc=2) \n",
    "axes.view_init(-150, -45) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>StartDate</th>\n",
       "      <th>EndDate</th>\n",
       "      <th>Duration__in_seconds_</th>\n",
       "      <th>Finished</th>\n",
       "      <th>RecordedDate</th>\n",
       "      <th>ResponseId</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age_classes</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Party_to_vote_for</th>\n",
       "      <th>...</th>\n",
       "      <th>Type_of_house</th>\n",
       "      <th>Net_income_household</th>\n",
       "      <th>Postal_code</th>\n",
       "      <th>INHABITANTS_postal_cod</th>\n",
       "      <th>unemployment_socia_ass</th>\n",
       "      <th>share_owner_occupied_h</th>\n",
       "      <th>share_rented_house</th>\n",
       "      <th>Area_address_density</th>\n",
       "      <th>Urban_density_class</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021/11/12 12:41</td>\n",
       "      <td>2021/11/12 12:53</td>\n",
       "      <td>723</td>\n",
       "      <td>True</td>\n",
       "      <td>2021/11/12 12:53</td>\n",
       "      <td>R_7U1zhjT10Fj6Oyd</td>\n",
       "      <td>77</td>\n",
       "      <td>75+</td>\n",
       "      <td>Male</td>\n",
       "      <td>VVD</td>\n",
       "      <td>...</td>\n",
       "      <td>Owner-occupied house</td>\n",
       "      <td>3000 - 3999 Euros</td>\n",
       "      <td>1521</td>\n",
       "      <td>11305.0</td>\n",
       "      <td>10.747457</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>Highly urban (address density of 1 500 to 2 500)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021/11/12 12:43</td>\n",
       "      <td>2021/11/12 13:11</td>\n",
       "      <td>1689</td>\n",
       "      <td>True</td>\n",
       "      <td>2021/11/12 13:11</td>\n",
       "      <td>R_2SfCEOKAWZpIB4U</td>\n",
       "      <td>68</td>\n",
       "      <td>65-75</td>\n",
       "      <td>Male</td>\n",
       "      <td>VVD</td>\n",
       "      <td>...</td>\n",
       "      <td>Owner-occupied house</td>\n",
       "      <td>2000 - 2999 Euros</td>\n",
       "      <td>1461</td>\n",
       "      <td>3630.0</td>\n",
       "      <td>4.683196</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>Moderately urban (address density of 1 000 to ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021/11/12 12:43</td>\n",
       "      <td>2021/11/12 12:55</td>\n",
       "      <td>733</td>\n",
       "      <td>True</td>\n",
       "      <td>2021/11/12 12:55</td>\n",
       "      <td>R_XBTkVGK5l28mcZH</td>\n",
       "      <td>56</td>\n",
       "      <td>55-65</td>\n",
       "      <td>Male</td>\n",
       "      <td>Votes blank or not</td>\n",
       "      <td>...</td>\n",
       "      <td>Owner-occupied house</td>\n",
       "      <td>2000 - 2999 Euros</td>\n",
       "      <td>9403</td>\n",
       "      <td>14180.0</td>\n",
       "      <td>7.193230</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1187.0</td>\n",
       "      <td>Moderately urban (address density of 1 000 to ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021/11/12 12:44</td>\n",
       "      <td>2021/11/12 13:06</td>\n",
       "      <td>1326</td>\n",
       "      <td>True</td>\n",
       "      <td>2021/11/12 13:06</td>\n",
       "      <td>R_12Sb67J1DHTIPPp</td>\n",
       "      <td>55</td>\n",
       "      <td>55-65</td>\n",
       "      <td>Male</td>\n",
       "      <td>PvdA (Labour Party)</td>\n",
       "      <td>...</td>\n",
       "      <td>Owner-occupied house</td>\n",
       "      <td>3000 - 3999 Euros</td>\n",
       "      <td>6932</td>\n",
       "      <td>7425.0</td>\n",
       "      <td>10.303030</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1228.0</td>\n",
       "      <td>Moderately urban (address density of 1 000 to ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021/11/12 12:44</td>\n",
       "      <td>2021/11/12 12:58</td>\n",
       "      <td>859</td>\n",
       "      <td>True</td>\n",
       "      <td>2021/11/12 12:58</td>\n",
       "      <td>R_3qtZ9hRp8cL0eSR</td>\n",
       "      <td>67</td>\n",
       "      <td>65-75</td>\n",
       "      <td>Male</td>\n",
       "      <td>VVD</td>\n",
       "      <td>...</td>\n",
       "      <td>Rented house</td>\n",
       "      <td>2000 - 2999 Euros</td>\n",
       "      <td>5235</td>\n",
       "      <td>6930.0</td>\n",
       "      <td>9.740260</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1755.0</td>\n",
       "      <td>Highly urban (address density of 1 500 to 2 500)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          StartDate           EndDate  Duration__in_seconds_  Finished  \\\n",
       "0  2021/11/12 12:41  2021/11/12 12:53                    723      True   \n",
       "1  2021/11/12 12:43  2021/11/12 13:11                   1689      True   \n",
       "2  2021/11/12 12:43  2021/11/12 12:55                    733      True   \n",
       "3  2021/11/12 12:44  2021/11/12 13:06                   1326      True   \n",
       "4  2021/11/12 12:44  2021/11/12 12:58                    859      True   \n",
       "\n",
       "       RecordedDate         ResponseId  Age Age_classes Gender  \\\n",
       "0  2021/11/12 12:53  R_7U1zhjT10Fj6Oyd   77         75+   Male   \n",
       "1  2021/11/12 13:11  R_2SfCEOKAWZpIB4U   68       65-75   Male   \n",
       "2  2021/11/12 12:55  R_XBTkVGK5l28mcZH   56       55-65   Male   \n",
       "3  2021/11/12 13:06  R_12Sb67J1DHTIPPp   55       55-65   Male   \n",
       "4  2021/11/12 12:58  R_3qtZ9hRp8cL0eSR   67       65-75   Male   \n",
       "\n",
       "     Party_to_vote_for  ...         Type_of_house Net_income_household  \\\n",
       "0                  VVD  ...  Owner-occupied house    3000 - 3999 Euros   \n",
       "1                  VVD  ...  Owner-occupied house    2000 - 2999 Euros   \n",
       "2   Votes blank or not  ...  Owner-occupied house    2000 - 2999 Euros   \n",
       "3  PvdA (Labour Party)  ...  Owner-occupied house    3000 - 3999 Euros   \n",
       "4                  VVD  ...          Rented house    2000 - 2999 Euros   \n",
       "\n",
       "  Postal_code INHABITANTS_postal_cod unemployment_socia_ass  \\\n",
       "0        1521                11305.0              10.747457   \n",
       "1        1461                 3630.0               4.683196   \n",
       "2        9403                14180.0               7.193230   \n",
       "3        6932                 7425.0              10.303030   \n",
       "4        5235                 6930.0               9.740260   \n",
       "\n",
       "  share_owner_occupied_h  share_rented_house Area_address_density  \\\n",
       "0                   50.0                50.0               1700.0   \n",
       "1                   80.0                20.0               1061.0   \n",
       "2                   80.0                20.0               1187.0   \n",
       "3                   50.0                50.0               1228.0   \n",
       "4                   60.0                40.0               1755.0   \n",
       "\n",
       "                                 Urban_density_class  cluster  \n",
       "0   Highly urban (address density of 1 500 to 2 500)        3  \n",
       "1  Moderately urban (address density of 1 000 to ...        3  \n",
       "2  Moderately urban (address density of 1 000 to ...        3  \n",
       "3  Moderately urban (address density of 1 000 to ...        3  \n",
       "4   Highly urban (address density of 1 500 to 2 500)        3  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding the clusters column to the original dataset\n",
    "df_original['cluster'] = cluster_labels_PCs\n",
    "df_original.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reverting the dictionaries \n",
    "\n",
    "# dict_edu_rev = {key:value for key, value in zip(dict_edu.values(), dict_edu.keys())}\n",
    "# dict_gender_rev = {key:value for key, value in zip(dict_gender.values(), dict_gender.keys())}\n",
    "# dict_inc_rev = {key:value for key, value in zip(dict_inc.values(), dict_inc.keys())}\n",
    "# dict_politics_rev = {key:value for key, value in zip(dict_politics.values(), dict_politics.keys())}\n",
    "\n",
    "# df.replace({\"Education_classes\": dict_edu_rev}, inplace = True)\n",
    "# df.replace({\"Gender\": dict_gender_rev}, inplace = True)\n",
    "# df.replace({\"Net_income_household\": dict_inc_rev}, inplace = True)\n",
    "# df.replace({\"PoliticalSpectrum_Left\": dict_politics_rev}, inplace = True)\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Gaining insights from the 4 clusters through visualization\n",
    "\n",
    "# Setting up the graphs\n",
    "gs = gridspec.GridSpec(3, 2)\n",
    "fig = plt.figure(figsize=(20, 20), dpi=100)\n",
    "\n",
    "# Creating subplots for each graph\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax1.set_title('Cluster Size')\n",
    "ax2 = fig.add_subplot(gs[0, 1])\n",
    "ax2.set_title('Average Age per Cluster')\n",
    "ax3 = fig.add_subplot(gs[1, 0])\n",
    "ax3.set_title('Education Level per Cluster')\n",
    "ax4 = fig.add_subplot(gs[1, 1])\n",
    "ax4.set_title('Number of Males and Females per Cluster')\n",
    "ax5 = fig.add_subplot(gs[2, 0])\n",
    "ax5.set_title('Household Income per Cluster')\n",
    "ax6 = fig.add_subplot(gs[2, 1])\n",
    "ax6.set_title('Political Parties per Cluster')\n",
    "\n",
    "# Plotting cluster size\n",
    "sns.histplot(data=df_original, x='cluster', discrete=True, ax=ax1)\n",
    "for p in ax1.patches:\n",
    "    ax1.annotate('{:.1f}%'.format((p.get_height() / len(df_original)) * 100), (p.get_x() + 0.5, p.get_height() + 10), fontsize=12)\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Plotting average age per cluster\n",
    "age = df_original.groupby('cluster')['Age'].mean()\n",
    "age.plot(kind='bar', rot=0, ax=ax2)\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Plotting education level per cluster\n",
    "counted = df_original.groupby('cluster').Education_classes.value_counts(normalize=True).reset_index(name=\"proportion\")\n",
    "sns.barplot(data=counted, x='cluster', y='proportion', hue='Education_classes', ax=ax3)\n",
    "ax3.legend(loc=1, bbox_to_anchor=(0.65, 1), fontsize=8)\n",
    "\n",
    "# Plotting gender per cluster\n",
    "counted = df_original.groupby('cluster').Gender.value_counts(normalize=True).reset_index(name=\"proportion\")\n",
    "sns.barplot(data=counted, x='cluster', y='proportion', hue='Gender', ax=ax4)\n",
    "ax4.legend(loc=1, bbox_to_anchor=(0.65, 1), fontsize=8)\n",
    "\n",
    "# Plotting household income per cluster\n",
    "counted = df_original.groupby('cluster').Net_income_household.value_counts(normalize=True).reset_index(name=\"proportion\")\n",
    "sns.barplot(data=counted, x='cluster', y='proportion', hue='Net_income_household', ax=ax5)\n",
    "ax5.legend(loc=1, bbox_to_anchor=(0.65, 1), fontsize=8)\n",
    "\n",
    "# Plotting political parties per cluster\n",
    "counted = df_original.groupby('cluster').PoliticalSpectrum_Left.value_counts(normalize=True).reset_index(name=\"proportion\")\n",
    "sns.barplot(data=counted, x='cluster', y='proportion', hue='PoliticalSpectrum_Left', ax=ax6)\n",
    "ax6.legend(loc=1, bbox_to_anchor=(0.65, 1), fontsize=8)\n",
    "\n",
    "# Removing the top and right spines for a cleaner look\n",
    "sns.despine()\n"
   ]
  },
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   "source": [
    "Here are some conlusions that can be drawn from the above graphs:\n",
    "* Cluster 1 is the largest cluster, with an average respondent age of approximately 65, making it the oldest cluster. All respondents in this cluster are female, and most have a medium education level. However, it also has the highest number of low education respondents. The majority of respondents in this cluster earn between 1,000 to 2,999 EUR. This cluster contains the highest number of respondents who identify with the left political party. \n",
    "* Cluster 2 is the smallest cluster, primarily consisting of males with an average age of approximately 52. This cluster has the highest number of respondents with high education and the lowest number of respondents with low and medium education. Most respondents in this cluster earn between 3,000 and 4,999 EUR, and it also contains the highest number of respondents in the 10,000 EUR or more income group. This cluster has the highest number of respondents associated with the center-right, center, and right political parties.\n",
    "* Cluster 3 is the second smallest cluster, with an average respondent age of approximately 38, making it the youngest cluster. It is predominantly composed of females with mostly medium and high education levels. Notably, this cluster has the highest number of respondents in the medium education level. The majority of respondents in this cluster earn between 2,000 and 4,999 EUR and are associated with the center-right political party.\n",
    "* Cluster 4 is the second largest cluster, exclusively consisting of male respondents with an average age of approximately 62. Most respondents in this cluster have either medium or low education levels, and it has the smallest number of high education level respondents. The majority of respondents in this cluster fall within the 2,000-2,999 income group and are primarily associated with the center political party.\n",
    "\n",
    "These clusters were summarized into 4 personas that can be found in [this](https://public.tableau.com/app/profile/cmihva/viz/FromFossilFoolToGreenBeanWhatCanTheDutchiesDo/Stimulating-Sustainability-among-Dutch-Consumers) visualisation. "
   ]
  }
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