Identify_Customer_Segments.ipynb

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    "# Project: Identify Customer Segments\n",
    "\n",
    "In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.\n",
    "\n",
    "This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. **Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks.** The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.\n",
    "\n",
    "It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. **There will be times in the project where you will need to make and justify your own decisions on how to treat the data.** These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.\n",
    "\n",
    "At the end of most sections, there will be a Markdown cell labeled **Discussion**. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. **Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.**"
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    "# import libraries here; add more as necessary\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import ast, re\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Seed\n",
    "np.random.seed(42)"
   ]
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    "### Step 0: Load the Data\n",
    "\n",
    "There are four files associated with this project (not including this one):\n",
    "\n",
    "- `Udacity_AZDIAS_Subset.csv`: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).\n",
    "- `Udacity_CUSTOMERS_Subset.csv`: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).\n",
    "- `Data_Dictionary.md`: Detailed information file about the features in the provided datasets.\n",
    "- `AZDIAS_Feature_Summary.csv`: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns\n",
    "\n",
    "Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.\n",
    "\n",
    "To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the `.csv` data files in this project: they're semicolon (`;`) delimited, so you'll need an additional argument in your [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.\n",
    "\n",
    "Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings."
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    "# Load in the general demographics data.\n",
    "azdias = pd.read_csv('Udacity_AZDIAS_Subset.csv', sep=';')\n",
    "\n",
    "# Load in the feature summary file.\n",
    "feat_info = pd.read_csv('AZDIAS_Feature_Summary.csv', sep=';')"
   ]
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      "azdias shape: (891221, 85)\n",
      "azdias head:\n"
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       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0        -1                     2          1            2.0   \n",
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       "\n",
       "   FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
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      "feat_info shape: (85, 4)\n",
      "feat_info head:\n"
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    "# Check the structure of the data after it's loaded (e.g. print the number of\n",
    "# rows and columns, print the first few rows).\n",
    "print(\"azdias shape:\", azdias.shape)\n",
    "print(\"azdias head:\")\n",
    "display(azdias.head())\n",
    "\n",
    "print(\"feat_info shape:\", feat_info.shape)\n",
    "print(\"feat_info head:\")\n",
    "display(feat_info.head())"
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    "> **Tip**: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut `esc --> a` (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and `esc --> b` adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use `esc --> m` and to convert to a code cell, use `esc --> y`. \n",
    "\n",
    "## Step 1: Preprocessing\n",
    "\n",
    "### Step 1.1: Assess Missing Data\n",
    "\n",
    "The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the **Discussion** cell with your findings and decisions at the end of each step that has one!\n",
    "\n",
    "#### Step 1.1.1: Convert Missing Value Codes to NaNs\n",
    "The fourth column of the feature attributes summary (loaded in above as `feat_info`) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. `[-1,0]`), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.\n",
    "\n",
    "**As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.**"
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       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0       NaN                   2.0          1            2.0   \n",
       "1       NaN                   1.0          2            5.0   \n",
       "2       NaN                   3.0          2            3.0   \n",
       "3       2.0                   4.0          2            2.0   \n",
       "4       NaN                   3.0          1            5.0   \n",
       "\n",
       "   FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
       "0                  3              4                 3               5   \n",
       "1                  1              5                 2               5   \n",
       "2                  1              4                 1               2   \n",
       "3                  4              2                 5               2   \n",
       "4                  4              3                 4               1   \n",
       "\n",
       "   FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  ...  PLZ8_ANTG1  PLZ8_ANTG2  \\\n",
       "0                      5                 3  ...         NaN         NaN   \n",
       "1                      4                 5  ...         2.0         3.0   \n",
       "2                      3                 5  ...         3.0         3.0   \n",
       "3                      1                 2  ...         2.0         2.0   \n",
       "4                      3                 2  ...         2.0         4.0   \n",
       "\n",
       "   PLZ8_ANTG3  PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  \\\n",
       "0         NaN         NaN          NaN       NaN       NaN     NaN   \n",
       "1         2.0         1.0          1.0       5.0       4.0     3.0   \n",
       "2         1.0         0.0          1.0       4.0       4.0     3.0   \n",
       "3         2.0         0.0          1.0       3.0       4.0     2.0   \n",
       "4         2.0         1.0          2.0       3.0       3.0     4.0   \n",
       "\n",
       "   ORTSGR_KLS9  RELAT_AB  \n",
       "0          NaN       NaN  \n",
       "1          5.0       4.0  \n",
       "2          5.0       2.0  \n",
       "3          3.0       3.0  \n",
       "4          6.0       5.0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify missing or unknown data values and convert them to NaNs.\n",
    "\n",
    "def to_list(list_string):    \n",
    "    # Put quotes around Xs\n",
    "    list_string = re.sub(\"(X{1,2})\", r\"'\\1'\", list_string)\n",
    "\n",
    "    # Parse into a list and return\n",
    "    return ast.literal_eval(list_string)\n",
    "\n",
    "\n",
    "def missing_value_codes(column, feat_info):\n",
    "    missing_value_codes = feat_info.query(\"attribute == @column\").missing_or_unknown.item()\n",
    "    return to_list(missing_value_codes)\n",
    "    \n",
    "\n",
    "def convert_missing_or_unkown_to_nan(data, feat_info):\n",
    "    data_copy = data.copy()\n",
    "    for column in data_copy.columns:\n",
    "        mvc_list = missing_value_codes(column, feat_info)\n",
    "        if len(mvc_list) > 0:\n",
    "            data_copy[column] = data_copy[column].replace(mvc_list, np.NaN)\n",
    "    return data_copy\n",
    "\n",
    "\n",
    "azdias = convert_missing_or_unkown_to_nan(azdias, feat_info)\n",
    "azdias.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's [`hist()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html) function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)\n",
    "\n",
    "For the remaining features, are there any patterns in which columns have, or share, missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb46a2a518>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform an assessment of how much missing data there is in each column of the\n",
    "# dataset.\n",
    "\n",
    "def nan_ratios(data, axis=0):\n",
    "    nan_counts = data.isnull().sum(axis=axis)\n",
    "    row_count = data.shape[axis]\n",
    "    return nan_counts / row_count\n",
    "\n",
    "nan_ratios(azdias, axis=0).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "      <th>nan_ratios</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attribute</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_SOZ</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_VERT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KULT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_RAT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KRIT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_DOM</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KAEM</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_PFLICHT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_TRADV</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      information_level         type missing_or_unknown  \\\n",
       "attribute                                                                 \n",
       "ANREDE_KZ                        person  categorical             [-1,0]   \n",
       "FINANZ_MINIMALIST                person      ordinal               [-1]   \n",
       "FINANZ_SPARER                    person      ordinal               [-1]   \n",
       "FINANZ_VORSORGER                 person      ordinal               [-1]   \n",
       "FINANZ_ANLEGER                   person      ordinal               [-1]   \n",
       "FINANZ_UNAUFFAELLIGER            person      ordinal               [-1]   \n",
       "FINANZ_HAUSBAUER                 person      ordinal               [-1]   \n",
       "FINANZTYP                        person  categorical               [-1]   \n",
       "GREEN_AVANTGARDE                 person  categorical                 []   \n",
       "SEMIO_SOZ                        person      ordinal             [-1,9]   \n",
       "SEMIO_FAM                        person      ordinal             [-1,9]   \n",
       "SEMIO_REL                        person      ordinal             [-1,9]   \n",
       "SEMIO_MAT                        person      ordinal             [-1,9]   \n",
       "SEMIO_VERT                       person      ordinal             [-1,9]   \n",
       "SEMIO_LUST                       person      ordinal             [-1,9]   \n",
       "SEMIO_ERL                        person      ordinal             [-1,9]   \n",
       "SEMIO_KULT                       person      ordinal             [-1,9]   \n",
       "SEMIO_RAT                        person      ordinal             [-1,9]   \n",
       "SEMIO_KRIT                       person      ordinal             [-1,9]   \n",
       "SEMIO_DOM                        person      ordinal             [-1,9]   \n",
       "SEMIO_KAEM                       person      ordinal             [-1,9]   \n",
       "SEMIO_PFLICHT                    person      ordinal             [-1,9]   \n",
       "SEMIO_TRADV                      person      ordinal             [-1,9]   \n",
       "ZABEOTYP                         person  categorical             [-1,9]   \n",
       "\n",
       "                       nan_ratios  \n",
       "attribute                          \n",
       "ANREDE_KZ                     0.0  \n",
       "FINANZ_MINIMALIST             0.0  \n",
       "FINANZ_SPARER                 0.0  \n",
       "FINANZ_VORSORGER              0.0  \n",
       "FINANZ_ANLEGER                0.0  \n",
       "FINANZ_UNAUFFAELLIGER         0.0  \n",
       "FINANZ_HAUSBAUER              0.0  \n",
       "FINANZTYP                     0.0  \n",
       "GREEN_AVANTGARDE              0.0  \n",
       "SEMIO_SOZ                     0.0  \n",
       "SEMIO_FAM                     0.0  \n",
       "SEMIO_REL                     0.0  \n",
       "SEMIO_MAT                     0.0  \n",
       "SEMIO_VERT                    0.0  \n",
       "SEMIO_LUST                    0.0  \n",
       "SEMIO_ERL                     0.0  \n",
       "SEMIO_KULT                    0.0  \n",
       "SEMIO_RAT                     0.0  \n",
       "SEMIO_KRIT                    0.0  \n",
       "SEMIO_DOM                     0.0  \n",
       "SEMIO_KAEM                    0.0  \n",
       "SEMIO_PFLICHT                 0.0  \n",
       "SEMIO_TRADV                   0.0  \n",
       "ZABEOTYP                      0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "      <th>nan_ratios</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attribute</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALTER_HH</th>\n",
       "      <td>household</td>\n",
       "      <td>interval</td>\n",
       "      <td>[0]</td>\n",
       "      <td>0.348137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBURTSJAHR</th>\n",
       "      <td>person</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[0]</td>\n",
       "      <td>0.440203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_BAUMAX</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>0.534687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KK_KUNDENTYP</th>\n",
       "      <td>household</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>0.655967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGER_TYP</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>0.769554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TITEL_KZ</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>0.997576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             information_level         type missing_or_unknown  nan_ratios\n",
       "attribute                                                                 \n",
       "ALTER_HH             household     interval                [0]    0.348137\n",
       "GEBURTSJAHR             person      numeric                [0]    0.440203\n",
       "KBA05_BAUMAX     microcell_rr3        mixed             [-1,0]    0.534687\n",
       "KK_KUNDENTYP         household  categorical               [-1]    0.655967\n",
       "AGER_TYP                person  categorical             [-1,0]    0.769554\n",
       "TITEL_KZ                person  categorical             [-1,0]    0.997576"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate patterns in the amount of missing data in each column.\n",
    "\n",
    "def view_zero_missing_data_features_info(data, feat_info):\n",
    "    feat_info = feat_info.merge(\n",
    "        pd.DataFrame(data={'nan_ratios': nan_ratios(data)}),\n",
    "        left_on='attribute',\n",
    "        right_index=True\n",
    "    )\n",
    "    feat_info = feat_info.set_index('attribute')\n",
    "    zero_nan_features = feat_info.query(\"nan_ratios == 0\")\n",
    "    display(zero_nan_features)\n",
    "\n",
    "\n",
    "def view_high_missing_data_features_info(data, feat_info, threshold=0.2):\n",
    "    feat_info = feat_info.merge(\n",
    "        pd.DataFrame(data={'nan_ratios': nan_ratios(data)}),\n",
    "        left_on='attribute',\n",
    "        right_index=True\n",
    "    )\n",
    "    feat_info = feat_info.set_index('attribute')\n",
    "    outlier_features = feat_info.query(\"nan_ratios > @threshold\").sort_values(by='nan_ratios')\n",
    "    display(outlier_features)\n",
    "\n",
    "\n",
    "view_zero_missing_data_features_info(azdias, feat_info)\n",
    "view_high_missing_data_features_info(azdias, feat_info, threshold=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping: ['AGER_TYP', 'GEBURTSJAHR', 'TITEL_KZ', 'ALTER_HH', 'KK_KUNDENTYP', 'KBA05_BAUMAX']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(891221, 79)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the outlier columns from the dataset. (You'll perform other data\n",
    "# engineering tasks such as re-encoding and imputation later.)\n",
    "\n",
    "def split_via_nan_ratio(data, threshold=0.2, axis=0):\n",
    "    nan_ratios_ = nan_ratios(data, axis=axis)\n",
    "    low_nan_ratios = nan_ratios_[nan_ratios_ <= threshold]\n",
    "    high_nan_ratios = nan_ratios_[nan_ratios_ > threshold]\n",
    "    return low_nan_ratios, high_nan_ratios\n",
    "\n",
    "\n",
    "def drop_outlier_columns(data, feat_info, outlier_columns, verbose=False):\n",
    "    if verbose:\n",
    "        print(\"Dropping:\", outlier_columns)\n",
    "\n",
    "    data = data.drop(columns=outlier_columns)\n",
    "    feat_info = feat_info.drop(index=feat_info.index[feat_info.attribute.isin(outlier_columns)])\n",
    "    return data, feat_info\n",
    "\n",
    "\n",
    "_low_nan_ratios, high_nan_ratios  = split_via_nan_ratio(azdias, threshold=0.2, axis=0)\n",
    "outlier_columns = high_nan_ratios.index.tolist()\n",
    "azdias, feat_info = drop_outlier_columns(azdias, feat_info, outlier_columns, verbose=True)\n",
    "azdias.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "(Double click this cell and replace this text with your own text, reporting your observations regarding the amount of missing data in each column. Are there any patterns in missing values? Which columns were removed from the dataset?)\n",
    "\n",
    "- The columns AGER_TYP, GEBURTSJAHR, TITEL_KZ, ALTER_HH, KK_KUNDENTYP and KBA05_BAUMAX have more than 20% of their data missing making them outliers, as all the other columns have less than that proportion missing. They were thus dropped from the data.\n",
    "- All the columns with no missing values were at the person information_level\n",
    "- The outlier columns (columns with more than 20% of their data missing) were at the person, microcell_rr3 and household information_level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.\n",
    "\n",
    "In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.\n",
    "- You can use seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) function to create a bar chart of code frequencies and matplotlib's [`subplot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html) function to put bar charts for the two subplots side by side.\n",
    "- To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.\n",
    "\n",
    "Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. **Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb469d7828>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REXdFxHhEPBkRF/Uca21pfyAi1vbUL46I/WWfuyIiZupDktSMmjONo8AtmflLwArg5og4D9gA7MnM5cCesg5wBbC8vNYDm6AbAMBG4FLgEmBjTwhsKm2P7beq1KfrQ5LUgIGhkZmHMvNbZfk14BlgMbAa2FqabQWuLsurgW3Z9RhwRkScC1wO7M7MI5n5CrAbWFW2nZ6Zj2ZmAtumHKtfH5KkBiyYTeOIWAZ8FHgcGMnMQ9ANlog4pzRbDLzQs9tEqc1Un+hTZ4Y+po5rPd0zFUZGRuh0OrOZ1nEjC+GWC44Ote+JGHa8/UxOTs7p8ZrgHNrBObRD2+ZQHRoR8beA/wb8dmb+sNx26Nu0Ty2HqFfLzM3AZoDR0dEcGxubze7H3X3/du7YP6scnRMHbxibs2N1Oh2GnX9bOId2cA7t0LY5VD09FRE/Qzcw7s/M/17KL5VLS5T3w6U+ASzt2X0J8OKA+pI+9Zn6kCQ1oObpqQDuBZ7JzN/r2bQDOPYE1Fpge099TXmKagXwarnEtAtYGRGLyg3wlcCusu21iFhR+loz5Vj9+pAkNaDmWszHgN8A9kfEE6X2L4DbgQcjYh3wPHBt2bYTuBIYB94AbgTIzCMRcRuwt7S7NTOPlOWbgPuAhcDD5cUMfUiSGjAwNDLz6/S/7wBwWZ/2Cdw8zbG2AFv61PcB5/epv9yvD0lSM/xGuCSpmqEhSapmaEiSqhkakqRqhoYkqZqhIUmqZmhIkqoZGpKkaoaGJKmaoSFJqmZoSJKqGRqSpGqGhiSpmqEhSapmaEiSqhkakqRqhoYkqZqhIUmqZmhIkqoZGpKkagNDIyK2RMThiPizntqZEbE7Ig6U90WlHhFxV0SMR8STEXFRzz5rS/sDEbG2p35xROwv+9wVETFTH5Kk5tScadwHrJpS2wDsyczlwJ6yDnAFsLy81gOboBsAwEbgUuASYGNPCGwqbY/tt2pAH5KkhgwMjcz8U+DIlPJqYGtZ3gpc3VPfll2PAWdExLnA5cDuzDySma8Au4FVZdvpmfloZiawbcqx+vUhSWrIsPc0RjLzEEB5P6fUFwMv9LSbKLWZ6hN96jP1IUlqyII5Pl70qeUQ9dl1GrGe7iUuRkZG6HQ6sz0EACML4ZYLjg6174kYdrz9TE5OzunxmuAc2sE5tEPb5jBsaLwUEedm5qFyielwqU8AS3vaLQFeLPWxKfVOqS/p036mPt4iMzcDmwFGR0dzbGxsuqYzuvv+7dyxf65zdLCDN4zN2bE6nQ7Dzr8tnEM7OId2aNschr08tQM49gTUWmB7T31NeYpqBfBqubS0C1gZEYvKDfCVwK6y7bWIWFGemloz5Vj9+pAkNWTgP6sj4o/oniWcHRETdJ+Cuh14MCLWAc8D15bmO4ErgXHgDeBGgMw8EhG3AXtLu1sz89jN9ZvoPqG1EHi4vJihD0lSQwaGRmZeP82my/q0TeDmaY6zBdjSp74POL9P/eV+fUiSmuM3wiVJ1QwNSVK1+X9USG+xbMMfz9mxbrngKJ+cxfEO3v6JOetb0jufZxqSpGqGhiSpmqEhSapmaEiSqhkakqRqhoYkqZqhIUmqZmhIkqr55T5JmkNz+WVdqP/C7nx9UdczDUlSNUNDklTN0JAkVTM0JEnVvBEu6R1p2YY/nvVvfdZgnmlIkqoZGpKkaoaGJKmaoSFJqtb6G+ERsQr4AnAK8MXMvL3hIb2jzPW3V2v5v5mdX8N8znNxE9nP+Z2n1aEREacA9wC/DkwAeyNiR2Y+3ezIdKJm+iH2dj/x4g8yaXitDg3gEmA8M58DiIgHgNWAoSH9FGjqTFZvn7bf01gMvNCzPlFqkqQGRGY2PYZpRcS1wOWZ+Ztl/TeASzLz01ParQfWl9UPAc8O2eXZwPeH3LctnEM7OId2cA71fi4z3z+oUdsvT00AS3vWlwAvTm2UmZuBzSfaWUTsy8zREz1Ok5xDOziHdnAOc6/tl6f2Assj4gMRcSpwHbCj4TFJ0kmr1WcamXk0Ij4F7KL7yO2WzHyq4WFJ0kmr1aEBkJk7gZ3z1N0JX+JqAefQDs6hHZzDHGv1jXBJUru0/Z6GJKlFTsrQiIhVEfFsRIxHxIY+298dEV8q2x+PiGXzP8qZVczhVyLiWxFxNCKuaWKMg1TM4TMR8XREPBkReyLi55oY50wq5vBPImJ/RDwREV+PiPOaGOdMBs2hp901EZER0ZoneY6p+Bw+GRF/WT6HJyLiN5sY50xqPoeI+Afl78RTEfGH8z1GADLzpHrRvaH+XeCDwKnAd4DzprT5p8B/LMvXAV9qetxDzGEZ8MvANuCapsc85Bx+FfjZsnzTT+nncHrP8lXAV5se92znUNq9F/hT4DFgtOlxD/E5fBL4D02P9QTnsBz4NrCorJ/TxFhPxjON47+aJDN/Ahz71SS9VgNby/JDwGUREfM4xkEGziEzD2bmk8BfNzHACjVzeCQz3yirj9H9nk6b1Mzhhz2rpwFtu4lY8/cB4Dbg3wI/ms/BVaqdQ5vVzOEfAfdk5isAmXl4nscInJyXp2p+NcnxNpl5FHgVOGteRlfnnfDrVWY7h3XAw2/riGavag4RcXNEfJfuD91/Nk9jqzVwDhHxUWBpZn5lPgc2C7X/Lf39cqnzoYhY2md7k2rm8AvAL0TE/46Ix8pvAJ93J2No9DtjmPqvv5o2TWr7+GpUzyEi/iEwCvy7t3VEs1c1h8y8JzP/NvBZ4F++7aOanRnnEBHvAu4Ebpm3Ec1ezefwP4FlmfnLwJ/wN1cS2qJmDgvoXqIaA64HvhgRZ7zN43qLkzE0an41yfE2EbEAeB9wZF5GV6fq16u0XNUcIuLXgN8BrsrMH8/T2GrN9nN4ALj6bR3R7A2aw3uB84FORBwEVgA7WnYzfODnkJkv9/z385+Ai+dpbLVqfy5tz8z/l5nfo/s79pbP0/iOOxlDo+ZXk+wA1pbla4CvZbnz1BLvhF+vMnAO5bLIH9ANjEau3w5QM4fev9SfAA7M4/hqzDiHzHw1M8/OzGWZuYzuvaWrMnNfM8Ptq+ZzOLdn9SrgmXkcX42av9P/g+7DIUTE2XQvVz03r6OEk+/pqfKz/0rg/9B9WuF3Su1Wun8ZAN4D/FdgHPgG8MGmxzzEHP4O3X+ZvA68DDzV9JiHmMOfAC8BT5TXjqbHPMQcvgA8Vcb/CPDhpsc82zlMaduhZU9PVX4O/6Z8Dt8pn8MvNj3mIeYQwO/R/f8J7Qeua2KcfiNcklTtZLw8JUkakqEhSapmaEiSqhkakqRqhoYkqZqhIUmqZmhIkqoZGpKkav8fxysAgMWeIQAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How much data is missing in each row of the dataset?\n",
    "\n",
    "nan_ratios(azdias, axis=1).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code to divide the data into two subsets based on the number of missing\n",
    "# values in each row.\n",
    "\n",
    "low_nan_ratio_rows, high_nan_ratio_rows = split_via_nan_ratio(azdias, threshold=0.05, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(684502, 79)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the distribution of values for at least five columns where there are\n",
    "# no or few missing values, between the two subsets.\n",
    "\n",
    "def compare_non_nan_distributions(data, low_nan_ratio_rows, high_nan_ratio_rows):\n",
    "    nan_ratios_ = nan_ratios(data, axis=0)\n",
    "    no_nan_columns = nan_ratios_[nan_ratios_ == 0].index\n",
    "    \n",
    "    for column in no_nan_columns[:5]:\n",
    "        fig, axes = plt.subplots(1,2, figsize=(20, 5))\n",
    "        sns.countplot(data.loc[low_nan_ratio_rows.index, column], ax=axes[0], color='g')\n",
    "        sns.countplot(data.loc[high_nan_ratio_rows.index, column], ax=axes[1], color='g')\n",
    "\n",
    "\n",
    "compare_non_nan_distributions(azdias, low_nan_ratio_rows, high_nan_ratio_rows)\n",
    "azdias = azdias.loc[low_nan_ratio_rows.index, :]\n",
    "azdias.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your observations regarding missing data in rows. Are the data with lots of missing values are qualitatively different from data with few or no missing values?)\n",
    "\n",
    "- The majority of rows have less than 0.05 (5%) of their data missing.\n",
    "- When comparing the distribution of missing between rows with a low proportion of missing values and rows with a high proportion of missing values, we realise that, apart from ANREDE_KZ the distributions are very different accross the various columns that were compared.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "### Step 1.2: Select and Re-Encode Features\n",
    "\n",
    "Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (`feat_info`) for a summary of types of measurement.\n",
    "- For numeric and interval data, these features can be kept without changes.\n",
    "- Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).\n",
    "- Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.\n",
    "\n",
    "In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.\n",
    "\n",
    "Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many features are there of each data type?\n",
    "sns.countplot(feat_info.type);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:\n",
    "- For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.\n",
    "- There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.\n",
    "- For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via [OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section."
   ]
  },
  {
   "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>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "      <th>number_of_categories</th>\n",
       "      <th>category_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>2</td>\n",
       "      <td>[2, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>building</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>2</td>\n",
       "      <td>[W, O]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>VERS_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>2</td>\n",
       "      <td>[2.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SOHO_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>2</td>\n",
       "      <td>[1.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>[0, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NATIONALITAET_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>3</td>\n",
       "      <td>[1.0, 2.0, 3.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>SHOPPER_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>4</td>\n",
       "      <td>[3.0, 2.0, 0.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "      <td>5</td>\n",
       "      <td>[1.0, 2.0, 5.0, 4.0, 3.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "      <td>5</td>\n",
       "      <td>[3.0, 1.0, 5.0, 2.0, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FINANZTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>6</td>\n",
       "      <td>[1, 5, 2, 4, 6, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ZABEOTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,9]</td>\n",
       "      <td>6</td>\n",
       "      <td>[5, 4, 1, 6, 3, 2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CJT_GESAMTTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "      <td>6</td>\n",
       "      <td>[5.0, 3.0, 2.0, 4.0, 1.0, 6.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>GEBAEUDETYP</td>\n",
       "      <td>building</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>7</td>\n",
       "      <td>[8.0, 1.0, 3.0, 6.0, 2.0, 4.0, 5.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,X]</td>\n",
       "      <td>9</td>\n",
       "      <td>[8, 4, 6, 2, 1, 9, 5, 7, 3, nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP_STATUS_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "      <td>10</td>\n",
       "      <td>[2.0, 3.0, 4.0, 1.0, 10.0, 8.0, 9.0, 5.0, 6.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "      <td>11</td>\n",
       "      <td>[5.0, 1.0, 10.0, 2.0, 7.0, 11.0, 8.0, 4.0, 6.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GFK_URLAUBERTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "      <td>12</td>\n",
       "      <td>[10.0, 5.0, 1.0, 12.0, 9.0, 3.0, 8.0, 11.0, 4....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[XX]</td>\n",
       "      <td>44</td>\n",
       "      <td>[8A, 4C, 6B, 8C, 4A, 2D, 1A, 1E, 9D, 5D, 9E, 9...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           attribute information_level         type missing_or_unknown  \\\n",
       "2          ANREDE_KZ            person  categorical             [-1,0]   \n",
       "55       OST_WEST_KZ          building  categorical               [-1]   \n",
       "41          VERS_TYP            person  categorical               [-1]   \n",
       "39           SOHO_KZ            person  categorical               [-1]   \n",
       "13  GREEN_AVANTGARDE            person  categorical                 []   \n",
       "21  NATIONALITAET_KZ            person  categorical             [-1,0]   \n",
       "38       SHOPPER_TYP            person  categorical               [-1]   \n",
       "20    LP_STATUS_GROB            person  categorical                [0]   \n",
       "18   LP_FAMILIE_GROB            person  categorical                [0]   \n",
       "10         FINANZTYP            person  categorical               [-1]   \n",
       "42          ZABEOTYP            person  categorical             [-1,9]   \n",
       "3      CJT_GESAMTTYP            person  categorical                [0]   \n",
       "52       GEBAEUDETYP          building  categorical             [-1,0]   \n",
       "57   CAMEO_DEUG_2015     microcell_rr4  categorical             [-1,X]   \n",
       "19    LP_STATUS_FEIN            person  categorical                [0]   \n",
       "17   LP_FAMILIE_FEIN            person  categorical                [0]   \n",
       "12   GFK_URLAUBERTYP            person  categorical                 []   \n",
       "58    CAMEO_DEU_2015     microcell_rr4  categorical               [XX]   \n",
       "\n",
       "    number_of_categories                                    category_values  \n",
       "2                      2                                             [2, 1]  \n",
       "55                     2                                             [W, O]  \n",
       "41                     2                                         [2.0, 1.0]  \n",
       "39                     2                                         [1.0, 0.0]  \n",
       "13                     2                                             [0, 1]  \n",
       "21                     3                                    [1.0, 2.0, 3.0]  \n",
       "38                     4                               [3.0, 2.0, 0.0, 1.0]  \n",
       "20                     5                          [1.0, 2.0, 5.0, 4.0, 3.0]  \n",
       "18                     5                          [3.0, 1.0, 5.0, 2.0, 4.0]  \n",
       "10                     6                                 [1, 5, 2, 4, 6, 3]  \n",
       "42                     6                                 [5, 4, 1, 6, 3, 2]  \n",
       "3                      6                     [5.0, 3.0, 2.0, 4.0, 1.0, 6.0]  \n",
       "52                     7                [8.0, 1.0, 3.0, 6.0, 2.0, 4.0, 5.0]  \n",
       "57                     9                   [8, 4, 6, 2, 1, 9, 5, 7, 3, nan]  \n",
       "19                    10  [2.0, 3.0, 4.0, 1.0, 10.0, 8.0, 9.0, 5.0, 6.0,...  \n",
       "17                    11  [5.0, 1.0, 10.0, 2.0, 7.0, 11.0, 8.0, 4.0, 6.0...  \n",
       "12                    12  [10.0, 5.0, 1.0, 12.0, 9.0, 3.0, 8.0, 11.0, 4....  \n",
       "58                    44  [8A, 4C, 6B, 8C, 4A, 2D, 1A, 1E, 9D, 5D, 9E, 9...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assess categorical variables: which are binary, which are multi-level, and\n",
    "# which one needs to be re-encoded?\n",
    "\n",
    "def feature_levels(data, feat_info, type='categorical'):\n",
    "    return data.loc[:,feat_info.query(\"type == @type\").attribute].nunique()\n",
    "\n",
    "\n",
    "def categorical_features_info(data, feat_info):\n",
    "    categorical_features = feat_info.query(\"type == 'categorical'\")\n",
    "    \n",
    "    categorical_features = categorical_features.merge(\n",
    "        pd.DataFrame(data={'number_of_categories': feature_levels(data, feat_info)}),\n",
    "        left_on='attribute',\n",
    "        right_index=True\n",
    "    )\n",
    "    \n",
    "    categorical_features['category_values'] = categorical_features['attribute'].apply(lambda column: data[column].unique())\n",
    "    return categorical_features\n",
    "\n",
    "\n",
    "def get_multi_level_categorical_features(data, feat_info):\n",
    "    feature_levels_ = feature_levels(data, feat_info, type='categorical')\n",
    "    return feature_levels_[feature_levels_ > 2].index.tolist()\n",
    "\n",
    "\n",
    "display(categorical_features_info(azdias, feat_info).sort_values(by='number_of_categories'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-encode categorical variable(s) to be kept in the analysis.\n",
    "   \n",
    "def re_encode_OST_WEST_KZ(data):\n",
    "    df = data.copy()\n",
    "    df['OST_WEST_KZ'] = df['OST_WEST_KZ'].apply(lambda x: 1 if x == 'W' else 0)\n",
    "    return df\n",
    "\n",
    "\n",
    "azdias = re_encode_OST_WEST_KZ(azdias)\n",
    "azdias['OST_WEST_KZ'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping: ['CJT_GESAMTTYP', 'FINANZTYP', 'GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN', 'LP_FAMILIE_GROB', 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP', 'GEBAEUDETYP', 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(684502, 66)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def drop_multi_level_categorical_features(data, feat_info, verbose=False):\n",
    "    multi_level_categorical_columns = get_multi_level_categorical_features(data, feat_info)\n",
    "\n",
    "    if verbose:\n",
    "        print(\"Dropping:\", multi_level_categorical_columns)\n",
    "\n",
    "    data = data.drop(columns=multi_level_categorical_columns)\n",
    "    feat_info = feat_info.drop(index=feat_info.index[feat_info.attribute.isin(multi_level_categorical_columns)])\n",
    "    return data, feat_info\n",
    "\n",
    "\n",
    "azdias, feat_info = drop_multi_level_categorical_features(azdias, feat_info, verbose=True)\n",
    "azdias.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding categorical features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)\n",
    "\n",
    "- I kept the binary features ANREDE_KZ, VERS_TYP, SOHO_KZ, GREEN_AVANTGARDE and OST_WEST_KZ.\n",
    "- I re-encoded OST_WEST_KZ to subtitute 'W' and 'O' with the numeric values 1 and 0 respectively, since dimensional reduction and kmeans clustering algorithms will only work with numerical values.\n",
    "- I dropped all multi-level categorical features because encoding them will further increase the number of features and I wanted to avoid that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "There are a handful of features that are marked as \"mixed\" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:\n",
    "- \"PRAEGENDE_JUGENDJAHRE\" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.\n",
    "- \"CAMEO_INTL_2015\" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).\n",
    "- If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.\n",
    "\n",
    "Be sure to check `Data_Dictionary.md` for the details needed to finish these tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "      <th>number_of_categories</th>\n",
       "      <th>category_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "      <td>40</td>\n",
       "      <td>[21.0, 3.0, 32.0, 8.0, 2.0, 5.0, 10.0, 4.0, 6....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "      <td>12</td>\n",
       "      <td>[6.0, 1.0, 10.0, 2.0, 3.0, 5.0, 7.0, 12.0, 9.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PRAEGENDE_JUGENDJAHRE</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>15</td>\n",
       "      <td>[14.0, 15.0, 8.0, 3.0, 10.0, 11.0, 9.0, 6.0, 5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>building</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1]</td>\n",
       "      <td>8</td>\n",
       "      <td>[4.0, 2.0, 3.0, 7.0, 5.0, 1.0, 8.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,XX]</td>\n",
       "      <td>21</td>\n",
       "      <td>[51, 24, 43, 54, 22, 14, 13, 15, 34, 55, 12, 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>PLZ8_BAUMAX</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>5</td>\n",
       "      <td>[1.0, 2.0, 4.0, 5.0, 3.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                attribute information_level   type missing_or_unknown  \\\n",
       "15    LP_LEBENSPHASE_FEIN            person  mixed                [0]   \n",
       "16    LP_LEBENSPHASE_GROB            person  mixed                [0]   \n",
       "22  PRAEGENDE_JUGENDJAHRE            person  mixed             [-1,0]   \n",
       "56               WOHNLAGE          building  mixed               [-1]   \n",
       "59        CAMEO_INTL_2015     microcell_rr4  mixed            [-1,XX]   \n",
       "79            PLZ8_BAUMAX    macrocell_plz8  mixed             [-1,0]   \n",
       "\n",
       "    number_of_categories                                    category_values  \n",
       "15                    40  [21.0, 3.0, 32.0, 8.0, 2.0, 5.0, 10.0, 4.0, 6....  \n",
       "16                    12  [6.0, 1.0, 10.0, 2.0, 3.0, 5.0, 7.0, 12.0, 9.0...  \n",
       "22                    15  [14.0, 15.0, 8.0, 3.0, 10.0, 11.0, 9.0, 6.0, 5...  \n",
       "56                     8           [4.0, 2.0, 3.0, 7.0, 5.0, 1.0, 8.0, 0.0]  \n",
       "59                    21  [51, 24, 43, 54, 22, 14, 13, 15, 34, 55, 12, 4...  \n",
       "79                     5                          [1.0, 2.0, 4.0, 5.0, 3.0]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def mixed_features_info(data, feat_info):\n",
    "    mixed_features = feat_info.query(\"type == 'mixed'\")\n",
    "        \n",
    "    mixed_features = mixed_features.merge(\n",
    "        pd.DataFrame(data={'number_of_categories': feature_levels(data, feat_info, type='mixed')}),\n",
    "        left_on='attribute',\n",
    "        right_index=True\n",
    "    )\n",
    "    \n",
    "    mixed_features['category_values'] = mixed_features['attribute'].apply(lambda column: data[column].unique())\n",
    "    return mixed_features\n",
    "\n",
    "\n",
    "display(mixed_features_info(azdias, feat_info))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(684502, 67)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n",
    "def movement(x):\n",
    "    if x in [1, 3, 5, 8, 10, 12, 14]:\n",
    "        return 0\n",
    "    elif x in [2, 4, 6, 7, 9, 11, 13, 15]:\n",
    "        return 1\n",
    "    else:\n",
    "        return np.NaN\n",
    "\n",
    "\n",
    "def decade(x):\n",
    "    if x in [1, 2]:\n",
    "        return 40\n",
    "    elif x in [3, 4]:\n",
    "        return 50\n",
    "    elif x in [5, 6, 7]:\n",
    "        return 60\n",
    "    elif x in [8, 9]:\n",
    "        return 70\n",
    "    elif x in [10, 11, 12, 13]:\n",
    "        return 80\n",
    "    elif x in [14, 15]:\n",
    "        return 90\n",
    "    else:\n",
    "        return np.NaN\n",
    "\n",
    "\n",
    "def add_movement_and_decade_from_PRAEGENDE_JUGENDJAHRE(data, feat_info):\n",
    "    df = data.copy()\n",
    "    df['movement'] = df.PRAEGENDE_JUGENDJAHRE.apply(movement)\n",
    "    df['decade'] = df.PRAEGENDE_JUGENDJAHRE.apply(decade)\n",
    "    return df.drop(columns='PRAEGENDE_JUGENDJAHRE'), feat_info.drop(index=feat_info.index[feat_info.attribute=='PRAEGENDE_JUGENDJAHRE'])\n",
    "\n",
    "\n",
    "azdias, feat_info = add_movement_and_decade_from_PRAEGENDE_JUGENDJAHRE(azdias, feat_info)\n",
    "azdias.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(684502, 68)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate \"CAMEO_INTL_2015\" and engineer two new variables.\n",
    "def wealth(x):\n",
    "    x = float(x)\n",
    "    if np.isnan(x):\n",
    "        return x\n",
    "    return x // 10\n",
    "\n",
    "def life_style(x):\n",
    "    x = float(x)\n",
    "    if np.isnan(x):\n",
    "        return x\n",
    "    return x % 10\n",
    "\n",
    "def add_wealth_and_life_style_from_CAMEO_INTL_2015(data, feat_info):\n",
    "    df = data.copy()\n",
    "    df['wealth'] = df.CAMEO_INTL_2015.apply(wealth)\n",
    "    df['life_style'] = df.CAMEO_INTL_2015.apply(life_style)\n",
    "    return df.drop(columns='CAMEO_INTL_2015'), feat_info.drop(index=feat_info.index[feat_info.attribute=='CAMEO_INTL_2015'])\n",
    "    \n",
    "    \n",
    "azdias, feat_info = add_wealth_and_life_style_from_CAMEO_INTL_2015(azdias, feat_info)\n",
    "azdias.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(684502, 67)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def life_stage(x):\n",
    "    # single\n",
    "    if x in [1,2,3,4,5,6,7,8,9,10,11,12,13]:\n",
    "        return 1\n",
    "    # couples\n",
    "    elif x in [14,15,16,17,18,19,20]:\n",
    "        return 2\n",
    "    # single parents\n",
    "    if x in [21,22,23]:\n",
    "        return 3\n",
    "    # families\n",
    "    elif x in [24,25,26,27,28]:\n",
    "        return 4\n",
    "    # multiperson\n",
    "    elif x in [29,30,31,32,33,34,35,36,37,38,39,40]:\n",
    "        return 5\n",
    "    else:\n",
    "        return np.NaN\n",
    "\n",
    "\n",
    "def add_life_stage_from_LP_LEBENSPHASE_columns(data, feat_info):\n",
    "    df = data.copy()\n",
    "    df['life_stage'] = df.LP_LEBENSPHASE_FEIN.apply(wealth)\n",
    "    df = df.drop(columns='LP_LEBENSPHASE_FEIN')\n",
    "    df = df.drop(columns='LP_LEBENSPHASE_GROB')\n",
    "    feat_info = feat_info.drop(index=feat_info.index[feat_info.attribute.isin(['LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB'])])\n",
    "    return df, feat_info\n",
    "\n",
    "\n",
    "azdias, feat_info = add_life_stage_from_LP_LEBENSPHASE_columns(azdias, feat_info)\n",
    "azdias.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding mixed-value features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)\n",
    "\n",
    "- I engineered movement and decade from PRAEGENDE_JUGENDJAHRE then dropped that column\n",
    "- I engineered wealth and life_style from CAMEO_INTL_2015 then dropped that column\n",
    "- I engineered life_stage from LP_LEBENSPHASE_FEIN then dropped that column\n",
    "- I dropped LP_LEBENSPHASE_GROB because it's just an abstraction of LP_LEBENSPHASE_FEIN based on information in Data_Dictionary.md\n",
    "- I kept the others (WOHNLAGE and PLZ8_BAUMAX) as they are since they are granular enough and consist of numeric values only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.3: Complete Feature Selection\n",
    "\n",
    "In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:\n",
    "- All numeric, interval, and ordinal type columns from the original dataset.\n",
    "- Binary categorical features (all numerically-encoded).\n",
    "- Engineered features from other multi-level categorical features and mixed features.\n",
    "\n",
    "Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep \"PRAEGENDE_JUGENDJAHRE\", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from **the subset with few or no missing values**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If there are other re-engineering tasks you need to perform, make sure you\n",
    "# take care of them here. (Dealing with missing data will come in step 2.1.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do whatever you need to in order to ensure that the dataframe only contains\n",
    "# the columns that should be passed to the algorithm functions.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### Step 1.3: Create a Cleaning Function\n",
    "\n",
    "Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/peter/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:1494: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  return self._getitem_tuple(key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clean_data(df, feat_info, outlier_columns):\n",
    "    \"\"\"\n",
    "    Perform feature trimming, re-encoding, and engineering for demographics\n",
    "    data\n",
    "    \n",
    "    INPUT: Demographics DataFrame\n",
    "    OUTPUT: Trimmed and cleaned demographics DataFrame\n",
    "    \"\"\"\n",
    "    \n",
    "    # Put in code here to execute all main cleaning steps:\n",
    "    # convert missing value codes into NaNs, ...\n",
    "    df = convert_missing_or_unkown_to_nan(df, feat_info)    \n",
    "    \n",
    "    # remove selected columns and rows, ...\n",
    "    df = df.drop(columns=outlier_columns)\n",
    "    \n",
    "    low_nan_ratio_rows, high_nan_ratio_rows = split_via_nan_ratio(df, threshold=0.05, axis=1)\n",
    "    df = df.loc[low_nan_ratio_rows.index, :]\n",
    "    \n",
    "    # select, re-encode, and engineer column values.\n",
    "    df = re_encode_OST_WEST_KZ(df)\n",
    "    df, feat_info = drop_multi_level_categorical_features(df, feat_info)\n",
    "    \n",
    "    df, feat_info = add_movement_and_decade_from_PRAEGENDE_JUGENDJAHRE(df, feat_info)\n",
    "    df, feat_info = add_wealth_and_life_style_from_CAMEO_INTL_2015(df, feat_info)\n",
    "    df, feat_info = add_life_stage_from_LP_LEBENSPHASE_columns(df, feat_info)\n",
    "\n",
    "    # Return the cleaned dataframe.\n",
    "    return df\n",
    "\n",
    "\n",
    "def test_clean_data(data, outlier_columns):\n",
    "    df = pd.read_csv('Udacity_AZDIAS_Subset.csv', sep=';')\n",
    "    feat_info = pd.read_csv('AZDIAS_Feature_Summary.csv', sep=';')\n",
    "    return clean_data(df, feat_info, outlier_columns).equals(data)\n",
    "\n",
    "\n",
    "# Run Udacity_AZDIAS_Subset.csv through the clean_data function and confirm the result\n",
    "# is the same as azdias\n",
    "test_clean_data(azdias, outlier_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## Step 2: Feature Transformation\n",
    "\n",
    "### Step 2.1: Apply Feature Scaling\n",
    "\n",
    "Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the [API reference page for sklearn](http://scikit-learn.org/stable/modules/classes.html) to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:\n",
    "\n",
    "- sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an [Imputer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html) to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.\n",
    "- For the actual scaling function, a [StandardScaler](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) instance is suggested, scaling each feature to mean 0 and standard deviation 1.\n",
    "- For these classes, you can make use of the `.fit_transform()` method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08954393120838215"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you've not yet cleaned the dataset of all NaN values, then investigate and\n",
    "# do that now.\n",
    "\n",
    "def ratio_of_rows_with_nan(data):\n",
    "    rows_with_nan = data[data.isnull().any(axis=1)]\n",
    "    return rows_with_nan.shape[0] / data.shape[0]\n",
    "\n",
    "ratio_of_rows_with_nan(azdias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/peter/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/peter/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Apply feature scaling to the general population demographics data.\n",
    "\n",
    "ss = StandardScaler()\n",
    "ss = ss.fit(azdias.dropna())\n",
    "\n",
    "imp = Imputer(strategy='median')\n",
    "imp = imp.fit(azdias)\n",
    "azdias_imputed = imp.transform(azdias)\n",
    "\n",
    "# Transform original\n",
    "azdias_scaled = ss.transform(azdias_imputed)\n",
    "\n",
    "# Change back to dataframe\n",
    "azdias_scaled = pd.DataFrame(data=azdias_scaled, columns=azdias.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.1: Apply Feature Scaling\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your decisions regarding feature scaling.)\n",
    "- Less than 10% of the data has missing values, so I decided to temporarily remove missing values in order to compute the scaling parameters for StandardScaler.\n",
    "- I then imputed the data with the median values for each column.\n",
    "- Finally, I scaled the data using the already fitted StandardScaler."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "On your scaled data, you are now ready to apply dimensionality reduction techniques.\n",
    "\n",
    "- Use sklearn's [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).\n",
    "- Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's [`plot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html) function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.\n",
    "- Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply PCA to the data.\n",
    "\n",
    "pca = PCA()\n",
    "pca.fit(azdias_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/peter/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the variance accounted for by each principal component.\n",
    "\n",
    "def scree_plot(pca):\n",
    "    '''\n",
    "    Creates a scree plot associated with the principal components \n",
    "    \n",
    "    INPUT: pca - the result of instantian of PCA in scikit learn\n",
    "            \n",
    "    OUTPUT:\n",
    "            None\n",
    "    '''\n",
    "    num_components = len(pca.explained_variance_ratio_)\n",
    "    ind = np.arange(num_components)\n",
    "    vals = pca.explained_variance_ratio_\n",
    " \n",
    "    plt.figure(figsize=(20, 10))\n",
    "    plt.grid(True)\n",
    "    ax = plt.subplot(111)\n",
    "    cumvals = np.cumsum(vals)\n",
    "    ax.bar(ind, vals)\n",
    "    ax.plot(ind, cumvals)\n",
    "    for i in range(num_components):\n",
    "        ax.annotate(f\"{cumvals[i]*100:.1f}\", (ind[i]+0.2, vals[i]), va=\"bottom\", ha=\"center\", fontsize=10)\n",
    " \n",
    "    ax.xaxis.set_tick_params(width=0)\n",
    "#     ax.yaxis.set_tick_params(width=2, length=12)\n",
    " \n",
    "    ax.set_xlabel(\"Principal Component\")\n",
    "    ax.set_ylabel(\"Variance Explained (%)\")\n",
    "    plt.title('Explained Variance Per Principal Component')\n",
    "\n",
    "scree_plot(pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-apply PCA to the data while selecting for number of components to retain.\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "pca = pca.fit(azdias_scaled)\n",
    "azdias_pca = pca.transform(azdias_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding dimensionality reduction. How many principal components / transformed features are you retaining for the next step of the analysis?)\n",
    "\n",
    "- According to the screeplot gained by applying pca on all 67 of the remaining columnsfrom data cleaning, over 60% of the variance is explained by the first 10 principal components of the data. Therefore, I decided to retain these 10 principal components in the next phase of the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.3: Interpret Principal Components\n",
    "\n",
    "Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.\n",
    "\n",
    "As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.\n",
    "\n",
    "- To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.\n",
    "- You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the *i*-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Map weights for the first principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "# HINT: Try defining a function here or in a new cell that you can reuse in the\n",
    "# other cells.\n",
    "\n",
    "def map_feature_names_to_principal_component_weights(pca, component):\n",
    "    components = pd.DataFrame(pca.components_, \n",
    "                              columns=azdias_scaled.columns,\n",
    "                              index = [n for n in range(pca.n_components_)])\n",
    "\n",
    "    return components.loc[component, :].sort_values()\n",
    "\n",
    "\n",
    "def plot_feature_to_principal_component_coefficients(pca, component):\n",
    "    map_feature_names_to_principal_component_weights(pca, component).plot(kind='barh', figsize=(10, 20))\n",
    "\n",
    "plot_feature_to_principal_component_coefficients(pca, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Map weights for the second principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "\n",
    "plot_feature_to_principal_component_coefficients(pca, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Map weights for the third principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "\n",
    "plot_feature_to_principal_component_coefficients(pca, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.3: Interpret Principal Components\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your observations from detailed investigation of the first few principal components generated. Can we interpret positive and negative values from them in a meaningful way?)\n",
    "\n",
    "#### 1st principal component\n",
    "\n",
    "Has large positive coefficients with \n",
    "- high share of 6-10 family houses in the PLZ8 region (PLZ8_ANTG3)\n",
    "- high share of 10+ family houses in the PLZ8 region (PLZ8_ANTG4)\n",
    "- mainly 10+ family homes (PLZ8_BAUMAX)\n",
    "- poverty (low wealth)\n",
    "- low estimated household net income (HH_EINKOMMEN_SCORE)\n",
    "\n",
    "Has large negative coefficents with\n",
    "- low minimalism in finances (FINANZ_MINIMALIST)\n",
    "- large number of buildings in the microcell (KBA05_GBZ)\n",
    "- large number of 1-2 family houses in the microcell (KBA05_ANTG1)\n",
    "- large number of 1-2 family houses in the PLZ8 region (PLZ8_ANTG1)\n",
    "- high movement patterns within the region (MOBI_REGIO)\n",
    "\n",
    "So a positive value within the 1st principal component indicates that the person lives in home with a large number of people and is generally poor.\n",
    "\n",
    "-------------------------------------------\n",
    "\n",
    "#### 2nd principal component\n",
    "Has large positive coefficients with\n",
    "- high estimated age based on given name analysis (ALTERSKATEGORIE_GROB)\n",
    "- low financial preparedness (FINANZ_VORSORGER)\n",
    "- not being event-oriented (SEMIO_ERL)\n",
    "- low sensual-mindedness (SEMIO_LUST)\n",
    "- tendency to not return bought items in a shop(RETOURTYP_BK_S)\n",
    "\n",
    "Has Large negative coefficients with \n",
    "- low religiousness (SEMIO_REL)\n",
    "- decade the person developed in (decade)\n",
    "- low tendency to save money (FINANZ_SPARER)\n",
    "- low affinity to dutifulness (SEMIO_PFLICHT)\n",
    "- low traditional mindedness (SEMIO_TRADV )\n",
    "\n",
    "So a positive value within the 2nd principal component indicates that the person is an old, religious, traditional, and generally conservative person.\n",
    "\n",
    "-------------------------------------------\n",
    "\n",
    "#### 3rd principal component\n",
    "Has large positive coefficients with\n",
    "- low dreamfulness (SEMIO_VERT)\n",
    "- low social-mindedness (SEMIO_SOZ)\n",
    "- low family mindedness (SEMIO_FAM)\n",
    "- low cultural mindedness (SEMIO_KULT)\n",
    "- financial minimalism (FINANZ_MINIMALIST)\n",
    "\n",
    "Has large negative coefficients with\n",
    "- Being female (ANREDE_KZ)\n",
    "- low combative attitude (SEMIO_KAEM)\n",
    "- low dominant-mindedness (SEMIO_DOM)\n",
    "- low critical-mindedness (SEMIO_KRIT)\n",
    "- low rational-mindedness (SEMIO_RAT)\n",
    "\n",
    "So a positive value withing the 3rd principal component indicates that the person is most likely male, disinterested in family, culture and socialization, and generally an analytical free-thinker."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Clustering\n",
    "\n",
    "### Step 3.1: Apply Clustering to General Population\n",
    "\n",
    "You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.\n",
    "\n",
    "- Use sklearn's [KMeans](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans) class to perform k-means clustering on the PCA-transformed data.\n",
    "- Then, compute the average difference from each point to its assigned cluster's center. **Hint**: The KMeans object's `.score()` method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.\n",
    "- Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. **Warning**: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.\n",
    "- Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Over a number of different cluster counts...\n",
    "centers = list(range(1,20))\n",
    "scores = list()\n",
    "for center in centers:\n",
    "    # run k-means clustering on the data and...\n",
    "    kmeans = KMeans(n_clusters=center, random_state=0)\n",
    "    kmeans.fit(azdias_pca)\n",
    "\n",
    "    # compute the average within-cluster distances.\n",
    "    score = np.abs(kmeans.score(azdias_pca))\n",
    "    scores.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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kSVKlMBHs4LbbDhYs8FxBSZIkSU1MBDu4gQPhO9+BK6+EF14odzSSJEmSKoGJYA048URYYQU47bRyRyJJkiSpEpgI1oC6OjjqKLj2Wpg1q9zRSJIkSSo3E8EaccIJ8Oyz0L9/uSORJEmSVG6eI1gjVl01XwAffZSnikqSJEmqTY4I1pivfS1fkiRJkmqXiWCNGTYMrrsOHn+83JFIkiRJKhcTwRrz/e/DSivBKaeUOxJJkiRJ5WIiWGP69oXjjoObboJJk8odjSRJkqRyMBGsQcccA6usAmedVe5IJEmSJJWDu4bWoD594MYbYZNNyh2JJEmSpHIwEaxR222XX1OCiPLGIkmSJKl9OTW0hr3wAnzuc3D//eWORJIkSVJ7MhGsYWusAbNmwcknlzsSSZIkSe2pZIlgRKwZEfdExOSIeCYijmmmzeiIeLJw/T0iNi2qmxoRT0XE4xHh/pYl0KMHnHQS1NdDXR106gRDh8KECeWOTJIkSVIplXKN4HzguJTSYxHRG3g0Iu5IKT1b1OZlYIeU0pyI+CIwHtiyqH5USunNEsZY8/r0yWsEZ83K99OmwZgx+f3o0eWLS5IkSVLplGxEMKX0akrpscL7d4HJwBqLtfl7SmlO4fYhYFCp4lHzTj01bxhTbN48GDu2LOFIkiRJagftskYwIoYCmwH/aKXZYcBfiu4TcHtEPBoRY0oXXW2bPv3TlUuSJEmqfpEWHw5a3j8gohdwLzAupXR9C21GARcC26aU3iqUDUwpzYyI/sAdwNEppfuaeXYMMAagrq5u+MSJE0v0m3RMBxywFa+/3v0T5XV1HzBx4kPL/PkNDQ306tVrmT9HpWU/VT77qDrYT9XBfqp89lF1sJ8q06hRox5NKY1oq11JE8GI6ArcDNyWUjqnhTafBW4AvphSmtJCm1OBhpTSL1r7eSNGjEiTJrmvzKcxYUJeEzhv3qLlY8fCT36y7J9fX1/PyJEjl/2DVFL2U+Wzj6qD/VQd7KfKZx9VB/upMkXEEiWCpdw1NIDLgMmtJIGDgeuBg4qTwIjoWdhghojoCewKPF2qWGvZ6NEwfjwMGZI3jVljDRg4EM4+G265pdzRSZIkSSqFUq4R3AY4CNixcATE4xGxR0QcERFHFNqcDKwKXLjYMRF1wAMR8QTwMHBLSumvJYy1po0eDVOnwsKFMGMGPPEEbLQR7LMPXHNNuaOTJEmStLyV7PiIlNIDQLTR5nDg8GbKXwI2/eQTag/9+sFdd8Gee8Jvfwv7759HCyVJkiR1DKU8R1BVbKWV4Lbb8tESEfDhh9CtW7mjkiRJkrQ8tMvxEapOPXpAz57Q0ADbbw+nnfbJMwclSZIkVR8TQbWpe3fYcEM45RQ4/niTQUmSJKnaOTVUberSBS67DHr3zruJzp0LF10EnTuXOzJJkiRJS8NEUEukUyc491zo0wfGjctrCH/+83JHJUmSJGlpmAhqiUXkQ+ZXXx322KPc0UiSJElaWq4R1Kd25JGw1lr53MGzz86byUiSJEmqHiaCWmqTJsEJJ8Auu8CcOeWORpIkSdKSMhHUUttiC/jjH+Gxx2DkSHj99XJHJEmSJGlJmAhqmeyzD9x8M7z4Yj5rcPr0ckckSZIkqS0mglpmu+wCt98Ob78N//pXuaORJEmS1BZ3DdVysc028NJL0LNnvn/7bejbt7wxSZIkSWqeI4JabhqTwGuugXXXhYcfLm88kiRJkppnIqjlbost8oHzO+0Ejz++UrnDkSRJkrQYE0Etd2utBfffD4MHwwknfJZbby13RJIkSZKKmQiqJAYOhHvvhaFD57H33jBlSrkjkiRJktTIzWJUMv36wdlnP86sWdvxmc+UOxpJkiRJjRwRVEn16rWAb34zv580CS64oLzxSJIkSTIRVDu6+GI46ig4/XRIqdzRSJIkSbXLqaFqNxddBB99BCefDHPnws9+BhHljkqSJEmqPSaCajddusBvfwu9e8MvfpGTwQsvhM6dyx2ZJEmSVFtMBNWuOnWCX/8a+vSBp56ChQtNBCVJkqT2ZiKodhcBP/0pzJ+fRwnffBN69YLu3csdmSRJklQb3CxGZdOlS04Gd90V9twTGhrKHZEkSZJUG0wEVVZdusD3vw/19TkhfPvtckckSZIkdXwmgiq7gw6Ca6/N5wyOHAmzZpU7IkmSJKljK1kiGBFrRsQ9ETE5Ip6JiGOaaRMRcV5EvBgRT0bE5kV1h0TEC4XrkFLFqcrw3/8Nf/4zTJkC3/pWuaORJEmSOrZWN4uJiD4ppbkt1A1OKU1v5fH5wHEppcciojfwaETckVJ6tqjNF4FhhWtL4CJgy4hYBTgFGAGkwrM3pZTmLPFvpqqz225w552w5prljkSSJEnq2NoaEaxvfBMRdy1Wd2NrD6aUXk0pPVZ4/y4wGVhjsWZ7A1em7CGgb0SsDuwG3JFSml1I/u4Adm/rl1H1+8IXciK4YAH8z//A00+XOyJJkiSp42nr+Igoer9KK3Wtf0jEUGAz4B+LVa0BvFJ0P6NQ1lJ5c589BhgDUFdXR319/ZKGpXbQ0NCwVH0ya1Y3rr56c373u06cddaTrL/+u8s/OP3H0vaT2o99VB3sp+pgP1U++6g62E/Vra1EMLXwvrn7ZkVEL+A64HvNTDNtLplMrZR/sjCl8cB4gBEjRqSRI0cuSVhqJ/X19Sxtn4wYATvvDMcfP5ybb4Yddli+sanJsvST2od9VB3sp+pgP1U++6g62E/Vra2pof0j4tiIOK7ofeP9am19eER0JSeBE1JK1zfTZAZQvCJsEDCzlXLVkLXXhvvvz1NFd98d/t//g6FDoVOn/DphQrkjlCRJkqpTW4ngJUBvoFfR+8b7S1t7MCICuAyYnFI6p4VmNwEHF3YP3Qp4J6X0KnAbsGtErBwRKwO7FspUY9ZYA+67D4YMgfPOg2nTIKX8OmaMyaAkSZK0NFqdGppS+vEyfPY2wEHAUxHxeKHsh8Dgwmf/BrgV2AN4EZgHfKNQNzsiTgceKTx3Wkpp9jLEoirWrx+8/z58+OGi5fPmwdixMHp0eeKSJEmSqlVbx0d8C6hPKb1QNML3FWAacEhK6Z8tPZtSeoA2NpRJKSXgyBbqLgcubz181YpXXmm+fHprB5hIkiRJalZbU0OPAaYW3h8IbAqsDRwLnFe6sKRFDR7cfHnv3tDQ0L6xSJIkSdWurURwfkrp48L7Pcln/r2VUroT6Fna0KQm48ZBjx6LlnXuDHPnwrbb5nWDkiRJkpZMW8dHLCwc8D4H2AkYV1S3YsmikhbTuA5w7Ng8HXTw4JwcrrUWvP46RORD6F9+GdZdt7yxSpIkSZWurUTwZGAS0Bm4KaX0DEBE7AC8VOLYpEWMHt36xjCXXQZHHQXHHgs/+hH06tV+sUmSJEnVpK2poa8DWwMbpJS+FREHR8SfgNHAmJJHJ30K++yTE8WzzoINNoBrr3XKqCRJktScthLBi4GGlNKciNgeOBO4kpwgnlvq4KRPo39/+O1v4W9/y0dO7L8/fOc75Y5KkiRJqjxtTQ3tXHR+31eB8Sml64Dris4GlCrKF74AjzwCv/lNHhkEeO+9PDrodFFJkiSp7RHBzhHRmCzuBNxdVNdWEimVTZcueb3gTjvl+9NOy0nhH//odFFJkiSprUTwauDewrrA94H7ASJiXeCdEscmLTd7752ni+63H+y6Kzz3XLkjkiRJksqn1UQwpTQOOA64Atg2pf+MpXQCji5taNLy0zhd9Ne/zq+f/WxeTyhJkiTVorZGBEkpPZRSuiGl9F5R2ZSU0mOlDU1avhqni06ZAgcdBFtumcvnzXO6qCRJkmpLm4mg1NH075/PHNxww3x/6KFOF5UkSVJtMRFUTUsJtt++abroiSdCQ0O5o5IkSZJKy0RQNS0iTxd9/nn42teaDqN/+OFyRyZJkiSVjomgBNTVwRVXwAMPwLrrwlpr5fIFC8oaliRJklQSJoJSkW22gXvugdVWg4ULYeRI+OEP84H0kiRJUkdhIii14P338+jgGWfk6aLXXefuopIkSeoYTASlFvTsmc8afOABWGUV2Hdf2G03eO21ckcmSZIkLRsTQakN22wDkyblw+jnzIG+fcsdkSRJkrRsTASlJdB4GP3DD0P37nnN4HbbOV1UkiRJ1clEUPoUIvLra6/Bu+82TRd9/vnyxiVJkiR9GiaC0lJYZ52m6aIPPwybbAInnQQffVTuyCRJkqS2mQhKS6lxumjjYfQPPghdu8KECTB0KHTqlF8nTCh3pJIkSdKiupQ7AKnaNR5G/+GHcNVV8K1v5aMnAKZNgzFj8vvRo8sWoiRJkrQIRwSl5aRbNxg7tikJbDRvXj6UXpIkSaoUJUsEI+LyiJgVEU+3UH98RDxeuJ6OiAURsUqhbmpEPFWom1SqGKXlbfr0lss//rh9Y5EkSZJaUsoRwSuA3VuqTCn9PKX0uZTS54CTgHtTSrOLmowq1I8oYYzScjV4cPPlK62U1w8CnHIK3HUXLFzYfnFJkiRJxUqWCKaU7gNmt9kwOxC4ulSxSO1l3Djo0WPRsh494IIL8vs334Tzz4edd4b11oOf/QxmzWr/OCVJklTbIpXwNOyIGArcnFLauJU2PYAZwLqNI4IR8TIwB0jAxSml8a08PwYYA1BXVzd84sSJyy1+LbuGhgZ69epV7jDa1Z139ufSS9dm1qxu9O//IYcf/hI779yU7X30USfuvXc1br55dZ58si9duixk3Lin2WKLJf13k+WvFvup2thH1cF+qg72U+Wzj6qD/VSZRo0a9eiSzKqshETwq8DXU0p7FZUNTCnNjIj+wB3A0YURxlaNGDEiTZrkksJKUl9fz8iRI8sdRsV69lm49FI4+WTo2xeuuw5efhkOPRT69Wu/OOynymcfVQf7qTrYT5XPPqoO9lNlioglSgQrYdfQA1hsWmhKaWbhdRZwA7BFGeKSSm7DDeGcc3ISCHDbbXD88bDGGvlswnvvhRL+W40kSZJqVFkTwYhYCdgB+FNRWc+I6N34HtgVaHbnUamjGT8ennoKvv1tuPVWGDkyJ4SSJEnS8lTK4yOuBh4E1ouIGRFxWEQcERFHFDX7b+D2lNJ7RWV1wAMR8QTwMHBLSumvpYpTqjQbbwznnQczZ+aD6r/+9Vw+ezYccgjcf7+jhJIkSVo2XUr1wSmlA5egzRXkYyaKy14CNi1NVFL16NEjJ36NnngCbrwRrrwyTykdMwYOPhhWXrl8MUqSJKk6VcIaQUlLYNSoPEp4+eXQuzd873t5LeEbb5Q7MkmSJFWbko0ISlr+evaEb3wjX088AXffDautlutOOCEnhgcd5CihJEmSWueIoFSlNt0Uvv/9/H7+fHjgATjmGBg4MB8/8eCDriWUJElS80wEpQ6gSxf429/gscdyEnj99fCFL+RNZyRJkqTFmQhKHchmm8FFF+W1hJdcAl/+ci6/9Vb45jfhoYccJZQkSZKJoNQh9eoFhx8Oa66Z719+Ga69FrbeGj73ObjwQnjnHZgwAYYOhR133IGhQ/O9JEmSOj4TQakGHHlkHiX8zW+gc+d8v/HG+QiKadMgpWDatHxvMihJktTxmQhKNaJ3b/j2t+HRR+Hhh+Gjj2DevEXbzJsHY8eWJz5JkiS1HxNBqcZEwOc/3/L5g9OmwTnnwKxZ7RuXJEmS2o+JoFSjBg9uvrxrVzjuOHjppXw/cya89Vb7xSVJkqTSMxGUatS4cdCjx6JlPXrAb38LU6bAllvmstNPhwED4Etfgj/8Ad59t/1jlSRJ0vJlIijVqNGjYfx4GDIEIhJDhuT70aNh2LA8hRTgu9+FY4+Fp5+Ggw6C/v3hO98pb+ySJElaNiaCUg0bPRqmToW7776XqVPz/eI22QTOOisfQfHAA3DYYbDKKrkupTyN9PbbYf789oxckiRJy6JLuQOQVB06dYJttslXo6lT4dJL8+Yy/fvDfvvBgQfm8wo7+c9MkiRJFcv/VJO01NZaC15/Ha67DrbfHi67DLbdFm65Jdd//HEeNZQkSVJlMRGUtEy6d4cvfxmuvTYfOfH738Muu+S6M86ADTeEH/84b0AjSZKkymAiKGm56d0bvv71nBxCTgLr6nIiuN56MGLQTuAqAAAetklEQVQE/PrX5Y1RkiRJJoKSSmjffaG+HqZPh1/8IpfddVdT/XXXwZtvliU0SZKkmmYiKKnkBg3Ku4tOmgQTJ+ayV17JieLqq8Mee+QppZ5RKEmS1D5MBCW1q8Zpo4MGweOP5wTx2Wfh4IPzzqN//esnn5kwAYYOzTuRDh2a7yVJkrT0PD5CUllEwKab5uuMM+DBB+Hqq2H48Fz/hz/AnXfmNYbnnw/z5uXyadNgzJj8vrlzDyVJktQ2E0FJZRcBX/hCvhq99hrccAPMnfvJ9vPmwdixJoKSJElLy6mhkirSD36QzyiMaL5++nR4++32jUmSJKmjMBGUVLG6d4fBg5uvSwlWWw123BF++Ut48cX2jU2SJKmamQhKqmjjxkGPHouW9egBp5wCxx8Pb7wBxx4Lw4bltYQAH38M8+e3f6ySJEnVomSJYERcHhGzIuLpFupHRsQ7EfF44Tq5qG73iHg+Il6MiBNLFaOkyjd6NIwfD0OG5GmiQ4bk+1NPhZ/+FJ56Cl5+OR9Uv9tu+Zmbb86bzHz963DNNfDOO2X9FSRJkipOKTeLuQI4H7iylTb3p5T2LC6IiM7ABcAuwAzgkYi4KaX0bKkClVTZRo9ufWOYoUPhqKOa7gcNgj33hFtvzUdNdOkC22+fD7Dv27fk4UqSJFW8kiWCKaX7ImLoUjy6BfBiSuklgIiYCOwNmAhKWiKf/zz87newYAH84x/w5z/DY4/BSivl+lNPzTuP7rUXbL11ThQlSZJqSbnXCG4dEU9ExF8iYqNC2RrAK0VtZhTKJOlT6dw5H0lxxhlw221NO5C+8AL86ld5lLCuDg46CP7yl/LGKkmS1J4ipVS6D88jgjenlDZupq4PsDCl1BARewDnppSGRcR+wG4ppcML7Q4CtkgpHd3CzxgDjAGoq6sbPnHixNL8MloqDQ0N9OrVq9xhqA212E8NDZ2ZNGkV/v73VfnHP1Zlxx1nccwxL7BwIdx000C22GI2Awd+UO4w/6MW+6ga2U/VwX6qfPZRdbCfKtOoUaMeTSmNaKtd2RLBZtpOBUYAw4BTU0q7FcpPAkgpndHWZ4wYMSJNmjRpGSLW8lZfX8/IkSPLHYbaUOv9tGABNDTkqaOPPQbDh+fyDTfM00f32gu22iqPMJZLrfdRtbCfqoP9VPnso+pgP1WmiFiiRLBsU0MjYkBEnqgVEVsUYnkLeAQYFhFrRcQKwAHATeWKU1LH17lz0/rBzTfPZxL+8pcwYACcfTZsuy3ceWeuf+stmDu3fLFKkiQtD6U8PuJq4EFgvYiYERGHRcQREXFEocm+wNMR8QRwHnBAyuYDRwG3AZOB/0spPVOqOCVpceusA9/7Htx1Vz6ncOJE2GGHXHfOOdCvH+y6az6y4uWXm56bMCHvYNqpU36dMKEc0UuSJLWtlLuGHthG/fnk4yWaq7sVuLUUcUnSp9G3L3z1q033X/4yfPRR3on0f/4nX1ttlY+vGDMm70YKMG1avofWj76QJEkqh3LvGipJVWX4cPj5z+G552DKlDx1dLfdYOzYpiSw0bx58MMflidOSZKk1pgIStJSGjYMjj02n0s4fXrzbaZPh/MLcx9SyhvTSJIklZuJoCQtB4MHN1/eowf075/fv/QSrLoq7LknnHUWPPhgnmYqSZLU3kwEJWk5GDcuJ33FevSA8eNh//2byvbfP+9KeuKJ+bD7vn2hvj7XzZ0L773XbiFLkqQaZiIoScvB6NE56RsyBCLy6/jxi24Us846uey55+C11+CPf8wbymywQa6/7LKcGG61Ffy//5c3pJkzpzy/jyRJ6thKtmuoJNWa0aOXfIfQujr4ylfy1Wj77eH44+H+++Hcc/OmNF27wjvv5Pqnn4ZVVoGBA5d/7JIkqbaYCEpShRg+PF8A778PjzySdyZdccVcdtRRcO+9eWRxu+1y4rjDDrD22uWLWZIkVSenhkpSBVpxxZzoHX54U9k55+TjKjbeOE8b/eY34Tvfaaq/+mp46ilYuLD945UkSdXFRFCSqsTmm+fjKm68EWbNgmeegTPOyHXvvJOnpX72s9CvH+y1V55a+vzzzX/WhAkwdCh06pRfJ0xor99CkiRVAqeGSlIV6tQJNtyw6b5Pn3w8xX335TWG990HN9+cdy5dbz2YORMuuSSPMk6dmqeZzpuXn502LW9aA0u+xlGSJFU3E0FJ6gAi8sje0KFw8MG57LXXoFu3/P6xx+DHP86H2jdn3jwYO9ZEUJKkWmEiKEkd1IABTe/33BNmz4a//x2+9KXm20+bBocckqeXNl51de0TqyRJal8mgpJUI/r2hT32yGccTpv2yfru3eGOO+DKK/N9p07Q0JA3rvnzn+Gtt2CTTfKU1MadTCVJUnVysxhJqjHjxuW1g8V69IBLL81rCd94A+6+Ox9w35jwXXwxfOMbMGIE9OoFG2yw6I6ls2e3PO1UkiRVHkcEJanGNK4DHDsWpk+HwYNzcthY3q8fjBq16DN/+hP861/w5JP5iIonn2zabAbyJjSvvJJHDBunlW61FXzuc+3zO0mSpE/HRFCSatDo0Z9uY5jOneEzn8nXvvt+sv77388b0jz5JFx1FVx0ERx0UJ5mmhJ87Wuw7rpNSeK66+bPlCRJ5eHUUEnSMjvsMLjggnx0xZw5eQ3iqafmunfeyQniGWfA/vvD+uvn6aXnnZfrP/ggT0V9881Pfq7nHUqSVBqOCEqSlquIPN20Ud++8MwzOeGbPDknhU8+CRtvnOuffBJ22im/HzCgadSwf/+cTHreoSRJy5+JoCSpXXTvDpttlq9iG26YdyttTBCfegp+/eucQBavQ4R8f8wx+ZmNNoIVVmi/+CVJ6khMBCVJZdWrF+y8c74azZ/fcpL31luw+eYwZQoMG5aPtrj33pwcbrBBvvr2bZ/YJUmqViaCkqSK06VLnl7a3HmHq68O554La62V7594As4/Hz78sKnNGmvASy/lZPLBB3PdBhvk6aYR7fM7SJJUydwsRpJUkVo67/DnP4f99svJIsCPfgTvvQcvvgg33QRnnZU3pWkcUfzpT/NxGAMGwKqrwrbbwg9+0PSZc+Z4BqIkqfY4IihJqkhtnXdYrHNnWGedfO2116J1F1+cN6uZPBmefTa/vvBCU/2OO+b79ddvml669dYwcmTJfjVJksrORFCSVLE+7XmHzRk4MF+77NJ8/THHwOOP5yTxnnvg97/PI4qNieAOO0BdXVOSuOGG+TzFbt2aPmPChMaEdYdWE1ZJkiqFiaAkqaYdeuii93PnQkNDfv/hh7DSSvDYY/DHPzZNIT32WDj7bHj//Zw03n47fPQRQHjMhSSpKpQsEYyIy4E9gVkppY2bqR8NnFC4bQC+k1J6olA3FXgXWADMTymNKFWckiQV69MnX5BH/W66Kb9///28U+nkyXm3Usib2dx88yc/Y968PEK4445w3XUwdGje3GboUOjZsz1+C0mSWlfKEcErgPOBK1uofxnYIaU0JyK+CIwHtiyqH5VSerOE8UmStMRWXBE23TRfjdZfP+9C2txmM9On5ymnRx+9aPlqq8H//V+eejplCtx1V1OSOHRoPm9RkqRSK1kimFK6LyKGtlL/96Lbh4BBpYpFkqRSaemYi8GDYffd4bXX4OWXYerUptdBhb/x6uvhu99d9LkBA3L5euvBpEnwz382jSgOHtzy+YqSJH0albJG8DDgL0X3Cbg9IhJwcUppfHnCkiSpdePG5TWB8+Y1lfXokcsj8kYzdXWw1VaffPbww+FLX1o0SXz55dwe4E9/gp/8pKl9RD4j8amnoG/fvLnN1KlNieKgQU3HajSnaVOb1ndhlSR1fJFKeHhSYUTw5ubWCBa1GQVcCGybUnqrUDYwpTQzIvoDdwBHp5Tua+H5McAYgLq6uuETJ05cvr+ElklDQwO9evUqdxhqg/1U+eyjynbnnf259NK1mTWrG/37f8jhh7/EzjvPWubPXbAgeOONFXjtte689tqKvPZad2bN6sbxxz9PBJx55vrcdtuA/7Tv1CkxaNA8rrjiESLgb39blYaGLqy++ge88EJPLrlkHT78sPN/2nfrtoAf/OD55RJrNfH7VPnso+pgP1WmUaNGPboke6yUNRGMiM8CNwBfTClNaaHNqUBDSukXbf28ESNGpEmTJi11vFr+6uvrGelhXBXPfqp89lF1aO9++ugjmDFj0dHEDz6AXxT+xtxpJ7j77tY/Y+WV4dJL80jjwIF5amrXriUPvaz8PlU++6g62E+VKSKWKBEs29TQiBgMXA8cVJwERkRPoFNK6d3C+12B08oUpiRJFWuFFWDttfPVnFtuydNAp06F3XZrvs2cOfCVrzTd77QT3Hlnfv/tbzdNRx04ML8OGwbrrLNcfw1JUhmU8viIq4GRQL+ImAGcAnQFSCn9BjgZWBW4MCKg6ZiIOuCGQlkX4KqU0l9LFackSR1V9+7wmc/ka8iQ5je1GTQoH5Excyb8+9/Qr19T3dNP551N3yzaw/uQQ+CKK/JOqRttBKus0pQkDhwI222X10OmlNdNLu1xGa5nlKTSKuWuoQe2UX84cHgz5S8Bm37yCUmStLRa2tTmzDNhs83ytbi//S2/fvghvPpqThYbz1j86CPYfPNc9sQTcOut8N578KMf5URw9uycVK600qKJ4kEHwc4753MZn3wyl9fVLToddcKERWOdNi3fg8mgJC0vlbJrqCRJKqHGBGppRtm6dWs657C47A9/WLTd3LlNZyp27gxnnNE00jhzZt7ldPvtc/3zzzftpNq4u+rAgTmmsWMXTVgh3590Enzta7m9JGnZmAhKklQjRo8u7Yha42gh5OMtTjyx5bZrrQV//vOiieK//52nkk6f3vwzr7wCt9+e1zvW18NZZ8Fqq0H//vl1tdXgv/4rj0TOmwcLF+bPM3GUpE8yEZQkSe1upZVgzz2brxs8uPn1jH37woYb5vfvvw9vvQWTJ8OsWfke8hmL/frB5ZfD0UfndZLFyeIVV+R2kybltsVJZP/+n35No2sZJVUrE0FJklRRWlrPeP75sOaa+f6LX8xXo/fegzfeyNNLAbbZJo8YvvFGThTfeCNf3brl+uuuy+sjF9fQkJPBc8+F225bNIns3z9vlhOR10DeeGNONl3LKKkamQhKkqSKsjTrGXv2XHQ0r6UNcBr97//mpK0xQZw1K48wNn7Gxx/nsqefzvUffJBHMQ89NNd/97twzTWf/Nx583LcXbrk51Zdtemqq8u7tEpSJTARlCRJFafU6xl79MjrFNdaq/n6H/wgX5A3wHnvvXzmYqNDD20+EYScvF5wAdx//6Lln/sc/POf+f2ee+YRxMYksV+/nLgecUSuv/fevJNqv365vm/fvAHP0nIKq6TFmQhKkiS1IgJ69cpXo913b/lsxsGD4e678/TRt95qulZYoanNppvmRO+tt+C55/JZja+/3pQIjh6dN88pjuHrX4crr2yq79p10RHHzTaDLbbI9a++ms947NbN4zgkNc9EUJIkaSm0tJZx3Lg8NbR//3y19GxrbropT019882mRHL99XNdSjBlSk4c33qr6ecfdVROBD/6qGmtZM+eeVrrggWLfv68eXl94/z5OWFcZRVYd908fVVSbTARlCRJWgrLcjZjWzbfvOW6CHjkkab7Dz7ICWHXrvk+JbjooqYE8pe/bP5z5sxpWvMIcPbZcOyxOcncbLOmBLHxOuooGDUqJ6A33ZTLVl65qb6urmkznqXVNIV1B6ewSiVmIihJkrSUSr2WcUl07w5rrNF0361b0xRTgOuvb34K66BBeS3i7Nn5GjYsl/fsmZ9vLJ8zB55/Ht59N9c/+2zT1NJiN9wA++wD99wDRx65aJK48so5kVx33Xwe5DPPLJpkrrQSTJxYPMIaTmGVSsxEUJIkqQNraQrrmWfC2mvnq9gaa+TRwZZsu21O5hqTxMaEsXEUs2dP2GijXPfvf+fzGmfPhq99LdffcQccdtiinxkBAwYsGiPk++9+N49CHn449OkDM2fC2283JZjLOgop1SoTQUmSpA5seU9h7do1jya2dBTGFlvAtde2/Pzee8MGGzQlkI3X6ac3337uXDjuODj44Hx/4YWLrrHs0SMnhc8/n99feWXesXXxEcl9980J55w5eQ1nr175fmm5E6uqnYmgJElSB1cJU1gbrboqbL31J8t/97uWd2F98kno3Tvfjx4Nm2yy6GjknDmw4oq5/oUX4JZbcvmHH+ayXr1gv/3y+6OOgquuyslgY6K4zjpw8825/vLL84hn8dTVAQOazqVMKT/vTqyqdiaCkiRJKruWprD+9Kd5DWGjDTbIV0tOP71pdPH993NCOHduU/0hh+Skrng0sjGJBLjuOrj11kU/c5NNcjIKsM028NBDOSEsNm9eHiH8+OO8G2vjiOTKK+dEcsCAJf/fQmoPJoKSJEkqu0WnsCYGD45lnm654op5zWPxZjq77pqvltxyS07k3n67KVFcPM4HH2z+2enT4ZRT8muxvfeGG2/M7zfaKCeLxYniLrvAN76R63//+zz62Vi38srQr1/eFGhpOIVVLTERlCRJUkVonMJaX38vI0eOLFscXbrk5Ktfv0/WHXkk/PznLU9jffrppmmrc+bka9VVm9rsskve/Gb27Hy8x4svNq23nD+/aS1kse99Lx8DMm8eDB++aBK58so50dxpp3yUyJ13NpXX18PxxzuFVc0zEZQkSZI+hZamsY4bl9cj9uoFa67Z/LO/+lXLn9upE7z88qJJ5OzZeRQR4KOPYOONc9nMmfkYjjlzcgK6004wYwbstVfrsc+bl3dgvf76vAtrnz45+Rw+PCeo99zTVN54DRiw9COSS8qRy/ZnIihJkiR9Cst7J9ZGnTrB0KH5ak7fvq3vyDpoEPzjH01J5IEHNt/ugw9gypS8dnLuXNh++5wIPvlk88/ccgvssUd+PeywvGazTx9YsGBThgzJ6zg32ACeeAL++temBLKx3fDh+ViRDz/MO7WusMKinz9hgpvvlIOJoCRJkvQpVdJOrI26d8/HdzQ68cTmp7AOGZLPd1zcNtvA5MlNCWLjtemmuX7AgDwNtbH8lVeCl17KU1ohb6Jz4omf/Nxnn82J4oUXwrHH5rMfi5PFWbOaP0Py6KNhwQI44ICcPM6cCQ0NTc+uuOKyHQGytDrK6KWJoCRJktQBtTaFtTk9esD667f8ecOHw8UXN93X1z++yFrOMWPgoIM+mUgOGZLrt9kGfvKTReveeQf++c/mf96cOXmX169+Nd+fdRacd15TfadOeS3kG2/khPCss/K6yN69m5LF1VaDk07K7R96KH9m8bTXlVbKI61LqiONXpoISpIkSR1QqaawtiQiJ5M9ejR/XMYWWyw6Ytlo6NDmRy4HDcqJXbdu+f7QQ/Pzc+fCu+/m148+ahoV/PjjvH5y6tSmRLNPn6ZEcNy4pvMiG62zTt6wB2D//eHxxxdNFDfaqClxvvLKvHFPc6OXY8eaCEqSJEmqEJU4hXVxLY1cnnlmTtQabbZZvlryox/lq9jChU3vzzsvJ2zFiWTxesXNNoPOnZvqpk5dtP5nP8sjis1Z/MiQamAiKEmSJKlsSjly2alT0/u11spXSxpHDlsyaRIMG5Z3Z13c4MFLF185dWq7iSRJkiSVzujReQRu4cL8WomjmN2751HKHj0WLW9t3WUlMxGUJEmSpCUwejSMH583wInIr+PHV2bi2paSJoIRcXlEzIqIp1uoj4g4LyJejIgnI2LzorpDIuKFwnVIKeOUJEmSpCVRDaOXS6LUI4JXALu3Uv9FYFjhGgNcBBARqwCnAFsCWwCnRMTKJY1UkiRJkmpESRPBlNJ9wOxWmuwNXJmyh4C+EbE6sBtwR0ppdkppDnAHrSeUkiRJkqQlVO41gmsArxTdzyiUtVQuSZIkSVpG5T4+IpopS62Uf/IDIsaQp5VSV1dHfX39cgtOy66hocE+qQL2U+Wzj6qD/VQd7KfKZx9VB/upupU7EZwBrFl0PwiYWSgfuVh5fXMfkFIaD4wHGDFiRBo5cmRzzVQm9fX12CeVz36qfPZRdbCfqoP9VPnso+pgP1W3ck8NvQk4uLB76FbAOymlV4HbgF0jYuXCJjG7FsokSZIkScuopCOCEXE1eWSvX0TMIO8E2hUgpfQb4FZgD+BFYB7wjULd7Ig4HXik8FGnpZRa23RGkiRJkrSESpoIppQObKM+AUe2UHc5cHkp4pIkSZKkWhY5F+sYIuINYFq549Ai+gFvljsItcl+qnz2UXWwn6qD/VT57KPqYD9VpiEppdXaatShEkFVnoiYlFIaUe441Dr7qfLZR9XBfqoO9lPls4+qg/1U3cq9WYwkSZIkqZ2ZCEqSJElSjTERVKmNL3cAWiL2U+Wzj6qD/VQd7KfKZx9VB/upirlGUJIkSZJqjCOCkiRJklRjTAS1zCJizYi4JyImR8QzEXFMM21GRsQ7EfF44Tq5HLHWuoiYGhFPFfpgUjP1ERHnRcSLEfFkRGxejjhrVUSsV/QdeTwi5kbE9xZr43epDCLi8oiYFRFPF5WtEhF3RMQLhdeVW3j2kEKbFyLikPaLuva00E8/j4jnCn+m3RARfVt4ttU/H7V8tNBHp0bEv4v+XNujhWd3j4jnC39Hndh+UdeeFvrpmqI+mhoRj7fwrN+lKuHUUC2ziFgdWD2l9FhE9AYeBfZJKT1b1GYk8IOU0p5lClPkP5yBESmlZs/8KfzlezSwB7AlcG5Kacv2i1CNIqIz8G9gy5TStKLykfhdancRsT3QAFyZUtq4UPYzYHZK6czCf5SunFI6YbHnVgEmASOARP7zcXhKaU67/gI1ooV+2hW4O6U0PyLOAli8nwrtptLKn49aPlroo1OBhpTSL1p5rjMwBdgFmAE8AhxY/N8aWn6a66fF6s8G3kkpndZM3VT8LlUFRwS1zFJKr6aUHiu8fxeYDKxR3qi0lPYm/6GfUkoPAX0Lib7a307Av4qTQJVPSuk+YPZixXsDvyu8/x2wTzOP7gbckVKaXUj+7gB2L1mgNa65fkop3Z5Sml+4fQgY1O6B6T9a+C4tiS2AF1NKL6WUPgImkr+DKoHW+ikiAtgfuLpdg9JyZyKo5SoihgKbAf9opnrriHgiIv4SERu1a2BqlIDbI+LRiBjTTP0awCtF9zMwqS+XA2j5L1m/S5WhLqX0KuR/EAP6N9PG71Rl+Sbwlxbq2vrzUaV1VGH67uUtTLP2u1Q5tgNeTym90EK936UqYSKo5SYiegHXAd9LKc1drPoxYEhKaVPg18CN7R2fANgmpbQ58EXgyMLUj2LRzDPOH29nEbEC8F/Atc1U+12qLn6nKkREjAXmAxNaaNLWn48qnYuAdYDPAa8CZzfTxu9S5TiQ1kcD/S5VCRNBLRcR0ZWcBE5IKV2/eH1KaW5KqaHw/laga0T0a+cwa15KaWbhdRZwA3mqTbEZwJpF94OAme0TnYp8EXgspfT64hV+lyrK641Tpwuvs5pp43eqAhQ26dkTGJ1a2BxhCf58VImklF5PKS1IKS0ELqH5/+39LlWAiOgCfBm4pqU2fpeqh4mglllhrvhlwOSU0jkttBlQaEdEbEH+/95b7RelIqJnYTMfIqInsCvw9GLNbgIOzpuHxlbkheCvtnOoauVfW/0uVZSbgMZdQA8B/tRMm9uAXSNi5cJ0t10LZWonEbE7cALwXymleS20WZI/H1Uii61F/2+a/9/+EWBYRKxVmDVxAPk7qPa1M/BcSmlGc5V+l6pLl3IHoA5hG+Ag4KmirYR/CAwGSCn9BtgX+E5EzAfeBw5o6V9lVTJ1wA2FHKILcFVK6a8RcQT8p59uJe8Y+iIwD/hGmWKtWRHRg7wr3reLyor7yO9SGUTE1cBIoF9EzABOAc4E/i8iDgOmA/sV2o4AjkgpHZ5Smh0Rp5P/IxbgtJTS0myUoSXQQj+dBHQD7ij8+fdQSumIiBj4/9u7f9QqojgKwOfgPlyDjaUQEESCtRuwdQVuw1LjHtyCggsQrEQQO0FsFJuAP4ukSPFeIrwHg97vq6Y8l8sMnOH+SfJyZk6z5/u4wRD+e3vm6KTtnVws9fycy+/f1Tm6PPX1aS5+pNxK8mpmPmwwhCXsmqeZOcuO/evepX+X6yMAAAAWY2koAADAYhRBAACAxSiCAAAAi1EEAQAAFqMIAgAALEYRBIADtf155fm07ce2t7fMBADXcY8gABxJ2/tJnid5MDNfts4DAPsoggBwBG3vJXmR5HRmPm2dBwCu40J5ADhQ2/MkP5KczMz7rfMAwE3sEQSAw50neZfkydZBAOBvKIIAcLjfSR4nudv22dZhAOAm9ggCwBHMzK+2j5K8bft1Zs62zgQA+yiCAHAkM/O97cMkb9p+m5nXW2cCgF0cFgMAALAYewQBAAAWowgCAAAsRhEEAABYjCIIAACwGEUQAABgMYogAADAYhRBAACAxSiCAAAAi/kDhf0ypo+qzXYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the change in within-cluster distance across number of clusters.\n",
    "# HINT: Use matplotlib's plot function to visualize this relationship.\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.grid(True)\n",
    "plt.plot(centers, scores, linestyle='--', marker='o', color='b')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('SSE')\n",
    "plt.title('SSE vs, K');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 0, 2, ..., 3, 4, 2], dtype=int32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-fit the k-means model with the selected number of clusters and obtain\n",
    "# cluster predictions for the general population demographics data.\n",
    "\n",
    "kmeans = KMeans(n_clusters=5, random_state=0)\n",
    "kmeans = kmeans.fit(azdias_pca)\n",
    "labels_azdias = kmeans.predict(azdias_pca)\n",
    "labels_azdias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.1: Apply Clustering to General Population\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding clustering. Into how many clusters have you decided to segment the population?)\n",
    "\n",
    "- From the scree plot of running K-means with n_components between 1 and 20, we see that after K=5 a change in K causes a negligible change in SSE\n",
    "- I concluded therefore, that the best K (number of clusters) for this dataset is 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.2: Apply All Steps to the Customer Data\n",
    "\n",
    "Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.\n",
    "\n",
    "- Don't forget when loading in the customers data, that it is semicolon (`;`) delimited.\n",
    "- Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the `clean_data()` function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)\n",
    "- Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a `.fit()` or `.fit_transform()` method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the customer demographics data.\n",
    "customers = pd.read_csv('Udacity_CUSTOMERS_Subset.csv', sep=';')\n",
    "\n",
    "# Load in the feature summary file.\n",
    "feat_info = pd.read_csv('AZDIAS_Feature_Summary.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/peter/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:1494: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  return self._getitem_tuple(key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 0, 4, ..., 2, 0, 2], dtype=int32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply preprocessing, feature transformation, and clustering from the general\n",
    "# demographics onto the customer data, obtaining cluster predictions for the\n",
    "# customer demographics data.\n",
    "\n",
    "df = clean_data(customers, feat_info, outlier_columns)\n",
    "\n",
    "\n",
    "# Impute the data\n",
    "df_imputed = imp.transform(df)\n",
    "\n",
    "# Apply feature scaling to the customer data.\n",
    "df_scaled = ss.transform(df_imputed)\n",
    "df_scaled = pd.DataFrame(data=df_scaled, columns=df.columns)\n",
    "\n",
    "# Apply PCA to the data.\n",
    "df_pca = pca.transform(df_scaled)\n",
    "\n",
    "labels_customers = kmeans.predict(df_pca)\n",
    "labels_customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.\n",
    "\n",
    "Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.\n",
    "\n",
    "Take a look at the following points in this step:\n",
    "\n",
    "- Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) or [`barplot()`](https://seaborn.pydata.org/generated/seaborn.barplot.html) function could be handy.\n",
    "  - Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!\n",
    "- Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the `.inverse_transform()` method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.\n",
    "- Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the proportion of data in each cluster for the customer data to the\n",
    "# proportion of data in each cluster for the general population.\n",
    "\n",
    "azdias_proportions = list()\n",
    "df_proportions = list()\n",
    "for k in range(kmeans.n_clusters):\n",
    "    azdias_k = azdias_pca[labels_azdias == k]\n",
    "    azdias_proportions.append(azdias_k.shape[0] / azdias_pca.shape[0]) \n",
    "    \n",
    "    df_k = df_pca[labels_customers == k]\n",
    "    df_proportions.append(df_k.shape[0] / df_pca.shape[0])\n",
    "\n",
    "\n",
    "data = pd.DataFrame(data={'azdias': azdias_proportions,\n",
    "                          'customers': df_proportions})\n",
    "\n",
    "\n",
    "data.plot(kind='bar', figsize=(10,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What kinds of people are part of a cluster that is overrepresented in the\n",
    "# customer data compared to the general population?\n",
    "\n",
    "def distribution_with_respect_to_first_3_principal_components(data, labels, cluster):\n",
    "    data = pd.DataFrame(data=data[:,:3], \n",
    "                        columns=['PC-1 (poor)', \n",
    "                                 'PC-2 (old and conservative)', \n",
    "                                 'PC-3 - male analytical free-thinker'])\n",
    "    data['labels'] = labels\n",
    "    data['target_audience'] = data['labels'].apply(lambda x: x == cluster)\n",
    "    \n",
    "    fig, axes = plt.subplots(1,3, figsize=(15, 5))\n",
    "    sns.boxplot(x=\"target_audience\", y=\"PC-1 (poor)\", data=data, ax=axes[0])\n",
    "    sns.boxplot(x=\"target_audience\", y=\"PC-2 (old and conservative)\", data=data, ax=axes[1])\n",
    "    sns.boxplot(x=\"target_audience\", y=\"PC-3 - male analytical free-thinker\", data=data, ax=axes[2])\n",
    "    \n",
    "distribution_with_respect_to_first_3_principal_components(df_pca, labels_customers, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What kinds of people are part of a cluster that is underrepresented in the\n",
    "# customer data compared to the general population?\n",
    "\n",
    "distribution_with_respect_to_first_3_principal_components(df_pca, labels_customers, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "### Discussion 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting findings and conclusions from the clustering analysis. Can we describe segments of the population that are relatively popular with the mail-order company, or relatively unpopular with the company?)\n",
    "\n",
    "- People in cluster 2 are popular with the mail order company. These people are: \n",
    "    - Lives in a rich neighbourhood and is most likely rich, based on the 1st principal component being low for the target audience.\n",
    "    - Male, analytical and free-thinkers, based on the 3rd principal component being high for the target audience.\n",
    "\n",
    "\n",
    "- People in cluster 4 are **not** popular with the mail order company. These people are:\n",
    "    - Lives in a poor neighbourhood and is most likely poor, based on the 1st principal component being generally high for the target audience.\n",
    "    - Young, not religious, not traditionl, capricious, not dutiful, based on the 2nd principal component being generally low for the target audience."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in **Discussion** cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to **File -> Download as -> HTML (.html)**. You will submit both that document and this notebook for your project submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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