personal_transaction_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "import pymysql.cursors\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pwd = \"password\"\n",
    "user_name = \"root\"\n",
    "host_loc = \"localhost\"\n",
    "data_base = \"database_name\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dbCON  = pymysql.connect(user=user_name, password=pwd, host=host_loc, database=data_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "selector = dbCON.cursor()\n",
    "selector.execute(\"SELECT * FROM transaction\")\n",
    "rows = selector.fetchall()\n",
    "df = pd.DataFrame(data = np.array(rows))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dbCON.close() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns = [3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "del df[6], df[1], df[2], df[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
      "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df = df.convert_objects(convert_numeric = True).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>5</th>\n",
       "      <th>3_</th>\n",
       "      <th>3_ATRID</th>\n",
       "      <th>3_DIGRID</th>\n",
       "      <th>3_DIGTRID</th>\n",
       "      <th>3_FTRID</th>\n",
       "      <th>3_FTRID</th>\n",
       "      <th>3_HTRID</th>\n",
       "      <th>3_PHTRID</th>\n",
       "      <th>3_PRTRID</th>\n",
       "      <th>3_SVTRID</th>\n",
       "      <th>3_TTRID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>302</td>\n",
       "      <td>-8.34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>302</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>501</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>302</td>\n",
       "      <td>-25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0      5  3_  3_ATRID  3_DIGRID  3_DIGTRID  3_FTRID  3_FTRID   3_HTRID  \\\n",
       "0    0   0.00   1        0         0          0        0         0        0   \n",
       "1  302  -8.34   0        0         0          0        1         0        0   \n",
       "2  302  -4.20   0        0         0          0        1         0        0   \n",
       "3  501 -10.00   0        0         0          0        0         0        0   \n",
       "4  302 -25.00   0        0         0          0        1         0        0   \n",
       "\n",
       "   3_PHTRID  3_PRTRID  3_SVTRID  3_TTRID  \n",
       "0         0         0         0        0  \n",
       "1         0         0         0        0  \n",
       "2         0         0         0        0  \n",
       "3         0         0         0        1  \n",
       "4         0         0         0        0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.  ,   0.  ,   1.  , ...,   0.  ,   0.  ,   0.  ],\n",
       "       [302.  ,  -8.34,   0.  , ...,   0.  ,   0.  ,   0.  ],\n",
       "       [302.  ,  -4.2 ,   0.  , ...,   0.  ,   0.  ,   0.  ],\n",
       "       ...,\n",
       "       [302.  ,  -0.79,   0.  , ...,   0.  ,   0.  ,   0.  ],\n",
       "       [302.  ,  -4.  ,   0.  , ...,   0.  ,   0.  ,   0.  ],\n",
       "       [302.  , -11.62,   0.  , ...,   0.  ,   0.  ,   0.  ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating the \"Best\" Value for `k`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans_fit = [KMeans(n_clusters = iter_val).fit(X) for iter_val in range(5,50,5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = [(iter_val, model.score(X, model.predict(X))) for iter_val, model in enumerate(kmeans_fit)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoresDF = pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1144d0668>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mbxDcDuyrqt9c7P4kSfMz6DLQ9yc5AFwOfDbJ7u742iQPdM2uAH4JeHeSJ7qf9w5UtSRpYIOuAroHuGeG44eA93bbfwjMvkxIktQLnwSWpEYZAJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRAz0HMIru3XOQ7bv3c+jYcdauHmPb5g1s2biu77IkaeQsqwC4d89Bbtq1l+MvnQDg4LHj3LRrL4AhIEnTLKspoO279/9w8D/p+Esn2L57f08VSdLoWlYBcOjY8QUdl6SWLasAWLt6bEHHJallyyoAtm3ewNiqFa84NrZqBds2b+ipIkkaXcvqJvDJG72uApKkU1tWAQCTIeCAL0mntqymgCRJ82cASFKjDABJapQBIEmNMgAkqVGpqr5rmFWSo8A3TvPl5wDfHmI5w2JdC2NdC2NdC7Mc6/rJqjp3Pg1HOgAGkWSiqsb7rmM661oY61oY61qY1utyCkiSGmUASFKjlnMA7Oi7gFlY18JY18JY18I0XdeyvQcgSZrbcr4CkCTNwQCQpEYtywBIcnWS/UmeTXJj3/UAJPlEkiNJnuy7lqmSvDnJ55M8neSpJB/ruyaAJK9J8n+SfKWr61/2XdNJSVYk2ZPkf/Vdy1RJvp5kb5Inkkz0XQ9AktVJ7k7yx0n2Jbl8BGra0P0Znfz5XpKP910XQJJ/3P19fzLJHUles6j9Lbd7AElWAF8FrgIOAI8B11fV0z3X9S7gReCTVfWWPmuZKskaYE1VfTnJXwIeB7aMwJ9XgNdV1YtJVgF/CHysqr7YZ10ASX4dGAfeUFU/33c9JyX5OjBeVSPzYFOSncAXquq2JGcBr62qY33XdVI3XhwE3lZVp/vQ6bBqWcfk3/NLqup4kruAB6rqvy5Wn8vxCuAy4Nmqeq6qvg/cCVzTc01U1aPAd/uuY7qqOlxVX+62/xzYB/T+DyrUpBe73VXdT++fVpJcAPxd4La+axl1SX4CeBdwO0BVfX+UBv/OlcDX+h78p1gJjCVZCbwWOLSYnS3HAFgHfGvK/gFGYEA7EyRZD2wEvtRvJZO6qZYngCPAg1U1CnX9NvBPgR/0XcgMCvhckseTbO27GOAi4Cjwe92U2W1JXtd3UdNcB9zRdxEAVXUQ+PfAN4HDwJ9V1ecWs8/lGAA6DUleD3wG+HhVfa/vegCq6kRVvRW4ALgsSa9TZ0l+HjhSVY/3Wccc/lZV/SzwHuAj3bRjn1YCPwv8blVtBP4vMBL35AC6Kan3Ab/fdy0ASc5mcrbiImAt8Lokv7iYfS7HADgIvHnK/gXdMc2im2P/DPCpqtrVdz3TddMGnweu7rmUK4D3dXPtdwLvTvLf+y3pR7pPkFTVEeAeJqdD+3QAODDlyu1uJgNhVLwH+HJVvdB3IZ1NwJ9U1dGqegnYBbxjMTtcjgHwGHBxkou6hL8OuL/nmkZWd7P1dmBfVf1m3/WclOTcJKu77TEmb+r/cZ81VdVNVXVBVa1n8u/VI1W1qJ/Q5ivJ67qb+HTTLH8H6HXFWVU9D3wryYbu0JVAr4sLprmeEZn+6XwTeHuS13b/X17J5D25RbPs/lH4qno5yUeB3cAK4BNV9VTPZZHkDuBvA+ckOQD8i6q6vd+qgMlPtb8E7O3m2wH+WVU90GNNAGuAnd0qjVcBd1XVSC27HDHnAfdMjhusBP5HVf1BvyUB8GvAp7oPY88B/7DneoAfhuRVwC/3XctJVfWlJHcDXwZeBvawyF8JseyWgUqS5mc5TgFJkubBAJCkRhkAktQoA0CSGmUASFKjDABJapQBIEmN+v81+BkcFk2HJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = scoresDF[0], y = scoresDF[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = KMeans(n_clusters = 2).fit_predict(X)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}