Churn Prediction Model.ipynb

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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "import datetime\n",
    "%matplotlib inline\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         amount\n",
      "count    1.934M\n",
      "mean    301.837\n",
      "std      1.122k\n",
      "min    -605.167\n",
      "25%      15.327\n",
      "50%      66.135\n",
      "75%     234.994\n",
      "max    307.370k\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1934135 entries, 0 to 1934134\n",
      "Data columns (total 3 columns):\n",
      "account    object\n",
      "date       datetime64[ns]\n",
      "amount     float64\n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 44.3+ MB\n",
      "None\n",
      "        count\n",
      "count  1.934M\n",
      "mean   41.459\n",
      "std    74.995\n",
      "min    10.000\n",
      "25%    15.000\n",
      "50%    24.000\n",
      "75%    44.000\n",
      "max    5.643k\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1934028 entries, 0 to 1934027\n",
      "Data columns (total 3 columns):\n",
      "account    object\n",
      "date       datetime64[ns]\n",
      "count      int64\n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 44.3+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "spend = pd.read_csv(\"spend.csv\")\n",
    "count = pd.read_csv(\"counts.csv\")\n",
    "spend['date'] = pd.to_datetime(spend['date'],unit='s')\n",
    "count['date'] = pd.to_datetime(count['date'])\n",
    "print spend.describe()\n",
    "print spend.info()\n",
    "print count.describe()\n",
    "print count.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            account                date   amount    new_date  \\\n",
      "0  5e0a5eb5575517efdddb2799c90e2e9b 2017-08-16 00:00:00   15.144  2017-08-16   \n",
      "1  a04e063493c55bc6cecd9056712e9e47 1973-04-05 07:06:40  730.070  1973-04-05   \n",
      "2  ff6cc8cadcb7457f9ddb2dd238a13733 2017-08-16 00:00:00   3.651k  2017-08-16   \n",
      "3  324a05f1f917b8a0af83a54e55a1ef63 2017-08-16 00:00:00  113.219  2017-08-16   \n",
      "4  b9abff45dc08172b556cb10b7c3c2252 2017-08-16 00:00:00  697.224  2017-08-16   \n",
      "\n",
      "   new_time  \n",
      "0  00:00:00  \n",
      "1  07:06:40  \n",
      "2  00:00:00  \n",
      "3  00:00:00  \n",
      "4  00:00:00  \n",
      "                            account       date  count    new_date\n",
      "0  5e0a5eb5575517efdddb2799c90e2e9b 2017-08-16     56  2017-08-16\n",
      "1  a04e063493c55bc6cecd9056712e9e47 2017-08-16    180  2017-08-16\n",
      "2  ff6cc8cadcb7457f9ddb2dd238a13733 2017-08-16    432  2017-08-16\n",
      "3  324a05f1f917b8a0af83a54e55a1ef63 2017-08-16     50  2017-08-16\n",
      "4  b9abff45dc08172b556cb10b7c3c2252 2017-08-16    135  2017-08-16\n"
     ]
    }
   ],
   "source": [
    "spend['new_date'] = [d.date() for d in spend['date']]\n",
    "spend['new_time'] = [d.time() for d in spend['date']]\n",
    "print spend.head()\n",
    "count['new_date'] = [d.date() for d in count['date']]\n",
    "print count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spend.drop(['year_quarter'],inplace=True,axis=1)\n",
    "spend.drop(['date'],inplace=True,axis=1)\n",
    "count.drop(['date'],inplace=True,axis=1)\n",
    "print spend.isnull().sum()\n",
    "print count.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account</th>\n",
       "      <th>amount</th>\n",
       "      <th>new_date</th>\n",
       "      <th>new_time</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>quarter</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5e0a5eb5575517efdddb2799c90e2e9b</td>\n",
       "      <td>15.144</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a04e063493c55bc6cecd9056712e9e47</td>\n",
       "      <td>730.070</td>\n",
       "      <td>1973-04-05</td>\n",
       "      <td>07:06:40</td>\n",
       "      <td>1973</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1973004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ff6cc8cadcb7457f9ddb2dd238a13733</td>\n",
       "      <td>3.651k</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>324a05f1f917b8a0af83a54e55a1ef63</td>\n",
       "      <td>113.219</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b9abff45dc08172b556cb10b7c3c2252</td>\n",
       "      <td>697.224</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                            account   amount    new_date  new_time  year  \\\n",
       "0  5e0a5eb5575517efdddb2799c90e2e9b   15.144  2017-08-16  00:00:00  2017   \n",
       "1  a04e063493c55bc6cecd9056712e9e47  730.070  1973-04-05  07:06:40  1973   \n",
       "2  ff6cc8cadcb7457f9ddb2dd238a13733   3.651k  2017-08-16  00:00:00  2017   \n",
       "3  324a05f1f917b8a0af83a54e55a1ef63  113.219  2017-08-16  00:00:00  2017   \n",
       "4  b9abff45dc08172b556cb10b7c3c2252  697.224  2017-08-16  00:00:00  2017   \n",
       "\n",
       "   month  day  quarter  year_month  \n",
       "0      8   16        3     2017008  \n",
       "1      4    5        2     1973004  \n",
       "2      8   16        3     2017008  \n",
       "3      8   16        3     2017008  \n",
       "4      8   16        3     2017008  "
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spend[\"year\"] = pd.DatetimeIndex(spend[\"new_date\"]).year\n",
    "spend[\"month\"] = pd.DatetimeIndex(spend[\"new_date\"]).month\n",
    "spend[\"day\"] = pd.DatetimeIndex(spend[\"new_date\"]).day\n",
    "spend[\"quarter\"] = pd.DatetimeIndex(spend[\"new_date\"]).quarter\n",
    "spend[\"year_month\"] = spend['new_date'].map(lambda x: 1000*x.year + x.month)\n",
    "spend.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most spending by account "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               account  amount\n",
      "8911  e5a2f63c3ec4d5dfddcdc3abb6510aa5  8.947M\n",
      "160   040b3d8490a0be543c3394bb5c1c9653  4.144M\n",
      "1683  2ae5455b659642e7196a63cde82f4789  3.625M\n",
      "7932  cead1808d4ea0a56c6f9b97f0f26db1e  2.460M\n",
      "7909  ce23b9bef1753ef640eb57922badb87d  2.385M\n",
      "         amount\n",
      "count   10.001k\n",
      "mean    58.373k\n",
      "std    151.696k\n",
      "min      12.907\n",
      "25%     13.570k\n",
      "50%     25.948k\n",
      "75%     54.553k\n",
      "max      8.947M\n"
     ]
    },
    {
     "data": {
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     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
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ME+wRYMVIGSeE5UZgKjZB6nTMpHNlRPlnymzriim76yLr0A+bjX0Z1kM5Izwr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\n6DCRyC1vZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x79b59b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#groupbyacc = spend.groupby(['account'],sort=True)['amount'].sum().reset_index()\n",
    "#print groupbyacc.head()\n",
    "np.set_printoptions(suppress=False)\n",
    "pd.set_eng_float_format(accuracy=3, use_eng_prefix=True)\n",
    "print groupbyacc.sort_values('amount',ascending=False).head(5)\n",
    "print groupbyacc.describe()\n",
    "sns.barplot(x = 'account',y='amount',data=groupbyacc.sort_values('amount',ascending=False).head(20))\n",
    "plt.xticks(Rotation= 'vertical')\n",
    "#sns.barplot(x='account',y = 'amount', data = groupbyacc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total Monthly spending "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20]), <a list of 21 Text xticklabel objects>)"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RxzAlSZKkO6TXQp1kOU2Z/nBVnTDHImuAnWe83gn4Xp9jkiRJkhZSb4U6SYAjgYs3clfF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\nSZLUgYVakiRJ6sBCLUmSJHXw/wEMWRHLXPzh9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe19705f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupbydate = spend.groupby(['year_month'],sort=True)['amount'].sum().reset_index()\n",
    "pd.set_eng_float_format(accuracy=3, use_eng_prefix=True)\n",
    "groupbydate.columns = [\"Date\",\"Total\"]\n",
    "\n",
    "#fig, ax =plt.subplot(1,grp_store_year[\"Store_new\"].nunique(),1)\n",
    "pv = pd.pivot_table(groupbydate, index=groupbydate.Date, columns=groupbydate.Total,\n",
    "                   values='Total', aggfunc='sum')\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(data=groupbydate, x=\"Date\", y=\"Total\")\n",
    "plt.xticks(Rotation= 'vertical')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf49b11d0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LXBsRh9J5rgMeBOqBuyNiU7rETcC9kv4Y+AFwV4rfBXxZUjtZD+nyodbZzMyqS0PpREj6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\nz3AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf4e75da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spend.plot(y=\"amount\",x=\"year_month\",kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20]), <a list of 21 Text xticklabel objects>)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf82a5780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupbyaccountcount = spend.groupby(['year_month'])['account'].nunique().reset_index()\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(data= groupbyaccountcount,x=\"year_month\",y=\"account\",)\n",
    "plt.xticks(Rotation= 'vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   year_month                           account   amount\n",
      "0     1973004  a04e063493c55bc6cecd9056712e9e47  730.070\n",
      "1     2017008  000b2159adf33dcb44291291e0575122  10.506k\n",
      "2     2017008  000cff3bf5b20e8bb5734eb0aeef6673   2.673k\n",
      "3     2017008  00115bed7ec756ccb004660899b6a992  193.773\n",
      "4     2017008  0013d81cb06a39cc2fc5fb57b2123115   1.152k\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>1973004</th>\n",
       "      <th>2017008</th>\n",
       "      <th>2017009</th>\n",
       "      <th>2017010</th>\n",
       "      <th>2017011</th>\n",
       "      <th>2017012</th>\n",
       "      <th>2018001</th>\n",
       "      <th>2018002</th>\n",
       "      <th>2018003</th>\n",
       "      <th>2018004</th>\n",
       "      <th>...</th>\n",
       "      <th>2018006</th>\n",
       "      <th>2018007</th>\n",
       "      <th>2018008</th>\n",
       "      <th>2018009</th>\n",
       "      <th>2018010</th>\n",
       "      <th>2018011</th>\n",
       "      <th>2018012</th>\n",
       "      <th>2019001</th>\n",
       "      <th>2019002</th>\n",
       "      <th>2019003</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00023fda963eb48839a670cbdf74cd21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>839.150</td>\n",
       "      <td>706.625</td>\n",
       "      <td>111.255</td>\n",
       "      <td>893.436</td>\n",
       "      <td>917.848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.524</td>\n",
       "      <td>556.384</td>\n",
       "      <td>5.008k</td>\n",
       "      <td>405.073</td>\n",
       "      <td>2.511k</td>\n",
       "      <td>1.487k</td>\n",
       "      <td>2.712k</td>\n",
       "      <td>1.871k</td>\n",
       "      <td>1.984k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000458145e8ff76fb2907417d806d098</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>363.683</td>\n",
       "      <td>1.193k</td>\n",
       "      <td>1.699k</td>\n",
       "      <td>...</td>\n",
       "      <td>1.272k</td>\n",
       "      <td>2.478k</td>\n",
       "      <td>593.378</td>\n",
       "      <td>497.734</td>\n",
       "      <td>1.375k</td>\n",
       "      <td>2.019k</td>\n",
       "      <td>2.535k</td>\n",
       "      <td>4.671k</td>\n",
       "      <td>1.277k</td>\n",
       "      <td>899.158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000b2159adf33dcb44291291e0575122</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.506k</td>\n",
       "      <td>19.340k</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000cff3bf5b20e8bb5734eb0aeef6673</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.673k</td>\n",
       "      <td>2.110k</td>\n",
       "      <td>4.519k</td>\n",
       "      <td>7.536k</td>\n",
       "      <td>8.764k</td>\n",
       "      <td>14.422k</td>\n",
       "      <td>9.382k</td>\n",
       "      <td>17.029k</td>\n",
       "      <td>16.799k</td>\n",
       "      <td>...</td>\n",
       "      <td>10.474k</td>\n",
       "      <td>8.704k</td>\n",
       "      <td>10.045k</td>\n",
       "      <td>11.230k</td>\n",
       "      <td>15.203k</td>\n",
       "      <td>9.330k</td>\n",
       "      <td>11.444k</td>\n",
       "      <td>379.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000f6054933c882efb96bc34b91c8924</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>236.117</td>\n",
       "      <td>26.519</td>\n",
       "      <td>69.626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>599.241</td>\n",
       "      <td>411.273</td>\n",
       "      <td>...</td>\n",
       "      <td>820.885</td>\n",
       "      <td>784.981</td>\n",
       "      <td>785.364</td>\n",
       "      <td>1.083k</td>\n",
       "      <td>1.478k</td>\n",
       "      <td>716.021</td>\n",
       "      <td>343.746</td>\n",
       "      <td>2.417k</td>\n",
       "      <td>2.219k</td>\n",
       "      <td>2.131k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month                        1973004  2017008  2017009  2017010  2017011  \\\n",
       "account                                                                         \n",
       "00023fda963eb48839a670cbdf74cd21      NaN      NaN      NaN      NaN  839.150   \n",
       "000458145e8ff76fb2907417d806d098      NaN      NaN      NaN      NaN      NaN   \n",
       "000b2159adf33dcb44291291e0575122      NaN  10.506k  19.340k      NaN      NaN   \n",
       "000cff3bf5b20e8bb5734eb0aeef6673      NaN   2.673k   2.110k   4.519k   7.536k   \n",
       "000f6054933c882efb96bc34b91c8924      NaN      NaN      NaN      NaN  236.117   \n",
       "\n",
       "year_month                        2017012  2018001  2018002  2018003  2018004  \\\n",
       "account                                                                         \n",
       "00023fda963eb48839a670cbdf74cd21  706.625  111.255  893.436  917.848      NaN   \n",
       "000458145e8ff76fb2907417d806d098      NaN      NaN  363.683   1.193k   1.699k   \n",
       "000b2159adf33dcb44291291e0575122      NaN      NaN      NaN      NaN      NaN   \n",
       "000cff3bf5b20e8bb5734eb0aeef6673   8.764k  14.422k   9.382k  17.029k  16.799k   \n",
       "000f6054933c882efb96bc34b91c8924   26.519   69.626      NaN  599.241  411.273   \n",
       "\n",
       "year_month                         ...     2018006  2018007  2018008  2018009  \\\n",
       "account                            ...                                          \n",
       "00023fda963eb48839a670cbdf74cd21   ...         NaN   39.524  556.384   5.008k   \n",
       "000458145e8ff76fb2907417d806d098   ...      1.272k   2.478k  593.378  497.734   \n",
       "000b2159adf33dcb44291291e0575122   ...         NaN      NaN      NaN      NaN   \n",
       "000cff3bf5b20e8bb5734eb0aeef6673   ...     10.474k   8.704k  10.045k  11.230k   \n",
       "000f6054933c882efb96bc34b91c8924   ...     820.885  784.981  785.364   1.083k   \n",
       "\n",
       "year_month                        2018010  2018011  2018012  2019001  2019002  \\\n",
       "account                                                                         \n",
       "00023fda963eb48839a670cbdf74cd21  405.073   2.511k   1.487k   2.712k   1.871k   \n",
       "000458145e8ff76fb2907417d806d098   1.375k   2.019k   2.535k   4.671k   1.277k   \n",
       "000b2159adf33dcb44291291e0575122      NaN      NaN      NaN      NaN      NaN   \n",
       "000cff3bf5b20e8bb5734eb0aeef6673  15.203k   9.330k  11.444k  379.010      NaN   \n",
       "000f6054933c882efb96bc34b91c8924   1.478k  716.021  343.746   2.417k   2.219k   \n",
       "\n",
       "year_month                        2019003  \n",
       "account                                    \n",
       "00023fda963eb48839a670cbdf74cd21   1.984k  \n",
       "000458145e8ff76fb2907417d806d098  899.158  \n",
       "000b2159adf33dcb44291291e0575122      NaN  \n",
       "000cff3bf5b20e8bb5734eb0aeef6673      NaN  \n",
       "000f6054933c882efb96bc34b91c8924   2.131k  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupbymonthaccount = spend.groupby(['year_month','account'])['amount'].sum().reset_index()\n",
    "groupbymonthaccount.columns = ['year_month','account','amount']\n",
    "print groupbymonthaccount.head()\n",
    "pvv = pd.pivot_table(groupbymonthaccount, index=groupbymonthaccount.account, columns=groupbymonthaccount.year_month,\n",
    "                   values='amount', aggfunc='sum')\n",
    "pvv.head()\n",
    "#plt.figure(figsize=(12,6))\n",
    "#sns.barplot(data= groupbyaccountcount,x=\"year_month\",y=\"account\",)\n",
    "#plt.xticks(Rotation= 'vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "groupbycount = count.groupby('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "account         0\n",
      "amount          3\n",
      "new_date        0\n",
      "new_time        3\n",
      "count         110\n",
      "year            0\n",
      "month           0\n",
      "day             0\n",
      "quarter         0\n",
      "year_month      0\n",
      "dtype: int64\n",
      "                            account   amount    new_date  new_time    count  \\\n",
      "0  5e0a5eb5575517efdddb2799c90e2e9b   15.144  2017-08-16  00:00:00   56.000   \n",
      "1  a04e063493c55bc6cecd9056712e9e47  730.070  1973-04-05  07:06:40    0.000   \n",
      "2  ff6cc8cadcb7457f9ddb2dd238a13733   3.651k  2017-08-16  00:00:00  432.000   \n",
      "3  324a05f1f917b8a0af83a54e55a1ef63  113.219  2017-08-16  00:00:00   50.000   \n",
      "4  b9abff45dc08172b556cb10b7c3c2252  697.224  2017-08-16  00:00:00  135.000   \n",
      "\n",
      "   year  month  day  quarter  year_month  \n",
      "0  2017      8   16        3     2017008  \n",
      "1  1973      4    5        2     1973004  \n",
      "2  2017      8   16        3     2017008  \n",
      "3  2017      8   16        3     2017008  \n",
      "4  2017      8   16        3     2017008  \n",
      "account       0\n",
      "amount        0\n",
      "new_date      0\n",
      "new_time      0\n",
      "count         0\n",
      "year          0\n",
      "month         0\n",
      "day           0\n",
      "quarter       0\n",
      "year_month    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#df = pd.merge(spend,count,how='outer')#, left_on=['account','new_date'], right_on = ['account','date'])\n",
    "#print df.head()\n",
    "print df.isnull().sum()\n",
    "df = df.fillna(0)\n",
    "print df.head()\n",
    "print df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account</th>\n",
       "      <th>amount</th>\n",
       "      <th>new_date</th>\n",
       "      <th>new_time</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>quarter</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5e0a5eb5575517efdddb2799c90e2e9b</td>\n",
       "      <td>15.144</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>56.000</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a04e063493c55bc6cecd9056712e9e47</td>\n",
       "      <td>730.070</td>\n",
       "      <td>1973-04-05</td>\n",
       "      <td>07:06:40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1973004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ff6cc8cadcb7457f9ddb2dd238a13733</td>\n",
       "      <td>3.651k</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>432.000</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>324a05f1f917b8a0af83a54e55a1ef63</td>\n",
       "      <td>113.219</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>50.000</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b9abff45dc08172b556cb10b7c3c2252</td>\n",
       "      <td>697.224</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>135.000</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            account   amount    new_date  new_time    count  \\\n",
       "0  5e0a5eb5575517efdddb2799c90e2e9b   15.144  2017-08-16  00:00:00   56.000   \n",
       "1  a04e063493c55bc6cecd9056712e9e47  730.070  1973-04-05  07:06:40      NaN   \n",
       "2  ff6cc8cadcb7457f9ddb2dd238a13733   3.651k  2017-08-16  00:00:00  432.000   \n",
       "3  324a05f1f917b8a0af83a54e55a1ef63  113.219  2017-08-16  00:00:00   50.000   \n",
       "4  b9abff45dc08172b556cb10b7c3c2252  697.224  2017-08-16  00:00:00  135.000   \n",
       "\n",
       "   year  month  day  quarter  year_month  \n",
       "0  2017      8   16        3     2017008  \n",
       "1  1973      4    5        2     1973004  \n",
       "2  2017      8   16        3     2017008  \n",
       "3  2017      8   16        3     2017008  \n",
       "4  2017      8   16        3     2017008  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"year\"] = pd.DatetimeIndex(df[\"new_date\"]).year\n",
    "df[\"month\"] = pd.DatetimeIndex(df[\"new_date\"]).month\n",
    "df[\"day\"] = pd.DatetimeIndex(df[\"new_date\"]).day\n",
    "df[\"quarter\"] = pd.DatetimeIndex(df[\"new_date\"]).quarter\n",
    "df[\"year_month\"] = df['new_date'].map(lambda x: 1000*x.year + x.month)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"year_quarter\"] = pd.DatetimeIndex(df[\"new_date\"]).map(lambda x: 1000*x.year + x.quarter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         amount   count      year   month     day  quarter  year_month  \\\n",
      "count    1.934M  1.934M    1.934M  1.934M  1.934M   1.934M      1.934M   \n",
      "mean    301.837  41.456    2.018k   6.874  15.717    2.616      2.018M   \n",
      "std      1.122k  74.993  551.689m   3.722   8.837    1.199     549.333   \n",
      "min       0.000   0.000    1.973k   1.000   1.000    1.000      1.973M   \n",
      "25%      15.327  15.000    2.018k   3.000   8.000    1.000      2.018M   \n",
      "50%      66.134  24.000    2.018k   7.000  16.000    3.000      2.018M   \n",
      "75%     234.994  44.000    2.018k  10.000  23.000    4.000      2.018M   \n",
      "max    307.370k  5.643k    2.019k  12.000  31.000    4.000      2.019M   \n",
      "\n",
      "       year_quarter  \n",
      "count        1.934M  \n",
      "mean         2.018M  \n",
      "std         550.939  \n",
      "min          1.973M  \n",
      "25%          2.018M  \n",
      "50%          2.018M  \n",
      "75%          2.018M  \n",
      "max          2.019M  \n"
     ]
    }
   ],
   "source": [
    "print df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account</th>\n",
       "      <th>amount</th>\n",
       "      <th>new_date</th>\n",
       "      <th>new_time</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>quarter</th>\n",
       "      <th>year_month</th>\n",
       "      <th>year_quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5e0a5eb5575517efdddb2799c90e2e9b</td>\n",
       "      <td>15.144</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>56.000</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "      <td>2017003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a04e063493c55bc6cecd9056712e9e47</td>\n",
       "      <td>730.070</td>\n",
       "      <td>1973-04-05</td>\n",
       "      <td>07:06:40</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1973</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1973004</td>\n",
       "      <td>1973002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ff6cc8cadcb7457f9ddb2dd238a13733</td>\n",
       "      <td>3.651k</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>432.000</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "      <td>2017003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>324a05f1f917b8a0af83a54e55a1ef63</td>\n",
       "      <td>113.219</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>50.000</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "      <td>2017003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b9abff45dc08172b556cb10b7c3c2252</td>\n",
       "      <td>697.224</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>135.000</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2017008</td>\n",
       "      <td>2017003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            account   amount    new_date  new_time    count  \\\n",
       "0  5e0a5eb5575517efdddb2799c90e2e9b   15.144  2017-08-16  00:00:00   56.000   \n",
       "1  a04e063493c55bc6cecd9056712e9e47  730.070  1973-04-05  07:06:40    0.000   \n",
       "2  ff6cc8cadcb7457f9ddb2dd238a13733   3.651k  2017-08-16  00:00:00  432.000   \n",
       "3  324a05f1f917b8a0af83a54e55a1ef63  113.219  2017-08-16  00:00:00   50.000   \n",
       "4  b9abff45dc08172b556cb10b7c3c2252  697.224  2017-08-16  00:00:00  135.000   \n",
       "\n",
       "   year  month  day  quarter  year_month  year_quarter  \n",
       "0  2017      8   16        3     2017008       2017003  \n",
       "1  1973      4    5        2     1973004       1973002  \n",
       "2  2017      8   16        3     2017008       2017003  \n",
       "3  2017      8   16        3     2017008       2017003  \n",
       "4  2017      8   16        3     2017008       2017003  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AMOUNT:\n",
    "##### There is large difference between mean and median for amount feature. Which is kind of expected and which shows that amount data is not normailly distributed. There looks to be large difference between min and max values.  -ve value can be a outlier for current scenario.\n",
    "\n",
    "#### COUNT:\n",
    "##### Counts seems to be normally distributed with few outliers.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.93309800e+06,   8.19000000e+02,   1.43000000e+02,\n",
       "          3.00000000e+01,   1.70000000e+01,   9.00000000e+00,\n",
       "          4.00000000e+00,   7.00000000e+00,   4.00000000e+00,\n",
       "          0.00000000e+00,   1.00000000e+00,   1.00000000e+00,\n",
       "          1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   2.00000000e+00]),\n",
       " array([      0.        ,   15368.52288498,   30737.04576995,\n",
       "          46105.56865493,   61474.0915399 ,   76842.61442488,\n",
       "          92211.13730985,  107579.66019483,  122948.1830798 ,\n",
       "         138316.70596478,  153685.22884975,  169053.75173473,\n",
       "         184422.2746197 ,  199790.79750468,  215159.32038966,\n",
       "         230527.84327463,  245896.36615961,  261264.88904458,\n",
       "         276633.41192956,  292001.93481453,  307370.45769951]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/PNa/k+RrWfy1Z5+NE+wQY7EWn41XyPjv+9+Pp68bS+fIz9/xHKMNRRB/5ZyV\n5Kml53tz6B8EHJlO8mdV9WAt/oJqkry5u59JFj+Ik7xp1Ncai0PV965SZ20n4tyvdQx+2PvGdIed\nS9MUjnQsfjzJt7v7xYPqL+trbH9htCfJ+Of0n87i6p/PxkQHjUXis3HCVdVJVfVQkuey+MXyiRz5\n+TueY7ShCOKvnNV+A3aLmuPnZ7r77UmuSHJDVf3cIdquNRZHWufIOfcn3i1JfjLJBUmeSfK7o348\nx8I4raGqfiTJp5P8Wnf/7aGarlLz2TiOVhkLn40Juvul7r4gi7+AflGSf7xas/F4vMZi04yDIP7K\n2ZvknKXnZyd5etJredXp7qfH43NJPpPFh/vZ8c+3GY/PjeZrjcWh6mevUmdtJ+Lcr3UMlnT3s+N/\nfD9I8kdZfDaSIx+Lb2YxXeLkg+ov62ts/7Ec/hSZV62qel0Wwe9PuvtPR9lnY4LVxsJnY67u/naS\n/zuLOeJHev6O5xhtKIL4K+eBJFvHt3ZPyeJLB3dPfk2vClX1D6vqRw+sJ7k0ySNZnN8DdxjYnuSu\nsX53kutq4eIkL4x/vr03yaVVddr4J8pLk9w7tn2nqi4e88yuW+qL1Z2Ic7/WMVhyIJANv5DFZyNZ\nnL9rxl0JzkuyNYsv/636s2rMQ/5ckneN/Q8e1wNj8a4kfz7av2aN/15vS/K17v69pU0+GyfYWmPh\ns3HiVdWWqjp1rL8hyT/NYs7+kZ6/4zlGG8vx+tanZdVvC1+Zxbe1n0jy67Nfz6tlyeLb0X8xlkcP\nnNss5oTdl+Tx8Xj6qFeSj41x+GqSbUt9/cssvvSxkuQ9S/VtWfyQfiLJ/5Hxx68snSSfyOKfdf+/\nLK5GXH8izv1ax3gtL2uMxR+Pc/1wFv/zOnOp/a+P8/pYlu4EtNbPqvFZ+9IYo3+d5PWj/g/G85Wx\n/Sdmn4vZS5L/Oot/+n44yUNjudJnY0ONhc/GiR+Lf5LkK+OcP5Lkfzva83e8xmijLf6yJgAATGBq\nCgAATCCIAwDABII4AABMIIgDAMAEgjgAAEwgiAMAwASCOAAATCCIAwDABP8/eqRQPxpelKgAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd492ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.hist(df.amount, 20, facecolor='blue', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Removing negative value from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00023fda963eb48839a670cbdf74cd21</th>\n",
       "      <td>261.877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000458145e8ff76fb2907417d806d098</th>\n",
       "      <td>152.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000b2159adf33dcb44291291e0575122</th>\n",
       "      <td>678.304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000cff3bf5b20e8bb5734eb0aeef6673</th>\n",
       "      <td>360.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000f6054933c882efb96bc34b91c8924</th>\n",
       "      <td>77.269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  avg_amount\n",
       "account                                     \n",
       "00023fda963eb48839a670cbdf74cd21     261.877\n",
       "000458145e8ff76fb2907417d806d098     152.247\n",
       "000b2159adf33dcb44291291e0575122     678.304\n",
       "000cff3bf5b20e8bb5734eb0aeef6673     360.440\n",
       "000f6054933c882efb96bc34b91c8924      77.269"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df = pd.DataFrame()\n",
    "model_df[\"avg_amount\"] = df.groupby(\"account\")['amount'].mean()\n",
    "model_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_amount</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>avg_transaction</th>\n",
       "      <th>active_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00023fda963eb48839a670cbdf74cd21</th>\n",
       "      <td>261.877</td>\n",
       "      <td>2017-11-26</td>\n",
       "      <td>2019-03-14</td>\n",
       "      <td>16.364</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000458145e8ff76fb2907417d806d098</th>\n",
       "      <td>152.247</td>\n",
       "      <td>2018-02-15</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>23.611</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000b2159adf33dcb44291291e0575122</th>\n",
       "      <td>678.304</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>2017-09-28</td>\n",
       "      <td>219.000</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000cff3bf5b20e8bb5734eb0aeef6673</th>\n",
       "      <td>360.440</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>65.188</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000f6054933c882efb96bc34b91c8924</th>\n",
       "      <td>77.269</td>\n",
       "      <td>2017-11-09</td>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>22.120</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  avg_amount  start_date    end_date  \\\n",
       "account                                                                \n",
       "00023fda963eb48839a670cbdf74cd21     261.877  2017-11-26  2019-03-14   \n",
       "000458145e8ff76fb2907417d806d098     152.247  2018-02-15  2019-03-15   \n",
       "000b2159adf33dcb44291291e0575122     678.304  2017-08-16  2017-09-28   \n",
       "000cff3bf5b20e8bb5734eb0aeef6673     360.440  2017-08-16  2019-01-02   \n",
       "000f6054933c882efb96bc34b91c8924      77.269  2017-11-09  2019-03-15   \n",
       "\n",
       "                                  avg_transaction  active_days  \n",
       "account                                                         \n",
       "00023fda963eb48839a670cbdf74cd21           16.364           77  \n",
       "000458145e8ff76fb2907417d806d098           23.611          144  \n",
       "000b2159adf33dcb44291291e0575122          219.000           44  \n",
       "000cff3bf5b20e8bb5734eb0aeef6673           65.188          485  \n",
       "000f6054933c882efb96bc34b91c8924           22.120          200  "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model_df = model_df.set_index(\"account\",inplace=True)\n",
    "account_start_date = pd.DataFrame()\n",
    "account_end_date = pd.DataFrame()\n",
    "account_avg_transaction = pd.DataFrame()\n",
    "account_total_active_days = pd.DataFrame()\n",
    "account_start_date[\"start_date\"] = df.groupby(\"account\")['new_date'].min()\n",
    "model_df = model_df.join(account_start_date)\n",
    "account_end_date[\"end_date\"] = df.groupby(\"account\")['new_date'].max()\n",
    "model_df = model_df.join(account_end_date)\n",
    "account_avg_transaction[\"avg_transaction\"] = df.groupby(\"account\")['count'].mean()\n",
    "model_df = model_df.join(account_avg_transaction)\n",
    "account_total_active_days['active_days'] = df.groupby(\"account\")['new_date'].count()\n",
    "model_df = model_df.join(account_total_active_days)\n",
    "model_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del account_start_date ,account_end_date ,account_avg_transaction,account_total_active_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model_df['days_seen_last_transaction'] = df.new_date.max() - model_df['end_date']\n",
    "model_df['days_seen_last_transaction'] = model_df['days_seen_last_transaction'].dt.days\n",
    "model_df.drop([\"days_seen_last_tran\"],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  avg_amount  start_date    end_date  \\\n",
      "account                                                                \n",
      "00023fda963eb48839a670cbdf74cd21     261.877  2017-11-26  2019-03-14   \n",
      "000458145e8ff76fb2907417d806d098     152.247  2018-02-15  2019-03-15   \n",
      "000b2159adf33dcb44291291e0575122     678.304  2017-08-16  2017-09-28   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673     360.440  2017-08-16  2019-01-02   \n",
      "000f6054933c882efb96bc34b91c8924      77.269  2017-11-09  2019-03-15   \n",
      "\n",
      "                                  avg_transaction  active_days  \\\n",
      "account                                                          \n",
      "00023fda963eb48839a670cbdf74cd21           16.364           77   \n",
      "000458145e8ff76fb2907417d806d098           23.611          144   \n",
      "000b2159adf33dcb44291291e0575122          219.000           44   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673           65.188          485   \n",
      "000f6054933c882efb96bc34b91c8924           22.120          200   \n",
      "\n",
      "                                  days_seen_last_transaction  \\\n",
      "account                                                        \n",
      "00023fda963eb48839a670cbdf74cd21                           1   \n",
      "000458145e8ff76fb2907417d806d098                           0   \n",
      "000b2159adf33dcb44291291e0575122                         533   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673                          72   \n",
      "000f6054933c882efb96bc34b91c8924                           0   \n",
      "\n",
      "                                  risky_by_last_activity  \n",
      "account                                                   \n",
      "00023fda963eb48839a670cbdf74cd21                       0  \n",
      "000458145e8ff76fb2907417d806d098                       0  \n",
      "000b2159adf33dcb44291291e0575122                       1  \n",
      "000cff3bf5b20e8bb5734eb0aeef6673                       0  \n",
      "000f6054933c882efb96bc34b91c8924                       0  \n",
      "2074\n"
     ]
    }
   ],
   "source": [
    "model_df[\"risky_by_last_activity\"]=model_df.apply(lambda x: 1 if x[\"days_seen_last_transaction\"]> 120 else 0, axis=1)\n",
    "print model_df.head()\n",
    "print model_df.risky_by_last_activity.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Quarterly avarage count and no of active days per quarter count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_quarter</th>\n",
       "      <th>1973002</th>\n",
       "      <th>2017003</th>\n",
       "      <th>2017004</th>\n",
       "      <th>2018001</th>\n",
       "      <th>2018002</th>\n",
       "      <th>2018003</th>\n",
       "      <th>2018004</th>\n",
       "      <th>2019001</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00023fda963eb48839a670cbdf74cd21</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>84.000</td>\n",
       "      <td>140.000</td>\n",
       "      <td>28.000</td>\n",
       "      <td>126.000</td>\n",
       "      <td>364.000</td>\n",
       "      <td>518.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000458145e8ff76fb2907417d806d098</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>153.000</td>\n",
       "      <td>595.000</td>\n",
       "      <td>561.000</td>\n",
       "      <td>1.037k</td>\n",
       "      <td>1.054k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000b2159adf33dcb44291291e0575122</th>\n",
       "      <td>0.000</td>\n",
       "      <td>9.636k</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000cff3bf5b20e8bb5734eb0aeef6673</th>\n",
       "      <td>0.000</td>\n",
       "      <td>1.056k</td>\n",
       "      <td>4.704k</td>\n",
       "      <td>5.152k</td>\n",
       "      <td>6.720k</td>\n",
       "      <td>4.992k</td>\n",
       "      <td>8.912k</td>\n",
       "      <td>80.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000f6054933c882efb96bc34b91c8924</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>84.000</td>\n",
       "      <td>126.000</td>\n",
       "      <td>980.000</td>\n",
       "      <td>952.000</td>\n",
       "      <td>1.036k</td>\n",
       "      <td>1.246k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year_quarter                      1973002  2017003  2017004  2018001  2018002  \\\n",
       "account                                                                         \n",
       "00023fda963eb48839a670cbdf74cd21    0.000    0.000   84.000  140.000   28.000   \n",
       "000458145e8ff76fb2907417d806d098    0.000    0.000    0.000  153.000  595.000   \n",
       "000b2159adf33dcb44291291e0575122    0.000   9.636k    0.000    0.000    0.000   \n",
       "000cff3bf5b20e8bb5734eb0aeef6673    0.000   1.056k   4.704k   5.152k   6.720k   \n",
       "000f6054933c882efb96bc34b91c8924    0.000    0.000   84.000  126.000  980.000   \n",
       "\n",
       "year_quarter                      2018003  2018004  2019001  \n",
       "account                                                      \n",
       "00023fda963eb48839a670cbdf74cd21  126.000  364.000  518.000  \n",
       "000458145e8ff76fb2907417d806d098  561.000   1.037k   1.054k  \n",
       "000b2159adf33dcb44291291e0575122    0.000    0.000    0.000  \n",
       "000cff3bf5b20e8bb5734eb0aeef6673   4.992k   8.912k   80.000  \n",
       "000f6054933c882efb96bc34b91c8924  952.000   1.036k   1.246k  "
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account_start_date = df.groupby([\"account\",\"year_quarter\"])['count'].sum().reset_index()\n",
    "account_start_date.columns = [\"account\",\"year_quarter\",\"avg_transaction\"]\n",
    "account_start_date= pd.pivot_table(data=account_start_date,index=account_start_date.account,columns=account_start_date.year_quarter,\n",
    "                                  values='avg_transaction',aggfunc='mean' )\n",
    "account_start_date.fillna(0,inplace=True)\n",
    "\n",
    "account_start_date.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Risk calculation by avg transaction count decrease by quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41\n"
     ]
    }
   ],
   "source": [
    "\n",
    "risky_avg_transaction = []\n",
    "for idx,row in account_start_date.iterrows():\n",
    "    if  (row[2017004] > row[2018001] ) & (row[2018001] > row[2018002] ) & (row[2018002] > row[2018003] ) & (row[2018003] > row[2018004] ) & (row[2018004] > row[2019001] )   :\n",
    "        risky_avg_transaction.append(idx)\n",
    "        \n",
    "print len(risky_avg_transaction)\n",
    "account_start_date =account_start_date.reset_index()\n",
    "account_start_date[\"risky_by_avg_tran\"] = map(lambda x: 1 if x in (risky_avg_transaction)  else 0,account_start_date.account)\n",
    "account_start_date[account_start_date[\"risky_by_avg_tran\"]>0]\n",
    "account_start_date.set_index(\"account\",inplace=True)\n",
    "\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "account_active_days_q = df.groupby([\"account\",\"year_quarter\"])['new_date'].count().reset_index()\n",
    "account_active_days_q.columns = [\"account\",\"year_quarter\",\"active_days_q\"]\n",
    "account_active_days_q= pd.pivot_table(data=account_active_days_q,index=account_active_days_q.account,columns=account_active_days_q.year_quarter,\n",
    "                                  values='active_days_q',aggfunc='sum' )\n",
    "account_active_days_q.fillna(0,inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Risk calculation by id avg active days count decrease by quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35\n"
     ]
    }
   ],
   "source": [
    "risky_avg_active_days = []\n",
    "for idx,row in account_active_days_q.iterrows():\n",
    "    if  (row[2017004] > row[2018001] ) & (row[2018001] > row[2018002] ) & (row[2018002] > row[2018003] ) & (row[2018003] > row[2018004] ) & (row[2018004] > row[2019001] )   :\n",
    "        risky_avg_active_days.append(idx)\n",
    "        \n",
    "print len(risky_avg_active_days)\n",
    "account_active_days_q =account_active_days_q.reset_index()\n",
    "account_active_days_q[\"risky_by_avg_active_days\"] = map(lambda x: 1 if x in (risky_avg_active_days)  else 0,account_active_days_q.account)\n",
    "account_active_days_q.set_index(\"account\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "account_active_amount_q = df.groupby([\"account\",\"year_quarter\"])['amount'].sum().reset_index()\n",
    "account_active_amount_q.columns = [\"account\",\"year_quarter\",\"active_amount_q\"]\n",
    "account_active_amount_q= pd.pivot_table(data=account_active_amount_q,index=account_active_amount_q.account,columns=account_active_amount_q.year_quarter,\n",
    "                                  values='active_amount_q',aggfunc='mean' )\n",
    "account_active_amount_q.fillna(0,inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Risk calculation by avg amount decrease by quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "risky_avg_amount = []\n",
    "for idx,row in account_active_amount_q.iterrows():\n",
    "    if  (row[2017004] > row[2018001] ) & (row[2018001] > row[2018002] ) & (row[2018002] > row[2018003] ) & (row[2018003] > row[2018004] ) & (row[2018004] > row[2019001] )   :\n",
    "        risky_avg_amount.append(idx)\n",
    "        \n",
    "print len(risky_avg_amount)\n",
    "account_active_amount_q =account_active_amount_q.reset_index()\n",
    "account_active_amount_q[\"risky_by_avg_amount\"] = map(lambda x: 1 if x in (risky_avg_amount)  else 0,account_active_amount_q.account)\n",
    "account_active_amount_q.set_index(\"account\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "account_active_amount_q.columns = [\"1973002_amount\", \"2017003_amount\", \"2017004_amount\", \"2018001_amount\", \"2018002_amount\", \"2018003_amount\", \"2018004_amount\",\"2019001_amount\",\"risky_by_amount\"]\n",
    "account_active_amount_q.drop(\"1973002_amount\",inplace=True,axis=1)\n",
    "account_active_days_q.columns = [\"1973002_days\", \"2017003_days\", \"2017004_days\", \"2018001_days\", \"2018002_days\", \"2018003_days\", \"2018004_days\",\"2019001_days\",\"risky_by_active_days\"]\n",
    "account_active_days_q.drop(\"1973002_days\",inplace=True,axis=1)\n",
    "account_start_date.columns = [\"1973002_avg_tran\", \"2017003_avg_tran\", \"2017004_avg_tran\", \"2018001_avg_tran\", \"2018002_avg_tran\", \"2018003_avg_tran\", \"2018004_avg_tran\",\"2019001_avg_tran\",\"risky_by_avg_trans\"]\n",
    "account_start_date.drop(\"1973002_avg_tran\",inplace=True,axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  2017003_amount  2017004_amount  \\\n",
      "account                                                            \n",
      "00023fda963eb48839a670cbdf74cd21           0.000          1.546k   \n",
      "000458145e8ff76fb2907417d806d098           0.000           0.000   \n",
      "000b2159adf33dcb44291291e0575122         29.845k           0.000   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673          4.783k         20.820k   \n",
      "000f6054933c882efb96bc34b91c8924           0.000         262.636   \n",
      "\n",
      "                                  2018001_amount  2018002_amount  \\\n",
      "account                                                            \n",
      "00023fda963eb48839a670cbdf74cd21          1.923k         121.726   \n",
      "000458145e8ff76fb2907417d806d098          1.556k          4.022k   \n",
      "000b2159adf33dcb44291291e0575122           0.000           0.000   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673         40.833k         42.041k   \n",
      "000f6054933c882efb96bc34b91c8924         668.867          2.565k   \n",
      "\n",
      "                                  2018003_amount  2018004_amount  \\\n",
      "account                                                            \n",
      "00023fda963eb48839a670cbdf74cd21          5.604k          4.403k   \n",
      "000458145e8ff76fb2907417d806d098          3.569k          5.929k   \n",
      "000b2159adf33dcb44291291e0575122           0.000           0.000   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673         29.980k         35.977k   \n",
      "000f6054933c882efb96bc34b91c8924          2.653k          2.538k   \n",
      "\n",
      "                                  2019001_amount  risky_by_amount  \\\n",
      "account                                                             \n",
      "00023fda963eb48839a670cbdf74cd21          6.567k                0   \n",
      "000458145e8ff76fb2907417d806d098          6.847k                0   \n",
      "000b2159adf33dcb44291291e0575122           0.000                0   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673         379.010                0   \n",
      "000f6054933c882efb96bc34b91c8924          6.767k                0   \n",
      "\n",
      "                                  2017003_avg_tran  2017004_avg_tran  \\\n",
      "account                                                                \n",
      "00023fda963eb48839a670cbdf74cd21             0.000            84.000   \n",
      "000458145e8ff76fb2907417d806d098             0.000             0.000   \n",
      "000b2159adf33dcb44291291e0575122            9.636k             0.000   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673            1.056k            4.704k   \n",
      "000f6054933c882efb96bc34b91c8924             0.000            84.000   \n",
      "\n",
      "                                          ...           2019001_avg_tran  \\\n",
      "account                                   ...                              \n",
      "00023fda963eb48839a670cbdf74cd21          ...                    518.000   \n",
      "000458145e8ff76fb2907417d806d098          ...                     1.054k   \n",
      "000b2159adf33dcb44291291e0575122          ...                      0.000   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673          ...                     80.000   \n",
      "000f6054933c882efb96bc34b91c8924          ...                     1.246k   \n",
      "\n",
      "                                  risky_by_avg_trans  2017003_days  \\\n",
      "account                                                              \n",
      "00023fda963eb48839a670cbdf74cd21                   0         0.000   \n",
      "000458145e8ff76fb2907417d806d098                   0         0.000   \n",
      "000b2159adf33dcb44291291e0575122                   0        44.000   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673                   0        32.000   \n",
      "000f6054933c882efb96bc34b91c8924                   0         0.000   \n",
      "\n",
      "                                  2017004_days  2018001_days  2018002_days  \\\n",
      "account                                                                      \n",
      "00023fda963eb48839a670cbdf74cd21         6.000         9.000         2.000   \n",
      "000458145e8ff76fb2907417d806d098         0.000         7.000        26.000   \n",
      "000b2159adf33dcb44291291e0575122         0.000         0.000         0.000   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673        89.000        90.000        91.000   \n",
      "000f6054933c882efb96bc34b91c8924         6.000         7.000        46.000   \n",
      "\n",
      "                                  2018003_days  2018004_days  2019001_days  \\\n",
      "account                                                                      \n",
      "00023fda963eb48839a670cbdf74cd21         8.000        20.000        32.000   \n",
      "000458145e8ff76fb2907417d806d098        27.000        45.000        39.000   \n",
      "000b2159adf33dcb44291291e0575122         0.000         0.000         0.000   \n",
      "000cff3bf5b20e8bb5734eb0aeef6673        89.000        92.000         2.000   \n",
      "000f6054933c882efb96bc34b91c8924        46.000        46.000        49.000   \n",
      "\n",
      "                                  risky_by_active_days  \n",
      "account                                                 \n",
      "00023fda963eb48839a670cbdf74cd21                     0  \n",
      "000458145e8ff76fb2907417d806d098                     0  \n",
      "000b2159adf33dcb44291291e0575122                     0  \n",
      "000cff3bf5b20e8bb5734eb0aeef6673                     0  \n",
      "000f6054933c882efb96bc34b91c8924                     0  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "account_active_amount_q=account_active_amount_q.join(account_start_date)\n",
    "print account_active_amount_q.head(1)\n",
    "account_active_amount_q = account_active_amount_q.join(account_active_days_q)\n",
    "print account_active_amount_q.head()\n",
    "model_df = model_df.join(account_active_amount_q)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_amount</th>\n",
       "      <th>avg_transaction</th>\n",
       "      <th>active_days</th>\n",
       "      <th>days_seen_last_transaction</th>\n",
       "      <th>risky_by_last_activity</th>\n",
       "      <th>2017003_amount</th>\n",
       "      <th>2017004_amount</th>\n",
       "      <th>2018001_amount</th>\n",
       "      <th>2018002_amount</th>\n",
       "      <th>2018003_amount</th>\n",
       "      <th>...</th>\n",
       "      <th>2019001_avg_tran</th>\n",
       "      <th>risky_by_avg_trans</th>\n",
       "      <th>2017003_days</th>\n",
       "      <th>2017004_days</th>\n",
       "      <th>2018001_days</th>\n",
       "      <th>2018002_days</th>\n",
       "      <th>2018003_days</th>\n",
       "      <th>2018004_days</th>\n",
       "      <th>2019001_days</th>\n",
       "      <th>risky_by_active_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>...</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "      <td>10.001k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>352.651</td>\n",
       "      <td>35.666</td>\n",
       "      <td>193.394</td>\n",
       "      <td>62.929</td>\n",
       "      <td>207.379m</td>\n",
       "      <td>2.542k</td>\n",
       "      <td>6.821k</td>\n",
       "      <td>7.189k</td>\n",
       "      <td>9.146k</td>\n",
       "      <td>10.420k</td>\n",
       "      <td>...</td>\n",
       "      <td>1.278k</td>\n",
       "      <td>4.100m</td>\n",
       "      <td>7.783</td>\n",
       "      <td>21.844</td>\n",
       "      <td>23.962</td>\n",
       "      <td>31.359</td>\n",
       "      <td>38.105</td>\n",
       "      <td>41.307</td>\n",
       "      <td>29.034</td>\n",
       "      <td>3.500m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.093k</td>\n",
       "      <td>46.044</td>\n",
       "      <td>106.555</td>\n",
       "      <td>109.211</td>\n",
       "      <td>405.450m</td>\n",
       "      <td>12.344k</td>\n",
       "      <td>29.354k</td>\n",
       "      <td>24.056k</td>\n",
       "      <td>29.484k</td>\n",
       "      <td>29.843k</td>\n",
       "      <td>...</td>\n",
       "      <td>3.616k</td>\n",
       "      <td>63.900m</td>\n",
       "      <td>12.746</td>\n",
       "      <td>27.187</td>\n",
       "      <td>25.786</td>\n",
       "      <td>27.481</td>\n",
       "      <td>26.622</td>\n",
       "      <td>28.746</td>\n",
       "      <td>24.280</td>\n",
       "      <td>59.057m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.727</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>88.750</td>\n",
       "      <td>20.042</td>\n",
       "      <td>116.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>186.100</td>\n",
       "      <td>1.127k</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>16.000</td>\n",
       "      <td>16.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>175.610</td>\n",
       "      <td>25.754</td>\n",
       "      <td>168.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>375.368</td>\n",
       "      <td>1.196k</td>\n",
       "      <td>2.590k</td>\n",
       "      <td>3.823k</td>\n",
       "      <td>...</td>\n",
       "      <td>611.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>17.000</td>\n",
       "      <td>27.000</td>\n",
       "      <td>36.000</td>\n",
       "      <td>42.000</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>332.518</td>\n",
       "      <td>36.100</td>\n",
       "      <td>242.000</td>\n",
       "      <td>86.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>972.266</td>\n",
       "      <td>4.540k</td>\n",
       "      <td>5.680k</td>\n",
       "      <td>8.035k</td>\n",
       "      <td>9.878k</td>\n",
       "      <td>...</td>\n",
       "      <td>1.504k</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>39.000</td>\n",
       "      <td>41.000</td>\n",
       "      <td>51.000</td>\n",
       "      <td>58.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>50.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>130.614k</td>\n",
       "      <td>2.212k</td>\n",
       "      <td>578.000</td>\n",
       "      <td>547.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>499.286k</td>\n",
       "      <td>1.148M</td>\n",
       "      <td>912.049k</td>\n",
       "      <td>1.093M</td>\n",
       "      <td>1.469M</td>\n",
       "      <td>...</td>\n",
       "      <td>228.239k</td>\n",
       "      <td>1.000</td>\n",
       "      <td>46.000</td>\n",
       "      <td>92.000</td>\n",
       "      <td>90.000</td>\n",
       "      <td>91.000</td>\n",
       "      <td>92.000</td>\n",
       "      <td>92.000</td>\n",
       "      <td>74.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       avg_amount  avg_transaction  active_days  days_seen_last_transaction  \\\n",
       "count     10.001k          10.001k      10.001k                     10.001k   \n",
       "mean      352.651           35.666      193.394                      62.929   \n",
       "std        2.093k           46.044      106.555                     109.211   \n",
       "min         5.727            0.000        1.000                       0.000   \n",
       "25%        88.750           20.042      116.000                       0.000   \n",
       "50%       175.610           25.754      168.000                       2.000   \n",
       "75%       332.518           36.100      242.000                      86.000   \n",
       "max      130.614k           2.212k      578.000                     547.000   \n",
       "\n",
       "       risky_by_last_activity  2017003_amount  2017004_amount  2018001_amount  \\\n",
       "count                 10.001k         10.001k         10.001k         10.001k   \n",
       "mean                 207.379m          2.542k          6.821k          7.189k   \n",
       "std                  405.450m         12.344k         29.354k         24.056k   \n",
       "min                     0.000           0.000           0.000           0.000   \n",
       "25%                     0.000           0.000           0.000           0.000   \n",
       "50%                     0.000           0.000         375.368          1.196k   \n",
       "75%                     0.000         972.266          4.540k          5.680k   \n",
       "max                     1.000        499.286k          1.148M        912.049k   \n",
       "\n",
       "       2018002_amount  2018003_amount          ...           2019001_avg_tran  \\\n",
       "count         10.001k         10.001k          ...                    10.001k   \n",
       "mean           9.146k         10.420k          ...                     1.278k   \n",
       "std           29.484k         29.843k          ...                     3.616k   \n",
       "min             0.000           0.000          ...                      0.000   \n",
       "25%           186.100          1.127k          ...                      0.000   \n",
       "50%            2.590k          3.823k          ...                    611.000   \n",
       "75%            8.035k          9.878k          ...                     1.504k   \n",
       "max            1.093M          1.469M          ...                   228.239k   \n",
       "\n",
       "       risky_by_avg_trans  2017003_days  2017004_days  2018001_days  \\\n",
       "count             10.001k       10.001k       10.001k       10.001k   \n",
       "mean               4.100m         7.783        21.844        23.962   \n",
       "std               63.900m        12.746        27.187        25.786   \n",
       "min                 0.000         0.000         0.000         0.000   \n",
       "25%                 0.000         0.000         0.000         0.000   \n",
       "50%                 0.000         0.000         8.000        17.000   \n",
       "75%                 0.000        12.000        39.000        41.000   \n",
       "max                 1.000        46.000        92.000        90.000   \n",
       "\n",
       "       2018002_days  2018003_days  2018004_days  2019001_days  \\\n",
       "count       10.001k       10.001k       10.001k       10.001k   \n",
       "mean         31.359        38.105        41.307        29.034   \n",
       "std          27.481        26.622        28.746        24.280   \n",
       "min           0.000         0.000         0.000         0.000   \n",
       "25%           4.000        16.000        16.000         0.000   \n",
       "50%          27.000        36.000        42.000        29.000   \n",
       "75%          51.000        58.000        65.000        50.000   \n",
       "max          91.000        92.000        92.000        74.000   \n",
       "\n",
       "       risky_by_active_days  \n",
       "count               10.001k  \n",
       "mean                 3.500m  \n",
       "std                 59.057m  \n",
       "min                   0.000  \n",
       "25%                   0.000  \n",
       "50%                   0.000  \n",
       "75%                   0.000  \n",
       "max                   1.000  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding target variable depending upong risk featurs from all risky features\n",
    "\n",
    "## Equally weighted model\n",
    "#### In this model I am giving equal weightage to all three risky factors (risky_by_active_days,risky_by_amount,risky_by_avg_trans)\n",
    "#### So if either of them is true then target account is consider to be risky."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_df.reset_index(inplace=True)\n",
    "model_df['target'] = model_df.apply(lambda x: 1 if (x.risky_by_active_days==1) or (x.risky_by_amount==1) or (x.risky_by_avg_trans == 1) or x.risky_by_last_activity == 1 else 0,axis=1)\n",
    "target = model_df[\"target\"]\n",
    "model_df.drop(['target'],inplace=True,axis=1)\n",
    "model_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7879\n",
       "1    2122\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 8000 samples.\n",
      "Testing set has 2001 samples.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(model_df, \n",
    "                                                    target, \n",
    "                                                    test_size = 0.2, \n",
    "                                                    random_state = 0)\n",
    "\n",
    "# Show the results of the split\n",
    "print \"Training set has {} samples.\".format(X_train.shape[0])\n",
    "print \"Testing set has {} samples.\".format(X_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline model\n",
    "\n",
    "#### Naive predictor considering model predicated all account risky accounts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2122 7879\n",
      "Naive Predictor: [Accuracy score: 0.2122, F-score: 0.2519]\n"
     ]
    }
   ],
   "source": [
    "TP = np.sum(target) # Counting the ones as this is the naive case. \n",
    "\n",
    "FP = target.count() - TP # Specific to the naive case\n",
    "\n",
    "TN = 0 # No predicted negatives in the naive case\n",
    "FN = 0 # No predicted negatives in the naive case\n",
    "print TP,FP\n",
    "# TODO: Calculate accuracy, precision and recall\n",
    "\n",
    "accuracy = float(TP)/float(target.count())\n",
    "recall = 1\n",
    "precision = accuracy\n",
    "beta= 0.5\n",
    "\n",
    "\n",
    "# The formula for fbeta score can be written as (1 + beta**2) * (precision * recall) / ((beta**2 * precision) + recall)\n",
    "fscore = (1 + 0.25) * (precision * recall) / ((0.25 * precision) + recall)\n",
    "\n",
    "# Print the results \n",
    "print \"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Building a train predictor to compare multiple models\n",
    "### Here metrics that I am going to use to compare different models is F1 score, accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import fbeta_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import make_scorer, precision_recall_curve,confusion_matrix\n",
    "from sklearn import cross_validation, metrics\n",
    "import visuals as vs\n",
    "import time\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "\n",
    "    results = {}\n",
    "    \n",
    "  \n",
    "    start = time.time()\n",
    "    learner = learner.fit(X_train[:sample_size], y_train[:sample_size])\n",
    "    end = time.time()\n",
    "    \n",
    "    results['train_time'] = end - start\n",
    "    \n",
    "    start = time.time()\n",
    "    predictions_test = learner.predict(X_test)\n",
    "    predictions_train = learner.predict(X_train)\n",
    "    end = time.time()\n",
    "    \n",
    "    results['pred_time'] = end - start\n",
    "    results['acc_train'] = accuracy_score(y_train, predictions_train)\n",
    "        \n",
    "    results['acc_test'] = accuracy_score(y_test, predictions_test)\n",
    "    \n",
    "    results['f_train'] = fbeta_score(y_train, predictions_train, 0.5,average='weighted')\n",
    "        \n",
    "    results['f_test'] = fbeta_score(y_test, predictions_test, 0.5,average='weighted')\n",
    "    matrix =  confusion_matrix(y_test, predictions_test)\n",
    "    TP =  matrix[0][0]\n",
    "    FP = matrix[0][1]  # Specific to the naive case\n",
    "\n",
    "    TN = matrix[1][1] # No predicted negatives in the naive case\n",
    "    FN = matrix[1][0] # No predicted negatives in the naive case\n",
    "\n",
    "    # TODO: Calculate accuracy, precision and recall\n",
    "    accuracy = metrics.accuracy_score(y_test, predictions_test)\n",
    "    recall = float(TP )/  float(TP + FN)\n",
    "    precision = float(TP )/ float(TP + FP)\n",
    "    #print \"precision: {}, recall: {}\".format(recall, precision) \n",
    "    beta = 0.5\n",
    "    \n",
    "    # TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n",
    "    # HINT: The formula above can be written as (1 + beta**2) * (precision * recall) / ((beta**2 * precision) + recall)\n",
    "    fscore = (1 + 0.25) * (precision * recall) / ((0.25 * precision) + recall)\n",
    "    print \"Test fscore with beta 0.5 : \", fscore   \n",
    "    print \"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size)\n",
    "    dataframe = pd.DataFrame(predictions_train,y_train)  \n",
    "    dataframe.to_csv(learner.__class__.__name__+\".txt\")\n",
    "    # Return the results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test fscore with beta 0.5 :  0.974649273936\n",
      "LogisticRegressionCV trained on 80 samples.\n",
      "Test fscore with beta 0.5 :  0.98071931776\n",
      "LogisticRegressionCV trained on 800 samples.\n",
      "Test fscore with beta 0.5 :  0.98071931776\n",
      "LogisticRegressionCV trained on 8000 samples.\n",
      "Test fscore with beta 0.5 :  0.961397954213\n",
      "KNeighborsClassifier trained on 80 samples.\n",
      "Test fscore with beta 0.5 :  0.95799876467\n",
      "KNeighborsClassifier trained on 800 samples.\n",
      "Test fscore with beta 0.5 :  0.960871718673\n",
      "KNeighborsClassifier trained on 8000 samples.\n",
      "Test fscore with beta 0.5 :  0.9988914891\n",
      "RandomForestClassifier trained on 80 samples.\n",
      "Test fscore with beta 0.5 :  0.999630223099\n",
      "RandomForestClassifier trained on 800 samples.\n",
      "Test fscore with beta 0.5 :  1.0\n",
      "RandomForestClassifier trained on 8000 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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UNJGVSGocqShIHjmMsStENB/Al0TkC94o54Pb+j8PYLnSdr8a4ZYQ0VTwGb814Pak7uAN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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf5d5438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import linear_model\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import svm\n",
    "# TODO: Initialize the three models\n",
    "clf_A =  linear_model.LogisticRegressionCV(solver='lbfgs',random_state=40)\n",
    "clf_B = KNeighborsClassifier()\n",
    "clf_C = RandomForestClassifier(random_state=40)\n",
    "#clf_D = RandomForestClassifier(random_state = 40)\n",
    "\n",
    "samples_100 = len(y_train)\n",
    "samples_10 = int(samples_100 * 0.1)\n",
    "samples_1 = int(samples_100 * 0.01)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1,samples_10,samples_100]):\n",
    "        results[clf_name][i]=train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "\n",
    "vs.evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Looking at the graph I can say Randomforest is performing well on testing set. It outperfrom other two model in all the sample cases. With High accuracy and high FScore I choose Randomforest as best model. Also if you see at training time requried out of three models KNeighborsClassifier take highest time so training time wise also Randomforest is performing better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.9985\n",
      "Final F-score on the testing data: 0.9985\n"
     ]
    }
   ],
   "source": [
    "# Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=200,max_depth=15,min_samples_split=117,min_samples_leaf=3,max_leaf_nodes=52)\n",
    "\n",
    "k = np.arange(20)+1\n",
    "parameters = {\n",
    "              \"max_leaf_nodes\": np.arange(2,60,5),\n",
    "              #\"n_estimators\": np.arange(2, 300, 2),\n",
    "              #\"max_depth\": np.arange(1, 28, 1),\n",
    "              #\"min_samples_split\": np.arange(2,150,1),\n",
    "              #\"min_samples_leaf\": np.arange(1,60,1),\n",
    "              #\"max_leaf_nodes\": np.arange(2,60,1),\n",
    "              #\"min_weight_fraction_leaf\": np.arange(0.1,0.4, 0.1)}\n",
    "             }\n",
    "\n",
    "#  Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(fbeta_score, beta=0.5,average='weighted')\n",
    "\n",
    "#  Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "grid_obj = GridSearchCV(estimator=clf,param_grid=parameters,scoring=scorer,cv=10)\n",
    "\n",
    "#  Fit the grid search object to the training data and find the optimal parameters using fit()\n",
    "grid_fit = clf.fit(X_train,y_train)\n",
    "\n",
    "# Get the estimator\n",
    "#best_clf = grid_fit.best_estimator_\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "#best_predictions = best_clf.predict(X_test)\n",
    "\n",
    "# Report the before-and-afterscores\n",
    "\n",
    "print \"\\nOptimized Model\\n------\"\n",
    "print \"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, predictions))\n",
    "print \"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5,average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tuned model is not improving any score or accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename= \"RandomForest_equally_weighted_model.sav\"\n",
    "pickle.dump(clf_C,open(filename,\"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature days_seen_last_transaction (0.246365)\n",
      "2. feature risky_by_last_activity (0.220689)\n",
      "3. feature 2019001_days (0.110422)\n",
      "4. feature 2019001_avg_tran (0.093649)\n",
      "5. feature 2019001_amount (0.090252)\n",
      "6. feature 2018004_days (0.064807)\n",
      "7. feature 2018004_amount (0.062413)\n",
      "8. feature 2018004_avg_tran (0.059789)\n",
      "9. feature 2018003_days (0.009692)\n",
      "10. feature 2018003_avg_tran (0.009351)\n",
      "11. feature risky_by_avg_trans (0.006698)\n",
      "12. feature risky_by_active_days (0.005558)\n",
      "13. feature 2018003_amount (0.004926)\n",
      "14. feature 2017004_amount (0.003594)\n",
      "15. feature 2017003_avg_tran (0.002269)\n",
      "16. feature 2017003_amount (0.001406)\n",
      "17. feature 2018001_amount (0.001257)\n",
      "18. feature 2017003_days (0.001203)\n",
      "19. feature 2017004_avg_tran (0.001080)\n",
      "20. feature 2018001_days (0.000874)\n",
      "21. feature risky_by_amount (0.000707)\n",
      "22. feature 2018001_avg_tran (0.000695)\n",
      "23. feature active_days (0.000555)\n",
      "24. feature 2017004_days (0.000464)\n",
      "25. feature 2018002_amount (0.000346)\n",
      "26. feature 2018002_days (0.000286)\n",
      "27. feature avg_transaction (0.000262)\n",
      "28. feature 2018002_avg_tran (0.000250)\n",
      "29. feature avg_amount (0.000142)\n"
     ]
    },
    {
     "data": {
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ABgjOAAAwQHAGAIABgjMAAAwQnAEAYIDgfALb2NjIxsbGbncDAGBPEJwBAGCA4AwAAAME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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xce1ee9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = clf.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in clf.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "probabilities = clf.predict_proba(X_test)[:, 1]\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(X_train.shape[1]):\n",
    "    print(\"%d. feature %s (%f)\" % (f + 1, X_train.columns[indices[f]], importances[indices[f]]))\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(X_train.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(X_train.shape[1]), indices)\n",
    "plt.xlim([-1, X_train.shape[1]])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Below are important features which helps decide which accounts are in risk of churing.\n",
    "##### 1. feature days_seen_last_transaction (0.246365)\n",
    "##### 2. feature risky_by_last_activity (0.220689)\n",
    "##### 3. feature 2019001_days (0.110422)\n",
    "##### 4. feature 2019001_avg_tran (0.093649)\n",
    "##### 5. feature 2019001_amount (0.090252)\n",
    "\n",
    "\n",
    "##### So I belive we have found important features which can point out risky account. But the data is not sufficent to make predications. We need more data to relia on model prediction like demographic information, type of transactions, age of credit card etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unequel weightage model\n",
    "\n",
    "### features days since last activity(50%), decreasing trend per quarter for amount(25%) and decreasing trend per quarter for number of transacation(25%)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_df.drop([\"Churn_probability\"],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7926\n",
       "1    2075\n",
       "dtype: int64"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Treshold = 0.50\n",
    "model_df[\"total_weights\"] = model_df[\"risky_by_last_activity\"] * 0.5 + model_df[\"risky_by_avg_trans\"] * 0.25 +  model_df[\"risky_by_amount\"] * 0.25\n",
    "print model_df.head()\n",
    "target_unequel_Weight = model_df.apply(lambda x: 1 if x[\"total_weights\"] >= Treshold else 0,axis=1)\n",
    "\n",
    "target_unequel_Weight.value_counts()     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 8000 samples.\n",
      "Testing set has 2001 samples.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(model_df, \n",
    "                                                    target_unequel_Weight, \n",
    "                                                    test_size = 0.2, \n",
    "                                                    random_state = 0)\n",
    "\n",
    "# Show the results of the split\n",
    "print \"Training set has {} samples.\".format(X_train.shape[0])\n",
    "print \"Testing set has {} samples.\".format(X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test fscore with beta 0.5 :  0.975594800098\n",
      "LogisticRegressionCV trained on 80 samples.\n",
      "Test fscore with beta 0.5 :  0.981768908598\n",
      "LogisticRegressionCV trained on 800 samples.\n",
      "Test fscore with beta 0.5 :  0.984\n",
      "LogisticRegressionCV trained on 8000 samples.\n",
      "Test fscore with beta 0.5 :  0.961165048544\n",
      "KNeighborsClassifier trained on 80 samples.\n",
      "Test fscore with beta 0.5 :  0.957995578482\n",
      "KNeighborsClassifier trained on 800 samples.\n",
      "Test fscore with beta 0.5 :  0.960281238436\n",
      "KNeighborsClassifier trained on 8000 samples.\n",
      "Test fscore with beta 0.5 :  0.999754510863\n",
      "RandomForestClassifier trained on 80 samples.\n",
      "Test fscore with beta 0.5 :  1.0\n",
      "RandomForestClassifier trained on 800 samples.\n",
      "Test fscore with beta 0.5 :  1.0\n",
      "RandomForestClassifier trained on 8000 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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CNcX9prYSSY0iFQjJYwVj7AoRzQfwNRH5gjfW+eB7CV4AsFy5N6AK4ZYQ0XTwGcI14Paq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\n6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g+EJEmSpNZ6WkA4k6QkSZI0s/SsgHAmSUmSJGnm6WUPxAMzSVbV3cDQTJIPqKrzququ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\nJEmS1JoFhCRJkqTWLCAkSZIktfb/AYM7oe5Nml9TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe1c18358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import linear_model\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import svm\n",
    "# TODO: Initialize the three models\n",
    "clf_A =  linear_model.LogisticRegressionCV(solver='lbfgs',random_state=40)\n",
    "clf_B = KNeighborsClassifier()\n",
    "clf_C = RandomForestClassifier(random_state=40)\n",
    "#clf_D = RandomForestClassifier(random_state = 40)\n",
    "\n",
    "samples_100 = len(y_train)\n",
    "samples_10 = int(samples_100 * 0.1)\n",
    "samples_1 = int(samples_100 * 0.01)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1,samples_10,samples_100]):\n",
    "        results[clf_name][i]=train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "\n",
    "vs.evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature 2018004_avg_tran (0.208727)\n",
      "2. feature total_weights (0.197219)\n",
      "3. feature days_seen_last_transaction (0.151545)\n",
      "4. feature risky_by_last_activity (0.093938)\n",
      "5. feature 2019001_days (0.089488)\n",
      "6. feature 2019001_amount (0.080306)\n",
      "7. feature 2018004_days (0.075860)\n",
      "8. feature 2019001_avg_tran (0.065950)\n",
      "9. feature 2017004_amount (0.011936)\n",
      "10. feature 2017003_amount (0.009286)\n",
      "11. feature 2018004_amount (0.006573)\n",
      "12. feature 2018003_amount (0.003880)\n",
      "13. feature 2018003_days (0.002358)\n",
      "14. feature 2017004_avg_tran (0.000922)\n",
      "15. feature active_days (0.000451)\n",
      "16. feature 2018002_amount (0.000400)\n",
      "17. feature avg_amount (0.000317)\n",
      "18. feature 2018001_avg_tran (0.000302)\n",
      "19. feature 2018002_avg_tran (0.000165)\n",
      "20. feature 2018001_days (0.000123)\n",
      "21. feature 2017004_days (0.000105)\n",
      "22. feature risky_by_amount (0.000075)\n",
      "23. feature 2017003_days (0.000073)\n",
      "24. feature risky_by_active_days (0.000000)\n",
      "25. feature 2017003_avg_tran (0.000000)\n",
      "26. feature 2018003_avg_tran (0.000000)\n",
      "27. feature risky_by_avg_trans (0.000000)\n",
      "28. feature 2018001_amount (0.000000)\n",
      "29. feature 2018002_days (0.000000)\n",
      "30. feature avg_transaction (0.000000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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gDAAAAwRnAAAYIDgDAMAAwRkAAAYIzgAAMEBwBgCAAYIzAAAMEJwBAGCA4AwAAAMEZwAA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\nMwAADBCcAQBgwP8P1GEOuJcFmkUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe2368e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = clf_C\n",
    "importances = clf.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in clf.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "probabilities = clf.predict_proba(X_test)[:, 1]\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(X_train.shape[1]):\n",
    "    print(\"%d. feature %s (%f)\" % (f + 1, X_train.columns[indices[f]], importances[indices[f]]))\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(X_train.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(X_train.shape[1]), indices)\n",
    "plt.xlim([-1, X_train.shape[1]])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Below are important features which helps decide which accounts are in risk of churing.¶\n",
    "#### 1. feature 2018004_avg_tran (0.208727)\n",
    "#### 2. feature total_weights (0.197219)\n",
    "#### 3. feature days_seen_last_transaction (0.151545)\n",
    "#### 4. feature risky_by_last_activity (0.093938)\n",
    "#### 5. feature 2019001_days (0.089488)\n",
    "##### So I believe we have found important features which can point out risky account. But the data is not sufficient to make predications. We need more data to rely on model prediction like demographic information, type of transactions, age of credit card etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC curve to check model accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x48eHKq833QSnnw4zZ8KJJ8YdlUi1kEqL4n7geGA5gLtPBjqX9SJ3Xw9cCrwD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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe24da908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probabilities = clf.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, probabilities)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### With unequel weightage model we are getting perfect score of 1.0 on randomforest model. So model tuning not needed for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename= \"RandomForest_unweighted_model.sav\"\n",
    "pickle.dump(clf,open(filename,\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00023fda963eb48839a670cbdf74cd21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000458145e8ff76fb2907417d806d098</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000b2159adf33dcb44291291e0575122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000cff3bf5b20e8bb5734eb0aeef6673</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000f6054933c882efb96bc34b91c8924</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            account  target\n",
       "0  00023fda963eb48839a670cbdf74cd21       0\n",
       "1  000458145e8ff76fb2907417d806d098       0\n",
       "2  000b2159adf33dcb44291291e0575122       1\n",
       "3  000cff3bf5b20e8bb5734eb0aeef6673       0\n",
       "4  000f6054933c882efb96bc34b91c8924       0"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename= \"risky_account.csv\"\n",
    "target.reset_index()\n",
    "target = pd.DataFrame(target).reset_index()\n",
    "target.head()\n",
    "target[target[\"target\"] > 0][\"account\"].to_csv(filename)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}