{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_uuid": "e7b2d3f9b44af9c3f64f91d183b8e9cb6b7adddd" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Draw inline\n", "%matplotlib inline\n", "\n", "# Set figure aesthetics\n", "sns.set_style(\"white\", {'ytick.major.size': 10.0})\n", "sns.set_context(\"poster\", font_scale=1.1)" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "45f9941c0b93e026853c3ce875d158a63449f27d" }, "source": [ "I wanted to take a look at the user data we have for this competition so I made this little notebook to share my findings and discuss about those. At the moment I've started with the basic user data, I'll take a look at sessions and the other *csv* files later on this month.\n", "\n", "Please, feel free to comment with anything you think it can be improved or fixed. I am not a professional in this field and there will be mistakes or things that can be *improved*. This is the flow I took and there are some plots not really interesting but I thought on keeping it in case someone see something interesting.\n", "\n", "Let's see the data!" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "478b3dee9a3c2f3ff279955d0844664288bda621" }, "source": [ "## Data Exploration" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "7af41496c24fc81b911d97142b40a731fdf4e164" }, "source": [ "Generally, when I start with a Data Science project I'm looking to answer the following questions:\n", "\n", "- Is there any mistakes in the data?\n", "- Does the data have peculiar behavior?\n", "- Do I need to fix or remove any of the data to be more realistic?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_uuid": "ec61fd9d5451c342f2d811989bfc5f29b1ffdfbf" }, "outputs": [], "source": [ "# Load the data into DataFrames\n", "train_users = pd.read_csv('./train_users_2.csv')\n", "test_users = pd.read_csv('./test_users.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_uuid": "af3bcef5cdd04f64bd1a859250c0429c69950ab7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "We have 213451 users in the training set and 62096 in the test set.\n", "In total we have 275547 users.\n" ] } ], "source": [ "print(\"We have\", train_users.shape[0], \"users in the training set and\", \n", " test_users.shape[0], \"in the test set.\")\n", "print(\"In total we have\", train_users.shape[0] + test_users.shape[0], \"users.\")" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "4bce84d0f8b6a86daa318fd19c8fa145764eb63d" }, "source": [ "Let's get those together so we can work with all the data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_uuid": "8033a81ad6fb5902eafa15b7760b0fdb32418ff8" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", "of pandas will change to not sort by default.\n", "\n", "To accept the future behavior, pass 'sort=True'.\n", "\n", "To retain the current behavior and silence the warning, pass sort=False\n", "\n", " \n" ] }, { "data": { "text/html": [ "
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affiliate_channelaffiliate_provideragecountry_destinationdate_account_createddate_first_bookingfirst_affiliate_trackedfirst_browserfirst_device_typegenderlanguagesignup_appsignup_flowsignup_methodtimestamp_first_active
0directdirectNaNNDF2010-06-28NaNuntrackedChromeMac Desktop-unknown-enWeb0facebook20090319043255
1seogoogle38.0NDF2011-05-25NaNuntrackedChromeMac DesktopMALEenWeb0facebook20090523174809
2directdirect56.0US2010-09-282010-08-02untrackedIEWindows DesktopFEMALEenWeb3basic20090609231247
3directdirect42.0other2011-12-052012-09-08untrackedFirefoxMac DesktopFEMALEenWeb0facebook20091031060129
4directdirect41.0US2010-09-142010-02-18untrackedChromeMac Desktop-unknown-enWeb0basic20091208061105
\n", "
" ], "text/plain": [ " affiliate_channel affiliate_provider age country_destination \\\n", "0 direct direct NaN NDF \n", "1 seo google 38.0 NDF \n", "2 direct direct 56.0 US \n", "3 direct direct 42.0 other \n", "4 direct direct 41.0 US \n", "\n", " date_account_created date_first_booking first_affiliate_tracked \\\n", "0 2010-06-28 NaN untracked \n", "1 2011-05-25 NaN untracked \n", "2 2010-09-28 2010-08-02 untracked \n", "3 2011-12-05 2012-09-08 untracked \n", "4 2010-09-14 2010-02-18 untracked \n", "\n", " first_browser first_device_type gender language signup_app signup_flow \\\n", "0 Chrome Mac Desktop -unknown- en Web 0 \n", "1 Chrome Mac Desktop MALE en Web 0 \n", "2 IE Windows Desktop FEMALE en Web 3 \n", "3 Firefox Mac Desktop FEMALE en Web 0 \n", "4 Chrome Mac Desktop -unknown- en Web 0 \n", "\n", " signup_method timestamp_first_active \n", "0 facebook 20090319043255 \n", "1 facebook 20090523174809 \n", "2 basic 20090609231247 \n", "3 facebook 20091031060129 \n", "4 basic 20091208061105 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Merge train and test users\n", "users = pd.concat((train_users, test_users), axis=0, ignore_index=True)\n", "\n", "# Remove ID's since now we are not interested in making predictions\n", "users.drop('id',axis=1, inplace=True)\n", "\n", "users.head()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "27c1f39ed90cc3b15f30e8ea901803849707b1ea" }, "source": [ "The data seems to be in an ussable format so the next important thing is to take a look at the missing data." ] }, { "cell_type": "markdown", "metadata": { "_uuid": "568f74436c44a982d4cf80b5b03b7cb22a8e85db" }, "source": [ "### Missing Data" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "ab39dcffc071d2233d75cabf56c0c768314edac7" }, "source": [ "Usually the missing data comes in the way of *NaN*, but if we take a look at the DataFrame printed above we can see at the `gender` column some values being `-unknown-`. We will need to transform those values into *NaN* first:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_uuid": "2d610103e114272f25488d17be3ca2a0287185be" }, "outputs": [], "source": [ "users.gender.replace('-unknown-', np.nan, inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "7e83f0df87d396e0207687a18101dc9cfefa025b" }, "source": [ "Now let's see how much data we are missing. For this purpose let's compute the NaN percentage of each feature." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_uuid": "11ededb168ec1a98a691cae08419d214145484de" }, "outputs": [ { "data": { "text/plain": [ "age 42.412365\n", "date_first_booking 67.733998\n", "first_affiliate_tracked 2.208335\n", "gender 46.990169\n", "dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "users_nan = (users.isnull().sum() / users.shape[0]) * 100\n", "users_nan[users_nan > 0].drop('country_destination')" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "d6c982eccab3c25cc8b12b4f43e3db5de218033d" }, "source": [ "We have quite a lot of *NaN* in the `age` and `gender` wich will yield in lesser performance of the classifiers we will build. The feature `date_first_booking` has a 58% of NaN values because this feature is not present at the tests users, and therefore, we won't need it at the *modeling* part." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_uuid": "9b5872253cb3a5d810812444c42dd8889434ad99" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Just for the sake of curiosity; we have 58 % of missing values at date_first_booking in the training data\n" ] } ], "source": [ "print(\"Just for the sake of curiosity; we have\", \n", " int((train_users.date_first_booking.isnull().sum() / train_users.shape[0]) * 100), \n", " \"% of missing values at date_first_booking in the training data\")" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "cd18487ee9dbc89c3e2327946006079c3db73a05" }, "source": [ "The other feature with a high rate of *NaN* was `age`. Let's see:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_uuid": "d9663c4fadc7d7baa7b09b1c3ae2cc731251d8ee" }, "outputs": [ { "data": { "text/plain": [ "count 158681.000000\n", "mean 47.145310\n", "std 142.629468\n", "min 1.000000\n", "25% 28.000000\n", "50% 33.000000\n", "75% 42.000000\n", "max 2014.000000\n", "Name: age, dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "users.age.describe()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "c2653d5269506f302d9e7d27a6c4ddeeef862ebc" }, "source": [ "There is some inconsistency in the age of some users as we can see above. It could be because the `age` inpout field was not sanitized or there was some mistakes handlig the data." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_uuid": "912f1874ae34eb1fc4412a613034fd9c0c43b569" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "830\n", "188\n" ] } ], "source": [ "print(sum(users.age > 122))\n", "print(sum(users.age < 18))" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "3c74992f47ad94693023f64312541a5fad84b50e" }, "source": [ "So far, do we have 801 users with [the longest confirmed human lifespan record](https://en.wikipedia.org/wiki/Jeanne_Calment) and 176 little *gangsters* breaking the [Aribnb Eligibility Terms](https://www.airbnb.com/terms)?" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_uuid": "e9ff9b11ff962ffc4eadca0db70f81b4f65656f2" }, "outputs": [ { "data": { "text/plain": [ "count 830.000000\n", "mean 2002.620482\n", "std 94.201344\n", "min 132.000000\n", "25% 2014.000000\n", "50% 2014.000000\n", "75% 2014.000000\n", "max 2014.000000\n", "Name: age, dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "users[users.age > 122]['age'].describe()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "8b3c4d2dbb945a5eb9ad77208ad6b277576d94b5" }, "source": [ "It's seems that the weird values are caused by the appearance of 2014. I didn't figured why, but I supose that might be related with a wrong input being added with the new users." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_uuid": "fdfbd0811d7ef876bc229057fab799eeed91077a" }, "outputs": [ { "data": { "text/plain": [ "count 188.000000\n", "mean 12.718085\n", "std 5.764569\n", "min 1.000000\n", "25% 5.000000\n", "50% 16.000000\n", "75% 17.000000\n", "max 17.000000\n", "Name: age, dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "users[users.age < 18]['age'].describe()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "637d89dbbab749be6fb5890a63921b7617dcc8e0" }, "source": [ "The young users seems to be under an acceptable range being the 50% of those users above 16 years old. \n", "We will need to hande the outliers. The simple thing that came to my mind it's to set an acceptance range and put those out of it to NaN." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_uuid": "041b348822d6c4a21364f90912fc6993f5e7368c" }, "outputs": [], "source": [ "users.loc[users.age > 95, 'age'] = np.nan\n", "users.loc[users.age < 13, 'age'] = np.nan" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "091dac344fb72beb350158d4a28c06a61c55510b" }, "source": [ "### Data Types" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "ca4ab899546f7549f94baf1112a25dab08612a7c" }, "source": [ "Let's treat each feature as what they are. This means we need to transform into categorical those features that we treas as categories and the same with the dates:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_uuid": "114c74a207275fa035901fc2f05148921233a17f" }, "outputs": [], "source": [ "categorical_features = [\n", " 'affiliate_channel',\n", " 'affiliate_provider',\n", " 'country_destination',\n", " 'first_affiliate_tracked',\n", " 'first_browser',\n", " 'first_device_type',\n", " 'gender',\n", " 'language',\n", " 'signup_app',\n", " 'signup_method'\n", "]\n", "\n", "for categorical_feature in categorical_features:\n", " users[categorical_feature] = users[categorical_feature].astype('category')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_uuid": "d86a73330bb85b7d36106e9c1b8beb2c4ab56645" }, "outputs": [], "source": [ "users['date_account_created'] = pd.to_datetime(users['date_account_created'])\n", "users['date_first_booking'] = pd.to_datetime(users['date_first_booking'])\n", "users['date_first_active'] = pd.to_datetime((users.timestamp_first_active // 1000000), format='%Y%m%d')" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "de6a53b18c2b2927fc6c78373d552248170cab5b" }, "source": [ "### Visualizing the Data" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "898724647863410696646bc3a1bb2b93b4e8fa9b" }, "source": [ "Usually, looking at tables, percentiles, means, and other several measures at this state is rarely useful unless you know very well your data.\n", "\n", "For me, it's usually better to visualize the data in some way. Visualization makes me see the outliers and errors immediately!" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "b417e51a8817d440c699979af1f2c8ad18c5a574" }, "source": [ "#### Gender" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_uuid": "e394f626f5fdad39d30bb1e8172c7e04379637d5" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "users.gender.value_counts(dropna=False).plot(kind='bar', color='#FD5C64', rot=0)\n", "plt.xlabel('Gender')\n", "sns.despine()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "16df27fce438fe314bea67f8ddf4286ed100cf50" }, "source": [ "As we've seen before at this plot we can see the ammount of missing data in perspective. Also, notice that there is a slight difference between user gender.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "b06ee12d434262faf8b195bc2f18f8b512a96778" }, "source": [ "#### Age" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "4cc2a5c9fc0e90d6746ba4db12ea81e922093213" }, "source": [ "Now that I know there is no difference between male and female reservations at first sight I'll dig into the age." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_uuid": "b0a09382942c9c17c6662ee731e807ec734bba6d" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(users.age.dropna(), color='#FD5C64')\n", "plt.xlabel('Age')\n", "sns.despine()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "12667532fa27d54e87aa51a2475180bf633adbf9" }, "source": [ "As expected, the common age to travel is between 25 and 40. Let's see if, for example, older people travel in a different way. Let's pick an arbitrary age to split into two groups. Maybe 45?" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_uuid": "321166faf74f33f598d14f0f5f094d7742d7cf4c" }, "outputs": [ { "data": { "image/png": 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QIAYNGsT333/Pc889VzBd9//+9z8aNGhQ0ltdJ1SGXnDXAXUIJZsCZrYNodptRiL4RB4EfgO6V1gORaRKOO2006hVqxYXXnhh2p5rn376KSNHjsTMOPjgg9da36FDB1asWMETTxS+9p0zZ06h1+3bt+ebb76hcePGtG7duuCxYMECRo4cyY8//lhkHj/88EM6duxYcIzNN9+cXr160bt3b1asWFHstuuaWEtAZnYgcBbQEdg+ZXXL6Pmj5IXuvsrMvgDSl21FRIqwzTbbcMstt3DeeefRrVs3evfuzd/+9jdWrVrFyy+/zLRp02jatCljxoyhRo0aa23ftWtXpk6dyqWXXsqCBQto3rw5Tz75JC+88EKhdP369eORRx6hT58+9O/fn6233poPPviAsWPHsu222xZq80nVokUL6tevz1VXXcVPP/3E9ttvzzfffMPEiRNp2bIlO+ywQ84/l7jEFoDMrBFwD3Ctu79qZqkBqH70/EuazZcC9bI87hulSLb2N09EqoSOHTsye/ZspkyZwv333893331H9erV2W677TjnnHM4+uijqVWrVtptN9xwQyZNmsTIkSO54447+PXXX2nbti2XX345l156aUG6hg0bMn36dEaNGsXo0aP5+eef2XTTTenZsydnnnlmsR0hNtxwQyZOnMgtt9zC7bffzk8//UT9+vU54IADOPfcc9lgg8pQcZUbeel6elQEM3sQ2BTo6O5/mdkxwL+JesGZ2XHAFKCDu7+Qsu0LwEbuvlsWxy1tANr56aefTttlM9vxsTbdbERW22U7IZ0mwBORGOSVnCSIpQRkZqcQerHtAeSZ2YasaY+qFr1eEr1OV9KpC/yUzbHdfY9S5K8Z8EU2+xcRkdKJqwruWGBj4OM06/4TPW8XPbcACsbEMLPqhPaiZ8szgyIiUr7iqkw8Bdgr5TE4WncqsJe7zwc+BI42s+R8Hg5sRFJQEhGRdU8sJSB399RlZrZD0urXo78HAw8AD5nZBGAHYCjwtLs/WiGZFRGRclGpu1O4+4NAT2BrYAZwPjAJOCLOfImISNnFPhJCgrtPA6alWT4LmFXxORIRkfJUqUtAIiJSdSkAiYhILBSAREQkFgpAIiISi6w7IZhZC8I9Oc2AW4FfAXP3p3OTNRERqcqyCkBmNgS4HKgG5AP3EYbHmWVmU4ET3H11znIpIiJVTsZVcGZ2NHAlYZbSo1kz8NwbwKPAccBpOcqfiIhUUdm0AZ0LvODuPYD/Jha6+9fufhgwF/hnbrInIiJVVTYBqDXF3xh6H2HIHBERkSJlE4D+KmF9XUK7kIiISJGyCUBvAL3SrTCzmsAJwFtlyZSIiFR92QSgEcA+ZjYL+Ee0rJmZHQE8BxihW7aIiEiRMg5A0TQI5wHdCFNoA0wgtP3sDlzl7vfnLIciIlIlZXUfkLuPikpAPYAdCYHsC+ABd/80h/kTEZEqKuuRENz9a2BUDvMiIiLrkYwDkJn1LSFJPrAC+B54y92XZZMxERGp2rIpAd1F4W7WiZEQ0nW9XmFmg9395iyOIyIiVVg2veC6AYuBhcBlhOmxDyZMl/0JsBy4ELgEcGCEmXXPSW5FRKTKyKYE1BVYCuzh7j8nLX/SzCYCrwPbuvtAMxsFPAucDTxU5tyupw57/9Wsttt0sxxnREQkh7IpAfUAbk8JPgC4+6/AJMIgpbj7SkJX7V3KkkkREal6sglAtSh+OJ7VQJ2k178BNbI4joiIVGHZBKB3gZPNbOPUFWa2EXAS8H7S4vbA/KxyJyIiVVY2bUDXAQ8D75nZGELHgxXATsAAwlA8PQHMbALQFxiSk9yKiEiVkXEAcvfHzKwPcAthXLhE9+s84GfgRHd/wMwaEuYFehRQN2wRESkk26F4pprZ/cCBQAugOqEk9JS7/xYlWwZs4e4Lc5JTERGpUsoyFM8fwCPFrF9FuFdIRERkLVkFIDPbktAduy6FOzJsCNQD/s/dW5Y9eyIiUlVlMxZcJ+BxQtfqPEIbUOpwPN/nInMiIlJ1ZdMNezDwJ3AmcDoh6BwG9CGMgrCc0PVaRESkSNlUwe0BTHD3cWZWAxgNrHT3KWb2AOE+oSGEHnAlinrUnU/ozLCYMLHdFe7+S1KavsAgYAfgR+BuYGg00oKIiKyDsikB1SG60TQKAPOJhtpx998JwaFjaXZkZmcC9wAvAUcC1wK9gcfNLC9K0z/a50uE+4vuIgx0OjaLvIuISCWRTQloMbBJ0uv5wN+SXn8LNC1pJ2a2IaGkNNvdT0tavoowxXdHM3uVcOPrLHcfECV51MyWAcPNbKS7f5TFexARkZhlUwJ6GTjezGpGrz8E9jOzatHr1oR7gEqyGvgHcG7K8uXRcy1gb6AxMC0lzRRCx4fDMsu6iIhUFtmUgG4F5gCfmNmuwL3AWcAcM/sEOJFSTL3g7quB9xKvzawuoepuOPAO8Axr2pE+Stn2OzP7FWiVaebN7I1SJNPgqSIi5SzjEpC7/xc4ClgE/OLurxF6xu1PGAvuS+CiTPZpZtsBvxBubK0NnBbdyFo/SvJLms2WEu45EhGRdVC2Q/HMAmYlvR5mZpOBRsAH7v5nhrv8hVAdVwc4B/ifmfViTYBMN903hGq8jLj7HiWlMbNmwBeZ7ltEREov4xKQmU0ys7apy939G3d/B9jfzJ7MZJ/uvtjd/+vuswnTe38JXA0siZKkK+nUJX3JSERE1gHZdELoB2xfzPr9o0exzKyBmR1vZi2Sl7v7CsK9RNuwpu2nRcq2TQmlpQ9Kn20REalMSqyCiwLEeylp7zWze4vZ7P1i1iXkEe4BmgYcn3S8BsC+wFvAi4RS0LHAg0nbHk+olnu8FMcREZFKqMQA5O6fmNmVQOdo0X6Ak36k67+i5cNLsd/FZnYjcJGZ/ULoObcZYcSDOsAF7r7SzK4CRpnZcmAmoWv2YOBOdy9NoBMRkUqoVJ0Q3H0YMAzAzFYD17j71Bwc/1JgHmFMuROAPwjdr49K3GDq7reY2UrgbEJJaAFhxIShOTi+iIjEJJsZUbNpNypqX6sJox5MKCHdOGBcro4rIiLxy3pCOjNrDmwOVEu33t2fzXbfIiJS9WUzH9AWwHRCR4HipA1MIiIikF0J6AagA/Afwvw/K3KaIxERWS9kE4AOBu519765zoyIiKw/sulQUAv4X64zIiIi65dsAtDbRBPQiYiIZCubAHQ50M/MjkjMWioiIpKpbNqAzgV+Bu4DlpvZItYelTrf3ZuXNXMiIlJ1ZROA2hCG3PkqaVlqSUglIxERKVY2IyE0K4d8iIjIeiZnw+qIiIhkIquheMxsY+AS4EhgW6Ab8DtwBnCZu3+ZsxyKiEiVlM2MqJsALxBGsm5IuC8IYCvCPD3Pm9nWOcuhiIhUSdlUwQ0BWgGHAzsTdThw9/uAo4BGwGW5yqCIiFRN2QSgHsAd7j6bMCtpgSgITQD+Lwd5ExGRKiybALQl8EYx698HtsguOyIisr7IJgAtAopr42kJ/JRddkREZH2RTQB6AjjNzLZJXWFmbYFTgDllzZiIiFRt2QSgKwhtP28DE6O/TzGzh4DnCfMDDc1ZDkVEpErKOAC5+zdAe+A14DBCL7hehHuBXgb2c/fPc5lJERGperK6EdXdPwO6mFkDYAdCIPvC3X/IZeZERKTqynYkhM0IbT23uPtr0bILzKweMMLdf8lhHkVEpArKZiSEZoTqtyuAFkmrdgYGAy+bWeOc5E5ERKqsbDohXAXUAw5PlH4A3L0f0JlwD9CVuciciIhUXdkEoL8Do9z94dQV7v4fYCxwSFkzJiIiVVs2AagR8H0x679EIyGIiEgJsglAnwMHFrO+E4VnSxUREVlLNgFoKnC4md1gZo0SC81sEzO7hDAi9r9zlUEREamasumGfSNhtOsLgPPNbAnwB7AZUA14FriutDszsyOjfbUkjKLwJnC5u7+elKYvMIhwz9GPwN3AUHdfmUX+RUSkEshmJIRVhI4I/wQeARYASwnjv50KHFjawGBm/YBZwHygD3AWoY3pRTPrEKXpTwg4LwE9gbsIs7GOzTTvIiJSeWRcAjKzk4Bn3P1O4M4yHn8o8LS7H5e0/yeAL4CLzawnoTQ1y90HREkeNbNlwHAzG+nuH5UxDyIiEoNs2oBuAvqV9cBmVgeYTUpJxt2XEjoxbAXsDTQGpqVsPoUwBt1hZc2HiIjEI6uheIAyj/nm7r8CZ6QuN7MWhFEVphDahQAKlXLc/Tsz+5UwNbiIiKyDsglAI4ELzOwt4CV3zy9pg9Iys7qEXnYrgWHAEdGqdGPLLSWMyJDpMYqbzTWhRqb7FRGRzGQTgPYmdBR4DvjDzBYBf6WkyXf35pns1My2BB4mlH56urubWaKKsKggtzqTY4iISOWRTQBqTZhyO3na7byUNKmvi2Vm7Qm94TYGDnP3J6JVS6LnesC3KZvVJX3JqFjuvkcp8tOM0BFCRETKScYByN2b5TIDZnYsoTfdQqCLu7+btDrR9tMC+DBpm6ZAHeCDXOZFREQqTja94HLGzHoA9wLvAHulBB+AFwmloGNTlh9PqJZ7vNwzKSIi5SLbCek2JtwMeiSwLWE67t8Jvdouc/cvS7GPhsAdwG+E+4FaRD3gEn539zfN7CpglJktB2YS2qAGA3e6+/vZ5F9EROKXzY2omxA6ILQhdMeuFa3ailAy6WRm7d396xJ2dRBrerGtNbUD4MBO7n6Lma0EziaUhBYA1xKCloiIrKOyKQENIdx/czihiuwHAHe/z8yOAu4BLiNM2V0kd59K6HJdIncfB4zLIq8iIlJJZdMG1AO4w91nk9I92t3vAyYQBisVEREpUjYBaEuguJs530cT0omISAmyCUCLgK2LWd+SwvcIiYiIrCWbAPQEcJqZbZO6wszaEtp+5pQ1YyIiUrVlE4CuILT9vA1MjP4+xcweAp4nTCqnHmoiIlKsbCak+wbYF3iNMB1CHtCLcC/Qy8B+7v55LjMpIiJVT0bdsM2sHrChu38KdDGzBoRpsjcAvnD3Mk/TICIi64dSBSAzOwi4gWj+HTP7Arja3e8hlIREREQyUmIVnJl1IIxU0Iow+Od7hFEP7jSzYm82FRERKUpp2oAGAYuBPd29jbvvChghEF1enpkTEZGqqzQBqC3wL3d/K7EgGmx0MLCFmW1fXpkTEZGqqzQBqAGQbnTrtwk94DTqgYiIZKw0Aag6sCrN8j+i55q5y46IiKwvYp2QTkRE1l8KQCIiEovS3oja0cxS09aJnjub2VapG0T3CImIiKRV2gA0IHqkMyjldR5hfDgFIBERKVJpAtBV5Z4LERFZ75QYgNxdAUhERHJOnRBERCQWCkAiIhILBSAREYmFApCIiMRCAUhERGKhACQiIrFQABIRkVgoAImISCwUgEREJBalHQuu3EWDnf4PeM/dT01ZdwhwBbAzsAyYCQx296UVnlEREcmJSlECMrONCUGlfZp1BwGzgW+BY4AbgZOA+ysyjyIikluxl4Ci0s1NQOMiktwIvAn0cPf8aJuvgBlmdpC7P1ExORURkVyKtQRkZvWBR4B3gV3SrN+GUO02IxF8Ig8CvwHdKyKfIiKSe3FXwf0OtHL3o9z92zTrW0bPHyUvdPdVwBdAq3LOn4iIlJNYq+DcfSUpwSVF/ej5lzTrlgL1Mj2mmb1RimQ1Mt2viIhkJu4SUEkS+csvYv3qisqIiIjkVuydEEqwJHpOV9KpC/yU6Q7dfY+S0phZM0IVn4iIlJPKXgJKVM+1SF5oZtWB7YEPKjxHIiKSE5U6ALn7fOBD4GgzS87r4cBGwGNx5EtERMquslfBAQwGHgAeMrMJwA7AUOBpd3801pyJiEjWKnUJCMDdHwR6AlsDM4DzgUnAEXHmS0REyqZSlYDcPa+I5bOAWRWcHRERKUeVvgQkIiJVkwKQiIjEQgFIRERioQAkIiKxUAASEZFYKACJiEgsFIBERCQWCkAiIhILBSAREYlFpRoJQdZtJ/94VFbbTWwyI8c5EZF1gUpAIiISCwUgERGJhargZC2Hvf9qVtttulmOMyIiVZpKQCIiEgsFIBERiYUCkIiIxEIBSEREYqEAJCIisVAAEhGRWCgAiYhILBSAREQkFroRVWKTsZf6AAAXZ0lEQVSX7Y2vs3feO8c5EZGKpBKQiIjEQiUgWWdp9G2RdZtKQCIiEgsFIBERiYUCkIiIxEJtQLLeqehed9kcTz38ZH2gACRSStl2eoALcpoPkapinQlAZrYPcB2wB7AKeAwY5O7fx5oxkXJQ0T381KNQ4rBOBCAz2xX4D/AacAKwKXANsLuZ7e7uK+LMn0hlodlsZV2yTgQgYCiwGDjI3f8AMLM3gVeBE4HxMeZNZL2VfcAbkdV2KnFVLZU+AJlZDeBA4I5E8AFw99fM7FOgOwpAIusFBbyqpdIHIGB7oBbwUZp1DuxSsdkRkfVFRQe8HxZm12FlXQ2w60IAqh89/5Jm3VKgXiY7M7M3SpGsBsD336fv37Dqhx8zOWSBP1avzGq7VYt0vPXteFX5vel4led4B7/3aFbbNWo8och1r/X6qBnwjbv/WdJ+8vLz87PKQEUxs/bAC0Bvd5+Ssm4KcIi7N8hgf6UJQBsDllFGRUQkYTt3n19SonWhBLQkek5X0qlL+pJRkdx9j5LSmFlNYC9gAfBXJvsXERG+KU2idSEAzSPc99MizToDPsj1AaNu3c/ner8iIrJGpR8Lzt1XAnOAw82sdmK5me0F7Ei4IVVERNYxlb4NCMDM9gReBF4HRgINCaMi/ADskdw9W0RE1g3rRAACMLNOwLXA7sAy4AngQg3FIyKyblpnApCIiFQtlb4NSEREqiYFIBERiYUCkIiIxEIBSErFzPLizoOIVC3rwo2olYKZ3UWYi+gsdx+TZv0xwL+BA9x9rpnNB7ZNSpIPLAe+AmYDw919cdL2zYAvSshGN6AncIy71yoin/2AOxP5iJb1Ak4h9CDcmNB9fS4wwt3fKeGYmFlX4FSga0peL3H360vaPhNmdiVwRTFJfnL3xmk+34Tfge8IvSSHuPuSNGnSHbcp4TM6LNrvRtF+/guMdPePonTNWPv/lE8YkeMt4Dp3/09pjhntrx/h/7UPMK2I95SqVMOcRPu/i/C9Lc5p7j4+Sl+m70rScXcAzgAOBrYGfgM+Bm4Hprr76jTb3AscD4x294GlOMZdrP3eViTl+Xp3/zApfWl6XNUu7raOLM4D+cBt7n5qKY5dLDM7GZgAvOfubdKsnw987O4HFZd3dy/yYrKIz/QvQs/j9wn/mxnF/P5SFftdVQDK3A1m9h93/7gUaV9hzXzMGxCGDmoPnAP0MrMO7v5dyjbjgSmk9wEhAJWamV1CuGfqDmAs4UTZDDgLeNXMDnb3/5awmwuAzTM5bg50J8wBlWpV0t/Jn29CY+AQ4ExgZ+CAkg5kZp0JJ42VhM//deAPoCVwOnC8mR3p7o8nbZb8f9owOu4A4HEzO8DdsxlJoydh5PeE0YR73o5PSbcgw/2uBP5RzPrPIGfflcRJ+A7CxdY4QuCpAxwOTAYOMbO+yYNVmlkjwvt/G+hnZoPdfVkW721jYCfC96KXmR3i7s8krX8IKG7o6NJObpnJeSBXTiF8PruaWafEBWY5SP1MNwCaAOcD06OhynLyXVUAysxfwJ/AFDNr5+6rSkj/c5oT0SNm9hRhdIexhB9lsi+LO3mZlX6M1GgupcuBu9395JR1MwhTXFwP7F3qnVacV0txj1e6zxfgQTOrTzgB7VTcScLMtgVmAF8D+yeXSoGnzewO4Fngzihtwlr/JzN7FFhIOGFnHIDc/fWU/f1CuCIv67BQ+SXtI1fflWj24rsIn9lhKaWJWWb2MWE242cpPI9XP8KJ7p/AG4SJJm8t6Y2R/r09aWbTov1MMbMd3P33aN33Ofg8Mz0PlJmZ7Q7sCRxKCOoDCaW88pD2+2Jmcwi/k4vcfeeUdVl9V9UGlJk/gbMJ1RNXZruT6MplNtDdzLbJSc7SqwvUTrciuro8G7jLzA4ys/+Z2WIzW2pmT5nZvlBQrN8//Gn5UZVRwf7NbJyZ/WBmv5vZ82a2T/JxzKyJmd1mZt+Z2Qoz+8DMBqSkucvM3jazy4BB0eK/lfG9l6rqDbiIMNDtSSnBB4DoxHUx4YS5aXE7cvflhKqmdVGpviul2M8lhAvb/kVUZd1ICDw/pCwfAPzX3d8kfNZnlqXd0d0XAsOBLYAe2e6nCDk5D2ToVML0M08T/g+HpVwQlTt3X0ooze6Yq32qBJQhd7/TzLoBF5nZo+7+Ypa7egw4knByn5y0fAMzS/d/+cvdM7pr2N0XmdnzwAlRiWAm8Ky7fx2tn2VmZwOPE6omTiKchM4D5ppZd0JRewJritefENpHAC4E7ifUGTchXCE/ZmbbuPuy6JgvEk7wVxPaTroCt5nZFu5+VVJ2WwJHAw8CxwGfpPscUuYYyUtJswGhKqx7lKdnCZMWFqcX8KG7v1ZUgqhN5z9Q0AYEhf9PieMOJHxOt5RwzApXxHcKwtXuX6X5rpTyUF2Bt9z9y3Qro7EdT0vJ2wGEwYYvjxZNIPwmDqZsYz0+RihFdWLNbyz1O5Nsdbq2qXRyeB4okZltAhwLTHb3FVGp/DJCG9uF5XXcNPmoSZgg9Ktc7VMBKDsDCI3Gk81sF3f/NYt9JOpGt0hZfm30SHUipbsCTdUTmEg4KXcHMLNvCI3rUwh1/nPcvaAq0MweAD4kNDg2Ty1eJ52EX3H3o5K2qwXcBrQlnLDPAZoD+7j7K1GyR81sJTDYzCYktYFVJ/yg/h69Lmo49+Sr4s4UbhNK+I5wlX1FcUE7OtE2Bv6XZl21lGMBJJ+civo/DQVeKuqYMalJ+s8JQjtPYtLH4r4rt7v7C8UdxMwaEy5O5mWYv1OBHwkXHwD3EYL4QMoWgNL9xgZEj3RuIXxnSysX54HS6E1oQ7sdwN2/MrMngJPN7Mqk6sWcSQnSNQm/4yGE38uVuTqOAlAWoqvFfwKPEr60/yzD7lJPkGOAu9OkS/S8Km0pKB8KqiK6RVV9nYGOwH5A3+gBKYHN3f+IeiRdbmbbFXOMZ1JefxY9N4yeOxOult5I+ULPIJxcDgTuSVr+LmsC0EHAT8UcG8KJPtFbqj6h+m4fYJC7Ty1hWyi+CvoFQiBNdhVrPqvk/9MGhPd8COEqfgugfymOX1FWAvsWsa6gRFnSd8XMrnb34nooJubOKvV5xcw2JbSDjgdqRG1RANOBU83M3L2kUmxJkn8z9wPDiki3MJOd5vg8UJxTCD0sPzOzOtGyewnft95EgYnSnxtKUtQFyw+E8Tf/laPjKABly90fM7PxhB/Jw1nsYqvoOfVK/9vUxugUvxF+qHlFXN3XjJ4LXY25+1eEq9uJUDDC+P2ELrLV0+wncfVYP826hNQrvkQJIXFib0LoqlnU1fdWSX+vcPefkzpZvFOKTghLkz8rM5sLPAnca2ar3X1acRu7+2IzW0q4ukvVj3DVCdCI0K07Wbr/0xPRCeJkM/uXu79dQv4rSn4J36lCiviuTCJckExP7tqcst2SqLS8fXH7jwLcgqjx/kSgBuFCIl3X67MIPRqzke439mMmn0VJcnAeKJaZtQV2iV6m6xV4FmsC0G8U7pmWqiZr/2bTSb1g+RNYHH0vckqdEMrmfOBTwhdgswy37Uy4Ylyr+qcECwhVQ0V1i050alhgZueY2S9mtnVqouhHeFv0cvc0+9kyel6UYf6SLSF0Hd+riMddZdj3WqL2oT6ExtrbS9lIO4vQrbVVyr4+dvfXo8+p1Pe/EHpewTo2pXspvisXEr53rdbauLBHCZ9nsyKOU41Qcn0r6mTQn9DtvWOax8uENqm6Wb2p8BuD0HBfnspyHijJqYRbAv7O2p/POGBnM0vUGixg7Sr9ZNtQui78+YnvfvR4uzyCDygAlUlU99obaEAG9aLRVU134L409wGVJHEfxnFp9lud0OPng2i/bxN6N11Swj4LTVMeNTYeD8yLGqGznZb8P8B2wMLkLzThh3A9xf9YshK970HAJoRu7iW5jtAOcm9UHZTObhlkIXHlWNZqo4pW0nelFaGK590S9nMD4fsyPmoTTHUZ0JRwsv4/Qulzkrs/n/ognGDrEDrHZMTMGhK+B18SOtiUm2zPAyWJ2iiPAma7+zNpPp/hhFqHRMnxv0CLqMSauq/tCV3on8pV/nJBVXBl5O6vmtm1pL97v76ZdYj+3oDQG6w9oUrhc9JXOZR0vJfMbCrhRrjmhB5sywgn+gGEKq+uUdq5ZjYBOM3MWhA6HcwnnGgOAk4mlMD2N7MHCSWSWsC5hCBxRHTYJUBbCyMivJVBdm8iBMq5ZjY8es+7ExozP6Xkk1m2JhLaLQ4xs6PcfUZRCd39MzM7HJgKfGhmkwjtP78STo49CFfS8wn3biVsm/S/hdB78EhCb6XZlaj6DULPrw7FrF9cyu/KTSW1x7j7O2Z2FqGN7PWoeupjQnXs0YQLr3sINy7eR6jumV7E7mYSerGdaWa3FtFDLfW9bUwIlmcS2uUOcvfkm0s3L+Gz+MjdS2p7XEsJ54HWZpauc8NCd/93MbvtQ+jUcU+6le7+pZk9TWi3247wmZ4AzDGzEcBrhADVhvCbXkC4B6vSUADKjWsIXUZTb9JrCzyX9Po3QkP9SMKPeWmWx+tDuNGxN+FHXYfQQPgcYViV5JPfKYQro36EUkeDKB9vAb3dfbqFO9fPI4wGsIJQRbJ/0k1ltxICxyxC8CjqhFFI1EjbjvD5XEU4ISwgBIirvJxu4HP3fDNLNNzeYmZPufvPxaSfG1XB9ScE3RMJFws/EqqH+gIzoi6wzaLNTo0eCb8Ten9dQSgFVCY1KPw9TPUo4aKlxO9KaQ7m7uPN7C1CEDiPUF28lHAzay/C92hzwrBHD3ua+6+i/SyPLrZOIzS4P1KK97YC+JZQ+r7B3T9LSV/Qw68IvQiBMRtFnQfaR49UbxB+c0U5hfAdfLKYNBMJJckz3P0CM9ubcN/acdHzBoTPYyZhaKLU+69ipQnpREQkFmoDEhGRWCgAiYhILBSAREQkFgpAIiISCwUgERGJhQKQiIjEQvcBSbmz9NNsryZMUf414f6OG9z9xwrKT6FJ6izMeYS7N6uAYzcAqifux0j6bAqmUI+DmW3OmvugtiPckPwFYYTqEUXdqxMnM9sS+KUcR6GWcqYSkFSk2wk30fYhDK8ymDAEzHnA+2YZTPeaJTMby9pD/J9DZsPwZ3vsgwgjQLRMWnw/4fP4qLyPXxQzO4QwZt8lhJsjLydM1PcB4WbGN0o5rl6FMbMTCHNTNY47L5I9lYCkIr3k7vemLLvFzO4kBIXZZtYqZdK5XDskdYG7P5guYTloRxhZO/nY71J+QxKVyMzaEILgl8A/3D155OhbzexIwl30j5lZ69JO2FYBDmDNxIiyjlIJSGLn7k8BIwizYvaOOTvrm7GE4Wx6pQQfANz9fkIAakkY8kUkZ1QCkspiEqHapztJ0zRE0wNcSRhjqzFhXKtZwNXJY+mZ2WbAjYQpzjcnTCPxNGHMuXnRGG5fJKXPj9ZdmdoGlNQu05IwonJXwuja70bbFKrCi4bDP5tQwmlIGMj0DeBad38mSjM3yhvAM2b2pbs3K6oNKKpiOgNoTZiP5U1CW8zDSWn6AXcSppzuThgXsDFhJO6R7p5uYsPkfDcnjN49NyqJFeUcYGDyOGLRBINnEiZh25HQnvcScF3SGIJFtnEl/T/udvd+SZ9RY8JFyHDC+Gn5hIkPL0q020X/r0SV4Bdm9j9372RmdxFmdT0W+BdhANSphNJSXWCL1PEHo2PuDDT1MF24VCCVgKSy+JRwEiuYGsLMdiScyA8nnGjPIoxIfQ7wvJltEqWrHi3vSghepxFmjOwJPGtmGxMGdexDCEyLor/vLyFPjxEmV7uaMNBkC0I1YUFblZn1IAx8uSVhAM/TCDOltiNMUJeYX+da4IHo7+sops3JzMZF72M1oZ3sOsI8M7PNbFCaTe4inGRHAJcSBhG9y8wOLOH9JWZ8LW6gUtz9+5TgUy16LzcTpj8fBIwiTJw2NxrcNlubA3OB7wnz7NxD+L8+YWtm1T0nKc/nUnhq9FrRNncQPovEKO+NCBcxBaJ2rf2AqQo+8VAJSCqFaATrxYSr1oQxhOqh3dw9ufTyGOEEeAnhJLMroaRwobvfmJTuK8KIwi3d/TXCnD/XRMdLbYtK531375a0v/mEwNaXEBiInr8D9ovmhUmk/YRwFd4DGOXuc8xsX0IvszlF9Xgzs/0Jo2w/AXR197+i5bcArwDDzOyBlFGefwb2Tlzdm9nLhNHSTyQEx6IkJh3MdE6q3oSgMMHdByTlfSzwPmEuoCeKG4G8GI0IU6qPSNpvTcJ0EAcQPrsHoyk0OgIPuvv8pO2rEUaaH5q0/TuEUlgfYHZS2r6ESfbuyiKfkgMqAUllUoNoXvtoQrH/I8xXtMzMGicehJPrD4T5dyCcQFcDp5vZsVFXZ9z9X+7eJgo+2Zia8joxlXPybLR7A7ukBJ+arJmePNPZPHtFz0MTwQcKJj0bRjjB9kjZZkZK1VK6fKaT6OxRLcs8Xpm8MOpGfwthKosuGe4z2ZSU16V9PwmFZkCNZvP8L2HenHpJq/oC77r7m1nlUspMAUgqhah6pT4hsADsQLg6PYxQfZb62BRoBuDu3xLaYLYgBI1FZvaymV1mZluVIVsLU14nJjYrOGFHPfa2N7Nbzew/ZvYZYQ6dxGysmf7Gto+eP0yz7oPoebvi8pk0AVtJgeXb6DnTmWm3J9x/k67kVFQeM1Hi557h9hDaGGsSBc+oNLoDoWpXYqIAJJXFrkB11lztJr6b9xFKQukeXRMbu/sYQpVSP2AaoZF6KODRySYbJXY5NrNLgFejvHwDTIj+Lm7Ss+IU95tMnIBXpCzPtmv0s4QSZ8fiEpnZvmb2rJn1iRZlk8dURVb/56Crd7op5B8gVFUm3sMJwCrWLm1JBVIbkFQWiRNDYjbKRJtPLXdfqx3DzHoRTviJ6rrWwDtRz6+7zSyP0BtqCqGh+oVcZzjqYHAt4UT+f8kN2WZ2fJa7nRc9twReTFnXKnr+Kst9F+Lu3yd655lZm2J6wvUnBKnE1NDzADOzpmlKQal5TFTz1UxJV9rqtJxw9z/M7N/AKWbWlNCx5dGKGn1D0lMJSGJnZgcApxOqne4DcPeFhKBxSDStd3L6nsAMQjdggM6EnlMFDeLuns+aE3jyja1/kbvvfSNCNaGnBJ+NgIHRy+SLvMSVeXHHnxU9Xx71Nkve50WE0s5DZcx3svOj56npqiujrt4nELp2JwJQIo9XpqRtTPif/AY8FS1OBKg9KKwPZVOazzLVpCj99YTOLqp+i5lKQFKR9jGzRDDYgNBdeB9Co/oPwJEpoyCcTuhu+4yZ3UYIUK0IgWYBcFmU7iHCPTrXmNn2hK7b9aN0K4Fbk/a5ENjbzM4DXnT3l8vwfj4APgNONLPfgfcI1YAnsKZdJbnRO9E2cVpUelirJ567/9fM7iDcX/OCmU0nlB5OAHYCBqf0gCsTd38rqlq7E/jIzKYQhkeqC/yDENy/AronBdm7gaOA/lFX5kcI/8sBhPt4+rn7sijt/YSOCUPMrA4wn9AdejfgjzJkPfFZDjKzJ0szmoW7v25m7xGC3w+sPSSTVDCVgKQiDQAmR4+7CG00OxKuSNu4uycnjqqE9iCUdo4CRhPaVqYA+yS6Zrv7csKJchxwICHgXEK4at/f3ZOrsi4nnASvJ1QtZS3qeXYQIQAeG+WvLyFotiIEyc5Jm0wj6l4NjI7uT0qnP6H7eHXCPUAXE0oS3dz9urLkuYj3MZXQBnc34b6YG4CrCMF0KCn/m6h3XjfCZ7wV4f6jgYSbZfdz98lJaX8m/E9eJHQUGU5oH+pAye1ExRlHuPH1pCi/pZUo9dxbzkM+SSnk5efnx50HEZEKYWbnEG6g3dndPygpvZQvBSARWS+YWS3gLeAnd+8Qd35EbUAiUsWZWXvCME67AUbhalGJkQKQiFR1ywj3jf0JnOHuc2LOj0RUBSciIrFQLzgREYmFApCIiMRCAUhERGKhACQiIrFQABIRkVgoAImISCz+H16xBUTaL5BxAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "age = 45\n", "width = 0.5\n", "\n", "younger = sum(users.loc[users['age'] < age, 'country_destination'].value_counts())\n", "older = sum(users.loc[users['age'] > age, 'country_destination'].value_counts())\n", "\n", "younger_destinations = users.loc[users['age'] < age, 'country_destination'].value_counts() / younger * 100\n", "older_destinations = users.loc[users['age'] > age, 'country_destination'].value_counts() / older * 100\n", "\n", "younger_destinations.plot(kind='bar', width=width, color='#63EA55', position=0, label='Youngers', rot=0)\n", "older_destinations.plot(kind='bar', width=width, color='#4DD3C9', position=1, label='Olders', rot=0)\n", "\n", "plt.legend()\n", "plt.xlabel('Destination Country')\n", "plt.ylabel('Percentage')\n", "\n", "sns.despine()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "8d5f119396d0996f8adb412f05e8c1fbad92266c" }, "source": [ "#### Dates" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "09668bbf2e05e6297dc06505b66b5fedb13ac2c6" }, "source": [ "To see the dates of our users and the timespan of them, let's plot the number of accounts created by time:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_uuid": "127c9d43400298e7e0b4d43a7e822ed7e614f56d" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style(\"whitegrid\", {'axes.edgecolor': '0'})\n", "sns.set_context(\"poster\", font_scale=1.1)\n", "users.date_account_created.value_counts().plot(kind='line', linewidth=1.2, color='#FD5C64')" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "67e3ccce04569cb865a51511302754161c06e961" }, "source": [ "It's appreciable how fast Airbnb has grown over the last 3 years. Does this correlate with the date when the user was active for the first time? It should be very similar, so doing this is a way to check the data!" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_uuid": "3a9bfed3bd6e5ec0571a257b9df99797043c17fb" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "users.date_first_active.value_counts().plot(kind='line', linewidth=1.2, color='#FD5C64')" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "6d7257105670a95bcf69ea98a3e2bb2d574ea6f9" }, "source": [ "We can se that's almost the same as `date_account_created`, and also, notice the small peaks. We can, either smooth the graph or dig into those peaks. Let's dig in:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_uuid": "9168613b7763ce252ea452fefed0ff84626246a6" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "users_2013 = users[users['date_first_active'] > pd.to_datetime(20130101, format='%Y%m%d')]\n", "users_2013 = users_2013[users_2013['date_first_active'] < pd.to_datetime(20140101, format='%Y%m%d')]\n", "users_2013.date_first_active.value_counts().plot(kind='line', linewidth=2, color='#FD5C64')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "12563235c180e07ecc954b62bf75ce1196554c29" }, "source": [ "At first sight we can see a small pattern, there are some peaks at the same distance. Looking more closely:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_uuid": "5d63a90c5f71921ac7c5bc25f133acfbc8e20265" }, "outputs": [], "source": [ "weekdays = []\n", "for date in users.date_account_created:\n", " weekdays.append(date.weekday())\n", "weekdays = pd.Series(weekdays)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_uuid": "6cdd4de3bb9cc5df6d50c43c9061b5d465c20e54" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.barplot(x = weekdays.value_counts().index, y=weekdays.value_counts().values, order=range(0,7))\n", "plt.xlabel('Week Day')\n", "sns.despine()" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "d75bd64ad22f4d276482c24bc68841acdf2958c5" }, "source": [ "The local minimums where the Sundays(where the people use less *the Internet*), and it's usually to hit a maximum at Tuesdays!\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "_uuid": "e928b1bf4a0117ac4f05d27e1d582a0cdfb2767b" }, "source": [ "I'll make more plots about the devices and singup methods/flow later this week. I hope you all have enjoyed this little analysis that despine not being very rellevant to make the predictions, it is to understand the problem and the user behaviour. \n", "\n", "Again, criticism is welcomed!\n", "\n", " David Gasquez" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "users.affiliate_provider.value_counts(dropna=False)[:6].plot(kind='bar', color='#FD5C64', rot=0)\n", "plt.xlabel('Affiliate Provider')\n", "sns.despine()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }