diff --git a/.DS_Store b/.DS_Store index 1b6f11e..04ff096 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/.gitignore b/.gitignore index 466c375..8367bbd 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,7 @@ *.csv .DS_Store -Analyst-User-Dataset.ipynb \ No newline at end of file +Analyst-User-Dataset.ipynb +.DS_Store +.DS_Store +.DS_Store diff --git a/Regression_conflict_correction/Regression.ipynb b/Regression_conflict_correction/Regression.ipynb new file mode 100644 index 0000000..ad86051 --- /dev/null +++ b/Regression_conflict_correction/Regression.ipynb @@ -0,0 +1,2258 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import json\n", + "import numpy as np\n", + "import pandas as pd\n", + "from pandas import json_normalize\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "%matplotlib inline\n", + "from plotly import tools\n", + "import plotly.offline as py\n", + "py.init_notebook_mode(connected=True)\n", + "import plotly.graph_objs as go\n", + "\n", + "#import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "from sklearn import model_selection, preprocessing, metrics\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9z/gkk8fkkj0pz746hrzcw69dq40000gn/T/ipykernel_5836/1626016371.py:1: DtypeWarning:\n", + "\n", + "Columns (2,34) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df = pd.read_csv('/Users/macbookpro/Desktop/lxc/git/MMA_Enterprise/Revenue-Radar/Data/train_df.csv')\n", + "\n", + "null = []\n", + "for n in train_df.columns:\n", + " if train_df[n].nunique(dropna=False) == 1:\n", + " null.append(n)\n", + "\n", + "null" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['channelGrouping',\n", + " 'date',\n", + " 'fullVisitorId',\n", + " 'visitId',\n", + " 'visitNumber',\n", + " 'visitStartTime',\n", + " 'device.browser',\n", + " 'device.operatingSystem',\n", + " 'device.isMobile',\n", + " 'device.deviceCategory',\n", + " 'geoNetwork.continent',\n", + " 'geoNetwork.subContinent',\n", + " 'geoNetwork.country',\n", + " 'geoNetwork.region',\n", + " 'geoNetwork.metro',\n", + " 'geoNetwork.city',\n", + " 'geoNetwork.networkDomain',\n", + " 'totals.hits',\n", + " 'totals.pageviews',\n", + " 'totals.bounces',\n", + " 'totals.newVisits',\n", + " 'totals.transactionRevenue',\n", + " 'trafficSource.campaign',\n", + " 'trafficSource.source',\n", + " 'trafficSource.medium',\n", + " 'trafficSource.keyword',\n", + " 'trafficSource.isTrueDirect',\n", + " 'trafficSource.referralPath',\n", + " 'trafficSource.adwordsClickInfo.page',\n", + " 'trafficSource.adwordsClickInfo.slot',\n", + " 'trafficSource.adwordsClickInfo.gclId',\n", + " 'trafficSource.adwordsClickInfo.adNetworkType',\n", + " 'trafficSource.adwordsClickInfo.isVideoAd',\n", + " 'trafficSource.adContent',\n", + " 'trafficSource.campaignCode']" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df.head()\n", + "train_df.columns.to_list()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "channelGrouping\n", + "date, device.browser, device.deviceCategory, device.isMobile, device.operatingSystem\n", + "fullVisitorId\n", + "geoNetwork.city, geoNetwork.continent, geoNetwork.country, geoNetwork.metro, geoNetwork.networkDomain, geoNetwork.region, geoNetwork.subContinent\n", + "totals.bounces, totals.hits, totals.newVisits, totals.pageviews, totals.transactionRevenue, trafficSource.adContent, trafficSource.adwordsClickInfo.adNetworkType, trafficSource.adwordsClickInfo.gclId, trafficSource.adwordsClickInfo.isVideoAd, trafficSource.adwordsClickInfo.page, trafficSource.adwordsClickInfo.slot, trafficSource.campaign, trafficSource.campaignCode, trafficSource.isTrueDirect, trafficSource.keyword, trafficSource.medium, trafficSource.referralPath, trafficSource.source\n", + "visitId, visitNumber, visitStartTime\n" + ] + } + ], + "source": [ + "import itertools\n", + "\n", + "columns = sorted(train_df.columns.tolist())\n", + "grouped_columns = [list(group) for key, group in itertools.groupby(columns, lambda x: x[0])]\n", + "\n", + "for group in grouped_columns:\n", + " print(\", \".join(group))" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "#drop the columns that have not available in demo dataset\n", + "columns_to_drop = [col for col in train_df.columns if train_df[col].eq('NaN').any()]\n", + "df_dropped = train_df.drop(columns=columns_to_drop)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "trafficSource.campaignCode 0.999999\n", + "trafficSource.adContent 0.987887\n", + "totals.transactionRevenue 0.987257\n", + "trafficSource.adwordsClickInfo.isVideoAd 0.976252\n", + "trafficSource.adwordsClickInfo.adNetworkType 0.976252\n", + "trafficSource.adwordsClickInfo.slot 0.976252\n", + "trafficSource.adwordsClickInfo.page 0.976252\n", + "trafficSource.adwordsClickInfo.gclId 0.976140\n", + "trafficSource.isTrueDirect 0.696781\n", + "trafficSource.referralPath 0.633774\n", + "trafficSource.keyword 0.556551\n", + "totals.bounces 0.501324\n", + "totals.newVisits 0.221980\n", + "totals.pageviews 0.000111\n", + "trafficSource.medium 0.000000\n", + "trafficSource.source 0.000000\n", + "trafficSource.campaign 0.000000\n", + "channelGrouping 0.000000\n", + "date 0.000000\n", + "device.isMobile 0.000000\n", + "fullVisitorId 0.000000\n", + "visitId 0.000000\n", + "visitNumber 0.000000\n", + "visitStartTime 0.000000\n", + "device.browser 0.000000\n", + "device.operatingSystem 0.000000\n", + "device.deviceCategory 0.000000\n", + "geoNetwork.networkDomain 0.000000\n", + "geoNetwork.continent 0.000000\n", + "geoNetwork.subContinent 0.000000\n", + "geoNetwork.country 0.000000\n", + "geoNetwork.region 0.000000\n", + "geoNetwork.metro 0.000000\n", + "geoNetwork.city 0.000000\n", + "totals.hits 0.000000\n", + "dtype: float64" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check the na percentage compare to total data\n", + "na_percentage = df_dropped.isna().sum() / len(df_dropped)\n", + "na_percentage = na_percentage.sort_values(ascending=False)\n", + "\n", + "na_percentage\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Percentage of customers producing revenue: 1.40%" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of customers producing revenue: 1.40%\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "gdf = train_df.groupby(\"fullVisitorId\")[\"totals.transactionRevenue\"].sum().reset_index()\n", + "\n", + "gdf[\"totals.transactionRevenue\"] = pd.to_numeric(gdf[\"totals.transactionRevenue\"], errors=\"coerce\")\n", + "\n", + "gdf = gdf.dropna(subset=[\"totals.transactionRevenue\"])\n", + "\n", + "gdf[\"totals.transactionRevenue\"] = np.sort(gdf[\"totals.transactionRevenue\"])\n", + "\n", + "total_customers = gdf.shape[0]\n", + "revenue_customers = gdf[gdf[\"totals.transactionRevenue\"] > 0].shape[0]\n", + "percentage_revenue_customers = (revenue_customers / total_customers) * 100\n", + "\n", + "print(\"Percentage of customers producing revenue: {:.2f}%\".format(percentage_revenue_customers))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regression model on revenue (Only with data that percentage of customers producing data)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the distribution of the total revenue\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(gdf.shape[0]), np.sort(np.log1p(gdf[\"totals.transactionRevenue\"].values)))" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "channelGrouping 903653\n", + "geoNetwork.subContinent 903653\n", + "trafficSource.medium 903653\n", + "trafficSource.source 903653\n", + "trafficSource.campaign 903653\n", + "date 903653\n", + "geoNetwork.networkDomain 903653\n", + "geoNetwork.city 903653\n", + "geoNetwork.metro 903653\n", + "geoNetwork.region 903653\n", + "geoNetwork.country 903653\n", + "totals.hits 903653\n", + "geoNetwork.continent 903653\n", + "visitStartTime 903653\n", + "fullVisitorId 903653\n", + "device.deviceCategory 903653\n", + "visitNumber 903653\n", + "visitId 903653\n", + "device.browser 903653\n", + "device.operatingSystem 903653\n", + "device.isMobile 903653\n", + "totals.pageviews 903553\n", + "totals.newVisits 703060\n", + "totals.bounces 450630\n", + "trafficSource.keyword 400724\n", + "trafficSource.referralPath 330941\n", + "trafficSource.isTrueDirect 274005\n", + "trafficSource.adwordsClickInfo.gclId 21561\n", + "trafficSource.adwordsClickInfo.page 21460\n", + "trafficSource.adwordsClickInfo.slot 21460\n", + "trafficSource.adwordsClickInfo.adNetworkType 21460\n", + "trafficSource.adwordsClickInfo.isVideoAd 21460\n", + "totals.transactionRevenue 11515\n", + "trafficSource.adContent 10946\n", + "trafficSource.campaignCode 1\n", + "dtype: int64" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check how many data we have for each column\n", + "count = df_dropped.count()\n", + "count = count.sort_values(ascending=False)\n", + "count" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "channelGrouping 903653\n", + "geoNetwork.country 903653\n", + "trafficSource.source 903653\n", + "trafficSource.campaign 903653\n", + "totals.hits 903653\n", + "geoNetwork.networkDomain 903653\n", + "geoNetwork.city 903653\n", + "geoNetwork.metro 903653\n", + "date 903653\n", + "geoNetwork.region 903653\n", + "geoNetwork.subContinent 903653\n", + "geoNetwork.continent 903653\n", + "device.deviceCategory 903653\n", + "device.isMobile 903653\n", + "device.operatingSystem 903653\n", + "device.browser 903653\n", + "visitStartTime 903653\n", + "visitNumber 903653\n", + "trafficSource.medium 903653\n", + "totals.pageviews 903553\n", + "totals.newVisits 703060\n", + "totals.transactionRevenue 11515\n", + "dtype: int64\n", + "channelGrouping 4465\n", + "date 4465\n", + "trafficSource.source 4465\n", + "trafficSource.campaign 4465\n", + "totals.transactionRevenue 4465\n", + "totals.newVisits 4465\n", + "totals.pageviews 4465\n", + "totals.hits 4465\n", + "geoNetwork.networkDomain 4465\n", + "geoNetwork.city 4465\n", + "geoNetwork.metro 4465\n", + "geoNetwork.region 4465\n", + "geoNetwork.country 4465\n", + "geoNetwork.subContinent 4465\n", + "geoNetwork.continent 4465\n", + "device.deviceCategory 4465\n", + "device.isMobile 4465\n", + "device.operatingSystem 4465\n", + "device.browser 4465\n", + "visitStartTime 4465\n", + "visitNumber 4465\n", + "trafficSource.medium 4465\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "trafficSource.campaignCode 0.999999\n", + "trafficSource.adContent 0.987887\n", + "totals.transactionRevenue 0.987257\n", + "trafficSource.adwordsClickInfo.isVideoAd 0.976252\n", + "trafficSource.adwordsClickInfo.adNetworkType 0.976252\n", + "trafficSource.adwordsClickInfo.slot 0.976252\n", + "trafficSource.adwordsClickInfo.page 0.976252\n", + "trafficSource.adwordsClickInfo.gclId 0.976140\n", + "trafficSource.isTrueDirect 0.696781\n", + "trafficSource.referralPath 0.633774\n", + "trafficSource.keyword 0.556551\n", + "totals.bounces 0.501324\n", + "\"\"\"\n", + "#drop these columns\n", + "df_1 = df_dropped.drop(columns=['trafficSource.campaignCode', 'trafficSource.adContent', \n", + " 'trafficSource.adwordsClickInfo.isVideoAd', \n", + " 'trafficSource.adwordsClickInfo.adNetworkType', \n", + " 'trafficSource.adwordsClickInfo.slot', 'trafficSource.adwordsClickInfo.page', \n", + " 'trafficSource.adwordsClickInfo.gclId', \n", + " 'trafficSource.isTrueDirect', 'trafficSource.referralPath', \n", + " 'trafficSource.keyword', 'totals.bounces', 'fullVisitorId', 'visitId'])\n", + "\n", + "# what if drop na\n", + "df_2 = df_1.dropna()\n", + "\n", + "# check how many data left\n", + "count1 = df_1.count()\n", + "count1 = count1.sort_values(ascending=False)\n", + "\n", + "count2 = df_2.count()\n", + "count2 = count2.sort_values(ascending=False)\n", + "\n", + "\n", + "print(count1)\n", + "print(count2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "df_2 = df_1.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "channelGrouping 11515\n", + "date 11515\n", + "trafficSource.source 11515\n", + "trafficSource.campaign 11515\n", + "totals.transactionRevenue 11515\n", + "totals.pageviews 11515\n", + "totals.hits 11515\n", + "geoNetwork.networkDomain 11515\n", + "geoNetwork.city 11515\n", + "geoNetwork.metro 11515\n", + "geoNetwork.region 11515\n", + "geoNetwork.country 11515\n", + "geoNetwork.subContinent 11515\n", + "geoNetwork.continent 11515\n", + "device.deviceCategory 11515\n", + "device.isMobile 11515\n", + "device.operatingSystem 11515\n", + "device.browser 11515\n", + "visitStartTime 11515\n", + "visitNumber 11515\n", + "trafficSource.medium 11515\n", + "totals.newVisits 4465\n", + "dtype: int64" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# only use the data that has transaction revenue\n", + "df_2 = df_2[df_2['totals.transactionRevenue'] > 0]\n", + "\n", + "# check how many data left\n", + "count2 = df_2.count()\n", + "count2 = count2.sort_values(ascending=False)\n", + "count2" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unique values before: [1.0, NaN]\n", + "Categories (1, float64): [1.0]\n", + "Unique values after: [1, 0]\n", + "Categories (2, int64): [1, 0]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9z/gkk8fkkj0pz746hrzcw69dq40000gn/T/ipykernel_5836/805069265.py:9: FutureWarning:\n", + "\n", + "Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Assuming df_2 is your DataFrame and has already been loaded\n", + "\n", + "# Step 1: Convert 'totals.newVisits' to categorical\n", + "df_2['totals.newVisits'] = df_2['totals.newVisits'].astype('category')\n", + "\n", + "# Check unique values before making changes\n", + "print(\"Unique values before:\", df_2['totals.newVisits'].unique())\n", + "\n", + "# Step 2: Fill NA/NaN values with 0\n", + "df_2['totals.newVisits'] = df_2['totals.newVisits'].cat.add_categories([0]).fillna(0)\n", + "\n", + "# Step 3: Ensure all values are either 1 or 0\n", + "df_2['totals.newVisits'] = df_2['totals.newVisits'].apply(lambda x: 1 if x == 1 else 0).astype('category')\n", + "\n", + "# Check unique values after making changes\n", + "print(\"Unique values after:\", df_2['totals.newVisits'].unique())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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channelGroupingdatevisitNumbervisitStartTimedevice.browserdevice.operatingSystemdevice.isMobiledevice.deviceCategorygeoNetwork.continentgeoNetwork.subContinent...geoNetwork.metrogeoNetwork.citygeoNetwork.networkDomaintotals.hitstotals.pageviewstotals.newVisitstotals.transactionRevenuetrafficSource.campaigntrafficSource.sourcetrafficSource.medium
752Direct2016090211472843572ChromeLinuxFalsedesktopAmericasNorthern America...Detroit MIAnn Arbor(not set)1111.0137860000.0(not set)(direct)(none)
753Organic Search2016090231472844906ChromeMacintoshFalsedesktopAmericasNorthern America...New York NYNew York(not set)1110.00306670000.0(not set)googleorganic
799Referral2016090271472827393ChromeLinuxFalsedesktopAmericasNorthern America...New York NYNew York(not set)1311.0068030000.0(not set)mall.googleplex.comreferral
802Referral2016090261472846398ChromeWindowsFalsedesktopAmericasNorthern America...San Francisco-Oakland-San Jose CAMountain View(not set)1312.0026250000.0(not set)mall.googleplex.comreferral
859Referral2016090241472824817ChromeMacintoshFalsedesktopAmericasNorthern America...not available in demo datasetnot available in demo dataset(not set)1714.00574150000.0(not set)mall.googleplex.comreferral
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5 rows × 22 columns

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" + ], + "text/plain": [ + " channelGrouping date visitNumber visitStartTime device.browser \\\n", + "752 Direct 20160902 1 1472843572 Chrome \n", + "753 Organic Search 20160902 3 1472844906 Chrome \n", + "799 Referral 20160902 7 1472827393 Chrome \n", + "802 Referral 20160902 6 1472846398 Chrome \n", + "859 Referral 20160902 4 1472824817 Chrome \n", + "\n", + " device.operatingSystem device.isMobile device.deviceCategory \\\n", + "752 Linux False desktop \n", + "753 Macintosh False desktop \n", + "799 Linux False desktop \n", + "802 Windows False desktop \n", + "859 Macintosh False desktop \n", + "\n", + " geoNetwork.continent geoNetwork.subContinent ... \\\n", + "752 Americas Northern America ... \n", + "753 Americas Northern America ... \n", + "799 Americas Northern America ... \n", + "802 Americas Northern America ... \n", + "859 Americas Northern America ... \n", + "\n", + " geoNetwork.metro geoNetwork.city \\\n", + "752 Detroit MI Ann Arbor \n", + "753 New York NY New York \n", + "799 New York NY New York \n", + "802 San Francisco-Oakland-San Jose CA Mountain View \n", + "859 not available in demo dataset not available in demo dataset \n", + "\n", + " geoNetwork.networkDomain totals.hits totals.pageviews totals.newVisits \\\n", + "752 (not set) 11 11.0 1 \n", + "753 (not set) 11 10.0 0 \n", + "799 (not set) 13 11.0 0 \n", + "802 (not set) 13 12.0 0 \n", + "859 (not set) 17 14.0 0 \n", + "\n", + " totals.transactionRevenue trafficSource.campaign trafficSource.source \\\n", + "752 37860000.0 (not set) (direct) \n", + "753 306670000.0 (not set) google \n", + "799 68030000.0 (not set) mall.googleplex.com \n", + "802 26250000.0 (not set) mall.googleplex.com \n", + "859 574150000.0 (not set) mall.googleplex.com \n", + "\n", + " trafficSource.medium \n", + "752 (none) \n", + "753 organic \n", + "799 referral \n", + "802 referral \n", + "859 referral \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "# make date to datetime\n", + "df_2['date'] = pd.to_datetime(df_2['date'], format='%Y%m%d')" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "channelGrouping object\n", + "date datetime64[ns]\n", + "visitNumber int64\n", + "visitStartTime int64\n", + "device.browser object\n", + "device.operatingSystem object\n", + "device.isMobile bool\n", + "device.deviceCategory object\n", + "geoNetwork.continent object\n", + "geoNetwork.subContinent object\n", + "geoNetwork.country object\n", + "geoNetwork.region object\n", + "geoNetwork.metro object\n", + "geoNetwork.city object\n", + "geoNetwork.networkDomain object\n", + "totals.hits int64\n", + "totals.pageviews float64\n", + "totals.newVisits category\n", + "totals.transactionRevenue float64\n", + "trafficSource.campaign object\n", + "trafficSource.source object\n", + "trafficSource.medium object\n", + "dtype: object" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NOT include Time Series\n" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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channelGroupingdatedevice.browserdevice.operatingSystemdevice.isMobiledevice.deviceCategorygeoNetwork.continentgeoNetwork.subContinentgeoNetwork.countrygeoNetwork.regiontotals.hitstotals.pageviewstotals.newVisitstotals.transactionRevenuetrafficSource.campaigntrafficSource.medium
752Direct2016-09-02ChromeLinuxFalsedesktopAmericasNorthern AmericaUnited StatesMichigan1111.0137860000.0(not set)(none)
753Organic Search2016-09-02ChromeMacintoshFalsedesktopAmericasNorthern AmericaUnited StatesNew York1110.00306670000.0(not set)organic
799Referral2016-09-02ChromeLinuxFalsedesktopAmericasNorthern AmericaUnited StatesNew York1311.0068030000.0(not set)referral
802Referral2016-09-02ChromeWindowsFalsedesktopAmericasNorthern AmericaUnited StatesCalifornia1312.0026250000.0(not set)referral
859Referral2016-09-02ChromeMacintoshFalsedesktopAmericasNorthern AmericaUnited Statesnot available in demo dataset1714.00574150000.0(not set)referral
\n", + "
" + ], + "text/plain": [ + " channelGrouping date device.browser device.operatingSystem \\\n", + "752 Direct 2016-09-02 Chrome Linux \n", + "753 Organic Search 2016-09-02 Chrome Macintosh \n", + "799 Referral 2016-09-02 Chrome Linux \n", + "802 Referral 2016-09-02 Chrome Windows \n", + "859 Referral 2016-09-02 Chrome Macintosh \n", + "\n", + " device.isMobile device.deviceCategory geoNetwork.continent \\\n", + "752 False desktop Americas \n", + "753 False desktop Americas \n", + "799 False desktop Americas \n", + "802 False desktop Americas \n", + "859 False desktop Americas \n", + "\n", + " geoNetwork.subContinent geoNetwork.country geoNetwork.region \\\n", + "752 Northern America United States Michigan \n", + "753 Northern America United States New York \n", + "799 Northern America United States New York \n", + "802 Northern America United States California \n", + "859 Northern America United States not available in demo dataset \n", + "\n", + " totals.hits totals.pageviews totals.newVisits \\\n", + "752 11 11.0 1 \n", + "753 11 10.0 0 \n", + "799 13 11.0 0 \n", + "802 13 12.0 0 \n", + "859 17 14.0 0 \n", + "\n", + " totals.transactionRevenue trafficSource.campaign trafficSource.medium \n", + "752 37860000.0 (not set) (none) \n", + "753 306670000.0 (not set) organic \n", + "799 68030000.0 (not set) referral \n", + "802 26250000.0 (not set) referral \n", + "859 574150000.0 (not set) referral " + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#type of each row\n", + "df_2.dtypes\n", + "#drop the date\n", + "df_3 = df_2.drop(columns=['geoNetwork.metro','geoNetwork.networkDomain','visitNumber','geoNetwork.city',\n", + " 'visitStartTime','trafficSource.source'])\n", + "df_3.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "channelGrouping 8\n", + "\n", + "\n", + "['Direct' 'Organic Search' 'Referral' 'Display' 'Paid Search' 'Social'\n", + " 'Affiliates' '(Other)']\n", + "\n", + "\n", + "Referral 5311\n", + "Organic Search 3438\n", + "Direct 2042\n", + "Paid Search 468\n", + "Display 142\n", + "Social 104\n", + "Affiliates 9\n", + "(Other) 1\n", + "Name: channelGrouping, dtype: int64\n", + "device.browser 9\n", + "\n", + "\n", + "['Chrome' 'Safari' 'Firefox' 'Safari (in-app)' 'Edge' 'Internet Explorer'\n", + " 'Android Webview' 'Opera' 'Amazon Silk']\n", + "\n", + "\n", + "Chrome 10353\n", + "Safari 780\n", + "Firefox 191\n", + "Internet Explorer 109\n", + "Edge 58\n", + "Safari (in-app) 12\n", + "Android Webview 6\n", + "Opera 5\n", + "Amazon Silk 1\n", + "Name: device.browser, dtype: int64\n", + "device.operatingSystem 7\n", + "\n", + "\n", + "['Linux' 'Macintosh' 'Windows' 'Android' 'Chrome OS' 'iOS' 'Windows Phone']\n", + "\n", + "\n", + "Macintosh 6426\n", + "Windows 2309\n", + "Chrome OS 994\n", + "Linux 782\n", + "iOS 536\n", + "Android 467\n", + "Windows Phone 1\n", + "Name: device.operatingSystem, dtype: int64\n", + "device.deviceCategory 3\n", + "\n", + "\n", + "['desktop' 'mobile' 'tablet']\n", + "\n", + "\n", + "desktop 10495\n", + "mobile 852\n", + "tablet 168\n", + "Name: device.deviceCategory, dtype: int64\n", + "geoNetwork.continent 6\n", + "\n", + "\n", + "['Americas' 'Asia' 'Europe' 'Oceania' '(not set)' 'Africa']\n", + "\n", + "\n", + "Americas 11283\n", + "Asia 125\n", + "Europe 79\n", + "Oceania 14\n", + "Africa 8\n", + "(not set) 6\n", + "Name: geoNetwork.continent, dtype: int64\n", + "geoNetwork.subContinent 19\n", + "\n", + "\n", + "['Northern America' 'Caribbean' 'Eastern Asia' 'Western Europe'\n", + " 'Central America' 'Australasia' 'Northern Europe' 'Western Asia'\n", + " 'South America' 'Southern Asia' 'Southeast Asia' '(not set)'\n", + " 'Eastern Europe' 'Southern Europe' 'Western Africa' 'Eastern Africa'\n", + " 'Central Asia' 'Southern Africa' 'Northern Africa']\n", + "\n", + "\n", + "Northern America 11143\n", + "South America 98\n", + "Eastern Asia 59\n", + "Southeast Asia 32\n", + "Western Europe 30\n", + "Northern Europe 27\n", + "Central America 26\n", + "Western Asia 21\n", + "Caribbean 16\n", + "Eastern Europe 14\n", + "Australasia 14\n", + "Southern Asia 11\n", + "Southern Europe 8\n", + "(not set) 6\n", + "Eastern Africa 3\n", + "Western Africa 2\n", + "Central Asia 2\n", + "Southern Africa 2\n", + "Northern Africa 1\n", + "Name: geoNetwork.subContinent, dtype: int64\n", + "geoNetwork.country 69\n", + "\n", + "\n", + "['United States' 'Puerto Rico' 'Taiwan' 'Switzerland' 'Canada' 'Mexico'\n", + " 'Australia' 'Sweden' 'Ireland' 'Israel' 'Germany' 'United Kingdom'\n", + " 'New Zealand' 'Argentina' 'Chile' 'Finland' 'India' 'Venezuela'\n", + " 'Indonesia' 'St. Lucia' 'Japan' 'China' 'Colombia' 'Hong Kong' 'Brazil'\n", + " 'Philippines' '(not set)' 'Pakistan' 'South Korea' 'Ecuador' 'Singapore'\n", + " 'Hungary' 'Greece' 'Russia' 'Kuwait' 'Malaysia' 'Spain' 'Ukraine'\n", + " 'Cyprus' 'Romania' 'Nigeria' 'Uruguay' 'Belgium' 'Kenya' 'Kazakhstan'\n", + " 'Turkey' 'Saudi Arabia' 'Anguilla' 'Armenia' 'France'\n", + " 'United Arab Emirates' 'Thailand' 'Netherlands' 'South Africa' 'Poland'\n", + " 'Egypt' 'Italy' 'Guatemala' 'Georgia' 'El Salvador' 'Lebanon' 'Nicaragua'\n", + " 'Czechia' 'Denmark' 'Curaçao' 'Peru' 'Panama' 'Portugal' 'Guadeloupe']\n", + "\n", + "\n", + "United States 10953\n", + "Canada 190\n", + "Venezuela 63\n", + "Mexico 20\n", + "Taiwan 19\n", + " ... \n", + "Anguilla 1\n", + "Pakistan 1\n", + "Egypt 1\n", + "Hungary 1\n", + "Guadeloupe 1\n", + "Name: geoNetwork.country, Length: 69, dtype: int64\n", + "geoNetwork.region 79\n", + "\n", + "\n", + "['Michigan' 'New York' 'California' 'not available in demo dataset'\n", + " 'Washington' 'Illinois' 'Oregon' 'District of Columbia' 'Massachusetts'\n", + " '(not set)' 'Virginia' 'Zurich' 'Georgia' 'Texas' 'Ontario'\n", + " 'Pennsylvania' 'Nevada' 'North Carolina' 'State of Rio de Janeiro'\n", + " 'Victoria' 'County Dublin' 'Tel Aviv District' 'Taipei City' 'Colorado'\n", + " 'New Jersey' 'Mexico City' 'Florida' 'New Taipei City' 'England'\n", + " 'Santiago Metropolitan Region' 'Nebraska' 'South Carolina' 'Utah'\n", + " 'Tennessee' 'Zulia' 'Alberta' 'Jakarta' 'Stockholm County' 'Dublin City'\n", + " 'Arizona' 'Ohio' 'Seoul' 'Quebec' 'Attica' 'Iowa' 'British Columbia'\n", + " 'Gujarat' 'Catalonia' 'New South Wales'\n", + " 'Federal Territory of Kuala Lumpur' 'Missouri' 'Vienna' 'Bucharest'\n", + " 'Tokyo' 'State of Sao Paulo' 'Kanagawa Prefecture' 'Maryland' 'Minnesota'\n", + " 'Delhi' 'Istanbul' 'Indiana' 'Ile-de-France' 'Bangkok' 'Bogota' 'Tbilisi'\n", + " 'Hamburg' 'Prague' 'North Holland' 'Berlin' 'Maharashtra'\n", + " 'Masovian Voivodeship' 'Connecticut' 'Pichincha' 'Buenos Aires'\n", + " 'Wisconsin' 'Dubai' 'Metro Manila' 'Karnataka' 'Lima Region']\n", + "\n", + "\n", + "not available in demo dataset 4579\n", + "California 3305\n", + "New York 1507\n", + "Illinois 423\n", + "Washington 336\n", + " ... \n", + "Kanagawa Prefecture 1\n", + "Attica 1\n", + "Gujarat 1\n", + "Bucharest 1\n", + "Lima Region 1\n", + "Name: geoNetwork.region, Length: 79, dtype: int64\n", + "trafficSource.campaign 7\n", + "\n", + "\n", + "['(not set)' 'AW - Accessories' 'AW - Dynamic Search Ads Whole Site'\n", + " 'AW - Apparel' 'Retail (DO NOT EDIT owners nophakun and tianyu)'\n", + " 'Data Share Promo' 'test-liyuhz']\n", + "\n", + "\n", + "(not set) 11050\n", + "AW - Dynamic Search Ads Whole Site 323\n", + "AW - Accessories 130\n", + "Data Share Promo 9\n", + "AW - Apparel 1\n", + "Retail (DO NOT EDIT owners nophakun and tianyu) 1\n", + "test-liyuhz 1\n", + "Name: trafficSource.campaign, dtype: int64\n", + "trafficSource.medium 7\n", + "\n", + "\n", + "['(none)' 'organic' 'referral' 'cpm' 'cpc' 'affiliate' '(not set)']\n", + "\n", + "\n", + "referral 5415\n", + "organic 3438\n", + "(none) 2042\n", + "cpc 468\n", + "cpm 142\n", + "affiliate 9\n", + "(not set) 1\n", + "Name: trafficSource.medium, dtype: int64\n" + ] + } + ], + "source": [ + "#check the unique value of each column for categorical data\n", + "for col in df_3.columns:\n", + " if df_3[col].dtype == 'object':\n", + " print(col, df_3[col].nunique())\n", + " print('\\n')\n", + " # print each unique value\n", + " print(df_3[col].unique())\n", + " print('\\n')\n", + " # print the value counts\n", + " print(df_3[col].value_counts())\n", + " else:\n", + " continue\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "df_4 = df_3.drop(columns=['geoNetwork.subContinent','trafficSource.campaign','geoNetwork.continent'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Michigan' 'New York' 'California' 'not available in demo dataset'\n", + " 'Washington' 'Illinois' 'Oregon' 'District of Columbia' 'Massachusetts'\n", + " '(not set)' 'Virginia' 'Georgia' 'Texas' 'Pennsylvania' 'Nevada'\n", + " 'North Carolina' 'State of Rio de Janeiro' 'Colorado' 'New Jersey'\n", + " 'Florida' 'Nebraska' 'South Carolina' 'Utah' 'Tennessee' 'Zulia'\n", + " 'Ontario' 'Arizona' 'Ohio' 'Iowa' 'Catalonia' 'Missouri' 'Vienna'\n", + " 'Maryland' 'Minnesota' 'England' 'Indiana' 'Connecticut' 'Quebec'\n", + " 'Wisconsin']\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# Assuming your DataFrame is named df (replace with the actual name of your DataFrame)\n", + "\n", + "# Filter the DataFrame to include only rows where 'geoNetwork.country' is 'United States' or 'Canada'\n", + "df_4= df_4[(df_4['geoNetwork.country'] == 'United States')]\n", + "\n", + "# drop country column\n", + "df_4 = df_4.drop(columns=['geoNetwork.country'])\n", + "\n", + "# Now, filtered_df contains only the rows with 'United States' and 'Canada'\n", + "\n", + "\n", + "print(df_4['geoNetwork.region'].unique())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Referral 5197\n", + "Organic Search 3111\n", + "Direct 1939\n", + "Paid Search 463\n", + "Other 243\n", + "Name: channelGrouping, dtype: int64\n", + "Chrome 9881\n", + "Rest 1072\n", + "Name: device.browser, dtype: int64\n", + "Mac 6176\n", + "Mobile 2674\n", + "Windows 2103\n", + "Name: device.operatingSystem, dtype: int64\n" + ] + } + ], + "source": [ + "df_4['channelGrouping'] = df_4['channelGrouping'].replace(['Social', 'Display', 'Affiliates', '(Other)'], 'Other')\n", + "print(df_4['channelGrouping'].value_counts())\n", + "\n", + "\n", + "browsers_to_keep = ['Chrome']\n", + "df_4['device.browser'] = df_4['device.browser'].apply(lambda x: x if x in browsers_to_keep else 'Rest')\n", + "# After the modification, to see the count of each category, you can use value_counts()\n", + "print(df_4['device.browser'].value_counts())\n", + "\n", + "\n", + "# For the 'device.operatingSystem' column\n", + "# Replace 'Macintosh' with 'Mac', 'Windows' remains the same, and classify others as 'Mobile'\n", + "df_4['device.operatingSystem'] = df_4['device.operatingSystem'].replace(['Linux', 'Android', 'iOS', 'Chrome OS','Windows Phone'], 'Mobile')\n", + "df_4['device.operatingSystem'] = df_4['device.operatingSystem'].replace(['Macintosh'], 'Mac')\n", + "\n", + "# Assuming 'device.deviceCategory' needs to be updated based on 'device.operatingSystem'\n", + "# This step seems a bit confusing because 'deviceCategory' typically indicates the type of device (e.g., desktop, mobile, tablet)\n", + "# If you intended to classify 'deviceCategory' based on 'operatingSystem', let's clarify the approach:\n", + "# - 'Mac' and 'Windows' could be considered 'desktop' in many contexts\n", + "# - 'Mobile' could map to 'mobile' and 'tablet'\n", + "# If the task is to adjust 'device.deviceCategory' based on these new 'operatingSystem' categories:\n", + "df_4['device.deviceCategory'] = df_4['device.operatingSystem'].apply(lambda x: 'desktop' if x in ['Mac', 'Windows'] else 'mobile')\n", + "\n", + "# drop device category\n", + "df_4 = df_4.drop(columns=['device.deviceCategory'])\n", + "\n", + "# After reclassification, to see the count of each category in 'device.operatingSystem'\n", + "print(df_4['device.operatingSystem'].value_counts())\n", + "\n", + "\n", + "df_4['geoNetwork.region'] = df_4['geoNetwork.region'].replace(['not available in demo dataset', '(not set)'], 'Unknown')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "channelGrouping 5\n", + "\n", + "\n", + "['Direct' 'Organic Search' 'Referral' 'Other' 'Paid Search']\n", + "\n", + "\n", + "Referral 5197\n", + "Organic Search 3111\n", + "Direct 1939\n", + "Paid Search 463\n", + "Other 243\n", + "Name: channelGrouping, dtype: int64\n", + "device.browser 2\n", + "\n", + "\n", + "['Chrome' 'Rest']\n", + "\n", + "\n", + "Chrome 9881\n", + "Rest 1072\n", + "Name: device.browser, dtype: int64\n", + "device.operatingSystem 3\n", + "\n", + "\n", + "['Mobile' 'Mac' 'Windows']\n", + "\n", + "\n", + "Mac 6176\n", + "Mobile 2674\n", + "Windows 2103\n", + "Name: device.operatingSystem, dtype: int64\n", + "geoNetwork.region 38\n", + "\n", + "\n", + "['Michigan' 'New York' 'California' 'Unknown' 'Washington' 'Illinois'\n", + " 'Oregon' 'District of Columbia' 'Massachusetts' 'Virginia' 'Georgia'\n", + " 'Texas' 'Pennsylvania' 'Nevada' 'North Carolina'\n", + " 'State of Rio de Janeiro' 'Colorado' 'New Jersey' 'Florida' 'Nebraska'\n", + " 'South Carolina' 'Utah' 'Tennessee' 'Zulia' 'Ontario' 'Arizona' 'Ohio'\n", + " 'Iowa' 'Catalonia' 'Missouri' 'Vienna' 'Maryland' 'Minnesota' 'England'\n", + " 'Indiana' 'Connecticut' 'Quebec' 'Wisconsin']\n", + "\n", + "\n", + "Unknown 4312\n", + "California 3293\n", + "New York 1492\n", + "Illinois 422\n", + "Washington 335\n", + "Texas 303\n", + "Michigan 193\n", + "Massachusetts 137\n", + "Georgia 99\n", + "District of Columbia 98\n", + "Virginia 57\n", + "Pennsylvania 50\n", + "Colorado 40\n", + "Arizona 14\n", + "North Carolina 14\n", + "Oregon 13\n", + "New Jersey 11\n", + "Tennessee 11\n", + "Florida 9\n", + "Minnesota 6\n", + "Nevada 5\n", + "Missouri 5\n", + "Ohio 5\n", + "Iowa 4\n", + "Nebraska 4\n", + "Zulia 4\n", + "Ontario 3\n", + "South Carolina 3\n", + "Utah 2\n", + "Catalonia 1\n", + "Vienna 1\n", + "Maryland 1\n", + "State of Rio de Janeiro 1\n", + "England 1\n", + "Indiana 1\n", + "Connecticut 1\n", + "Quebec 1\n", + "Wisconsin 1\n", + "Name: geoNetwork.region, dtype: int64\n", + "trafficSource.medium 7\n", + "\n", + "\n", + "['(none)' 'organic' 'referral' 'cpm' 'cpc' 'affiliate' '(not set)']\n", + "\n", + "\n", + "referral 5289\n", + "organic 3111\n", + "(none) 1939\n", + "cpc 463\n", + "cpm 141\n", + "affiliate 9\n", + "(not set) 1\n", + "Name: trafficSource.medium, dtype: int64\n" + ] + } + ], + "source": [ + "for col in df_4.columns:\n", + " if df_4[col].dtype == 'object':\n", + " print(col, df_4[col].nunique())\n", + " print('\\n')\n", + " # print each unique value\n", + " print(df_4[col].unique())\n", + " print('\\n')\n", + " # print the value counts\n", + " print(df_4[col].value_counts())\n", + " else:\n", + " continue\n" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "referral 5289\n", + "organic 3111\n", + "none 1939\n", + "rest 614\n", + "Name: trafficSource.medium, dtype: int64\n" + ] + } + ], + "source": [ + "df_4['trafficSource.medium'] = df_4['trafficSource.medium'].replace('(none)', 'none')\n", + "\n", + "# Combine 'cpc', 'cpm', 'affiliate', and '(not set)' into 'rest'\n", + "df_4['trafficSource.medium'] = df_4['trafficSource.medium'].replace(['cpc', 'cpm', 'affiliate', '(not set)'], 'rest')\n", + "\n", + "# After reclassification, to see the count of each category\n", + "print(df_4['trafficSource.medium'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unknown 4338\n", + "West 4005\n", + "East 2610\n", + "Name: geoNetwork.region, dtype: int64\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Assuming your DataFrame is named df and already has 'Unknown' consolidated\n", + "\n", + "# Define the states as 'West' or 'East'\n", + "west_states = ['California', 'Washington', 'Texas', 'Colorado', 'Arizona', 'Oregon', 'Nevada', 'Utah']\n", + "east_states = ['New York', 'Illinois', 'Michigan', 'Massachusetts', 'Georgia', 'District of Columbia', \n", + " 'Pennsylvania', 'New Jersey', 'North Carolina', 'Minnesota', 'Missouri', 'Florida', \n", + " 'Tennessee', 'Iowa', 'Virginia', 'Indiana', 'Connecticut']\n", + "\n", + "# Function to categorize each state\n", + "def categorize_state(region):\n", + " if region in west_states:\n", + " return 'West'\n", + " elif region in east_states:\n", + " return 'East'\n", + " else:\n", + " return 'Unknown' # Keeps 'Unknown' and other unspecified regions as 'Unknown'\n", + "\n", + "# Apply the categorization\n", + "df_4['geoNetwork.region'] = df_4['geoNetwork.region'].apply(categorize_state)\n", + "\n", + "# Check the new categorization\n", + "print(df_4['geoNetwork.region'].value_counts())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "channelGrouping 5\n", + "\n", + "\n", + "['Direct' 'Organic Search' 'Referral' 'Other' 'Paid Search']\n", + "\n", + "\n", + "Referral 5197\n", + "Organic Search 3111\n", + "Direct 1939\n", + "Paid Search 463\n", + "Other 243\n", + "Name: channelGrouping, dtype: int64\n", + "device.browser 2\n", + "\n", + "\n", + "['Chrome' 'Rest']\n", + "\n", + "\n", + "Chrome 9881\n", + "Rest 1072\n", + "Name: device.browser, dtype: int64\n", + "device.operatingSystem 3\n", + "\n", + "\n", + "['Mobile' 'Mac' 'Windows']\n", + "\n", + "\n", + "Mac 6176\n", + "Mobile 2674\n", + "Windows 2103\n", + "Name: device.operatingSystem, dtype: int64\n", + "geoNetwork.region 3\n", + "\n", + "\n", + "['East' 'West' 'Unknown']\n", + "\n", + "\n", + "Unknown 4338\n", + "West 4005\n", + "East 2610\n", + "Name: geoNetwork.region, dtype: int64\n", + "trafficSource.medium 4\n", + "\n", + "\n", + "['none' 'organic' 'referral' 'rest']\n", + "\n", + "\n", + "referral 5289\n", + "organic 3111\n", + "none 1939\n", + "rest 614\n", + "Name: trafficSource.medium, dtype: int64\n" + ] + } + ], + "source": [ + "for col in df_4.columns:\n", + " if df_4[col].dtype == 'object':\n", + " print(col, df_4[col].nunique())\n", + " print('\\n')\n", + " # print each unique value\n", + " print(df_4[col].unique())\n", + " print('\\n')\n", + " # print the value counts\n", + " print(df_4[col].value_counts())\n", + " else:\n", + " continue\n" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "df_4.drop(columns=['device.isMobile'], inplace=True) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "# splitting the date column into year, month and day\n", + "df_4['month']=df_4['date'].dt.month\n", + "df_4['day']=df_4['date'].dt.day\n", + "df_4['year']=df_4['date'].dt.year" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "# bucketing month into quarters\n", + "def quarter(x):\n", + " if x in [1,2,3]:\n", + " return '1st_quarter'\n", + " elif x in [4,5,6]:\n", + " return '2nd_quarter'\n", + " elif x in [7,8,9]:\n", + " return '3rd_quarter'\n", + " else:\n", + " return '4th_quarter'\n", + " \n", + "df_4['quarter']=df_4['month'].apply(quarter)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "# bucking the day column into beginning, middle and end of the month\n", + "def day(x):\n", + " if x in range(1,11):\n", + " return 'beginning'\n", + " elif x in range(11,21):\n", + " return 'middle'\n", + " else:\n", + " return 'end'\n", + "\n", + "df_4['day_of_month']=df_4['day'].apply(day)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"['date'] not found in axis\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[132], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_4\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m], inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 3\u001b[0m df_4\u001b[38;5;241m.\u001b[39mhead()\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 326\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 327\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m 328\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 329\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 330\u001b[0m )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:5399\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 5251\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 5252\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m( \u001b[38;5;66;03m# type: ignore[override]\u001b[39;00m\n\u001b[1;32m 5253\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 5260\u001b[0m errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5261\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 5262\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 5263\u001b[0m \u001b[38;5;124;03m Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[1;32m 5264\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 5397\u001b[0m \u001b[38;5;124;03m weight 1.0 0.8\u001b[39;00m\n\u001b[1;32m 5398\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5399\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdrop(\n\u001b[1;32m 5400\u001b[0m labels\u001b[38;5;241m=\u001b[39mlabels,\n\u001b[1;32m 5401\u001b[0m axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[1;32m 5402\u001b[0m index\u001b[38;5;241m=\u001b[39mindex,\n\u001b[1;32m 5403\u001b[0m columns\u001b[38;5;241m=\u001b[39mcolumns,\n\u001b[1;32m 5404\u001b[0m level\u001b[38;5;241m=\u001b[39mlevel,\n\u001b[1;32m 5405\u001b[0m inplace\u001b[38;5;241m=\u001b[39minplace,\n\u001b[1;32m 5406\u001b[0m errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[1;32m 5407\u001b[0m )\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 326\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 327\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m 328\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 329\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 330\u001b[0m )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/generic.py:4505\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 4503\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 4504\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4505\u001b[0m obj \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_drop_axis(labels, axis, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m 4507\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[1;32m 4508\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/generic.py:4546\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m 4544\u001b[0m new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m 4545\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 4546\u001b[0m new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m 4547\u001b[0m indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[1;32m 4549\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[1;32m 4550\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6934\u001b[0m, in \u001b[0;36mIndex.drop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m 6932\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m 6933\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 6934\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6935\u001b[0m indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[1;32m 6936\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n", + "\u001b[0;31mKeyError\u001b[0m: \"['date'] not found in axis\"" + ] + } + ], + "source": [ + "df_4.drop(columns=['date'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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channelGroupingdevice.browserdevice.operatingSystemgeoNetwork.regiontotals.hitstotals.pageviewstotals.newVisitstotals.transactionRevenuetrafficSource.mediummonthdayyearquarterday_of_month
752DirectChromeMobileEast1111.0137860000.0none9220163rd_quarterbeginning
753Organic SearchChromeMacEast1110.00306670000.0organic9220163rd_quarterbeginning
799ReferralChromeMobileEast1311.0068030000.0referral9220163rd_quarterbeginning
802ReferralChromeWindowsWest1312.0026250000.0referral9220163rd_quarterbeginning
859ReferralChromeMacUnknown1714.00574150000.0referral9220163rd_quarterbeginning
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" + ], + "text/plain": [ + " channelGrouping device.browser device.operatingSystem geoNetwork.region \\\n", + "752 Direct Chrome Mobile East \n", + "753 Organic Search Chrome Mac East \n", + "799 Referral Chrome Mobile East \n", + "802 Referral Chrome Windows West \n", + "859 Referral Chrome Mac Unknown \n", + "\n", + " totals.hits totals.pageviews totals.newVisits \\\n", + "752 11 11.0 1 \n", + "753 11 10.0 0 \n", + "799 13 11.0 0 \n", + "802 13 12.0 0 \n", + "859 17 14.0 0 \n", + "\n", + " totals.transactionRevenue trafficSource.medium month day year \\\n", + "752 37860000.0 none 9 2 2016 \n", + "753 306670000.0 organic 9 2 2016 \n", + "799 68030000.0 referral 9 2 2016 \n", + "802 26250000.0 referral 9 2 2016 \n", + "859 574150000.0 referral 9 2 2016 \n", + "\n", + " quarter day_of_month \n", + "752 3rd_quarter beginning \n", + "753 3rd_quarter beginning \n", + "799 3rd_quarter beginning \n", + "802 3rd_quarter beginning \n", + "859 3rd_quarter beginning " + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_4.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "df_4.drop(columns=['month', 'day'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "channelGrouping object\n", + "device.browser object\n", + "device.operatingSystem object\n", + "geoNetwork.region object\n", + "totals.hits int64\n", + "totals.pageviews float64\n", + "totals.newVisits category\n", + "totals.transactionRevenue float64\n", + "trafficSource.medium object\n", + "year int64\n", + "quarter object\n", + "day_of_month object\n", + "dtype: object" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# give the type of each column\n", + "df_4.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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channelGroupingdevice.browserdevice.operatingSystemgeoNetwork.regiontotals.hitstotals.pageviewstotals.newVisitstotals.transactionRevenuetrafficSource.mediumyearquarterday_of_month
75200101111137860000.00201620
753100011100306670000.01201620
79940101311068030000.02201620
80240221312026250000.02201620
859400117140574150000.02201620
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" + ], + "text/plain": [ + " channelGrouping device.browser device.operatingSystem \\\n", + "752 0 0 1 \n", + "753 1 0 0 \n", + "799 4 0 1 \n", + "802 4 0 2 \n", + "859 4 0 0 \n", + "\n", + " geoNetwork.region totals.hits totals.pageviews totals.newVisits \\\n", + "752 0 11 11 1 \n", + "753 0 11 10 0 \n", + "799 0 13 11 0 \n", + "802 2 13 12 0 \n", + "859 1 17 14 0 \n", + "\n", + " totals.transactionRevenue trafficSource.medium year quarter \\\n", + "752 37860000.0 0 2016 2 \n", + "753 306670000.0 1 2016 2 \n", + "799 68030000.0 2 2016 2 \n", + "802 26250000.0 2 2016 2 \n", + "859 574150000.0 2 2016 2 \n", + "\n", + " day_of_month \n", + "752 0 \n", + "753 0 \n", + "799 0 \n", + "802 0 \n", + "859 0 " + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert the column to int\n", + "df_4['totals.pageviews'] = df_4['totals.pageviews'].astype(int)\n", + "\n", + "from sklearn.preprocessing import LabelEncoder\n", + "# Initialize a label encoder\n", + "label_encoder = LabelEncoder()\n", + "\n", + "# Iterate through the columns\n", + "for column in df_4.columns:\n", + " if df_4[column].dtype == 'object':\n", + " df_4[column] = label_encoder.fit_transform(df_4[column])\n", + " else:\n", + " continue\n", + "\n", + "df_4.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error: 8.804161590438723e+16\n", + "R-squared: 0.0334859344263867\n" + ] + } + ], + "source": [ + "# Split the data into features and target\n", + "X = df_4.drop(columns='totals.transactionRevenue')\n", + "y = df_4['totals.transactionRevenue']\n", + "\n", + "# Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "\n", + "# Initialize a standard scaler\n", + "scaler = StandardScaler()\n", + "\n", + "# Fit the scaler\n", + "scaler.fit(X_train)\n", + "\n", + "# Scale the train and test data\n", + "X_train = scaler.transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "# regression model\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "\n", + "# Initialize the model\n", + "model = LinearRegression()\n", + "\n", + "# Fit the model\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "\n", + "print(\"Mean Squared Error:\", mse)\n", + "print(\"R-squared:\", r2)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error: 1.20426572380097e+17\n", + "R-squared: -0.32203361874441105\n" + ] + } + ], + "source": [ + "# other regression model\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "# Initialize the model\n", + "model = RandomForestRegressor(n_estimators=100, random_state=42)\n", + "\n", + "# Fit the model\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "\n", + "print(\"Mean Squared Error:\", mse)\n", + "print(\"R-squared:\", r2)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/regression/Regression.ipynb b/regression/Regression.ipynb index 5871471..95b6ef0 100644 --- a/regression/Regression.ipynb +++ b/regression/Regression.ipynb @@ -1,8 +1,22 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Preprocessing and feature engineering " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Library\n" + ] + }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -52,16 +66,23 @@ "from sklearn.preprocessing import StandardScaler\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Importing" + ] + }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 117, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/9z/gkk8fkkj0pz746hrzcw69dq40000gn/T/ipykernel_4825/1626016371.py:1: DtypeWarning:\n", + "/var/folders/9z/gkk8fkkj0pz746hrzcw69dq40000gn/T/ipykernel_7875/1626016371.py:1: DtypeWarning:\n", "\n", "Columns (2,34) have mixed types. Specify dtype option on import or set low_memory=False.\n", "\n" @@ -73,7 +94,7 @@ "[]" ] }, - "execution_count": 125, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" } @@ -89,9 +110,16 @@ "null" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preprocessing \n" + ] + }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -134,7 +162,7 @@ " 'trafficSource.campaignCode']" ] }, - "execution_count": 126, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -147,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -175,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ @@ -188,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -232,7 +260,7 @@ "dtype: float64" ] }, - "execution_count": 129, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -256,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 122, "metadata": {}, "outputs": [ { @@ -290,21 +318,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Regression model on revenue (Only with data that percentage of customers producing data)" + "## Regression model on revenue (Only with data that percentage of customers producing data)\n" ] }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 131, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" }, @@ -327,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -371,7 +399,7 @@ "dtype: int64" ] }, - "execution_count": 132, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -385,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -482,7 +510,111 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "df_2 = df_1.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "channelGrouping 11515\n", + "date 11515\n", + "trafficSource.source 11515\n", + "trafficSource.campaign 11515\n", + "totals.transactionRevenue 11515\n", + "totals.pageviews 11515\n", + "totals.hits 11515\n", + "geoNetwork.networkDomain 11515\n", + "geoNetwork.city 11515\n", + "geoNetwork.metro 11515\n", + "geoNetwork.region 11515\n", + "geoNetwork.country 11515\n", + "geoNetwork.subContinent 11515\n", + "geoNetwork.continent 11515\n", + "device.deviceCategory 11515\n", + "device.isMobile 11515\n", + "device.operatingSystem 11515\n", + "device.browser 11515\n", + "visitStartTime 11515\n", + "visitNumber 11515\n", + "trafficSource.medium 11515\n", + "totals.newVisits 4465\n", + "dtype: int64" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# only use the data that has transaction revenue\n", + "df_2 = df_2[df_2['totals.transactionRevenue'] > 0]\n", + "\n", + "# check how many data left\n", + "count2 = df_2.count()\n", + "count2 = count2.sort_values(ascending=False)\n", + "count2" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unique values before: [1.0, NaN]\n", + "Categories (1, float64): [1.0]\n", + "Unique values after: [1, 0]\n", + "Categories (2, int64): [1, 0]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9z/gkk8fkkj0pz746hrzcw69dq40000gn/T/ipykernel_7875/805069265.py:9: FutureWarning:\n", + "\n", + "Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Assuming df_2 is your DataFrame and has already been loaded\n", + "\n", + "# Step 1: Convert 'totals.newVisits' to categorical\n", + "df_2['totals.newVisits'] = df_2['totals.newVisits'].astype('category')\n", + "\n", + "# Check unique values before making changes\n", + "print(\"Unique values before:\", df_2['totals.newVisits'].unique())\n", + "\n", + "# Step 2: Fill NA/NaN values with 0\n", + "df_2['totals.newVisits'] = df_2['totals.newVisits'].cat.add_categories([0]).fillna(0)\n", + "\n", + "# Step 3: Ensure all values are either 1 or 0\n", + "df_2['totals.newVisits'] = df_2['totals.newVisits'].apply(lambda x: 1 if x == 1 else 0).astype('category')\n", + "\n", + "# Check unique values after making changes\n", + "print(\"Unique values after:\", df_2['totals.newVisits'].unique())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 129, "metadata": {}, "outputs": [ { @@ -548,90 +680,90 @@ " (not set)\n", " 11\n", " 11.0\n", - " 1.0\n", + " 1\n", " 37860000.0\n", " (not set)\n", " (direct)\n", " (none)\n", " \n", " \n", - " 893\n", - " Direct\n", + " 753\n", + " Organic Search\n", " 20160902\n", - " 1\n", - " 1472861525\n", - " Safari\n", + " 3\n", + " 1472844906\n", + " Chrome\n", " Macintosh\n", " False\n", " desktop\n", " Americas\n", " Northern America\n", " ...\n", - " San Francisco-Oakland-San Jose CA\n", - " San Jose\n", - " comcast.net\n", - " 19\n", - " 16.0\n", - " 1.0\n", - " 395730000.0\n", + " New York NY\n", + " New York\n", " (not set)\n", - " (direct)\n", - " (none)\n", + " 11\n", + " 10.0\n", + " 0\n", + " 306670000.0\n", + " (not set)\n", + " google\n", + " organic\n", " \n", " \n", - " 922\n", - " Organic Search\n", + " 799\n", + " Referral\n", " 20160902\n", - " 1\n", - " 1472853332\n", + " 7\n", + " 1472827393\n", " Chrome\n", - " Android\n", - " True\n", - " mobile\n", + " Linux\n", + " False\n", + " desktop\n", " Americas\n", " Northern America\n", " ...\n", - " San Francisco-Oakland-San Jose CA\n", - " Mountain View\n", - " directmusicservice.com\n", - " 22\n", - " 16.0\n", - " 1.0\n", - " 35480000.0\n", + " New York NY\n", + " New York\n", " (not set)\n", - " google\n", - " organic\n", + " 13\n", + " 11.0\n", + " 0\n", + " 68030000.0\n", + " (not set)\n", + " mall.googleplex.com\n", + " referral\n", " \n", " \n", - " 974\n", - " Organic Search\n", + " 802\n", + " Referral\n", " 20160902\n", - " 1\n", - " 1472832496\n", - " Safari\n", - " iOS\n", - " True\n", - " mobile\n", + " 6\n", + " 1472846398\n", + " Chrome\n", + " Windows\n", + " False\n", + " desktop\n", " Americas\n", " Northern America\n", " ...\n", " San Francisco-Oakland-San Jose CA\n", - " San Francisco\n", - " att.net\n", - " 28\n", - " 20.0\n", - " 1.0\n", - " 117340000.0\n", + " Mountain View\n", " (not set)\n", - " google\n", - " organic\n", + " 13\n", + " 12.0\n", + " 0\n", + " 26250000.0\n", + " (not set)\n", + " mall.googleplex.com\n", + " referral\n", " \n", " \n", - " 976\n", + " 859\n", " Referral\n", " 20160902\n", - " 1\n", - " 1472879820\n", + " 4\n", + " 1472824817\n", " Chrome\n", " Macintosh\n", " False\n", @@ -642,10 +774,10 @@ " not available in demo dataset\n", " not available in demo dataset\n", " (not set)\n", - " 28\n", - " 21.0\n", - " 1.0\n", - " 43710000.0\n", + " 17\n", + " 14.0\n", + " 0\n", + " 574150000.0\n", " (not set)\n", " mall.googleplex.com\n", " referral\n", @@ -658,57 +790,57 @@ "text/plain": [ " channelGrouping date visitNumber visitStartTime device.browser \\\n", "752 Direct 20160902 1 1472843572 Chrome \n", - "893 Direct 20160902 1 1472861525 Safari \n", - "922 Organic Search 20160902 1 1472853332 Chrome \n", - "974 Organic Search 20160902 1 1472832496 Safari \n", - "976 Referral 20160902 1 1472879820 Chrome \n", + "753 Organic Search 20160902 3 1472844906 Chrome \n", + "799 Referral 20160902 7 1472827393 Chrome \n", + "802 Referral 20160902 6 1472846398 Chrome \n", + "859 Referral 20160902 4 1472824817 Chrome \n", "\n", " device.operatingSystem device.isMobile device.deviceCategory \\\n", "752 Linux False desktop \n", - "893 Macintosh False desktop \n", - "922 Android True mobile \n", - "974 iOS True mobile \n", - "976 Macintosh False desktop \n", + "753 Macintosh False desktop \n", + "799 Linux False desktop \n", + "802 Windows False desktop \n", + "859 Macintosh False desktop \n", "\n", " geoNetwork.continent geoNetwork.subContinent ... \\\n", "752 Americas Northern America ... \n", - "893 Americas Northern America ... \n", - "922 Americas Northern America ... \n", - "974 Americas Northern America ... \n", - "976 Americas Northern America ... \n", + "753 Americas Northern America ... \n", + "799 Americas Northern America ... \n", + "802 Americas Northern America ... \n", + "859 Americas Northern America ... \n", "\n", " geoNetwork.metro geoNetwork.city \\\n", "752 Detroit MI Ann Arbor \n", - "893 San Francisco-Oakland-San Jose CA San Jose \n", - "922 San Francisco-Oakland-San Jose CA Mountain View \n", - "974 San Francisco-Oakland-San Jose CA San Francisco \n", - "976 not available in demo dataset not available in demo dataset \n", + "753 New York NY New York \n", + "799 New York NY New York \n", + "802 San Francisco-Oakland-San Jose CA Mountain View \n", + "859 not available in demo dataset not available in demo dataset \n", "\n", " geoNetwork.networkDomain totals.hits totals.pageviews totals.newVisits \\\n", - "752 (not set) 11 11.0 1.0 \n", - "893 comcast.net 19 16.0 1.0 \n", - "922 directmusicservice.com 22 16.0 1.0 \n", - "974 att.net 28 20.0 1.0 \n", - "976 (not set) 28 21.0 1.0 \n", + "752 (not set) 11 11.0 1 \n", + "753 (not set) 11 10.0 0 \n", + "799 (not set) 13 11.0 0 \n", + "802 (not set) 13 12.0 0 \n", + "859 (not set) 17 14.0 0 \n", "\n", - " totals.transactionRevenue trafficSource.campaign trafficSource.source \\\n", - "752 37860000.0 (not set) (direct) \n", - "893 395730000.0 (not set) (direct) \n", - "922 35480000.0 (not set) google \n", - "974 117340000.0 (not set) google \n", - "976 43710000.0 (not set) mall.googleplex.com \n", + " totals.transactionRevenue trafficSource.campaign trafficSource.source \\\n", + "752 37860000.0 (not set) (direct) \n", + "753 306670000.0 (not set) google \n", + "799 68030000.0 (not set) mall.googleplex.com \n", + "802 26250000.0 (not set) mall.googleplex.com \n", + "859 574150000.0 (not set) mall.googleplex.com \n", "\n", " trafficSource.medium \n", "752 (none) \n", - "893 (none) \n", - "922 organic \n", - "974 organic \n", - "976 referral \n", + "753 organic \n", + "799 referral \n", + "802 referral \n", + "859 referral \n", "\n", "[5 rows x 22 columns]" ] }, - "execution_count": 134, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -719,7 +851,66 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "# make date to datetime\n", + "df_2['date'] = pd.to_datetime(df_2['date'], format='%Y%m%d')" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "channelGrouping object\n", + "date datetime64[ns]\n", + "visitNumber int64\n", + "visitStartTime int64\n", + "device.browser object\n", + "device.operatingSystem object\n", + "device.isMobile bool\n", + "device.deviceCategory object\n", + "geoNetwork.continent object\n", + "geoNetwork.subContinent object\n", + "geoNetwork.country object\n", + "geoNetwork.region object\n", + "geoNetwork.metro object\n", + "geoNetwork.city object\n", + "geoNetwork.networkDomain object\n", + "totals.hits int64\n", + "totals.pageviews float64\n", + "totals.newVisits category\n", + "totals.transactionRevenue float64\n", + "trafficSource.campaign object\n", + "trafficSource.source object\n", + "trafficSource.medium object\n", + "dtype: object" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NOT include Time Series - more data cleaning and feature selection " + ] + }, + { + "cell_type": "code", + "execution_count": 132, "metadata": {}, "outputs": [ { @@ -745,24 +936,19 @@ " \n", " channelGrouping\n", " date\n", - " visitNumber\n", - " visitStartTime\n", " device.browser\n", " device.operatingSystem\n", " device.isMobile\n", " device.deviceCategory\n", " geoNetwork.continent\n", " geoNetwork.subContinent\n", - " ...\n", - " geoNetwork.metro\n", - " geoNetwork.city\n", - " geoNetwork.networkDomain\n", + " geoNetwork.country\n", + " geoNetwork.region\n", " totals.hits\n", " totals.pageviews\n", " totals.newVisits\n", " totals.transactionRevenue\n", " trafficSource.campaign\n", - " trafficSource.source\n", " trafficSource.medium\n", " \n", " \n", @@ -770,415 +956,140 @@ " \n", " 752\n", " Direct\n", - " 20160902\n", - " 1\n", - " 1472843572\n", + " 2016-09-02\n", " Chrome\n", " Linux\n", " False\n", " desktop\n", " Americas\n", " Northern America\n", - " ...\n", - " Detroit MI\n", - " Ann Arbor\n", - " (not set)\n", + " United States\n", + " Michigan\n", " 11\n", " 11.0\n", - " 1.0\n", + " 1\n", " 37860000.0\n", " (not set)\n", - " (direct)\n", " (none)\n", " \n", " \n", - " 893\n", - " Direct\n", - " 20160902\n", - " 1\n", - " 1472861525\n", - " Safari\n", + " 753\n", + " Organic Search\n", + " 2016-09-02\n", + " Chrome\n", " Macintosh\n", " False\n", " desktop\n", " Americas\n", " Northern America\n", - " ...\n", - " San Francisco-Oakland-San Jose CA\n", - " San Jose\n", - " comcast.net\n", - " 19\n", - " 16.0\n", - " 1.0\n", - " 395730000.0\n", + " United States\n", + " New York\n", + " 11\n", + " 10.0\n", + " 0\n", + " 306670000.0\n", " (not set)\n", - " (direct)\n", - " (none)\n", + " organic\n", " \n", " \n", - " 922\n", - " Organic Search\n", - " 20160902\n", - " 1\n", - " 1472853332\n", + " 799\n", + " Referral\n", + " 2016-09-02\n", " Chrome\n", - " Android\n", - " True\n", - " mobile\n", + " Linux\n", + " False\n", + " desktop\n", " Americas\n", " Northern America\n", - " ...\n", - " San Francisco-Oakland-San Jose CA\n", - " Mountain View\n", - " directmusicservice.com\n", - " 22\n", - " 16.0\n", - " 1.0\n", - " 35480000.0\n", + " United States\n", + " New York\n", + " 13\n", + " 11.0\n", + " 0\n", + " 68030000.0\n", " (not set)\n", - " google\n", - " organic\n", + " referral\n", " \n", " \n", - " 974\n", - " Organic Search\n", - " 20160902\n", - " 1\n", - " 1472832496\n", - " Safari\n", - " iOS\n", - " True\n", - " mobile\n", + " 802\n", + " Referral\n", + " 2016-09-02\n", + " Chrome\n", + " Windows\n", + " False\n", + " desktop\n", " Americas\n", " Northern America\n", - " ...\n", - " San Francisco-Oakland-San Jose CA\n", - " San Francisco\n", - " att.net\n", - " 28\n", - " 20.0\n", - " 1.0\n", - " 117340000.0\n", + " United States\n", + " California\n", + " 13\n", + " 12.0\n", + " 0\n", + " 26250000.0\n", " (not set)\n", - " google\n", - " organic\n", + " referral\n", " \n", " \n", - " 976\n", + " 859\n", " Referral\n", - " 20160902\n", - " 1\n", - " 1472879820\n", + " 2016-09-02\n", " Chrome\n", " Macintosh\n", " False\n", " desktop\n", " Americas\n", " Northern America\n", - " ...\n", - " not available in demo dataset\n", + " United States\n", " not available in demo dataset\n", + " 17\n", + " 14.0\n", + " 0\n", + " 574150000.0\n", " (not set)\n", - " 28\n", - " 21.0\n", - " 1.0\n", - " 43710000.0\n", - " (not set)\n", - " mall.googleplex.com\n", " referral\n", " \n", " \n", "\n", - "

5 rows × 22 columns

\n", "" ], "text/plain": [ - " channelGrouping date visitNumber visitStartTime device.browser \\\n", - "752 Direct 20160902 1 1472843572 Chrome \n", - "893 Direct 20160902 1 1472861525 Safari \n", - "922 Organic Search 20160902 1 1472853332 Chrome \n", - "974 Organic Search 20160902 1 1472832496 Safari \n", - "976 Referral 20160902 1 1472879820 Chrome \n", + " channelGrouping date device.browser device.operatingSystem \\\n", + "752 Direct 2016-09-02 Chrome Linux \n", + "753 Organic Search 2016-09-02 Chrome Macintosh \n", + "799 Referral 2016-09-02 Chrome Linux \n", + "802 Referral 2016-09-02 Chrome Windows \n", + "859 Referral 2016-09-02 Chrome Macintosh \n", "\n", - " device.operatingSystem device.isMobile device.deviceCategory \\\n", - "752 Linux False desktop \n", - "893 Macintosh False desktop \n", - "922 Android True mobile \n", - "974 iOS True mobile \n", - "976 Macintosh False desktop \n", - "\n", - " geoNetwork.continent geoNetwork.subContinent ... \\\n", - "752 Americas Northern America ... \n", - "893 Americas Northern America ... \n", - "922 Americas Northern America ... \n", - "974 Americas Northern America ... \n", - "976 Americas Northern America ... \n", + " device.isMobile device.deviceCategory geoNetwork.continent \\\n", + "752 False desktop Americas \n", + "753 False desktop Americas \n", + "799 False desktop Americas \n", + "802 False desktop Americas \n", + "859 False desktop Americas \n", "\n", - " geoNetwork.metro geoNetwork.city \\\n", - "752 Detroit MI Ann Arbor \n", - "893 San Francisco-Oakland-San Jose CA San Jose \n", - "922 San Francisco-Oakland-San Jose CA Mountain View \n", - "974 San Francisco-Oakland-San Jose CA San Francisco \n", - "976 not available in demo dataset not available in demo dataset \n", + " geoNetwork.subContinent geoNetwork.country geoNetwork.region \\\n", + "752 Northern America United States Michigan \n", + "753 Northern America United States New York \n", + "799 Northern America United States New York \n", + "802 Northern America United States California \n", + "859 Northern America United States not available in demo dataset \n", "\n", - " geoNetwork.networkDomain totals.hits totals.pageviews totals.newVisits \\\n", - "752 (not set) 11 11.0 1.0 \n", - "893 comcast.net 19 16.0 1.0 \n", - "922 directmusicservice.com 22 16.0 1.0 \n", - "974 att.net 28 20.0 1.0 \n", - "976 (not set) 28 21.0 1.0 \n", + " totals.hits totals.pageviews totals.newVisits \\\n", + "752 11 11.0 1 \n", + "753 11 10.0 0 \n", + "799 13 11.0 0 \n", + "802 13 12.0 0 \n", + "859 17 14.0 0 \n", "\n", - " totals.transactionRevenue trafficSource.campaign trafficSource.source \\\n", - "752 37860000.0 (not set) (direct) \n", - "893 395730000.0 (not set) (direct) \n", - "922 35480000.0 (not set) google \n", - "974 117340000.0 (not set) google \n", - "976 43710000.0 (not set) mall.googleplex.com \n", - "\n", - " trafficSource.medium \n", - "752 (none) \n", - "893 (none) \n", - "922 organic \n", - "974 organic \n", - "976 referral \n", - "\n", - "[5 rows x 22 columns]" - ] - }, - "execution_count": 135, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_2.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "channelGrouping object\n", - "date int64\n", - "visitNumber int64\n", - "visitStartTime int64\n", - "device.browser object\n", - "device.operatingSystem object\n", - "device.isMobile bool\n", - "device.deviceCategory object\n", - "geoNetwork.continent object\n", - "geoNetwork.subContinent object\n", - "geoNetwork.country object\n", - "geoNetwork.region object\n", - "geoNetwork.metro object\n", - "geoNetwork.city object\n", - "geoNetwork.networkDomain object\n", - "totals.hits int64\n", - "totals.pageviews float64\n", - "totals.newVisits float64\n", - "totals.transactionRevenue float64\n", - "trafficSource.campaign object\n", - "trafficSource.source object\n", - "trafficSource.medium object\n", - "dtype: object" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_2.dtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NOT include Time Series\n" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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channelGroupingdevice.browserdevice.operatingSystemdevice.isMobiledevice.deviceCategorygeoNetwork.continentgeoNetwork.subContinentgeoNetwork.countrygeoNetwork.regiontotals.hitstotals.pageviewstotals.newVisitstotals.transactionRevenuetrafficSource.campaigntrafficSource.medium
752DirectChromeLinuxFalsedesktopAmericasNorthern AmericaUnited StatesMichigan1111.01.037860000.0(not set)(none)
893DirectSafariMacintoshFalsedesktopAmericasNorthern AmericaUnited StatesCalifornia1916.01.0395730000.0(not set)(none)
922Organic SearchChromeAndroidTruemobileAmericasNorthern AmericaUnited StatesCalifornia2216.01.035480000.0(not set)organic
974Organic SearchSafariiOSTruemobileAmericasNorthern AmericaUnited StatesCalifornia2820.01.0117340000.0(not set)organic
976ReferralChromeMacintoshFalsedesktopAmericasNorthern AmericaUnited Statesnot available in demo dataset2821.01.043710000.0(not set)referral
\n", - "
" - ], - "text/plain": [ - " channelGrouping device.browser device.operatingSystem device.isMobile \\\n", - "752 Direct Chrome Linux False \n", - "893 Direct Safari Macintosh False \n", - "922 Organic Search Chrome Android True \n", - "974 Organic Search Safari iOS True \n", - "976 Referral Chrome Macintosh False \n", - "\n", - " device.deviceCategory geoNetwork.continent geoNetwork.subContinent \\\n", - "752 desktop Americas Northern America \n", - "893 desktop Americas Northern America \n", - "922 mobile Americas Northern America \n", - "974 mobile Americas Northern America \n", - "976 desktop Americas Northern America \n", - "\n", - " geoNetwork.country geoNetwork.region totals.hits \\\n", - "752 United States Michigan 11 \n", - "893 United States California 19 \n", - "922 United States California 22 \n", - "974 United States California 28 \n", - "976 United States not available in demo dataset 28 \n", - "\n", - " totals.pageviews totals.newVisits totals.transactionRevenue \\\n", - "752 11.0 1.0 37860000.0 \n", - "893 16.0 1.0 395730000.0 \n", - "922 16.0 1.0 35480000.0 \n", - "974 20.0 1.0 117340000.0 \n", - "976 21.0 1.0 43710000.0 \n", - "\n", - " trafficSource.campaign trafficSource.medium \n", - "752 (not set) (none) \n", - "893 (not set) (none) \n", - "922 (not set) organic \n", - "974 (not set) organic \n", - "976 (not set) referral " + " totals.transactionRevenue trafficSource.campaign trafficSource.medium \n", + "752 37860000.0 (not set) (none) \n", + "753 306670000.0 (not set) organic \n", + "799 68030000.0 (not set) referral \n", + "802 26250000.0 (not set) referral \n", + "859 574150000.0 (not set) referral " ] }, - "execution_count": 137, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -1187,14 +1098,14 @@ "#type of each row\n", "df_2.dtypes\n", "#drop the date\n", - "df_3 = df_2.drop(columns=['date','geoNetwork.metro','geoNetwork.networkDomain','visitNumber','geoNetwork.city',\n", + "df_3 = df_2.drop(columns=['geoNetwork.metro','geoNetwork.networkDomain','visitNumber','geoNetwork.city',\n", " 'visitStartTime','trafficSource.source'])\n", "df_3.head()" ] }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 133, "metadata": {}, "outputs": [ { @@ -1204,47 +1115,49 @@ "channelGrouping 8\n", "\n", "\n", - "['Direct' 'Organic Search' 'Referral' 'Paid Search' 'Social' 'Display'\n", + "['Direct' 'Organic Search' 'Referral' 'Display' 'Paid Search' 'Social'\n", " 'Affiliates' '(Other)']\n", "\n", "\n", - "Organic Search 1657\n", - "Referral 1533\n", - "Direct 970\n", - "Paid Search 214\n", - "Social 47\n", - "Display 40\n", - "Affiliates 3\n", + "Referral 5311\n", + "Organic Search 3438\n", + "Direct 2042\n", + "Paid Search 468\n", + "Display 142\n", + "Social 104\n", + "Affiliates 9\n", "(Other) 1\n", "Name: channelGrouping, dtype: int64\n", - "device.browser 8\n", + "device.browser 9\n", "\n", "\n", - "['Chrome' 'Safari' 'Firefox' 'Safari (in-app)' 'Internet Explorer' 'Edge'\n", - " 'Android Webview' 'Opera']\n", + "['Chrome' 'Safari' 'Firefox' 'Safari (in-app)' 'Edge' 'Internet Explorer'\n", + " 'Android Webview' 'Opera' 'Amazon Silk']\n", "\n", "\n", - "Chrome 3821\n", - "Safari 428\n", - "Firefox 106\n", - "Internet Explorer 61\n", - "Edge 33\n", - "Safari (in-app) 8\n", - "Android Webview 5\n", - "Opera 3\n", + "Chrome 10353\n", + "Safari 780\n", + "Firefox 191\n", + "Internet Explorer 109\n", + "Edge 58\n", + "Safari (in-app) 12\n", + "Android Webview 6\n", + "Opera 5\n", + "Amazon Silk 1\n", "Name: device.browser, dtype: int64\n", - "device.operatingSystem 6\n", + "device.operatingSystem 7\n", "\n", "\n", - "['Linux' 'Macintosh' 'Android' 'iOS' 'Chrome OS' 'Windows']\n", + "['Linux' 'Macintosh' 'Windows' 'Android' 'Chrome OS' 'iOS' 'Windows Phone']\n", "\n", "\n", - "Macintosh 2210\n", - "Windows 1073\n", - "iOS 312\n", - "Chrome OS 312\n", - "Linux 309\n", - "Android 249\n", + "Macintosh 6426\n", + "Windows 2309\n", + "Chrome OS 994\n", + "Linux 782\n", + "iOS 536\n", + "Android 467\n", + "Windows Phone 1\n", "Name: device.operatingSystem, dtype: int64\n", "device.deviceCategory 3\n", "\n", @@ -1252,220 +1165,142 @@ "['desktop' 'mobile' 'tablet']\n", "\n", "\n", - "desktop 3895\n", - "mobile 475\n", - "tablet 95\n", + "desktop 10495\n", + "mobile 852\n", + "tablet 168\n", "Name: device.deviceCategory, dtype: int64\n", "geoNetwork.continent 6\n", "\n", "\n", - "['Americas' 'Asia' 'Oceania' 'Europe' '(not set)' 'Africa']\n", + "['Americas' 'Asia' 'Europe' 'Oceania' '(not set)' 'Africa']\n", "\n", "\n", - "Americas 4378\n", - "Asia 50\n", - "Europe 26\n", - "Oceania 6\n", - "(not set) 3\n", - "Africa 2\n", + "Americas 11283\n", + "Asia 125\n", + "Europe 79\n", + "Oceania 14\n", + "Africa 8\n", + "(not set) 6\n", "Name: geoNetwork.continent, dtype: int64\n", - "geoNetwork.subContinent 16\n", - "\n", - "\n", - "['Northern America' 'Eastern Asia' 'Australasia' 'Northern Europe'\n", - " 'Western Asia' 'South America' 'Southeast Asia' 'Caribbean'\n", - " 'Eastern Europe' 'Southern Europe' 'Southern Asia' '(not set)'\n", - " 'Central America' 'Western Europe' 'Central Asia' 'Southern Africa']\n", - "\n", - "\n", - "Northern America 4316\n", - "South America 40\n", - "Eastern Asia 24\n", - "Southeast Asia 14\n", - "Caribbean 11\n", - "Central America 11\n", - "Western Europe 10\n", - "Western Asia 9\n", - "Northern Europe 8\n", - "Australasia 6\n", - "Eastern Europe 4\n", - "Southern Europe 4\n", - "(not set) 3\n", - "Southern Asia 2\n", - "Southern Africa 2\n", - "Central Asia 1\n", + "geoNetwork.subContinent 19\n", + "\n", + "\n", + "['Northern America' 'Caribbean' 'Eastern Asia' 'Western Europe'\n", + " 'Central America' 'Australasia' 'Northern Europe' 'Western Asia'\n", + " 'South America' 'Southern Asia' 'Southeast Asia' '(not set)'\n", + " 'Eastern Europe' 'Southern Europe' 'Western Africa' 'Eastern Africa'\n", + " 'Central Asia' 'Southern Africa' 'Northern Africa']\n", + "\n", + "\n", + "Northern America 11143\n", + "South America 98\n", + "Eastern Asia 59\n", + "Southeast Asia 32\n", + "Western Europe 30\n", + "Northern Europe 27\n", + "Central America 26\n", + "Western Asia 21\n", + "Caribbean 16\n", + "Eastern Europe 14\n", + "Australasia 14\n", + "Southern Asia 11\n", + "Southern Europe 8\n", + "(not set) 6\n", + "Eastern Africa 3\n", + "Western Africa 2\n", + "Central Asia 2\n", + "Southern Africa 2\n", + "Northern Africa 1\n", "Name: geoNetwork.subContinent, dtype: int64\n", - "geoNetwork.country 53\n", - "\n", - "\n", - "['United States' 'Taiwan' 'Australia' 'Ireland' 'Israel' 'Canada' 'Chile'\n", - " 'Finland' 'Venezuela' 'Indonesia' 'St. Lucia' 'Japan' 'China' 'Sweden'\n", - " 'Ecuador' 'Hong Kong' 'Hungary' 'Greece' 'Puerto Rico' 'Philippines'\n", - " 'India' 'Ukraine' 'United Kingdom' 'Malaysia' 'Singapore' '(not set)'\n", - " 'Mexico' 'Germany' 'Belgium' 'Brazil' 'Kazakhstan' 'Saudi Arabia'\n", - " 'Anguilla' 'Thailand' 'South Korea' 'Netherlands' 'South Africa' 'Turkey'\n", - " 'Italy' 'Colombia' 'Georgia' 'El Salvador' 'Denmark' 'Curaçao' 'Peru'\n", - " 'Panama' 'Russia' 'Spain' 'France' 'Portugal' 'Switzerland' 'Argentina'\n", - " 'Guadeloupe']\n", - "\n", - "\n", - "United States 4238\n", - "Canada 78\n", - "Venezuela 29\n", - "Japan 8\n", - "Mexico 8\n", - "Indonesia 6\n", - "Taiwan 6\n", - "Australia 6\n", - "Puerto Rico 6\n", - "Germany 5\n", - "Israel 4\n", - "Hong Kong 4\n", - "South Korea 4\n", - "Brazil 4\n", - "Singapore 3\n", - "Saudi Arabia 3\n", - "(not set) 3\n", - "Ecuador 2\n", - "Panama 2\n", - "South Africa 2\n", - "Finland 2\n", - "Chile 2\n", - "Belgium 2\n", - "St. Lucia 2\n", - "Ireland 2\n", - "Malaysia 2\n", - "United Kingdom 2\n", - "Ukraine 2\n", - "India 2\n", - "Philippines 2\n", - "China 2\n", - "El Salvador 1\n", - "Portugal 1\n", - "France 1\n", - "Spain 1\n", - "Russia 1\n", - "Switzerland 1\n", - "Argentina 1\n", - "Peru 1\n", - "Curaçao 1\n", - "Denmark 1\n", - "Thailand 1\n", - "Georgia 1\n", - "Colombia 1\n", - "Italy 1\n", - "Turkey 1\n", - "Netherlands 1\n", - "Sweden 1\n", + "geoNetwork.country 69\n", + "\n", + "\n", + "['United States' 'Puerto Rico' 'Taiwan' 'Switzerland' 'Canada' 'Mexico'\n", + " 'Australia' 'Sweden' 'Ireland' 'Israel' 'Germany' 'United Kingdom'\n", + " 'New Zealand' 'Argentina' 'Chile' 'Finland' 'India' 'Venezuela'\n", + " 'Indonesia' 'St. Lucia' 'Japan' 'China' 'Colombia' 'Hong Kong' 'Brazil'\n", + " 'Philippines' '(not set)' 'Pakistan' 'South Korea' 'Ecuador' 'Singapore'\n", + " 'Hungary' 'Greece' 'Russia' 'Kuwait' 'Malaysia' 'Spain' 'Ukraine'\n", + " 'Cyprus' 'Romania' 'Nigeria' 'Uruguay' 'Belgium' 'Kenya' 'Kazakhstan'\n", + " 'Turkey' 'Saudi Arabia' 'Anguilla' 'Armenia' 'France'\n", + " 'United Arab Emirates' 'Thailand' 'Netherlands' 'South Africa' 'Poland'\n", + " 'Egypt' 'Italy' 'Guatemala' 'Georgia' 'El Salvador' 'Lebanon' 'Nicaragua'\n", + " 'Czechia' 'Denmark' 'Curaçao' 'Peru' 'Panama' 'Portugal' 'Guadeloupe']\n", + "\n", + "\n", + "United States 10953\n", + "Canada 190\n", + "Venezuela 63\n", + "Mexico 20\n", + "Taiwan 19\n", + " ... \n", "Anguilla 1\n", - "Kazakhstan 1\n", - "Greece 1\n", + "Pakistan 1\n", + "Egypt 1\n", "Hungary 1\n", "Guadeloupe 1\n", - "Name: geoNetwork.country, dtype: int64\n", - "geoNetwork.region 60\n", - "\n", - "\n", - "['Michigan' 'California' 'not available in demo dataset' 'Illinois'\n", - " 'Oregon' 'New York' 'District of Columbia' 'Washington' 'Massachusetts'\n", - " 'Victoria' 'Texas' 'County Dublin' 'Tel Aviv District' 'Taipei City'\n", - " 'Colorado' 'North Carolina' 'Ontario' 'New Jersey' 'Georgia'\n", - " 'Santiago Metropolitan Region' 'Utah' 'Pennsylvania' 'Zulia' 'Alberta'\n", - " 'Jakarta' 'Stockholm County' 'Arizona' '(not set)' 'Attica' 'Virginia'\n", - " 'Iowa' 'Quebec' 'Tennessee' 'New South Wales' 'England' 'Missouri'\n", - " 'Federal Territory of Kuala Lumpur' 'Tokyo' 'Mexico City' 'Minnesota'\n", - " 'State of Sao Paulo' 'Florida' 'Nevada' 'Indiana' 'Bangkok' 'Seoul'\n", - " 'Istanbul' 'Bogota' 'Tbilisi' 'Berlin' 'Connecticut' 'Pichincha'\n", - " 'Wisconsin' 'Catalonia' 'Ile-de-France' 'Metro Manila' 'British Columbia'\n", - " 'Zurich' 'Ohio' 'Karnataka']\n", - "\n", - "\n", - "not available in demo dataset 2049\n", - "California 1136\n", - "New York 516\n", - "Illinois 135\n", - "Washington 117\n", - "Texas 114\n", - "Michigan 55\n", - "Massachusetts 49\n", - "Georgia 44\n", - "District of Columbia 41\n", - "Pennsylvania 23\n", - "Ontario 22\n", - "Colorado 21\n", - "(not set) 19\n", - "Zulia 15\n", - "Arizona 8\n", - "New Jersey 8\n", - "North Carolina 7\n", - "Quebec 6\n", - "Oregon 5\n", - "Missouri 4\n", - "Minnesota 4\n", - "Nevada 4\n", - "New South Wales 4\n", - "Jakarta 3\n", - "Tel Aviv District 3\n", - "Taipei City 3\n", - "State of Sao Paulo 3\n", - "Florida 3\n", - "Tennessee 3\n", - "Seoul 2\n", - "Tokyo 2\n", - "Federal Territory of Kuala Lumpur 2\n", - "Mexico City 2\n", - "Iowa 2\n", - "England 2\n", - "Utah 2\n", - "Santiago Metropolitan Region 2\n", - "County Dublin 2\n", - "Victoria 2\n", - "Virginia 2\n", - "Connecticut 1\n", - "Ohio 1\n", - "Zurich 1\n", - "British Columbia 1\n", - "Metro Manila 1\n", - "Ile-de-France 1\n", - "Catalonia 1\n", - "Wisconsin 1\n", - "Pichincha 1\n", - "Bogota 1\n", - "Berlin 1\n", - "Tbilisi 1\n", - "Istanbul 1\n", - "Bangkok 1\n", - "Indiana 1\n", - "Alberta 1\n", - "Stockholm County 1\n", - "Attica 1\n", - "Karnataka 1\n", - "Name: geoNetwork.region, dtype: int64\n", - "trafficSource.campaign 5\n", + "Name: geoNetwork.country, Length: 69, dtype: int64\n", + "geoNetwork.region 79\n", + "\n", + "\n", + "['Michigan' 'New York' 'California' 'not available in demo dataset'\n", + " 'Washington' 'Illinois' 'Oregon' 'District of Columbia' 'Massachusetts'\n", + " '(not set)' 'Virginia' 'Zurich' 'Georgia' 'Texas' 'Ontario'\n", + " 'Pennsylvania' 'Nevada' 'North Carolina' 'State of Rio de Janeiro'\n", + " 'Victoria' 'County Dublin' 'Tel Aviv District' 'Taipei City' 'Colorado'\n", + " 'New Jersey' 'Mexico City' 'Florida' 'New Taipei City' 'England'\n", + " 'Santiago Metropolitan Region' 'Nebraska' 'South Carolina' 'Utah'\n", + " 'Tennessee' 'Zulia' 'Alberta' 'Jakarta' 'Stockholm County' 'Dublin City'\n", + " 'Arizona' 'Ohio' 'Seoul' 'Quebec' 'Attica' 'Iowa' 'British Columbia'\n", + " 'Gujarat' 'Catalonia' 'New South Wales'\n", + " 'Federal Territory of Kuala Lumpur' 'Missouri' 'Vienna' 'Bucharest'\n", + " 'Tokyo' 'State of Sao Paulo' 'Kanagawa Prefecture' 'Maryland' 'Minnesota'\n", + " 'Delhi' 'Istanbul' 'Indiana' 'Ile-de-France' 'Bangkok' 'Bogota' 'Tbilisi'\n", + " 'Hamburg' 'Prague' 'North Holland' 'Berlin' 'Maharashtra'\n", + " 'Masovian Voivodeship' 'Connecticut' 'Pichincha' 'Buenos Aires'\n", + " 'Wisconsin' 'Dubai' 'Metro Manila' 'Karnataka' 'Lima Region']\n", + "\n", + "\n", + "not available in demo dataset 4579\n", + "California 3305\n", + "New York 1507\n", + "Illinois 423\n", + "Washington 336\n", + " ... \n", + "Kanagawa Prefecture 1\n", + "Attica 1\n", + "Gujarat 1\n", + "Bucharest 1\n", + "Lima Region 1\n", + "Name: geoNetwork.region, Length: 79, dtype: int64\n", + "trafficSource.campaign 7\n", "\n", "\n", "['(not set)' 'AW - Accessories' 'AW - Dynamic Search Ads Whole Site'\n", - " 'Retail (DO NOT EDIT owners nophakun and tianyu)' 'Data Share Promo']\n", + " 'AW - Apparel' 'Retail (DO NOT EDIT owners nophakun and tianyu)'\n", + " 'Data Share Promo' 'test-liyuhz']\n", "\n", "\n", - "(not set) 4249\n", - "AW - Dynamic Search Ads Whole Site 160\n", - "AW - Accessories 52\n", - "Data Share Promo 3\n", - "Retail (DO NOT EDIT owners nophakun and tianyu) 1\n", + "(not set) 11050\n", + "AW - Dynamic Search Ads Whole Site 323\n", + "AW - Accessories 130\n", + "Data Share Promo 9\n", + "AW - Apparel 1\n", + "Retail (DO NOT EDIT owners nophakun and tianyu) 1\n", + "test-liyuhz 1\n", "Name: trafficSource.campaign, dtype: int64\n", "trafficSource.medium 7\n", "\n", "\n", - "['(none)' 'organic' 'referral' 'cpc' 'cpm' 'affiliate' '(not set)']\n", + "['(none)' 'organic' 'referral' 'cpm' 'cpc' 'affiliate' '(not set)']\n", "\n", "\n", - "organic 1657\n", - "referral 1580\n", - "(none) 970\n", - "cpc 214\n", - "cpm 40\n", - "affiliate 3\n", + "referral 5415\n", + "organic 3438\n", + "(none) 2042\n", + "cpc 468\n", + "cpm 142\n", + "affiliate 9\n", "(not set) 1\n", "Name: trafficSource.medium, dtype: int64\n" ] @@ -1491,7 +1326,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 134, "metadata": {}, "outputs": [], "source": [ @@ -1500,19 +1335,21 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['Michigan' 'California' 'not available in demo dataset' 'Illinois'\n", - " 'Oregon' 'New York' 'District of Columbia' 'Washington' 'Massachusetts'\n", - " 'Texas' 'Colorado' 'North Carolina' 'New Jersey' 'Georgia' 'Utah'\n", - " 'Pennsylvania' 'Arizona' 'Virginia' 'Iowa' 'Tennessee' '(not set)'\n", - " 'Missouri' 'Minnesota' 'Florida' 'Nevada' 'Zulia' 'Indiana' 'Connecticut'\n", - " 'Wisconsin' 'Ohio']\n" + "['Michigan' 'New York' 'California' 'not available in demo dataset'\n", + " 'Washington' 'Illinois' 'Oregon' 'District of Columbia' 'Massachusetts'\n", + " '(not set)' 'Virginia' 'Georgia' 'Texas' 'Pennsylvania' 'Nevada'\n", + " 'North Carolina' 'State of Rio de Janeiro' 'Colorado' 'New Jersey'\n", + " 'Florida' 'Nebraska' 'South Carolina' 'Utah' 'Tennessee' 'Zulia'\n", + " 'Ontario' 'Arizona' 'Ohio' 'Iowa' 'Catalonia' 'Missouri' 'Vienna'\n", + " 'Maryland' 'Minnesota' 'England' 'Indiana' 'Connecticut' 'Quebec'\n", + " 'Wisconsin']\n" ] } ], @@ -1535,25 +1372,25 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 136, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Organic Search 1520\n", - "Referral 1504\n", - "Direct 912\n", - "Paid Search 213\n", - "Other 89\n", + "Referral 5197\n", + "Organic Search 3111\n", + "Direct 1939\n", + "Paid Search 463\n", + "Other 243\n", "Name: channelGrouping, dtype: int64\n", - "Chrome 3644\n", - "Rest 594\n", + "Chrome 9881\n", + "Rest 1072\n", "Name: device.browser, dtype: int64\n", - "Mac 2119\n", - "Mobile 1135\n", - "Windows 984\n", + "Mac 6176\n", + "Mobile 2674\n", + "Windows 2103\n", "Name: device.operatingSystem, dtype: int64\n" ] } @@ -1571,7 +1408,7 @@ "\n", "# For the 'device.operatingSystem' column\n", "# Replace 'Macintosh' with 'Mac', 'Windows' remains the same, and classify others as 'Mobile'\n", - "df_4['device.operatingSystem'] = df_4['device.operatingSystem'].replace(['Linux', 'Android', 'iOS', 'Chrome OS'], 'Mobile')\n", + "df_4['device.operatingSystem'] = df_4['device.operatingSystem'].replace(['Linux', 'Android', 'iOS', 'Chrome OS','Windows Phone'], 'Mobile')\n", "df_4['device.operatingSystem'] = df_4['device.operatingSystem'].replace(['Macintosh'], 'Mac')\n", "\n", "# Assuming 'device.deviceCategory' needs to be updated based on 'device.operatingSystem'\n", @@ -1594,7 +1431,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 137, "metadata": {}, "outputs": [ { @@ -1604,14 +1441,14 @@ "channelGrouping 5\n", "\n", "\n", - "['Direct' 'Organic Search' 'Referral' 'Paid Search' 'Other']\n", + "['Direct' 'Organic Search' 'Referral' 'Other' 'Paid Search']\n", "\n", "\n", - "Organic Search 1520\n", - "Referral 1504\n", - "Direct 912\n", - "Paid Search 213\n", - "Other 89\n", + "Referral 5197\n", + "Organic Search 3111\n", + "Direct 1939\n", + "Paid Search 463\n", + "Other 243\n", "Name: channelGrouping, dtype: int64\n", "device.browser 2\n", "\n", @@ -1619,8 +1456,8 @@ "['Chrome' 'Rest']\n", "\n", "\n", - "Chrome 3644\n", - "Rest 594\n", + "Chrome 9881\n", + "Rest 1072\n", "Name: device.browser, dtype: int64\n", "device.operatingSystem 3\n", "\n", @@ -1628,62 +1465,73 @@ "['Mobile' 'Mac' 'Windows']\n", "\n", "\n", - "Mac 2119\n", - "Mobile 1135\n", - "Windows 984\n", + "Mac 6176\n", + "Mobile 2674\n", + "Windows 2103\n", "Name: device.operatingSystem, dtype: int64\n", - "geoNetwork.region 29\n", - "\n", - "\n", - "['Michigan' 'California' 'Unknown' 'Illinois' 'Oregon' 'New York'\n", - " 'District of Columbia' 'Washington' 'Massachusetts' 'Texas' 'Colorado'\n", - " 'North Carolina' 'New Jersey' 'Georgia' 'Utah' 'Pennsylvania' 'Arizona'\n", - " 'Virginia' 'Iowa' 'Tennessee' 'Missouri' 'Minnesota' 'Florida' 'Nevada'\n", - " 'Zulia' 'Indiana' 'Connecticut' 'Wisconsin' 'Ohio']\n", - "\n", - "\n", - "Unknown 1930\n", - "California 1136\n", - "New York 516\n", - "Illinois 135\n", - "Washington 117\n", - "Texas 114\n", - "Michigan 55\n", - "Massachusetts 49\n", - "Georgia 44\n", - "District of Columbia 41\n", - "Pennsylvania 23\n", - "Colorado 21\n", - "New Jersey 8\n", - "Arizona 8\n", - "North Carolina 7\n", - "Oregon 5\n", - "Minnesota 4\n", - "Nevada 4\n", - "Missouri 4\n", - "Florida 3\n", - "Tennessee 3\n", - "Utah 2\n", - "Iowa 2\n", - "Virginia 2\n", - "Zulia 1\n", - "Indiana 1\n", - "Connecticut 1\n", - "Wisconsin 1\n", - "Ohio 1\n", + "geoNetwork.region 38\n", + "\n", + "\n", + "['Michigan' 'New York' 'California' 'Unknown' 'Washington' 'Illinois'\n", + " 'Oregon' 'District of Columbia' 'Massachusetts' 'Virginia' 'Georgia'\n", + " 'Texas' 'Pennsylvania' 'Nevada' 'North Carolina'\n", + " 'State of Rio de Janeiro' 'Colorado' 'New Jersey' 'Florida' 'Nebraska'\n", + " 'South Carolina' 'Utah' 'Tennessee' 'Zulia' 'Ontario' 'Arizona' 'Ohio'\n", + " 'Iowa' 'Catalonia' 'Missouri' 'Vienna' 'Maryland' 'Minnesota' 'England'\n", + " 'Indiana' 'Connecticut' 'Quebec' 'Wisconsin']\n", + "\n", + "\n", + "Unknown 4312\n", + "California 3293\n", + "New York 1492\n", + "Illinois 422\n", + "Washington 335\n", + "Texas 303\n", + "Michigan 193\n", + "Massachusetts 137\n", + "Georgia 99\n", + "District of Columbia 98\n", + "Virginia 57\n", + "Pennsylvania 50\n", + "Colorado 40\n", + "Arizona 14\n", + "North Carolina 14\n", + "Oregon 13\n", + "New Jersey 11\n", + "Tennessee 11\n", + "Florida 9\n", + "Minnesota 6\n", + "Nevada 5\n", + "Missouri 5\n", + "Ohio 5\n", + "Iowa 4\n", + "Nebraska 4\n", + "Zulia 4\n", + "Ontario 3\n", + "South Carolina 3\n", + "Utah 2\n", + "Catalonia 1\n", + "Vienna 1\n", + "Maryland 1\n", + "State of Rio de Janeiro 1\n", + "England 1\n", + "Indiana 1\n", + "Connecticut 1\n", + "Quebec 1\n", + "Wisconsin 1\n", "Name: geoNetwork.region, dtype: int64\n", "trafficSource.medium 7\n", "\n", "\n", - "['(none)' 'organic' 'referral' 'cpc' 'cpm' 'affiliate' '(not set)']\n", + "['(none)' 'organic' 'referral' 'cpm' 'cpc' 'affiliate' '(not set)']\n", "\n", "\n", - "referral 1549\n", - "organic 1520\n", - "(none) 912\n", - "cpc 213\n", - "cpm 40\n", - "affiliate 3\n", + "referral 5289\n", + "organic 3111\n", + "(none) 1939\n", + "cpc 463\n", + "cpm 141\n", + "affiliate 9\n", "(not set) 1\n", "Name: trafficSource.medium, dtype: int64\n" ] @@ -1705,17 +1553,17 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 138, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "referral 1549\n", - "organic 1520\n", - "none 912\n", - "rest 257\n", + "referral 5289\n", + "organic 3111\n", + "none 1939\n", + "rest 614\n", "Name: trafficSource.medium, dtype: int64\n" ] } @@ -1732,16 +1580,16 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 139, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Unknown 1933\n", - "West 1407\n", - "East 898\n", + "Unknown 4338\n", + "West 4005\n", + "East 2610\n", "Name: geoNetwork.region, dtype: int64\n" ] } @@ -1775,7 +1623,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 140, "metadata": {}, "outputs": [ { @@ -1785,14 +1633,14 @@ "channelGrouping 5\n", "\n", "\n", - "['Direct' 'Organic Search' 'Referral' 'Paid Search' 'Other']\n", + "['Direct' 'Organic Search' 'Referral' 'Other' 'Paid Search']\n", "\n", "\n", - "Organic Search 1520\n", - "Referral 1504\n", - "Direct 912\n", - "Paid Search 213\n", - "Other 89\n", + "Referral 5197\n", + "Organic Search 3111\n", + "Direct 1939\n", + "Paid Search 463\n", + "Other 243\n", "Name: channelGrouping, dtype: int64\n", "device.browser 2\n", "\n", @@ -1800,8 +1648,8 @@ "['Chrome' 'Rest']\n", "\n", "\n", - "Chrome 3644\n", - "Rest 594\n", + "Chrome 9881\n", + "Rest 1072\n", "Name: device.browser, dtype: int64\n", "device.operatingSystem 3\n", "\n", @@ -1809,9 +1657,9 @@ "['Mobile' 'Mac' 'Windows']\n", "\n", "\n", - "Mac 2119\n", - "Mobile 1135\n", - "Windows 984\n", + "Mac 6176\n", + "Mobile 2674\n", + "Windows 2103\n", "Name: device.operatingSystem, dtype: int64\n", "geoNetwork.region 3\n", "\n", @@ -1819,9 +1667,9 @@ "['East' 'West' 'Unknown']\n", "\n", "\n", - "Unknown 1933\n", - "West 1407\n", - "East 898\n", + "Unknown 4338\n", + "West 4005\n", + "East 2610\n", "Name: geoNetwork.region, dtype: int64\n", "trafficSource.medium 4\n", "\n", @@ -1829,10 +1677,10 @@ "['none' 'organic' 'referral' 'rest']\n", "\n", "\n", - "referral 1549\n", - "organic 1520\n", - "none 912\n", - "rest 257\n", + "referral 5289\n", + "organic 3111\n", + "none 1939\n", + "rest 614\n", "Name: trafficSource.medium, dtype: int64\n" ] } @@ -1853,12 +1701,1858 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 141, "metadata": {}, "outputs": [], "source": [ "df_4.drop(columns=['device.isMobile'], inplace=True) \n" ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "# splitting the date column into year, month and day\n", + "df_4['month']=df_4['date'].dt.month\n", + "df_4['day']=df_4['date'].dt.day\n", + "df_4['year']=df_4['date'].dt.year" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "# bucketing month into quarters\n", + "def quarter(x):\n", + " if x in [1,2,3]:\n", + " return '1st_quarter'\n", + " elif x in [4,5,6]:\n", + " return '2nd_quarter'\n", + " elif x in [7,8,9]:\n", + " return '3rd_quarter'\n", + " else:\n", + " return '4th_quarter'\n", + " \n", + "df_4['quarter']=df_4['month'].apply(quarter)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "# bucking the day column into beginning, middle and end of the month\n", + "def day(x):\n", + " if x in range(1,11):\n", + " return 'beginning'\n", + " elif x in range(11,21):\n", + " return 'middle'\n", + " else:\n", + " return 'end'\n", + "\n", + "df_4['day_of_month']=df_4['day'].apply(day)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "df_4.drop(columns=['date'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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channelGroupingdevice.browserdevice.operatingSystemgeoNetwork.regiontotals.hitstotals.pageviewstotals.newVisitstotals.transactionRevenuetrafficSource.mediummonthdayyearquarterday_of_month
752DirectChromeMobileEast1111.0137860000.0none9220163rd_quarterbeginning
753Organic SearchChromeMacEast1110.00306670000.0organic9220163rd_quarterbeginning
799ReferralChromeMobileEast1311.0068030000.0referral9220163rd_quarterbeginning
802ReferralChromeWindowsWest1312.0026250000.0referral9220163rd_quarterbeginning
859ReferralChromeMacUnknown1714.00574150000.0referral9220163rd_quarterbeginning
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" + ], + "text/plain": [ + " channelGrouping device.browser device.operatingSystem geoNetwork.region \\\n", + "752 Direct Chrome Mobile East \n", + "753 Organic Search Chrome Mac East \n", + "799 Referral Chrome Mobile East \n", + "802 Referral Chrome Windows West \n", + "859 Referral Chrome Mac Unknown \n", + "\n", + " totals.hits totals.pageviews totals.newVisits \\\n", + "752 11 11.0 1 \n", + "753 11 10.0 0 \n", + "799 13 11.0 0 \n", + "802 13 12.0 0 \n", + "859 17 14.0 0 \n", + "\n", + " totals.transactionRevenue trafficSource.medium month day year \\\n", + "752 37860000.0 none 9 2 2016 \n", + "753 306670000.0 organic 9 2 2016 \n", + "799 68030000.0 referral 9 2 2016 \n", + "802 26250000.0 referral 9 2 2016 \n", + "859 574150000.0 referral 9 2 2016 \n", + "\n", + " quarter day_of_month \n", + "752 3rd_quarter beginning \n", + "753 3rd_quarter beginning \n", + "799 3rd_quarter beginning \n", + "802 3rd_quarter beginning \n", + "859 3rd_quarter beginning " + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_4.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "df_4.drop(columns=['month', 'day'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "channelGrouping object\n", + "device.browser object\n", + "device.operatingSystem object\n", + "geoNetwork.region object\n", + "totals.hits int64\n", + "totals.pageviews float64\n", + "totals.newVisits category\n", + "totals.transactionRevenue float64\n", + "trafficSource.medium object\n", + "year int64\n", + "quarter object\n", + "day_of_month object\n", + "dtype: object" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# give the type of each column\n", + "df_4.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "# from sklearn.preprocessing import LabelEncoder\n", + "# # Initialize a label encoder\n", + "# label_encoder = LabelEncoder()\n", + "\n", + "# # Iterate through the columns\n", + "# for column in df_4.columns:\n", + "# if df_4[column].dtype == 'object':\n", + "# df_4[column] = label_encoder.fit_transform(df_4[column])\n", + "# else:\n", + "# continue\n", + "\n", + "# df_4.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "# drop the column that has high correlation\n", + "df_4 = df_4.drop(columns=['totals.hits', 'year', 'channelGrouping'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dummify the categorical Variable - Heatmap " + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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859140574150000.00001001001000
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" + ], + "text/plain": [ + " totals.pageviews totals.newVisits totals.transactionRevenue \\\n", + "752 11 1 37860000.0 \n", + "753 10 0 306670000.0 \n", + "799 11 0 68030000.0 \n", + "802 12 0 26250000.0 \n", + "859 14 0 574150000.0 \n", + "\n", + " device.browser_Rest device.operatingSystem_Mobile \\\n", + "752 0 1 \n", + "753 0 0 \n", + "799 0 1 \n", + "802 0 0 \n", + "859 0 0 \n", + "\n", + " device.operatingSystem_Windows geoNetwork.region_Unknown \\\n", + "752 0 0 \n", + "753 0 0 \n", + "799 0 0 \n", + "802 1 0 \n", + "859 0 1 \n", + "\n", + " geoNetwork.region_West trafficSource.medium_organic \\\n", + "752 0 0 \n", + "753 0 1 \n", + "799 0 0 \n", + "802 1 0 \n", + "859 0 0 \n", + "\n", + " trafficSource.medium_referral trafficSource.medium_rest \\\n", + "752 0 0 \n", + "753 0 0 \n", + "799 1 0 \n", + "802 1 0 \n", + "859 1 0 \n", + "\n", + " quarter_2nd_quarter quarter_3rd_quarter quarter_4th_quarter \\\n", + "752 0 1 0 \n", + "753 0 1 0 \n", + "799 0 1 0 \n", + "802 0 1 0 \n", + "859 0 1 0 \n", + "\n", + " day_of_month_end day_of_month_middle \n", + "752 0 0 \n", + "753 0 0 \n", + "799 0 0 \n", + "802 0 0 \n", + "859 0 0 " + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert the column to int\n", + "df_4['totals.pageviews'] = df_4['totals.pageviews'].astype(int)\n", + "\n", + "# dummy the categorical data\n", + "df_4 = pd.get_dummies(df_4, columns=[ 'device.browser', 'device.operatingSystem', 'geoNetwork.region', 'trafficSource.medium', 'quarter', 'day_of_month'], drop_first=True)\n", + "\n", + "df_4.head()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16 0.041998\n", + "15 0.041359\n", + "14 0.041267\n", + "13 0.039898\n", + "18 0.039624\n", + " ... \n", + "121 0.000091\n", + "119 0.000091\n", + "233 0.000091\n", + "305 0.000091\n", + "169 0.000091\n", + "Name: totals.pageviews, Length: 154, dtype: float64\n", + "\n", + "\n", + "0 0.613074\n", + "1 0.386926\n", + "Name: totals.newVisits, dtype: float64\n", + "\n", + "\n", + "16990000.0 0.021638\n", + "33590000.0 0.016799\n", + "18990000.0 0.016708\n", + "44790000.0 0.015247\n", + "13590000.0 0.012143\n", + " ... \n", + "84470000.0 0.000091\n", + "86800000.0 0.000091\n", + "412250000.0 0.000091\n", + "49520000.0 0.000091\n", + "69390000.0 0.000091\n", + "Name: totals.transactionRevenue, Length: 5117, dtype: float64\n", + "\n", + "\n", + "0 0.902127\n", + "1 0.097873\n", + "Name: device.browser_Rest, dtype: float64\n", + "\n", + "\n", + "0 0.755866\n", + "1 0.244134\n", + "Name: device.operatingSystem_Mobile, dtype: float64\n", + "\n", + "\n", + "0 0.807998\n", + "1 0.192002\n", + "Name: device.operatingSystem_Windows, dtype: float64\n", + "\n", + "\n", + "0 0.603944\n", + "1 0.396056\n", + "Name: geoNetwork.region_Unknown, dtype: float64\n", + "\n", + "\n", + "0 0.634347\n", + "1 0.365653\n", + "Name: geoNetwork.region_West, dtype: float64\n", + "\n", + "\n", + "0 0.715968\n", + "1 0.284032\n", + "Name: trafficSource.medium_organic, dtype: float64\n", + "\n", + "\n", + "0 0.517119\n", + "1 0.482881\n", + "Name: trafficSource.medium_referral, dtype: float64\n", + "\n", + "\n", + "0 0.943942\n", + "1 0.056058\n", + "Name: trafficSource.medium_rest, dtype: float64\n", + "\n", + "\n", + "0 0.737789\n", + "1 0.262211\n", + "Name: quarter_2nd_quarter, dtype: float64\n", + "\n", + "\n", + "0 0.736693\n", + "1 0.263307\n", + "Name: quarter_3rd_quarter, dtype: float64\n", + "\n", + "\n", + "0 0.723637\n", + "1 0.276363\n", + "Name: quarter_4th_quarter, dtype: float64\n", + "\n", + "\n", + "0 0.682918\n", + "1 0.317082\n", + "Name: day_of_month_end, dtype: float64\n", + "\n", + "\n", + "0 0.637907\n", + "1 0.362093\n", + "Name: day_of_month_middle, dtype: float64\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9z/gkk8fkkj0pz746hrzcw69dq40000gn/T/ipykernel_7875/2938984261.py:7: FutureWarning:\n", + "\n", + "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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Xr+lY96G6c+KsLk1doEvTF8mrW1tTmZxdWil4/XYF/jRRd0+eVeBPExW8cac8u7R6U9V4azSpl00z513UPzuCde7iPQ0Zc0L29ilUvULi05HUqeoqh3R26jPkqA4fD9eNm5E6dCxcZ87H/TC2c+9tTfrjvP7Zkfi5U3JiNBq1b9NMlarRSd6+1ZUpq7dqtRyuRw8f6PjuxI9vtxyFVbHRN8pXvI5SJHJ8S5KNTQqldcxkeqRJnzwv2jEajfpn9SxVa9BRRUpWk5tHHrXoPFQPIx9o77aE/4ZLUsCqWfIuVFrVGnRQlmxeqtagg7wLllLA6lmmMp36TFCpig3k5pFb2XLk08efD1ZI8DVdPnfMElWzONry9diwYqZKV26oslUay9XdSx+2+UYZXFy15e95CZbfum6+Mri46cM238jV3UtlqzRW6coNtWHZDFOZTSv/UL7C76lGw/ZyzZZTNRq2V96CJbVp5R+WqpZV0Javj9FoVMCqP1S9YQcVKVVVWbPn0Sf/G6KoyAfau/V5x/cfylv4PVVr2D72+G7YPvb4XhXXXj5+5WNHV5V6s6M+gKSkW7dumjx5sqZOnarjx4/r66+/1sWLF9Wp0/+xd99hURwNGMDfowpSBQRRpIqCCFgjomLHErsmJkbsKRpNYtfkM2qMxhZL7FGsib0romBBFLsCFhClqAjSqyLluO+P0zsO7hAjHsX39zz3PHd7s8vMMHO7s7Mz8y0AYObMmfDy8pKE37VrF7y8vLBs2TK0bt0az58/x/Pnz5GRkfHB4lipO6lWrlwJNzc3jB07FvHx8YiPj4eFhQUAYNq0aVi4cCHCwsLg7OyM7Oxs9OzZE/7+/rh9+zY8PT3Ru3dvPHnyBABw8OBBjB07Fm5uboiPj8fBgwflbiuusLAQPXr0QFBQEHbu3In79+/jjz/+gKqq+Mk8MzMzXLlyBY8fPy41HcuWLcPSpUsRGhoKT09P9OnTBw8fPnyn/Hj58iUWLlyITZs24d69e6hduza8vLywe/durFq1CmFhYVi/fj10dHQAAPHx8fDw8ICrqytu3LgBX19fJCQkvHWRs5cvX2LVqlXYvXs3fH19cf78eQwYMAA+Pj7w8fHBjh07sHHjRuzfL73pOHLkSFy6dAm7d+9GaGgoBg8ejO7du0vSePXqVYwaNQrjxo1DcHAwOnbsiPnz579T+hVZvnw53N3dcfv2bfTq1QvDhg2Dl5cXvvrqK9y6dQt2dnbw8vKCSCR667Fu3ryJzz77DEOGDMGdO3cwZ84c/O9//8PWrVtlwi1ZsgROTk64efMm/ve//+HSpUv49ttv8cMPPyA4OBhdu3bF77//LrPP28roG8uWLUOLFi1w+/ZtjBs3Dt99953Cp9yfPXuG9u3bo0aNGjh79ixu3ryJUaNGlXl0obIV5OfjSWQYHF3dZLY7urRG5IMQuftEPQiFo0tr2fCubfA48j6EBfly98nLewWhsAA1dfTLJ+KVVFpSLLIyktHAyV2yTU1dA9aNWuLxw9sK93vyKAQNnGSflrBv4o4nRfYJu3UOda0b459VP2L+OHes+mUArp2Tf/Fd1eXnFyAiMhotXWWfNm/p6oy74RFlOkZhYSFe5uRAT1c6Veyl6zfRuKE9lm/wRj+vbzBiwhTs2HcIQmH1GgX7RkF+Ph5HhqGxa/H66oZH4fLrd+SD0BK/B05N3fA4MgwFRep37qscTP26J6aM6Y6V8yficVT1HvmTn5+Ph48eoVkz2WkSmzVtivthJaeYk6ewsBA5OTnQ1ZWOfgwLDy9xzObNmiHsfvW7IfPGm/rdylV2+teWrk1wN7xs12Hi+v0Kero6CsOc8D+Pzm3doFWjxnvFt7LLSInFy8wkWDZqK9mmpq6BurYtERet+LxTFlF3zsK0vhOOe0/E+llu2LmoH+4EVc/zzrsyaO2KJH/ZadGSTgdCv7kTBK9H1xu2dkWy/0WZMMl+gTB0q77TywKAuWkNGNfSxLXbaZJt+QUiBN9Nh1MjxaPw2n5ihLvhmZj8rR2ObnfD9tUtMGxwfahU6tbxh5WREosXmUmwcpCt3/XsWuLZe9ZvAEhLeox1s9pi4+xOOOb9E9KTn773MSujlMRYZKYno5Gz9FpbTV0Ddg4tEBMRrHC/mIchMvsAQCNn91L3yXkpHuGmXU3bPMzL91eQn4+nUWFwcJHNDwdnN0Q9CJa7T1RECBycZa/PHVza4HGUtP0dHRFS4piOru6IUtCmrw6Yl+VLUf22dWyO6AjFaY+OCEHD4vXbpQ2iS6nfRAR8/vnnWLFiBebNmwdXV1dcuHABPj4+sLQUP0QdHx8vc396w4YNKCgowPjx41GnTh3J64cffvhgcazUc4bp6+tDQ0MD2traMDMzAwDJzfp58+aha1fp+ilGRkZwcZGuBTJ//nwcOnQIR48exffff49atWpBW1sbGhoakmMBkLutKH9/f1y7dg1hYWGwt7cHANjY2Ei+//XXXzFgwABYWVnB3t4ebm5u6NmzJwYNGgSV162cpUuXYvr06RgyZAgAYNGiRTh37hxWrFiBNWvWlDk/8vPzsXbtWkk6IyIisHfvXvj5+UnmhSwat3Xr1qFZs2ZYsGCBZJu3tzcsLCwQEREhSY+8v7Nu3TrY2toCAAYNGoQdO3YgISEBOjo6cHR0RMeOHXHu3Dl8/vnniIyMxK5duxAbGwtzc3MA4vWlfH19sWXLFixYsAArV66Ep6cnZsyYAQCwt7dHUFCQzIiy/6pnz5745ptvAACzZ8/GunXr0LJlSwweLF6zaPr06XBzc0NCQoLC//Mbf/75Jzp37oz//e9/knjev38fS5YskRnB1alTJ0yZMkXy+ZdffkGPHj0k296k782IOwBwcXEptYwWTc+4ceMkcV++fDnOnz+PRo1Krp2xZs0a6OvrY/fu3VBXV5f87dLk5uYiNzdXZtu7zGX6PrKz0lBYKISevuyTk7oGRshMl/+UamZ6MnQNZC9C9PRroVBYgOysdOgblpxW5ODOlTCoVRsOztX7ifas13mmoy87qkRHzwjpKXEK98tOTy65j74xsjKk/4PUpKe4enY32nYfgY59vsbTqDs4tmMB1NQ10Kxtv/JLRCWQkZkJYWEhahnINkoNDfSRmpZepmPsOXwCr3Jz0dFd2giJf56I24n30MXDHYtmT0ds3HOs2OgNobAQI4YMLM8kVApZWeni+m0gO22rvkEt3E2Xv4ZHZloK9JvK/h7oGRhBKCxAdmY6DGqZoE5dK4yaMAf1LBsgJycb/sd34Y+ZozBn+W6Ymle/ERYAkJmZicLCQhgWGY0LAAaGhkhLS5O/UzEHDx7Eq1ev0L5dO8m2tLQ0mRG+AGBgYIDUMh6zKsrIyoKwsBCG8up3etmeAtt9xAevXuWik4JRUvcjIhH1JBbTx3/93vGt7F5mJgEAtPVk67m2njGyUhWfd8oiI+UpQi/uQrOOI9Gq67d4/iQU5w7Mh6qaBhxb9XuvY1d1mqbGyE2QvU7KS0yBiro6NIwNkfs8CZpmxshNkP2tzU1IgaaZ4unXqoNahuKRO6npstNvp6XnwbS24k5jczMtNHOuAb/zCZg69w7qmWth0rcNoKoqwNbdih8+rM5evK7fNXVl63dNPWNkvmf9rmPljJ5ei2BY2wovs1Jw2Xcd/l06BCN/OQ4tHcP3OnZl8+b6XFdfNh919Y2Qmqw4H7PSk+Xuo6iNJBKJcHjHYtg0bIY6FqWvRVtVMS/fn6T9Xez6vLT2d1Z6CnSLhdczMJJpf2emJ0O3eJtev5bkf1YdMS/LV9br9qG8upqmYPpE8X7vVr+JSGrcuHGS+83FFR+cUXx2NGWo1J1UpWnRooXM5xcvXmDu3Lk4fvw44uLiUFBQgJycnBKjVN5VcHAw6tWrp/DGf506dXD58mXcvXsXAQEBCAoKwvDhw7Fp0yb4+voiOzsbcXFxcHd3l9nP3d0dISHv9mSEhoYGnJ2lT/oHBwdDVVUVHh4ecsPfvHkT586dk4ysKioyMlJhmrS1tSUdVABgamoKKysrmeOYmppK1sW4desWRCJRiePl5uZK1pgKCwtD//79Zb53c3Mrl06qonnyZsG3Jk2alNiWmJj41k6qsLAw9O3bV2abu7s7VqxYAaFQKBlBV7z8PXjwoET6WrVqJdNJVdYyWjQ9AoEAZmZmCtcgCQ4ORrt27SQdVGWxcOFCzJ07V2bbr7/+ijlz5pT5GO9NIJD9LBIBEMgNKg4u+50Ib0bFldzn1OEtuH7RF5Pnbqp2a9bcvnQMh7fMkXwePnmd+E2JbCg9P8X7FMtTkUgmn0WFItS1bgzPz34CAJhbOSIh9hGunNld7TqpJEqUy5JlTx7/C5ewdfd+/D5risyN8EJRIQz09TBl3NdQVVVBQzsbJKelYfehY9Wyk0qR4mWrBDllUbxZvN22oTNsG0p/F+0auWLe5C9xxmc3vhwzrfwjXImU+O17W16+dv78eez85x/8Ont2iU6pkr+nZSvnVZ2g+G+iSFSiysvjHxiELXsOYuHMSSU6ut44ceY8bOrXg6O9rdzvq7Kw60dxZs+vks/9vtnw+p288/j7EYlEMLVwQtvekwAAtS0ckRL/CKEXd330nVQASubxmwJcdLu8MOXwv6lMunrUxtTx0jbHtHl3xG+KJ1MgKLmtCBUBkJ6Rh8VrIlBYCDyIzIZxLU18MaDeR9NJdf/aUfjtktbvAeNe128F5+X3YdNYtr1ax9oVm37tintXD6NF55HvffyKdOPicez9W9q2+nr6WvEbOe2Xt55v32GfA1t+R9zjCPwwt+QUyVUV8/JDkncd9A7tb1HJ9re8a8oyXVxVeczL/+JG4HHs+Vu6Fuc3M14/sC+nDf62tJfI7zK2kajyEvH/R6jCnVQ1a9aU+Tx16lScOnUKS5cuhZ2dHbS0tDBo0CDk5eUpOELZaGlplSmck5MTnJycMH78eFy8eBHt2rVDQEAAmjdvDqD0G01vRlwVbQDk55ecxkxLS0vmOG+LW2FhIXr37o1FixaV+K5OnToK9yve4SEQCORuKywslPwdVVVV3Lx5U9KJ88abjq3/0rhRUVEpsZ+8fCkatzf5I2/bm/iWRt4NQHlxL17+yrJfWctoaXldXFnLZ1EzZ87EpEmTZLYpYxQVAOjoGkJFRRWZxUZVZGWklngi6Q09A2NkpiUXC58GFVU16OjK3jA8fWQbTh7YjB9/3YB6VqWPKKuKHJt1goWd9Ga9MF9cdrLTk6FnIF13ITszFTr68vMTAHQMjJGdniSz7UVmCnSKPBmva2CM2nVlb7jWNrfBvRun3ysNlZG+nh5UVVRKjJpKy8hQeFP6jbOBQVj81wbMnf4jWhSbTszI0BBqqqpQVZXOHWRZzxypaenIzy+AunqVPQXLpatrILd+Z2aklRg9+YaeoREy0kr+HqiqqqGmrvy8V1FRgZVdYyTEvd9DKJWZnp4eVFRUSoxwykhPL9HpVFxAQABWrFyJWTNnomlT2Wm+DOWMxMpITy8xYqs60dfVFdfvIut+AkBaRiYM9Uuv32cuXsYfq//GvKkT0cLFSW6YV7m5OHPxMkYPGVReUa5UbJt0Qh0r6SjwggLxeedlZjJ09KXnnZdZKdDWe7+14mrqmcDITPa8U8vUBg9DTr3XcauD3ITkEiOiNExqoTA/H3kp6eIwz5OhaSb7P9CsXavECKyq7uK1FNyPkK41q6EuPsfWMtRASpr0mtpQX73E6KqiktPyICwQoegl9uPYlzCupQk1NQEKCqpX5548ds6y9Vv4un6/+AD1uzgNTW2Y1LVHWmJMuR63Ijg17wjLItfnBa+vz7PSk2VmfMjOSC3x9H9RugbGJUZOKNrnwJYFuHvjHCbM2QYDo9IfwqxKmJflT9r+Lt6eVpyHugZGctrfqTLtbz0D4xLX/G/7v1R1zMv349SiIywbvL1+Z2WmvLV+l/gfZFa//CL6GFX6Wbc1NDQgFArfGi4wMBAjRoxA//790aRJE5iZmSEmJua9/76zszNiY2MREVG2dUkAwNHREYB45Iyenh7Mzc1x8aLsHPFBQUFwcHAAAJiYiH+Q4+OlQ1qDg4Pf+neaNGmCwsJCBAQEyP2+WbNmuHfvHqysrGBnZyfzKt7J8j6aNm0KoVCIxMTEEn/nzcglR0dHXLlyRWa/4p+LMzExkckToVCIu3fvllu85XF0dJT7v7K3ty/RAVdUo0aNcO3aNZltN27ckPn8Icqos7MzAgMD5XbeKaKpqQk9PT2Zl7I6qdTU1VHf1gFhIZdltoeFXoVtQxe5+9g0dEZY6FWZbfeDL8PS1hGqatIOvVOHt+LE/r8x8X9rYWXXuPwjXwloatWEsaml5FW7rh109Y3x8G6QJExBQR6iw6/DsoHiNSjq27nI7AMAD+8GoX6RfSztmyE5PkYmTPLzGBgYmZdPYioRdXU12Nta40bIHZntN4LvwKmR4s5O/wuXsHDVOvxv8gS4tWhW4nsnB3s8e/5cppM5Ni4eRoaG1a6DChDXb0tbB9wLKVZfQ67ArpH8+m3b0Bn3Q2TPBfeCr8DS1gFqavJHiIpEIjyNeQB9w/K9YVaZqKuro4GdHW7fll0D5Nbt23B8fe0gz/nz5/Hn8uWYNnUqWrVqVeJ7h0aNcKv4MW/dgsPr65bq6E39vh4ie/1wPeQOnBopntLHPzAIC/7agNmTxqNNC8W/p2cvXUF+fgG6ebgrDFOVadTQgYGJpeRlZGYHbT0TPH4gXR9JWJCHZ5HXYW79fmsfmds0Q2pitMy2tKQY6BnWfa/jVgfpV4Jh3Fl26mOTrm2RcfMuRK/XIU27EgzjzrLl0LhLW6Rdfv+1hCqTnBwhnsW/kryin7xEcmouWrpKp4xTUxPA1ckAd8MzFR7nzv1M1K2jJfOwtoW5FpJTcj+KDipAXL8Na1tKXkZ17FBTzwSPw2Xrd+yj66j7nvW7uIL8PKQ8j0RN/ao/HWUNrZowMasveZnVs4WegTEe3JG2dwoK8vEo7Aas7F0VHseqgYvMPgAQHhoks49IJMJ+798Res0f4//nDaPa9co7ORWKeVn+1NTVYWHjgPDQ4vlxBTYNXeXuY2PvgvBQ2evzsJAgWNpI29/W9i4IK3bMsJAg2Cho01cHzMv3o7B+h8rW78j7N2Ftrzjt1vYuMvsAwIPQIFiX8ptARFVDpe+ksrKywtWrVxETE4Pk5GSFI0rs7Oxw8OBBBAcHIyQkBF9++WWZRs68jYeHB9q3b4+BAwfCz88P0dHROHnypGSauu+++w6//fYbLl26hMePH+PKlSvw8vKCiYkJ3NzEa5NMnToVixYtwp49e/DgwQPMmDEDwcHBksXG7OzsYGFhgTlz5iAiIgInTpzAsmXLypQ3w4cPx6hRo3D48GFER0fj/Pnz2LtXvMj0+PHjkZqaii+++ALXrl1DVFQUTp8+jVGjRkk6/lavXo3OnTu/Vx7Z29tj6NCh8PLywsGDBxEdHY3r169j0aJF8PHxAQBMnDgRvr6+WLx4MSIiIrB69eq3TvXXqVMnnDhxAidOnEB4eDjGjRuH9GJPQpe3yZMn48yZM/jtt98QERGBbdu2YfXq1TLrT8kzYcIE+Pj44M8//8TDhw+xYcMGnDx5UmZ01Ycoo99//z0yMzMxZMgQ3LhxAw8fPsSOHTvw4MGD9zruh9Sl9zBcPHMIl84cRnxsFPZuWYLU5Hi07yZ+Cv3QzlXYsuoXSXiPboORkhSHvVuWIj42CpfOHMals4fQrY+XJMypw1twdNcaDB83B0Ym5shIS0ZGWjJe5bxUevqUSSAQwL27F84f24h7N/zw/GkE9m+cBXWNGnB1+1QSbu/66fDd86fks3s3Lzy6G4SA438jMS4KAcf/xqN7l+HuKc1T9+7D8SQyBOeObkBywmMEBx3HtXP70LrLl0pNo7J81rcXTvidxQn/c4h5+gyrN21DYnIy+nQXr/e3cfsu/L5cuoag/4VLWLBiLcaNHAbHhg2QkpaOlLR0ZL+Qlrl+3bsiIzMbqzZtw9Nncbh84xZ27juC/j27KT19ytKtz1AE+h9CoP9hxD2Nwm7vpUhNfg4PT/H0hgd2/IVNK/8nCd/BcxBSkuKx23sZ4p5GIdD/MALPHIZnP2lZPLJnA+7eDkLS81g8iX6ALavn4ml0BDp4Vs+RK2/0798fp06dwqnTp/HkyRNs2LgRSUlJ6NmzJwBgy5YtWLp0qST8+fPnsXTZMowdMwaNGjVCamoqUlNT8eLFC0mYvn374tatW9i7bx+ePn2Kvfv24XZwMPoVm+a2uvm8Tw8c9z+HE/7nEfP0GVZ570Bicgr6eYqvf9bv2I35K9dJwvsHBmH+yvX4fsRQNLa3k1u/3zjhH4C2nzSHvp6u0tJTkQQCAZp5eOG63wY8CvFDclwETv0zE2rqNdCoufS847tjGi4elV7LCgvykBgbhsTYMAgL8pCdkYDE2DCkJ0mnVWvWYTiex4Tg2un1SE96jPAbx3AnaC9c2lW/845qTW3ouTSCnot4vVFt63rQc2mEGhbimQ4azp8Ely3SmRAeb9wNLUtzOCyZAZ1GNqg3YiAsRg5E1J/ekjAxq7fDuKs7bKaMRc2GNrCZMhbGnd0Q89c25SauAuw7+gzDBtdH+9ZGsK6vjZ9/bIjcXCFOB0iny/7lp4b4xsta8vnwyTjo66rhh7F2sDDXgluLWhg2uD4O+kjXudGqoQI765qwsxY/3FfHtAbsrGvC1KR6TSf9hkAgQLOOXrh6agMeBvshKS4CJ3fMhJpGDTi0lNZvn23TcOFIsfr9NAyJT8MgFOYhKz0BiU/DkJYord/nDy7C04fXkJ78FPHRITi6aSLyXmWj8Sey06VXBwKBAO17DIPf4b8Res0f8U8f4t+1P0NDswaau/eShNu5ZiaO7Vou+ezR4ys8CA2C/5HNSHgWBf8jmxFx9wo8egyThNnvPR83Lh7HsAmLoKlVE5npychMT0Ze3iulplFZmJflo/OnXgg6cxBBZw/heWwU9m9djNTkeLTtJl7D+8g/K7Htr1mS8G27DkZqchwObF2C57FRCDp7CJfPHkLnPsMlYTr2GorwkMs4fdgbz59F4/Rhb4TfuYqOvb5SevqUiXlZfgQCATx6fgW/w5sQcu0M4p48xD9rf4a6Zg00b1ukfq+ehWP/rpB8FtfvyzL1+8Gdq/DoKc2v3FcvERsTjtiYcABASuIzxMaEIzVZ8VpXRFTxKv2j3FOmTMHw4cPh6OiInJwcbNmyRW645cuXY9SoUWjTpg2MjY0xffp0ZGYqfnruXRw4cABTpkzBF198gRcvXsDOzg5//PEHAKBLly7w9vbGunXrkJKSAmNjY7i5ueHMmTOS9ZgmTpyIzMxMTJ48GYmJiXB0dMTRo0fRoIH4CV51dXXs2rUL3333HVxcXNCyZUvMnz8fgwcPfmvc1q1bh1mzZmHcuHFISUlB/fr1MWuW+KRobm6OS5cuYfr06fD09ERubi4sLS3RvXt3yRSDycnJiIyMfO882rJlC+bPn4/Jkyfj2bNnMDIygpubm+RmWuvWrbFp0ybJ2kddunTBL7/8gt9++03hMUeNGoWQkBB4eXlBTU0NP/30Ezp27PjecS1Ns2bNsHfvXsyePRu//fYb6tSpg3nz5mHEiBGl7ufu7o7169dj7ty5+OWXX+Dp6YmffvoJq1evloT5EGXUyMgIZ8+exdSpU+Hh4QFVVVW4urqWWAOtMmnp7okXWek4sW8DMtKSYV7fDt/PWg2j2uIROhlpSTIXD8amdTHh59XYu2UpAnz3QL+WCT4fNR3N3LpIwgT47kVBQT42LJXtTPz0s2/Q+/PvlJOwCtK+1xjk5+XiyNZ5yHmZCQsbZ4yatgmaWtLRkukp8RAIikw5Z98UQ8Yvg9/+lfDb/xdqmVrgi/HLUN9O+sSShU0TfPXDKpzauxxnD6+FoUk9fPrVDDR1763U9ClLp3ZtkJGVje17DiAlNR3WlhZYNHsGzGqLn/BNSUtDYrJ0WoFjp/whFAqxYoM3VmyQ3iTs3qk9Zv4gXoiytokxls6dhTWbt2PUD9NhbGSIgb2748sB1bdDoFVbT2RnZeDY3r+RkZaMuvVt8cMvq2D8un6npyUjNem5JLyJaV38+Mtf2L1lGc6d3AuDWib4cvQ0tHCTPjyR8yIL29bNR2ZaCrS0dVDfpiGmzf8bNvbyp1+rLjw8PJCVlYV///0XqampsLKywry5cyXrLKampSExSTptp8/JkxAKhVizdi3WrF0r2d6lSxdMfj3Fq6OjI2bMmIHt27djx44dqFOnDmbOmIFGjRopN3FK1rmtGzKzsrF17yGkpKXDun49LP5lapH6nY6EJOk0K0dOnYVQKMSfG7fiz41bJdu7d2yHnyd+K/n85Fk8QsMe4M9fZygtLZVBiy5jUZCfizP75iL3ZQbMLF0wYJw3NGpI1y7NSpM972RnJOKfxf0kn2+e9cbNs96oZ9cKgyfuAACYWTqj95jVuHjsT1zxXQN9o3roMGAWHFr2UVralEW/uRPczuyQfHZcKr52f7r9IEJHz4RmHRNoWUin5s6JicX13l/DcdlMWH43FLlxibj30+94fkg6BW/a5du4PXQSGs79EQ3nTsTLyKe4/eVPSL8WqryEVZB/DjyFpoYKJn3XALo66rgfkYmfZociJ0c6G4epSQ0UFhkglZici59m38HEMbbY+lcLJKfkYt+xZ/jngHQq2UZ2uvhroavk88QxdgAAnzPPsWBF5X0g7H206iqu3/575uLVywzUsXLBoO9l63emnPq9/Y9+ks83znjjxhlv1GvQCkN+FJfzrPTnOL5lEnKy06GtY4g61q74cspe6BtVz5GSnfuMQn7eK+z3no+XLzJhaeeM72ZtRI0i1+dpybL5aN2wKbwmLoHP3r9wcu9fMDK1wPAflsCqyPRYl/z2AABWz5Ndx+uLb+fjkw79PmyiKgjz8v01d++OF9npOLl/AzLTklDHwg7jZq2BkYm0/Z2WLL0+Nzath3Ez1+LAtsW4cGo39A1NMHjUDDRt3VUSxqahK0b+uAjHd6/G8d2rYWxmgdE/LYZ1kTyujpiX5Utcv3Oxf/Ob+t0E383aIFu/U+IhUJE+/G3d0BXDf1iME3v+gs+e1TA2tcCIYvX7SeQ9rJ43SvL58PYlAIBWHn0wdNzvSkgZEf0XAlF5rIRKRCWMHTsW4eHhCAwMrOiolNn5uzkVHYUqq4OTdH2wg9fefxTnx2xAK2kj83l49ZqmSNnMGkmn57l4/0UpIak0bR2lDaWocniw42NmYytdcyjx/o1SQtLb1HZsIXm/nss2/Wffekrfn1BvWHERqQZ65Us7btr2lj8dOZXNxWMekvd/+1dgRKq4sdJn2nDydtmnR6eSejSVTsPMvHw/RfPSPzS3AmNStXVxlo5qZT6+n6J56RuseD1HervurhoVHYUqKTIqqqKjUOXY2thUdBTKXaUfSUVUVSxduhRdu3ZFzZo1cfLkSWzbtg1rizzNTkRERERERERERERiIpHg7YGo2qv0a1IRlacePXpAR0dH7mvBggXvdexr166ha9euaNKkCdavX49Vq1ZhzJgx5RRzIiIiIiIiIiIiIqLqhSOp6KOyadMm5OTIn9KuVq1a73XsvXv3vtf+REREREREREREREQfE3ZS0Uelbt3quTgvEREREREREREREVFVw+n+iIiIiIiIiIiIiIiISOnYSUVERERERERERERERERKx+n+iIiIiIiIiIiIiIhIqUQcQ0PgSCoiIiIiIiIiIiIiIiKqAOykIiIiIiIiIiIiIiIiIqVjJxUREREREREREREREREpHTupiIiIiIiIiIiIiIiISOnYSUVERERERERERERERERKp1bRESAiIiIiIiIiIiIioo+LCIKKjgJVAhxJRURERERERERERERERErHTioiIiIiIiIiIiIiIiJSOnZSERERERERERERERERkdKxk4qIiIiIiIiIiIiIiIiUjp1UREREREREREREREREpHRqFR0BIiIiIiIiIiIiIiL6uIggqOgoUCXAkVRERERERERERERERESkdOykIiIiIiIiIiIiIiIiIqVjJxUREREREREREREREREpHTupiIiIiIiIiIiIiIiISOnYSUVERERERERERERERERKp1bRESAiIiIiIiIiIiIioo+LCIKKjgJVAhxJRURERERERERERERERErHTioiIiIiIiIiIiIiIiJSOnZSERERERERERERERERkdKxk4qIiIiIiIiIiIiIiIiUjp1UREREREREREREREREpHRqFR0BIiIiIiIiIiIiIiL6uIggqOgoUCXAkVRERERERERERERERESkdOykIiIiIiIiIiIiIiIiIqVjJxUREREREREREREREREpHTupiIiIiIiIiIiIiIiISOnYSUVERERERERERERERERKx04qIiIiIiIiIiIiIiIiUjq1io4AERERERERERERERF9XEQiQUVHgSoBjqQiIiIiIiIiIiIiIiIipWMnFRERERERERERERERESkdO6mIiIiIiIiIiIiIiIhI6dhJRURERERERERERERERErHTioiIiIiIiIiIiIiIiJSOrWKjgAREREREREREREREX1cRBBUdBSoEuBIKiIiIiIiIiIiIiIiIlI6gUgkElV0JIiIiIiIiIiIiIiI6ONx71F8RUehymlsV6eio1DuOJKKiIiIiIiIiIiIiIiIlI6dVERERERERERERERERKR0ahUdASKqPHyD8yo6ClVWd1cNyXufW/kVGJOqr2czdcn7WZtzKzAmVd+C0ZqS9/9e5Oy+/9WXbaULuc77p6ACY1L1zR4qvfScsu5lBcak6lv6nbbkfVRkZAXGpGqzsbWVvG/bO6ACY1L1XTzmIXl/Qr1hBcak6uuV/0DyftH+wgqMSdU2fZD0mdzTIWznvI9uLtK2zqX72RUYk6rP3VFH8p7l8r8rWib9QthmfB9dXaRtRv9Q5uX76OKs+fZARCQXO6mIiIiIiIiIiIiIiEipRBC8PRBVe5zuj4iIiIiIiIiIiIiIiJSOnVRERERERERERERERESkdOykIiIiIiIiIiIiIiIiIqVjJxUREREREREREREREREpHTupiIiIiIiIiIiIiIiISOnUKjoCRERERERERERERET0cRFBUNFRoEqAI6mIiIiIiIiIiIiIiIhI6dhJRURERERERERERERERErHTioiIiIiIiIiIiIiIiJSOnZSERERERERERERERERkdKxk4qIiIiIiIiIiIiIiIiUTq2iI0BERERERERERERERB8XkUhQ0VGgSoAjqYiIiIiIiIiIiIiIiEjp2ElFRERERERERERERERESsdOKiIiIiIiIiIiIiIiIlI6dlIRERERERERERERERGR0rGTioiIiIiIiIiIiIiIiJROraIjQEREREREREREREREH5dCCCo6ClQJcCQVERERERERERERERERKR07qYiIiIiIiIiIiIiIiEjp2ElFRERERERERERERERESsdOKiIiIiIiIiIiIiIiIlI6dlIRERERERERERERERGR0qlVdASIiIiIiIiIiIiIiOjjIoKgoqNAlQBHUhEREREREREREREREZHSsZOKiIiIiIiIiIiIiIiIlI6dVERERERERERERERERKR07KQiIiIiIiIiIiIiIiIipWMnFRERERERERERERERESmdWkVHgIiIiIiIiIiIiIiIPi4ikaCio0CVAEdSERERERERERERERERkdKxk4qIiIiIiIiIiIiIiIiUjp1UREREREREREREREREpHTspCIiIiIiIiIiIiIiIiKlYycVERERERERERERERERKZ1aRUeAiIiIiIiIiIiIiIg+LiIIKjoKVAlwJBUREREREREREREREREpHTupiIiIiIiIiIiIiIiISOnYSUVERERERERERERERERKx04qIiIiIiIiIiIiIiIiUrqPrpPq5cuXGDhwIPT09CAQCJCeni53m5WVFVasWFHR0a32tm7dCgMDA8nnOXPmwNXVtcLi874OHz4MOzs7qKqq4scff6yQOAgEAhw+fLhC/jYRERERERERERERUVmpVXQEyqJDhw5wdXUtl06jbdu2ITAwEEFBQTA2Noa+vj7Wr19fYtv169dRs2bNMh93w4YNWLt2LR49egR1dXVYW1tjyJAhmD59+nvH+WMyZcoUTJgwoaKj8Z998803GDlyJCZOnAhdXd2Kjk6lJhKJ4Lt/HYLO7EdOdiYsGzTBoFE/o46FXan7BV/1g8+e1UhOeApjUwv0GjIRLq06S75/dP8Gzh7biqfR95GZloTRU1bAuWXnUo5YtYlEIpw6sBaXz+xHzotM1LdrgoEjf3lrPoZc9cPJfX9J8rHn5xPh3LKL5PtLfrtxyW8PUpPjAABm9ezgOeBbOLi2+6DpqWidm6qiZUNVaGkCT5NEOBpUgMR0kcLwjS1V4OGiCiM9AVRVgORMES7eFSL4UaFMuE8cVNCuiRp0tYDEdBFOXClATILi41Z1IpEIAUdX42bAXrx6mYm6Ns7oOXQ2atdtoHCfxGcPcf7wKsQ9voeMlDh4DpmJ1l2Hlwh3/ey/CDq1GVnpSahd1w6eQ2bB0r7Fh0xOhfJoooJmdgLU0ACepQAnrwuRlKE4fFNbAVxsVGCiL/4cnyrC2ZBCxKXIhtPVAjo3VYGduQDqqkBKJnDsqhDxqR8uLRWtWwt1fOKoCm1NAZ4kFOJgYB4S0hTXQydrVXRupgZjfRWoqgBJGSIEhOTjVoRQEsatsRrcGquhlq4AAPA8tRD+N/MR/qRQ0WGrtOPHj2P/gQNITU2FpaUlvvn6azg5OckNe+nSJZw4cQKRUVHIz8+HpaUlvho6FM2bN5cJd/HiRWzfsQPx8fGoU6cOhg8fDvc2bZSRnAo36gtL9PGsA10dNdyPyMKf6x8i+snLUvfRqamKr4dZo72bMXR11BGfkIPVm6Nw5aa48ro01seXAyzQ0FYHxkaamPn7XQReSSn1mFVVrbYtYDN5NPSbOaGGeW3cGDgOCUfPlL5Pu5ZwXDoDOo4NkBuXiMhlm/Bk426ZMGb9u8F+zg/Qtq2Pl5FP8GD2ciQc8f+QSak0RCIRbp9dgwfX9yIvJxMmFs5w6/0/GJoqPn8/uL4Xj24fRVrCQwCAUV1HtOj6E0wsnOWGDwnYiJunl8OxzTC07jXrg6SjoolEIpzctw6XirRzPhtdhnbOFT+cKNLO+fSLku2cM0e34snrds6YKStkvq+Ozp7cC9/DO5Celoy6Fjb4YvQU2Ds2VRj+wd2b2L3lTzx7GgWDWibo0c8LHbsPknx/8exReP81t8R+G/YEQV1D84OkobJguSwfIpEIPvvW4dKZA5J8/Hz0rLfm4+0rfjixZ40kH3t/MaFEPl04tRtnjm5FRnoy6tSzxcAR02Dn0FzBEau+C6d2w/+INL2DRpae3of3buDAtiWIj42EvqEJuvYdiXbdPpN8H/f0EU7sWYMnUWFITYrDwBFT0anXMGUkhd6TSCSo6ChQJVAtRlKJRCIUFBSUKWxkZCQcHBzg5OQEMzMzCAQCudtMTEygra1dpmNu3rwZkyZNwsSJExESEoJLly5h2rRpyM7Ofp9kvZVQKERhYfW6CaKjowMjI6OKjoaMsuZzdnY2EhMT4enpCXNz8//cSZWXl/ef41CVnDnqjXMntmPQyFmYtGAXdPWNsfb3r/Eq54XCfaIjgrFtxVS0bNcb0xfvR8t2vbF1xRTEPAyVhMnLzUFdS3sMGlk9G73FnT3mjfM+2zFw5Cz89Ptu6BkYY/2CsaXmY0xEMLavmoIWbXtj6h8H0KJtb2xbOQWPH0nzUb+WGT794idM+n0PJv2+Bw0at8LmpRMQ//SRMpJVIdo7q8LdSRXHLhdg7dF8ZOeIMKq7OjTUFe/zMleE8yFCrD+Wj1WH8nDrYSEGtlNDg7rSi6wm1iro9YkazgcXYPXhfMQ8L8RwT3Xol/05iCrn0slNuHx6K3oO/R/G/rIPOnom2LFsFHJzFJ8X8/NewcDEAl0GToaOvoncMHev+cB390K06/Utvvn1EOo3aIF/VnyNjJS4D5WUCtXGUYDWDgKcvFGITb5CZOeI8FUnVWiU8oiRlakAd2MKsf2MEN6nhch4CXzVSRW6WtIwNTSAkd1UUVgI/HtOiLXHhfC7VYhXJU8/1UZHVzW0d1HDocB8rDzwCpkvRfi6tyY0S6nfObkinLlVgL8OvsKyva9wPbwAn3fUgL2F9PI5I1sEnyt5WLH/FVbsf4VHzwoxorsmTA2rX0MrICAAGzZuxJDPP8fqv/5C48aN8b/Zs5GYmCg3/J27d9G0aVPMmzcPf61aBRdnZ8yZOxePIiMlYcLCwrDwjz/QuVMnrF2zBp07dcLChQsRHh6urGRVmKEDLfB5v3r4c8MjjJl0CylpeVg+zxlaWqoK91FTE2D5b84wq10D//vjPr789hoWr45AckquJIxWDVU8is7Gnxuq7/n6DdWa2sgMfYB7P8wrU3gtq3poeWwjUi/exMWW/fBo0Xo0Xv4zzPp3k4QxaO2Kpv8ux7N/jiCweV88++cImu1aAYNW8jtcqps7gZtw79JWuPX+BX3G7YWWjjF8t4xGfq7i68r46Ouwce6JHqO34tNvd0FH3xynto7Bi4yEEmGTYu/gwfW9MDRr+CGTUeH8j4jbOYNHzcKUhbugZ2CM1fPf3s7ZsmIqWrbvjelL9qNl+97wXi7bzsnNzUFdK3sMHvVxtHOuXTyNXd7L8OmgUZiz7F80cGyK5b9NQEpSvNzwSQnPsHz+RDRwbIo5y/7FpwNH4t/NS3DjsmzntZZ2TSz3PiXzqu4dVADLZXnxP7IF507swGejZmLqwn+hZ2CMv+Z/U2o+RkWEYMuKaWjZ/lPMWLIfLdt/is3Lp8rk480gXxzYuhieA8ZixqK9sHVohrULxiE1WX55r+puXvLF/i2L4TlwLGYu3gs7h2ZY8/s4pCqo38kJsVi7cBzsHJph5uK98BwwBvu8/8DtK36SMPm5r2BUux76Dv0BegbGykoKEZWTSt9JNWLECAQEBGDlypUQCAQQCATYunUrBAIBTp06hRYtWkBTUxOBgYGIjIxE3759YWpqCh0dHbRs2RL+/tKn3jp06IBly5bhwoULEAgE6NChg9xtAEpM95eeno6vv/4apqamqFGjBpycnHD8+HEAwLFjx/DZZ59h9OjRsLOzQ+PGjfHFF1/gt99+k+xfWFiIefPmoV69etDU1ISrqyt8fX0l358/f14y1eAbwcHBEAgEiImJASCdGu/48eNwdHSEpqYmHj9+jNzcXEybNg0WFhbQ1NREgwYNsHnzZslx7t+/j549e0JHRwempqYYNmwYkpOTFeZ50b/TsGFDaGtrY9CgQXjx4gW2bdsGKysrGBoaYsKECRAKpU8T5+XlYdq0aahbty5q1qyJTz75BOfPny9x7Pr160NbWxv9+/dHSorsk53Fp/vr0KFDiWnz+vXrhxEjRkg+W1lZYf78+fDy8oKOjg4sLS1x5MgRJCUloW/fvtDR0UGTJk1w48YNhWlWlP6i+Vxa+s6fPy/plOrUqRMEAoHku6CgILRv3x5aWlqwsLDAxIkT8eKF9ALmTfxHjBgBfX19jB07VmEcrl+/jq5du0pG/Hl4eODWrVtlSldlIhKJEOCzE936j4XLJ11gXr8Bvhr/O/JzX+HmxRMK9wvw2YmGzq3Rtf8YmNa1Qdf+Y2Dv9AkCfHZKwjg2bSceXfVJF4XHqS5EIhECTu5A135fw7lVV9SxaIAvv1uAvLxXuHWplHw8uQP2TdzQpd9YmNa1QZd+Y2Hf+BME+OyQhHFq3gGOTdujdh0r1K5jhV6f/wDNGtp4/ChEGUmrEG0aq+J8iBD3HhciIU2EfQEFUFcDXG0Unyqjn4tw/3EhkjJESM0Cgu4J8TxVBEtT6T5tnVRxM6IQNyLE4U5cFSLjhQifOCi+EVmViUQiXPXfjna9voVD826oXc8e/Ub/gfy8V7hz9bjC/epaN0G3z6bB6ZNeUFWT33Nw5fRWNG03EM3aD4aJuS26fzEL+rXMcP38rg+VnAr1SSMVBN4tRPhTEZIygCOXC6GuBjhZKe4AORRUiBsPRUhIE4+OOn61EAIBYG0m3cfdUQWZL4GjV8QjrDJeANEJIqR92GdrKlQ7Z3WcuZmPu9HiOrr7bB401ARo2kBxj19kXCHuRguRmC5CSqYIF+8UID5FBGszad29/1iI8CeFSM4QITlDBN9r+cjLh8xvQHVx6NAhdOvWDd27d0f9+vXx7TffwMTEBCdOyD/ffPvNNxg8eDAa2tujbt26GDFiBMzNzXH16lVJmMOHD6NZ06b4/PPPYWFhgc8//xyurq44fOSIspJVYQb3qYvte5/gwuVkRD95id+Xh0NTUxXdPGor3KdXFzPo6ahj5u/3cCcsEwlJuQi9n4lHMdLryis3U/H3zhhcuKz4Wr+6SDp1ARG/rsDzw35vDwzA8ushePUkHvcnL0B2eBSeeu/H060HYTNplCSM9YThSPYPQuTijXjxIAqRizci+ewVWE0oObK3uhGJRLh3aTtcOnwDq8bdYGhqj/aD/oAw/xUiQxSfvzt8tgQOrb+EkbkDDExs4N5/HkSiQsRFXZYJl5/7AgF7p8K93zxoaul96ORUGJFIhPOv2zmuxdo5N0pp55w7IW7ndOs/BmZ1bdCt/xg0dPoE505I2zmNm7bDp0MmwvUjaOcAwKmjO9Guc1+079of5hbW+HL0FNQyMsU53/1yw58/dQBGxmb4cvQUmFtYo33X/mjXqS9OHd5RLKQA+obGMq/qjuWyfIhEIpzz2QnPIvk4bPz81/noo3C/8yd2opFza3j2HwOzutbw7D8GDZ1ayeTj2ePb4dapP9p0HgizejYYNGI6DI3NEHh6rzKSpnRnXqfX/U16R5ae3ot++2BoXAeDRk6HWT0buHceCLdO/XHm6DZJGEs7JwzwmowW7j2gpq6hrKQQUTmp9C3olStXws3NDWPHjkV8fDzi4+NhYWEBAJg2bRoWLlyIsLAwODs7Izs7Gz179oS/vz9u374NT09P9O7dG0+ePAEAHDx4EGPHjoWbmxvi4+Nx8OBBuduKKywsRI8ePRAUFISdO3fi/v37+OOPP6CqKr5JYWZmhitXruDx48elpmPZsmVYunQpQkND4enpiT59+uDhw4fvlB8vX77EwoULsWnTJty7dw+1a9eGl5cXdu/ejVWrViEsLAzr16+Hjo4OACA+Ph4eHh5wdXXFjRs34Ovri4SEBHz22Wdv/TurVq3C7t274evri/Pnz2PAgAHw8fGBj48PduzYgY0bN2L/fukF4siRI3Hp0iXs3r0boaGhGDx4MLp37y5J49WrVzFq1CiMGzcOwcHB6NixI+bPn/9O6Vdk+fLlcHd3x+3bt9GrVy8MGzYMXl5e+Oqrr3Dr1i3Y2dnBy8sLIlHZpteSl8+lpa9NmzZ48OABAODAgQOIj49HmzZtcOfOHXh6emLAgAEIDQ3Fnj17cPHiRXz//fcyf2/JkiVwcnLCzZs38b///U9hHLKysjB8+HAEBgbiypUraNCgAXr27ImsrKxyyUdlSUmMRWZ6Mho5S6fzUVPXgK1jc0RHKO4EiY4IQUNn2SmAGrm0QXRE8IeKaqWWkhiLrPRkNGwim492Di1KzZOYhyXzsaGLO2Ieyt+nsFCIW0E+yM3NgVUD13KIeeVjqAvoaQvw8Jl0xKKwEIh+Xoj673Cz2baOACb6AsQ8Fx9HVQUwN5Y9LgA8elYIy9qV/hT8n6QnxyI7Iwm2jd0l29TUNWDVsCViI2//5+MKC/IQ9/iezHEBwMbRHbGP/vtxKysDHUBXS4CoeOl5S1gIPE4QwcKk7KN01FUBFQGQU2SUlH09AeJSRBjUVgWTB6pibA9VNLWtfiN/3qilK4BeTQEexEofrBEWApFxQliZlb0e2tVVQW0DAaLihXK/FwgAVztVaKgDjxOq1+jn/Px8PHz0CM2aNZPZ3qxpU9wPCyvTMQoLC5GTkyMz0jwsPLzEMZs3a4aw+/ffP9KVmLlpDRjX0sS122mSbfkFIgTfTYdTI8U379t+YoS74ZmY/K0djm53w/bVLTBscH2oVM/TSbkzaO2KJP9LMtuSTgdCv7kTBGriDmvD1q5I9r8oEybZLxCGboqnF6sustJikZOdjLp20vOsqpoGzKxaIvFJ2c+zwvxXKBQWQFNLX2b75WO/waKhB+raVe/pPCXtHBdpOtXVNWDn2BzRDxS3c2IiQmTaRsDH3c4pyM/H48hwNHZtLbO9sWtrPAoPlbtP5IPQkuGbtkZM5H0UFORLtuW+ysHUr3th8pgeWDH/BzyOqv6jd1kuy0dK4rPX+egm2fYmH6MeBCvcL1pOPjq4uCPq9b2PgoJ8PI0Kg4NLsTDObogu5bhVVUG+4vQqyseoiBA4OLvJhndpg8dR9yEsUr+JqOqq9GtS6evrQ0NDA9ra2jAzMwMAyRQg8+bNQ9euXSVhjYyM4OLiIvk8f/58HDp0CEePHsX333+PWrVqQVtbGxoaGpJjAZC7rSh/f39cu3YNYWFhsLe3BwDY2NhIvv/1118xYMAAWFlZwd7eHm5ubujZsycGDRoEldetxqVLl2L69OkYMmQIAGDRokU4d+4cVqxYgTVr1pQ5P/Lz87F27VpJOiMiIrB37174+fmhS5cuJeK2bt06NGvWDAsWLJBs8/b2hoWFBSIiIiTpkfd31q1bB1tbWwDAoEGDsGPHDiQkJEBHRweOjo7o2LEjzp07h88//xyRkZHYtWsXYmNjYW5uDkC8vpSvry+2bNmCBQsWYOXKlfD09MSMGTMAAPb29ggKCpIZUfZf9ezZE9988w0AYPbs2Vi3bh1atmyJwYMHAwCmT58ONzc3JCQkKPw/F09/0XwuS/pq1xY/+VqrVi3J31iyZAm+/PJLyWiwBg0aYNWqVfDw8MC6detQo0YNAOLRV1OmTJH8/YsXL5aIw5twRW3YsAGGhoYICAjAp59++s75VlGy0sUj6HT1Zad21NU3QpqC4d3i/ZLl7pOZXv2fFpYnK0Oc7uJ5oqNvhLRkxdOflTUf455EYOXsoSjIz4NGDW2MmrQSZvVsyyn2lYuulvgGfXaObEd2dg5goFP6zXtNdWDGFxpQUwUKC4GjQQV4FCc+jnYNQFVFUOK4WTlAAy15R6v6sjOSAAA6erJlrKae0XtNy/cyKw2iQmGJ4+roGyHybvX7DdARnx6Q/Up2e/YrwOAdpors3FQFWTmQ6ewy1AFa2AtwJUyEi/eEMDcSoHsLFQgLCxEaXf3WStPVfl2/iy31k50DGL6lftfQAP7npQU1FaBQBBwMzMPDWNkOKLNaAkwYUANqqkBePrDVN7fUta6qoszMTBQWFsLQwEBmu4GhIdLS0uTvVMzBgwfx6tUrtG8nXdswLS0NBsWPaWCA1DIes6qqZSh+ujc1XXaOzbT0PJjWrqFwP3MzLTRzrgG/8wmYOvcO6plrYdK3DaCqKsDW3YofliMxTVNj5CbIni/yElOgoq4ODWND5D5PgqaZMXITZGd6yE1IgaaZ/Gloq5OcLHHeaOnIjiqpoWOEF+llP39fP7UM2nqmMLeV3nyMCj2BlLj76P3dvvKJbCWW+bqdoyfnWru0absy05OhZyC7j56BEbI+1nZOVjoKC4XQl5MnGeny19nLSEuBXlPZ8PoGRhAKhcjOTIdBLRPUqWuN0RPmoK6lHV7lvIDf8V1YOHMU5i7fDVPz+h8sPRWN5bJ8vGkvy2tLvy0fdQ1qye5jUEuSj9mZaSgsFH409zqys8TpLV62dA0UpzcrPQW6cspiobAA2Vnp0Des/udpouqu0ndSlaZFC9mF0l+8eIG5c+fi+PHjiIuLQ0FBAXJyciQjqf6r4OBg1KtXT2GHTp06dXD58mXcvXsXAQEBCAoKwvDhw7Fp0yb4+voiOzsbcXFxcHeXffrb3d0dISHvNnWWhoYGnJ2lc6IHBwdDVVUVHh4ecsPfvHkT586dk4ysKioyMlJhmrS1tSUdVABgamoKKysrmeOYmppK1iG4desWRCJRiePl5uZK1pgKCwtD//79Zb53c3Mrl06qonliamoKAGjSpEmJbYmJiWXqpCqez2VJnzw3b97Eo0eP8M8//0i2iUQiFBYWIjo6Gg4ODgBKlmV5cXgT/9mzZ+Ps2bNISEiAUCjEy5cv36mM5+bmIjc3V2abpqYmNDU/3DzcNwKPY8/f0vUCvpnxumNWUOzmoEjOtmIEJfYRldxWTd28eBx7N0kX+h07ba34jbw8wVvysfj3cvKxtrk1pvxxADkvMhF6zQ//rvsZ38/eWi06qlxsVdDPXXoK3H769dNXxe4rl6Vo5eUDfx3Kg6a6ALbmKuj5iRpSs/IR/Vx6sOK3q6tTiQ29cgzHt/8q+fzlD+vFb4plnqgM9btsPtRxK5aTlQCftpIOh9h1/vVoHTllsqzdH20cBXCyFGCbvxDCIv0qAgBxqcDZEPHG52kimOiL0KKBCkKj5Y8SqkqaNlDFIA/pFB+bT4jPeSI5Ofe2vMzNA/7c+wqa6kCDeqro00YDqZm5iIyTZmhSugh/7n0FLU0BmtioYkgnTaw78qradVQBJc/BojKeg8+fP4+d//yDX2fPLtEpVeKYcrZVdV09amPqeOk15LR5d8RvSpwcSq/gKgIgPSMPi9dEoLAQeBCZDeNamvhiQD12UpVV8VkV3pS1otvlhSnjbAxVSWTwMVw6MkfyuavXOgUhRWU+z4Ze2ISoUB/0HLMNauritkV2ejyuHF8Iz5GbJNuqk+uBx7F7o7Sd8+1M+e0ckUjO9XcJJX9jq8M1zvuRd94pJXSJJo7o9XbxF7YNm8C2ofQ+gV0jF8ydPBT+PrsxdMy08olyJcByWT6uB57AriL5+N3rfJR7T+ItxyrZ/kbJfCxxTVTd73W8270dedehco9DRFVSle6kqllT9lHiqVOn4tSpU1i6dCns7OygpaWFQYMGIS/v/VYC19Iq2+PuTk5OcHJywvjx43Hx4kW0a9cOAQEBaN68OYDSG/ZvRlwVnY4uP7/kkFUtLS2Z47wtboWFhejduzcWLVpU4rs6deoo3E9dXXY9EIFAIHdbYWGh5O+oqqri5s2bkmkQ33jTsVXWqfaKUlFRKbGfvHwpGrc3+SNv25v4vk3xfC5L+uQpLCzEN998g4kTJ5b4rn596ZNaxcuyvDgA4jXakpKSsGLFClhaWkJTUxNubm7vVMYXLlyIuXPnymz79ddfMWfOnDIf4105tegIywbSDreCfHF8s9KTZZ54ycpMKfH0UFG6BsYlnqzJykwtdZ/qpHHzjphi9/Z8zM5Mhc7b8jHj7fmopqYOEzNxOa1v64QnUfdwwXcnPhvzK6q6sCeFeJoorTdqquK6pqMtQFaRUU81a5QcXVWcCEBqlvhdfKoQJgYCeLioIfp5Pl6+AoSFotcjtaTH0dESj+KoDhq6dES9X4uUywJxvmZnJEPXQLq2ysuslBKjoN6Ftq4hBCqqyM6ULbsvMt/vuJVFRKwIG5KlHURqr081Olqyo6lqagIvio2uksfNQYC2jVWw44wQiemy32W9ApIyZMt1cqYIDvWrRwPvfowQfyZIM+lNXupqC5D1sng9fHv9TskUh4lLKUBtQxV0aqqOyDjpwx7CwjdhRIhNKoRFbRW0baKGAxeqz9Qjenp6UFFRKTHCKSM9vUSnU3EBAQFYsXIlZs2ciaZNZadMM5QzEisjPb3EiK2q7uK1FNyPkK6NqqEuvu6vZaiBlDTpuchQX73E6KqiktPyICwQoejl7OPYlzCupQk1NQEKCqpfR0p5yk1ILjEiSsOkFgrz85GXki4O8zwZmmayI4k0a9cqMQKrOqjv0AkmFtLzt/D1+TsnOxnaetLz96vsVGjpvP08eyfQG6EBG9F9pDdqmTWUbE+Ju4dXL1JwdO0gyTZRoRDPY24g7Mq/GD43BCoqVXedziYtOsJKTjsns8T1eentHD157ZyMj6edU5yurgFUVFSRISdPio8GekPf0AgZabKjrDIzUqGqqoqauvpy91FRUYG1nSMS4p6WT8QrCZbL8tGkRQdYNZB2airKx7fdkxDno2zZLJqPOnqGUFFRLTFCLbua5rWOrji971K2dA2MkJlWMryKqhp0FNRvqjre3s1LH4MqMYO5hoYGhMK3P9kbGBiIESNGoH///mjSpAnMzMwQExPz3n/f2dkZsbGxiIiIKPM+jo6OAMSju/T09GBubo6LF2XnNw8KCpKMpDExEZ/g4uOlQ4SDg4Pf+neaNGmCwsJCBAQEyP2+WbNmuHfvHqysrGBnZyfzktcx8l81bdoUQqEQiYmJJf7Om5FLjo6OuHLlisx+xT8XZ2JiIpMnQqEQd+/eLbd4l1VZ0ifPm/wvvo+dnR00NN59IcfAwEBMnDgRPXv2ROPGjaGpqYnk5HdrNM+cORMZGRkyr5kzZ75zXN5FDa2aMDGrL3mZ1bOFnoExHoRKF1QuKMhH5P2bsLZ3UXgca3sXmX0A4EFoEKztXT9U1CsVefmoa2CMB3dk8/FR2I1S88SqgQsi7pTMx7euNyUSSS7Mq7q8fHHH0ptXYroImS9FsDOXnhZVVQBrMxU8ece1ZQSQ3hQXFgJxySLY1ZU93dqZq+BxYvVYs0ZTSwe1TC0lLxNzO+jomyDqfpAkjLAgDzEPrqOe7X9f00NVTQPmlo0RdS9IZnvU/SDUs6v6a4XkFQBp2dJXUgaQlSOCTR3pBbuKCmBpKsDTpNJvRLs5CNDOSQX/nBUiPrXk90+TRDDWk20IGOkKkPGiXJJS4XLzxZ1Gb14JaSJkvhDBvp70JqiqCmBrripZP+5dqL3lXqr4N6B6NbTU1dXRwM4Ot2/Lrktz6/ZtOL6+lpXn/Pnz+HP5ckybOhWtWrUq8b1Do0a4VfyYt27B4fV1dHWRkyPEs/hXklf0k5dITs1FS1dDSRg1NQFcnQxwNzxT4XHu3M9E3TpaMg9aW5hrITkllx1UZZB+JRjGnWXXvzDp2hYZN+9CVFAAAEi7EgzjzrKzXxh3aYu0y9Vv7UN1zZrQM7KUvAxq20FLxxjPHsmev5/HXEft+qWfZ+8EbkbwuXXoNnwjjOs5yXxnbuuG/hOPoN/3ByUv47pOsHX5FP2+P1ilO6iAsrdzHt2/CeuGits5VvYuMtf0ABD+EbVzilNTV4elbSPcD7kqs/1eyFXYNXKWu49tQ2fcKx4++AqsbB2hpqYudx+RSIQnMREwMDSW+31VxXJZPhTlY7icfLRp6KrwONb2LgiXk482r+99qKmpw8LGQea44jBXYF3KcasqNXXF6VWUjzb2LggPlb1/GBYSBEsbR6gqqN9EVLVUiU4qKysrXL16FTExMUhOTlY4GsbOzg4HDx5EcHAwQkJC8OWXX5Z55ExpPDw80L59ewwcOBB+fn6Ijo7GyZMnJdPUfffdd/jtt99w6dIlPH78GFeuXIGXlxdMTEzg5iZe2G/q1KlYtGgR9uzZgwcPHmDGjBkIDg7GDz/8IIm7hYUF5syZg4iICJw4cQLLli0rU94MHz4co0aNwuHDhxEdHY3z589j7969AIDx48cjNTUVX3zxBa5du4aoqCicPn0ao0aNknT8rV69Gp07d36vPLK3t8fQoUPh5eWFgwcPIjo6GtevX8eiRYvg4+MDAJg4cSJ8fX2xePFiREREYPXq1W+d6q9Tp044ceIETpw4gfDwcIwbNw7p6envFdf/oizpk2f69Om4fPkyxo8fj+DgYDx8+BBHjx7FhAkT/lM87OzssGPHDoSFheHq1asYOnRomUf6vaGpqQk9PT2Z14ec6k8egUAAj55fwe/wJoRcO4O4Jw/xz9qfoa5ZA83b9pKE27l6Fo79u0Ly2aPHV3gQehn+RzYj4VkU/I9sxoM7V+HR8ytJmNxXLxEbE47YGPHadSmJzxAbE17qHNFVlUAggEePYfA/8jdCr/sj/ulD7Fr3MzQ0aqCZuzQf/1k7E8d3LZd8bt/jKzwIDcKZo+J8PHN0MyLuXoFHz2GSMCd2r0Bk+E2kJj1D3JMInNizEo/uX0fzIsetboLuCdHBRRWOliowNRRgUHs15BcAwVHS88ig9mro1kJ6M8XDWRV25gIY6gIm+gK4O6miaQMVBD+SPlhx8a4QLexV0LyBCkz0Bej5iSr0dQS4Fl71p1WTRyAQ4JMuXgg8sQFht/yQGBuBw94zoa5RA00+ka6dd2jTdPgfkJ7nhAV5eP4kDM+fhEFYkI/MtAQ8fxKG1ATpFFatu43ArcD9uB14AElxkfDdvRAZqfFo4TFEqWlUlqvhhWjbWAUN6wlgog/0dVNBfgFwN0Z6M7qvmwo6uUov59o4CtDRRQVHrxQi/YV4NGDNGoB6kbHzV8MKUdcYaNtYAEMd8VSDzRoIcD2ienScyhMYmo/OzdThZK0Ks1oCfN5JA3kFItx+WCAJM6STBnp8Im3kdmqqhgb1VFBLVwATAwHaO6uhhb0qbhbZp8cn6rCuowJDXQHMagnQvZU6bM1VcKtImOqif//+OHXqFE6dPo0nT55gw8aNSEpKQs+ePQEAW7ZswdKlSyXhz58/j6XLlmHsmDFo1KgRUlNTkZqaihcvpL2hffv2xa1bt7B33z48ffoUe/ftw+3gYPTr21fp6VO2fUefYdjg+mjf2gjW9bXx848NkZsrxOmAREmYX35qiG+8rCWfD5+Mg76uGn4YawcLcy24taiFYYPr46CPdL0grRoqsLOuCTtr8cNodUxrwM66JkxNqt80a6o1taHn0gh6Lo0AANrW9aDn0gg1LMSzRTScPwkuW6SzSTzeuBtaluZwWDIDOo1sUG/EQFiMHIioP70lYWJWb4dxV3fYTBmLmg1tYDNlLIw7uyHmr23KTVwFEAgEaOzuhdCAjYi554e0hAgEHpgFVfUasHWRnr8D9k3HjVN/Sj6HXtiEm34r0W7A79AxrIuXWUl4mZWE/FxxXVfXrAlDU3uZl5qGFjS1DWBoKn/q+apMIBCgQ8+vcPqQtJ2zc424ndOiSDtn++pZOFqkndOh51cID7kMv8Ob8fxZFPwOi9s5HXt9nO0cAPDs8xUu+B9GoP8RxD2Nxi7vZUhNfo4OnuJReft3/IW/V86WhO/gORApSfHY7f0n4p5GI9D/CALPHIFnP2kb58iejbh7OwiJz2PxJPoBtqyeh6fRD9DBc6DS06dMLJflQyAQoGPPr3D60GZJPu5Y88vrfOwpCbd99Swc+Xel5HOHnkNf56M3nj+Lht9hb4QXy8dOn3oh6MxBXD57CM9jo3Bg62KkJsejXdfBSk2jsnR+nd6g1+nd/zq9bbuJ03vkn5XY9tcsSfi2XQcjNTkOB7YuwfPYKASdPYTLZw+hc5/hkjAF+fl4Gh2Op9HhEBbkIz0lEU+jw5EY/35LwBCRclSJ6f6mTJmC4cOHw9HRETk5OdiyZYvccMuXL8eoUaPQpk0bGBsbY/r06cjMVPw04rs4cOAApkyZgi+++AIvXryAnZ0d/vjjDwBAly5d4O3tjXXr1iElJQXGxsZwc3PDmTNnJOsVTZw4EZmZmZg8eTISExPh6OiIo0ePokGDBgDET6ju2rUL3333HVxcXNCyZUvMnz8fgwe//YS0bt06zJo1C+PGjUNKSgrq16+PWbPEP+bm5ua4dOkSpk+fDk9PT+Tm5sLS0hLdu3eXTDGYnJyMyMjI986jLVu2YP78+Zg8eTKePXsGIyMjuLm5SW5etG7dGps2bZJMLdelSxf88ssv+O233xQec9SoUQgJCYGXlxfU1NTw008/oWPHju8d1//ibemTx9nZGQEBAfj555/Rrl07iEQi2Nra4vPPP/9PcfD29sbXX3+Npk2bon79+liwYAGmTJnyX5NUoTr3GYX8vFzs3zwfL19kwtKuCb6btQE1tKQj/NJS4iFQkT4ubN3QFcN/WIwTe/6Cz57VMDa1wIgflshMZfAk8h5Wzxsl+Xx4+xIAQCuPPhg67nclpEy5OvUehfy8V9jvPR85LzJhaeuMb2dtlM3H5HgIBNKb2Nb2TTFs4hKc3PsXTu79C0amFhg+cQksi0wlmJWRgn/WzERmehK0tHVRp749vpmxHg2dZZ9Ark4uhAqhrgr0aaMGLQ0gNkmELafykVdkxi4DHYHMshQa6uLw+jUFyBeK16bZe74Ad6KlN/vvRBdCu0YBOjVVg642kJAmwrbT+UjPVmLilMy9xxgU5L+Cz855yHmRgXo2zhg2aTM0taTTo2akxslMaZqVnogNc6XrFl4+5Y3Lp7xh2bAlRkzbAQBwatUTOdnpCDi2BtkZSahdtwGG/rABBsZ1lZc4JQq6L4K6qgg9W6lASwN4lgzsPCtEXpH+D/2aAplpcVs0UIGaqgCftZd9Mj0gtBABd8TlMi4V2HuhEJ1cVdC+iXjk1qkbhTKdX9XNueACqKsJMKCdBrQ0gSeJhfj7eC5yi9RvwxL1WxzeQEeA/AIgMb0Q/57JQ0iktINZR0uALzppQK+mAK/ygLiUQvx9IhcPY6tfh5+HhweysrLw77//IjU1FVZWVpg3d65k3c/UtDQkJiVJwvucPAmhUIg1a9dizdq1ku1dunTB5EmTAIhH2c+YMQPbt2/Hjh07UKdOHcycMQONGjVSbuIqwD8HnkJTQwWTvmsAXR113I/IxE+zQ5GTIy1fpiY1UFikTCYm5+Kn2XcwcYwttv7VAskpudh37Bn+OSC9+dLIThd/LXSVfJ44xg4A4HPmORasePDB06VM+s2d4HZmh+Sz41Jx++fp9oMIHT0TmnVMoGUhnd48JyYW13t/DcdlM2H53VDkxiXi3k+/4/mh05IwaZdv4/bQSWg490c0nDsRLyOf4vaXPyH9WqjyElaBmrQbg4L8XFw+Og95rzJhUs8Z3Udugrqm9LryRYbsdWX41V0oFObj7K4fZI7l2mk8mnX+Xmlxr0y69BW3c/ZuErdzrOyaYPzPG+Rcn0uvg2waumLEj4txfPdfOLFnNYzNLDDyx5LtnFVzpe2cQ0XaOcPGV792Tqu23ZCdlY6je/9GRloy6ta3xY+/rIJxbXG9zkhLRmrSc0l4E9O6+OmXVdi1ZRnOntwLg1om+HL0VLRwkz6Q+/JFFrat+x0ZaSnQ0tZBfZuGmD5/E2zsnUr8/eqG5bJ8dOk7Enl5r7Bn0++SfPz+5/Uy+Zia/Fzmd9KmoStG/rgIx3evxvHX+Tjqx8Uy+di8TXe8yErHyQMbkJmWhDoWdhg3cw1qmZgrNX3K0ty9O15kp+Pk/iLpnbUGRq/Tm5GWhLRkaf02Nq2HcTPX4sC2xbhwajf0DU0weNQMNG3dVRImIy0Rf0z7TPL5zLFtOHNsGxo4tsCPc6UPpBBR5SQQ/ZeFgoioWvINrh5TuVWE7q7S6Rt9blWfdUgqQs9m0pEMszbnlhKS3mbBaOmT8/9e5On+v/qyrbSxPu+f6jdCRplmD5U+HzVl3csKjEnVt/Q7bcn7qHJ42OhjZWNrK3nftrf86bOpbC4e85C8P6HesJSQ9Da98qUdiov2V79Ob2WZPkh6k/h0CNs576Obi7Stc+l+NX7SSgncHaUPbbFc/ndFy6RfCNuM76Ori7TN6B/KvHwfXZyr38h1Zbj+IL2io1DltGxoUNFRKHdVYro/IiIiIiIiIiIiIiIiql7YSUUfnR49ekBHR0fua8GCBRUdPSIiIiIiIiIiIqJqTyQS8PWOr+qoSqxJRVSeNm3ahJycHLnf1apVS8mxISIiIiIiIiIiIiL6OLGTij46devWregoEBERERERERERERF99DjdHxERERERERERERERESkdO6mIiIiIiIiIiIiIiIhI6dhJRURERERERERERERERErHNamIiIiIiIiIiIiIiEipCis6AlQpcCQVERERERERERERERERKR07qYiIiIiIiIiIiIiIiEjp2ElFRERERERERERERERESsdOKiIiIiIiIiIiIiIiIlI6dlIRERERERERERERERGR0qlVdASIiIiIiIiIiIiIiOjjIhIJKjoKVAlwJBUREREREREREREREREpHTupiIiIiIiIiIiIiIiISOnYSUVERERERERERERERERKx04qIiIiIiIiIiIiIiIiUjp2UhEREREREREREREREZHSqVV0BIiIiIiIiIiIiIiI6OMigqCio0CVAEdSERERERERERERERERkdKxk4qIiIiIiIiIiIiIiIiUjp1UREREREREREREREREpHTspCIiIiIiIiIiIiIiIiKlYycVERERERERERERERERKZ1aRUeAiIiIiIiIiIiIiIg+LiKRoKKjQJUAR1IRERERERERERERERGR0rGTioiIiIiIiIiIiIiIiJSOnVRERERERERERERERESkdOykIiIiIiIiIiIiIiIiIqVjJxUREREREREREREREREpnVpFR4CIiIiIiIiIiIiIiD4uIggqOgpUCXAkFRERERERERERERERESkdO6mIiIiIiIiIiIiIiIhI6dhJRURERERERERERERERErHTioiIiIiIiIiIiIiIiJSOnZSERERERERERERERERkdKpVXQEiIiIiIiIiIiIiIjo41IoqugYUGXAkVRERERERERERERERESkdOykIiIiIiIiIiIiIiIiIqVjJxUREREREREREREREREpHTupiIiIiIiIiIiIiIiISOnYSUVERERERERERERERERKp1bRESAiIiIiIiIiIiIioo+LCIKKjgJVAhxJRUREREREREREREREREonEIlEooqOBBERERERERERERERfTwC7r2s6ChUOR6NtSs6CuWOI6mIiIiIiIiIiIiIiIhI6dhJRUREREREREREREREREqnVtERIKLKwz80t6KjUGV1cdaUvL9w70UFxqTqa9+4puT9tfCMCoxJ1deqkb7k/aX72RUYk6rN3VFH8p71+/0Urd/XH6RXXESqgZYNDSTvj90sqLiIVHG9m0ubQ3/7V2BEqoGxXaTvF+0vrLiIVAPTB0mfJT2h3rACY1K19cp/IHnvG5xXgTGp+rq7akje+4Wwzfg+urpI243n7uRUYEyqto5NtCTvWSbfT9EyeebOqwqMSdXXuUmNio4CUZXFTioiIiIiIiIiIiIiIlIqkUhQ0VGgSoDT/REREREREREREREREZHSsZOKiIiIiIiIiIiIiIiIlI6dVERERERERERERERERKR07KQiIiIiIiIiIiIiIiIipWMnFRERERERERERERERESmdWkVHgIiIiIiIiIiIiIiIPi4iUUXHgCoDjqQiIiIiIiIiIiIiIiIipWMnFRERERERERERERERESkdO6mIiIiIiIiIiIiIiIhI6dhJRURERERERERERERERErHTioiIiIiIiIiIiIiIiJSOrWKjgAREREREREREREREX1cCiGo6ChQJcCRVERERERERERERERERKR07KQiIiIiIiIiIiIiIiIipWMnFRERERERERERERERESkdO6mIiIiIiIiIiIiIiIhI6dhJRUREREREREREREREREqnVtERICIiIiIiIiIiIiKij4tIJKjoKFAlwJFUREREREREREREREREpHTspCIiIiIiIiIiIiIiIiKlYycVERERERERERERERERKR07qYiIiIiIiIiIiIiIiEjp2ElFRERERERERERERERUDa1duxbW1taoUaMGmjdvjsDAwFLDBwQEoHnz5qhRowZsbGywfv36Dxo/dlIREREREREREREREZFSiUR8vevrXe3Zswc//vgjfv75Z9y+fRvt2rVDjx498OTJE7nho6Oj0bNnT7Rr1w63b9/GrFmzMHHiRBw4cOA9/9uKsZOKiIiIiIiIiIiIiIiomvnzzz8xevRojBkzBg4ODlixYgUsLCywbt06ueHXr1+P+vXrY8WKFXBwcMCYMWMwatQoLF269IPFkZ1URERERERERERERERElVxubi4yMzNlXrm5uXLD5uXl4ebNm+jWrZvM9m7duiEoKEjuPpcvXy4R3tPTEzdu3EB+fn75JKIYdlIRERERERERERERERFVcgsXLoS+vr7Ma+HChXLDJicnQygUwtTUVGa7qakpnj9/Lnef58+fyw1fUFCA5OTk8klEMWof5KhERERERERERERERERUbmbOnIlJkybJbNPU1Cx1H4FAIPNZJBKV2Pa28PK2lxd2UhEREREREREREREREVVympqab+2UesPY2BiqqqolRk0lJiaWGC31hpmZmdzwampqMDIy+m+RfgtO90dEREREREREREREREolgoCvd3y9Cw0NDTRv3hx+fn4y2/38/NCmTRu5+7i5uZUIf/r0abRo0QLq6urv9g8uI3ZSERERERERERERERERVTOTJk3Cpk2b4O3tjbCwMPz000948uQJvv32WwDi6QO9vLwk4b/99ls8fvwYkyZNQlhYGLy9vbF582ZMmTLlg8WR0/0RERERERERERERERFVM59//jlSUlIwb948xMfHw8nJCT4+PrC0tAQAxMfH48mTJ5Lw1tbW8PHxwU8//YQ1a9bA3Nwcq1atwsCBAz9YHNlJRUREREREREREREREVA2NGzcO48aNk/vd1q1bS2zz8PDArVu3PnCspDjdHxERERERERERERERESkdO6mIiIiIiIiIiIiIiIhI6TjdHxERERERERERERERKVWhqKJjQJUBR1IRERERERERERERERGR0rGTioiIiIiIiIiIiIiIiJSOnVRERERERERERERERESkdOykIiIiIiIiIiIiIiIiIqVjJxUREREREREREREREREpHTupytnLly8xcOBA6OnpQSAQID09Xe42KysrrFixoqKjW+1t3boVBgYGks9z5syBq6trhcWHiIiIiIiIiIiIiACRSMDXO76qI7WKjkBl0KFDB7i6upZLp9G2bdsQGBiIoKAgGBsbQ19fH+vXry+x7fr166hZs2aZj7thwwasXbsWjx49grq6OqytrTFkyBBMnz79veP8MZkyZQomTJhQ0dEod+fPn0fHjh2RlpYm0ylXWV04tRv+R7YiIz0ZderZYtDIabBzaK4w/MN7N3Bg2xLEx0ZC39AEXfuORLtun0m+j3v6CCf2rMGTqDCkJsVh4Iip6NRrmDKSUuHOndyLU0e2IyMtGeYWNvh81BTYOzZTGP7BvZvYu2UZ4p5GwaCWCTz7DUcHz0Fyw167eAp//zkTrq06YPyMPz9UEioNf5/9OHFoBzLSUlC3vg2+Gv0TGjZuqjB82N1b+Nd7BZ49iYJBLWP06j8MnXsMlAnje3QXzpw8gJTkBOjq6qNlm074zGs8NDQ0P3RyKszZk3vhe3gH0tOSUdfCBl+MngJ7R8X5+ODuTeze8ieevS6TPfp5oWN3aZm8ePYovP+aW2K/DXuCoF6N8xFg/S5Pfj774XNwJ9LTUlC3vjW+GvMTGr2lfv+zeQWePYmGQS1jfDpgGDr3GCATxvfILvj7HkRKUgJ09fTRqk0nfOY1rlrXbwAQiUQ4fWAtrp7dh5cvMlHfzhkDRv4Cs3p2pe4Xeu00fPf9hZSEpzAytUCPz35Ak5Zd5IY9c+RvnNyzAu26f4W+XjM/RDIqnEgkQpDPaoRe2oPcl5kws3JBl89mw9i8gcJ9kuMe4tKJVUh4cg+Zqc/QceBMNO80QibMpRN/4bLPaplt2rrGGPfHpQ+RjEpBJBLh9tk1eHB9L/JyMmFi4Qy33v+DoanivHxwfS8e3T6KtISHAACjuo5o0fUnmFg4yw0fErARN08vh2ObYWjda9YHSUdFqtW2BWwmj4Z+MyfUMK+NGwPHIeHomdL3adcSjktnQMexAXLjEhG5bBOebNwtE8asfzfYz/kB2rb18TLyCR7MXo6EI/4fMimVgkgkgu/+dQg6sx852ZmwbNAEg0b9jDoWpf9OBl/1g8+e1UhOeApjUwv0GjIRLq06S75/dP8Gzh7biqfR95GZloTRU1bAuWXnUo5Y9V04tRtnjkrbjQNHvKXdeP8GDhZpN3bpI9tuBIDbV/xwYs8aST73/mKCTD5XR+d998Dv6LbX15S2GDxiKhqUck0Zce8G9m9bhrinkTAwNEG3viPQ3nOw5PtAvwO4GnAccU8fAQDq2zii75ffw7pBkw+elorGMll+Anz3wP/oVmSkJaOOhS0Gj5gGu7eUywPbliL+6Zv7QiPQ3lP2vtDx3Wsl94UGjZiKTp9+pYykEFE54EiqMhCJRCgoKChT2MjISDg4OMDJyQlmZmYQCARyt5mYmEBbW7tMx9y8eTMmTZqEiRMnIiQkBJcuXcK0adOQnZ39Psl6K6FQiMLCwg/6N5RNR0cHRkZGFR0NGdUxn0tz85Iv9m9ZDM+BYzFz8V7YOTTDmt/HITUpXm745IRYrF04DnYOzTBz8V54DhiDfd5/4PYVP0mY/NxXMKpdD32H/gA9A2NlJaXCXb94Cnu2LEWvgaMxe9m/aODQFKvmT0CKgrxMSniGVfMnoIFDU8xe9i96DhiF3ZsX4+blkjchUhLjsG/rcjQopXOhOrkS6Iedm/9E38Ej8dvyHWjo6Iol835EctJzueETE55h6bwf0dDRFb8t34E+g0Zgx6ZluB50VhLm0nlf7N2+Bv2HjMGi1XswZsIvuHrRH3u3r1FWspTu2sXT2OW9DJ8OGoU5y/5FA8emWP5b6WVy+fyJaODYFHOW/YtPB47Ev5uX4EaxMqmlXRPLvU/JvKp7BxXrd/m5EuiHnZuWo89nIzF/xXZx/Z77k+L6/TwOS+f+hIaOrpi/Yjv6DB6B7X8vw7Vi9XvP9rUYMGQMFq/ZjbETfsaVi/7Yu32tspJVYc4d24wLJ7eh/4if8cP8PdDTN8bGBWPwKueFwn1iIoKxc9UUNG/bB5MXHkTztn2wY9VkPH4UWiLsk8g7uHJ2H+rUt/+Qyahw1/z+xs2zW9D5s9kYOm0/auoZY9/qkch7pfj6Pj8/B/pG9dC+72TU1DNRGM6oTgN8t+Ci5DXi52MfIgmVxp3ATbh3aSvcev+CPuP2QkvHGL5bRiM/V3GZjI++Dhvnnugxeis+/XYXdPTNcWrrGLzISCgRNin2Dh5c3wtDs4YfMhkVSrWmNjJDH+DeD/PKFF7Lqh5aHtuI1Is3cbFlPzxatB6Nl/8Ms/7dJGEMWrui6b/L8eyfIwhs3hfP/jmCZrtWwKCV/I7A6uTMUW+cO7Edg0bOwqQFu6Crb4y1v39d6u9kdEQwtq2YipbtemP64v1o2a43tq6YgpiH0t/JvNwc1LW0x6CR1a+jVJ6bQb44sHUxPAeMxYxFe2Hr0AxrF4xDarKCdmNiLNYtHAdbh2aYsWgvPPuPwf4tsu3GqIgQbFkxDS3bf4oZS/ajZftPsXn5VJl8rm5uXDqFfVuXoMeAMfh5yW7YOTTF6gXjS2l/P8PqBd/DzqEpfl6yG90HjMaeLYtw64q0gzni3g20aNsdP835G9MWbEctYzOs+u07pKWU/A2tTlgmy8+NS77Yv3Uxug8Yi5lL9ojvCy14y32hBePF94WW7EH3AWOwb8si3C5SLvNyX8HYtB76DZ34Ud0XIqouPvpOqhEjRiAgIAArV66EQCCAQCDA1q1bIRAIcOrUKbRo0QKampoIDAxEZGQk+vbtC1NTU+jo6KBly5bw95f+IHbo0AHLli3DhQsXIBAI0KFDB7nbAJSY7i89PR1ff/01TE1NUaNGDTg5OeH48eMAgGPHjuGzzz7D6NGjYWdnh8aNG+OLL77Ab7/9Jtm/sLAQ8+bNQ7169aCpqQlXV1f4+vpKvj9//rxkqsE3goODIRAIEBMTA0A6Nd7x48fh6OgITU1NPH78GLm5uZg2bRosLCygqamJBg0aYPPmzZLj3L9/Hz179oSOjg5MTU0xbNgwJCcnK8zzon+nYcOG0NbWxqBBg/DixQts27YNVlZWMDQ0xIQJEyAUCiX75eXlYdq0aahbty5q1qyJTz75BOfPny9x7Pr160NbWxv9+/dHSkqKzPfFp/vr0KEDfvzxR5kw/fr1w4gRIySfraysMH/+fHh5eUFHRweWlpY4cuQIkpKS0LdvX+jo6KBJkya4ceOGwjQrSn/RfH5b+h4/fozevXvD0NAQNWvWROPGjeHj44OYmBh07NgRAGBoaAiBQCAT/8rmzPHtcOvUH+6dB8Ksng0GjZwOQ2MzBJ7eKzf8Rb99MDSug0Ejp8Osng3cOw+EW6f+OHN0mySMpZ0TBnhNRgv3HlBT11BWUiqc37F/0LZzP7Tr2h916tlgyOipMDQyRcCp/XLDB5zaj1rGZhgyeirq1LNBu6794d6pL04f2S4TrlAoxKYVv6DPkG9hbFpPGUmpcCeP/AuPLn3QoVs/1LWwxldjJsHI2BRnTh6QG/6s70EYm5jhqzGTUNfCGh269YNH597wObxTEubRgzto4OCMNh7dYWJqjiZNW8OtfTdER4YpK1lKd+roTrTr3Bftu/aHuYU1vhw9BbWMTHHOV36ZPH/qAIyMzfDl6Ckwt7BG+6790a5TX5w6vKNYSAH0DY1lXtUd63f5OXlkFzp06YOO3fqiroU1ho19Xb99FNdvIxMzDBsrrt8du/WFR5fe8Dn0jyTMo/A39dtTWr/bdUPUo+pbvwHxg1uBvjvQue/XaNKqK+pYNMCQ7xYgL+8VbgedULhfoO8ONGjihs59x6J2XRt07jsWDRp/gsCTsuUz99UL/LtmOgaPmQutmvofOjkVRiQS4da57fjE81vYu3aDibk9egxbhIK8Vwi7flzhfnUsndFhwHQ0atELqmqKr3dUVFRRU99E8tLWrfUhklEpiEQi3Lu0HS4dvoFV424wNLVH+0F/QJj/CpEhivOyw2dL4ND6SxiZO8DAxAbu/edBJCpEXNRlmXD5uS8QsHcq3PvNg6aW3odOToVJOnUBEb+uwPPDfm8PDMDy6yF49SQe9ycvQHZ4FJ5678fTrQdhM2mUJIz1hOFI9g9C5OKNePEgCpGLNyL57BVYTRj+oZJRKYhEIgT47ES3/mPh8kkXmNdvgK/G/4783Fe4eVHx72SAz040dG6Nrv3HwLSuDbr2HwN7p08Q4CO9tnRs2k48uuoT+aNQq5uzr9uNbd60G0e8pd14+nW7cYS43dim80C07tgfZ45J243nT+xEI+fW8Ow/BmZ1reHZfwwaOrXCuRM75R6zOvA/tgPunfqjbZcBqFPPBp+NnAZDIzMEnN4nN/yF0/tQy7gOPhs5DXXq2aBtlwFo07Ef/I5Kz9mjf1yIDt0/h4V1I5jVtcZX386GSCTCgzvXlJWsCsEyWX7OHtuBNp36w/11uRw8choMjMxwQUFeBr7Oy8Gvy6V7lwFw69gP/kXuC1nZOWGA1yS0aPtx3Rciqi4++k6qlStXws3NDWPHjkV8fDzi4+NhYWEBAJg2bRoWLlyIsLAwODs7Izs7Gz179oS/vz9u374NT09P9O7dG0+ePAEAHDx4EGPHjoWbmxvi4+Nx8OBBuduKKywsRI8ePRAUFISdO3fi/v37+OOPP6CqqgoAMDMzw5UrV/D48eNS07Fs2TIsXboUoaGh8PT0RJ8+ffDw4cN3yo+XL19i4cKF2LRpE+7du4fatWvDy8sLu3fvxqpVqxAWFob169dDR0cHABAfHw8PDw+4urrixo0b8PX1RUJCAj777LO3/p1Vq1Zh9+7d8PX1xfnz5zFgwAD4+PjAx8cHO3bswMaNG7F/v/Rm3MiRI3Hp0iXs3r0boaGhGDx4MLp37y5J49WrVzFq1CiMGzcOwcHB6NixI+bPn/9O6Vdk+fLlcHd3x+3bt9GrVy8MGzYMXl5e+Oqrr3Dr1i3Y2dnBy8sLIpGoTMeTl89vS9/48eORm5uLCxcu4M6dO1i0aBF0dHRgYWGBAwfEN9oePHiA+Ph4rFy5slzSXd4K8vPxNCoMDi5tZLY7OLsh6kGw3H2iIkLg4OwmG96lDR5H3YewIP9DRbXSK8jPx+PIMDi6tJbZ3tjVDZHhIXL3iYoIRWNXtxLhH0eGoaBIXh7btxE6eoZo16Vfuce7MirIz0dMZDiauH4is93J9RM8DJf/BNuj8DtwKha+SdPWiH4UJhl5a+/ggpjIcERG3AMAJD5/hpCbQXBt3vYDpKLiictkOBq7Fi+TrfFIQT5GPggtGb5pa8RE3pcpk7mvcjD1616YPKYHVsz/AY+jwss/AZUI63f5KcjPR/SjcDg1LVa/m7bCw/A7cvd5GH4HTk1byWwrUb8dFdTvFu4fIBWVR2piLLLSk9HQWZpONXUN2Dq0QEzEbYX7PX4YjIZNZM/9DZ3dEfMwWGbbwS3z4dC0PeybyJbl6iYjJRYvMpNg5SA9H6ipa6CeXUs8i1acj2WVlvQY62a1xcbZnXDM+yekJz9972NWVllpscjJTkZdO2mZVFXTgJlVSyQ+KXteCvNfoVBYAE0t2c7Ry8d+g0VDD9S1a6Ngz4+TQWtXJPnLTiGZdDoQ+s2dIFATryhg2NoVyf4XZcIk+wXC0K16j+JNSYxFZnoyGjlLy4yaugZsHZsjOkL+ORwAoiNC0NBZtpw1cmmD6IjgDxXVSq2gQHG7MVpBuzH6Ycl2o6NrGzwp0m6MjgiR+d8AgIOLO6JK+d9UZQX5+XgSFQYHl+Lt6daIeqD4mtKh2DWoo2sbPI5U3P7Oy3sFobAA2jrV9wETlsnyo7hcuiksl9ERoSXCv61cElHV8tGvSaWvrw8NDQ1oa2vDzMwMABAeLr75NW/ePHTt2lUS1sjICC4uLpLP8+fPx6FDh3D06FF8//33qFWrFrS1taGhoSE5FgC524ry9/fHtWvXEBYWBnt78dQmNjY2ku9//fVXDBgwAFZWVrC3t4ebmxt69uyJQYMGQUVF3M+4dOlSTJ8+HUOGDAEALFq0COfOncOKFSuwZk3Zp5bKz8/H2rVrJemMiIjA3r174efnhy5dupSI27p169CsWTMsWLBAss3b2xsWFhaIiIiQpEfe31m3bh1sbW0BAIMGDcKOHTuQkJAAHR0dODo6omPHjjh37hw+//xzREZGYteuXYiNjYW5uTkA8fpSvr6+2LJlCxYsWICVK1fC09MTM2bMAADY29sjKChIZkTZf9WzZ0988803AIDZs2dj3bp1aNmyJQYPFs/LPH36dLi5uSEhIUHh/7l4+ovmc1nS9+TJEwwcOBBNmojneS76f6hVS/yEbO3atSv1mlTZWWkoLBRCz0B2ykVdAyNkpssffZeVngLdYuH1DIxQKCxAdlY69A0VT3dTnWVnpcvPS/1ayEhPkbtPRloKdF1ln6bWMzCCUFiA7Mx0GNQywaOwYFz0P4LZf+76YHGvbLIy5eelvkEtZKQpyMv0FOgbyMtL4eu8NIZb+27IykzDbzPHAiIRhEIhOvcYiN6DqucTxFmvy6S+nPpaWpnUa1o834vmownq1LXG6AlzUNfSDq9yXsDv+C4snDkKc5fvhql5/Q+WnorE+l1+3tTv4vVVX98I6elX5O6TkZ4Cff2SvwdCoRBZmekwlNTvdMyb8bVM/e5TTev3G1kZ4nO1TrH80dEzQlpynOL90pNL7qNvhKwi5/7bQT54FhOGH37bU44xrpxeZCYBAGrqyuZJTT1jZKYqzseyqGPljJ5ei2BY2wovs1Jw2Xcd/l06BCN/OQ4tHcP3OnZllJMlLkNaOrIjbGvoGOFFetnz8vqpZdDWM4W5rfRGYVToCaTE3Ufv7+SPNviYaZoaIzdB9to9LzEFKurq0DA2RO7zJGiaGSM3QfaclZuQAk2z6n3tnvX6PK2rX/wcboQ0BdNYifdLlruPojZSdZedKW43vkueZKanyA1ftN2YmZ4M3WLXBLoGtWTOR9WJpP2tX+wasdR8TIaefpti4WuV2v4+tHMlDGrVhoPzJyW+qy5YJsvPm3JZPG/eXi5Lz0siqto++k6q0rRo0ULm84sXLzB37lwcP34ccXFxKCgoQE5OjmQk1X8VHByMevXqKezQqVOnDi5fvoy7d+8iICAAQUFBGD58ODZt2gRfX19kZ2cjLi4O7u6yT++6u7sjJOTdnr7Q0NCAs7N0nvDg4GCoqqrCw8NDbvibN2/i3LlzkpFVRUVGRipMk7a2tqSDCgBMTU1hZWUlcxxTU1MkJiYCAG7dugWRSFTieLm5uZI1psLCwtC/f3+Z793c3Mqlk6ponpiamgKApLOo6LbExMQydVIVz+eypG/ixIn47rvvcPr0aXTp0gUDBw6UOUZZ5ebmIjc3V2abpqYmNDWVub6LQPajSASBQCA/KFDiO+mINcX7fCxKZtu75SVe56VAIMCrnBfYtPIXeI37H3T1qt+NrLcpmTWl52XJzH9dLl9vDrtzE0f3bcGIb6bB1t4JCfFPsXPTnzhsaIx+n48ut3hXPiXr67tko6hImQQA24ZNYNtQ+ntr18gFcycPhb/PbgwdM618olxJsX6Xn5L5JoKglHNIyeCy5fL+nZs4sncLRnw7DXb2jfE8PhY7//4ThwyN0H9I9anfty4ex/7NcySfR09bBwBy8u4tv5eQVz6l29JT4nFk+x/4eubGarne3P1rR+G361fJ5wHjNojfKLy++e9sGstes9exdsWmX7vi3tXDaNF55Hsfv6JFBh/DpSNzJJ+7eq1TEFIk70dUrtALmxAV6oOeY7ZBTV1c/rLT43Hl+EJ4jtwk2UbFFC+vb/K76HZ5YcqhnFcmNwKPY8/f0rW8vpnx+gFROb95byuT8s7jb/ttrfaK/06+7Xyj4He16D4lzmFl+N9UdSXa0+JEv2N4yN3n1OEtuH7JF5PmbKqW5/ASWCbLjbxyVtr1ucL2N+8LVXnV7NKA/iN2UpWiZs2aMp+nTp2KU6dOYenSpbCzs4OWlhYGDRqEvLy89/o7WlpaZQrn5OQEJycnjB8/HhcvXkS7du0QEBCA5s2bA5B/I//Ntjcjroo2fvPzSw6J1dLSkjnO2+JWWFiI3r17Y9GiRSW+q1OnjsL91NXVZT4LBAK52woLCyV/R1VVFTdv3pRMg/jGm46t/9KwV1FRKbGfvHwpGrc3+SNv25v4vk3xfC5L+saMGQNPT0+cOHECp0+fxsKFC7Fs2TJMmDChTH/zjYULF2Lu3Lky23799VfMmTPnnY7zX+joGkJFRbXE0zFZGaklnqJ5Q9fACJlpJcOrqKpBR7f6TifwNjq6BlBRUS0x0icrI63Ek3Jv6BsaIbPYKIzMjFSoqqqhpq4+4p5GISUxDqsX/Cj5XiQSl+lvBrXEb6sPoraZRfkmpBLQ1ZOfl5kZadAzUJCXBkYlw6enQlVVFTq6BgCA/f+uh3uHnujQrR8AwMLKDrm5r+C9ZgH6DB4p+V2uLnTflEk59bv4U29v6BvKyccMcT7WVFC/VVRUYG3niIS46jt9Fet3+XlTv9OL5WVGRmqJ0VVv6BsYIT29ePi01/VbXC73/7MB7h17oGO3vgBe1+9XOfBesxB9P6s+9duxeUdMspN2Er+ZOjIrIxl6RZ5Yzc5MLTFSqihdA+MSTwRnZ6ZI9omNuo/szBSs+Fk6XXRhoRDR4Tdw6fQu/LH9NlRUZK+PqhI7506oYyWdjUFYIG47vMhMho5+bcn2l1kp0NYr3zX3NDS1YVLXHmmJMeV63IpS36ETTCykD2m9ycuc7GRo60nz8lV2KrR0FJfJN+4EeiM0YCO6j/RGLbOGku0pcffw6kUKjq4dJNkmKhTiecwNhF35F8PnhlTpMvm+chOSS4yI0jCphcL8fOSlpIvDPE+GpplsedasXavECKyqzqlFR1g2kJbJgnxxmcxKT5Z5sj8rs+SIiqJ0DYxLtpEyFbeRqjsdPXG7scS5o5R2o56BkZxzjbjdWPP1NHR6BsYlrpdKa4tWdW/a38VH4mdlpJYYsf+GnoGxnOv5NLnt79NHtsH34Gb8OHsD6lnJf0C5umCZLD+l3hcqpVzKC/+x3xciqk7YSQXxqBahUPjWcIGBgRgxYoRktE52djZiYmLe++87OzsjNja21OnxinN0dAQgHt2lp6cHc3NzXLx4Ee3bt5eECQoKQqtW4jUVTEzEF8jx8fEwNBQ/QR0cHPzWv9OkSRMUFhYiICBAMt1fUc2aNcOBAwdgZWUFNbUPV5yaNm0KoVCIxMREtGvXTm4YR0dHXLkiO3VP8c/FmZiYID5eOu2CUCjE3bt30bFjx/eP9DsoS/oAwMLCAt9++y2+/fZbzJw5E3///TcmTJgADQ3xopBlKcczZ87EpEmTZLYpaxSVmro6LGwcEB56Ga6fdJZsDw+9AueW8vPcxt4Fd24EyGwLCwmCpY0jVNXU5e7zMVBTV4elrQPCQq6iWetOku33Q67AtVUHufvY2Dsj9MYFmW33Q67A0tYBamrqqFPXCnOWyy5UenjXWrzKeYEho6ailtHbRwlWRWrq6rCybYS7IdfQwk1aDu8GX0OzT9rL3ceuURPcvia7zsKd4KuwtnOQ/Bbm5eZCoCL78ICKior4ea9q+KiQuEw2wv2Qq2hepEzeC7mKpq3kj8a1beiM4OuyZfJe8BVY2TpCTUH9FolEeBITgXr17cov8pUM63f5UVNXh7VdI9wNvoaWbh0k2+8GX0PzVvLrd4NGTXDreqDMtru3i9fvV1ARyHZEVcf6XUOrJmpoSR/aEolE0DUwRsSdINS1cgAAFBTkITLsBnp9MUnRYWDZwBURdy6jfU/pdIgP7gTBqoErAMDOqTUmLzoss8+eDT+jtrkNOvYeXeU7AzRq6ECjhnS2AJFIhJp6JngcfgmmFuJremFBHmIfXUf7vlPK9W8X5Och5Xkk6to2L9fjVhR1zZpQ15Qtk1o6xnj2KAhG5tK8fB5zHS08J5d6rDuBmxF8bj08R/wN43pOMt+Z27qh/8QjMtsCD/wMfRNrOLcfU+XL5PtKvxKM2r1kr91NurZFxs27EL1euy/tSjCMO7sjeqV0UXvjLm2Rdvn9112rTOT9TuoZGONB6GXUs37zO5mPyPs30fvLHxUex9reBQ9CL6NjLy/JtgehQbC2d/1QUa/U1NSk7UaXVrLtxiYK2o3WDVxw92bJdmP9Iu1Ga3sXhN+5jE6fDityzCDY2LugOlJTV0d9GweEhV5G00+k15RhoVfh0rKD3H1s7J0RelP2mjIs5DIsbWXb36ePbIXPgU2Y+MtaWNo1/iDxr0xYJsuPtFxekXNfqIPcfaztnXGnDOWSiKqu6vGY53uysrLC1atXERMTg+TkZIWjYezs7HDw4EEEBwcjJCQEX375ZZlHzpTGw8MD7du3x8CBA+Hn54fo6GicPHlSMk3dd999h99++w2XLl3C48ePceXKFXh5ecHExARubuKFA6dOnYpFixZhz549ePDgAWbMmIHg4GD88MMPkrhbWFhgzpw5iIiIwIkTJ7Bs2bIy5c3w4cMxatQoHD58GNHR0Th//jz27hXf6Bo/fjxSU1PxxRdf4Nq1a4iKisLp06cxatQoSYfJ6tWr0blz59L+zFvZ29tj6NCh8PLywsGDBxEdHY3r169j0aJF8PHxASCeDs/X1xeLFy9GREQEVq9e/dap/jp16oQTJ07gxIkTCA8Px7hx45Cenv5ecf0vypK+H3/8EadOnUJ0dDRu3bqFs2fPwsFB3PCxtLSEQCDA8ePHkZSUhOzsbIV/S1NTE3p6ejIvZU711/lTLwSdOYigs4fwPDYK+7cuRmpyPNp2E6/vdeSfldj21yxJ+LZdByM1OQ4Hti7B89goBJ09hMtnD6FzH+mNroL8fDyNDsfT6HAIC/KRnpKIp9HhSIx/v6k4K7uuvYci8MwhXDxzGPGxUdjjvRSpyc/h0W0gAODgzr+weeX/JOE9PAchJSkee7YsQ3xsFC6eOYyLZw6jW19xY1hdQxN1Le1kXlo1dVFDqybqWtpBTb36Xvz16PslzvsdQYD/UTx7Go2dm/5ESvJzdO4+AACwZ/sarF8unaqpU/cBSE6Kxz+bl+PZ02gE+B9FgP9R9Oz3lSRM05ZtcebkQVy+cBqJCc9wJ/gq9v+zAc1atoOKavW8ueXZ5ytc8D+MQP8jiHsajV3ey5Ca/BwdPMVPou/f8Rf+XjlbEr6D50CkJMVjt/efiHsajUD/Iwg8cwSe/aSNtCN7NuLu7SAkPo/Fk+gH2LJ6Hp5GP0AHz4FKT58ysX6Xnx59vxDXb7839Xs5UpIS0LnH6/q9bQ3WL58jCd+p+wCkJD7Hzs0rxPXb7yjO+x9Fz/5DJWGatmwH/5MHxPX7eRzu3L6K/f9sRLNW1bd+A+KR4+26D8OZI3/jznV/xD99iD3rf4aGRg00bdNLEm7X2pnw2b1c8rld968QcScIZ49uQuKzKJw9ugkP715Bux7i8llDqybqWDSQeWloaqOmjj7qWDRQejo/NIFAgGYdvXD11AY8DPZDUlwETu6YCTWNGnBo+akknM+2abhwRHq9LizIQ+LTMCQ+DYNQmIes9AQkPg1DWuJjSZjzBxfh6cNrSE9+ivjoEBzdNBF5r7LR+BPZKbGrC4FAgMbuXggN2IiYe35IS4hA4IFZUFWvAVsXaV4G7JuOG6f+lHwOvbAJN/1Wot2A36FjWBcvs5LwMisJ+bkvAIg7wwxN7WVeahpa0NQ2gKFp9RstoFpTG3oujaDn0ggAoG1dD3oujVDDQjwzRsP5k+CyRTpzxuONu6FlaQ6HJTOg08gG9UYMhMXIgYj601sSJmb1dhh3dYfNlLGo2dAGNlPGwrizG2L+2obqTCAQwKPnV/A7vAkh184g7slD/LP2Z6hr1kDzttLfyZ2rZ+HYvysknz16fIUHoZfhf2QzEp5Fwf/IZjy4cxUePaXXlrmvXiI2JhyxMeI1tFMSnyE2JhypyYrXuqrKOr1uN15+3W488Lrd2K7r63bjvyuxfXWRdmO31+3GbeJ24+U37cbe0nZjh55DEfI4BdQAAQAASURBVB5yGX6HvfH8WTT8Dnsj/M5VdOz1VYm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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# give me the percentage of each value in the column\n", + "for column in df_4.columns:\n", + " print(df_4[column].value_counts(normalize=True))\n", + " print('\\n')\n", + "\n", + "# check the correlation between the columns\n", + "correlation = df_4.corr()\n", + "correlation\n", + "\n", + "# plot the correlation\n", + "plt.figure(figsize=(20,20))\n", + "sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt='.2f', linewidths=2)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MODELING \n", + "* above are data engineering\n", + "* below are modeling \n" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the revenue\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(df_4.shape[0]), np.sort(np.log1p(df_4[\"totals.transactionRevenue\"].values)))\n", + "plt.xlabel('index', fontsize=12)\n", + "plt.ylabel('TransactionRevenue', fontsize=12)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reveue before Log\n" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10000.0\n", + "23129500000.0\n" + ] + } + ], + "source": [ + "df_5 = df_4.copy()\n", + "# get me the stats of the revenue in a table\n", + "print(df_5['totals.transactionRevenue'].min())\n", + "print(df_5['totals.transactionRevenue'].max())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## After MIN MAX AND LOG\n" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [], + "source": [ + "# Min max on target\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "#df_5['totals.transactionRevenue'] = scaler.fit_transform(df_5['totals.transactionRevenue'].values.reshape(-1,1))\n", + "\n", + "df_5['totals.transactionRevenue'] = np.log1p(df_5['totals.transactionRevenue'].values)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.210440366976517\n", + "23.86437469605166\n" + ] + } + ], + "source": [ + "# get me the stats of the revenue in a table\n", + "print(df_5['totals.transactionRevenue'].min())\n", + "print(df_5['totals.transactionRevenue'].max())" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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totals.pageviewstotals.newVisitstotals.transactionRevenuedevice.browser_Restdevice.operatingSystem_Mobiledevice.operatingSystem_WindowsgeoNetwork.region_UnknowngeoNetwork.region_WesttrafficSource.medium_organictrafficSource.medium_referraltrafficSource.medium_restquarter_2nd_quarterquarter_3rd_quarterquarter_4th_quarterday_of_month_endday_of_month_middle
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80212017.0831770010101001000
85914020.1684010001001001000
\n", + "
" + ], + "text/plain": [ + " totals.pageviews totals.newVisits totals.transactionRevenue \\\n", + "752 11 1 17.449406 \n", + "753 10 0 19.541283 \n", + "799 11 0 18.035459 \n", + "802 12 0 17.083177 \n", + "859 14 0 20.168401 \n", + "\n", + " device.browser_Rest device.operatingSystem_Mobile \\\n", + "752 0 1 \n", + "753 0 0 \n", + "799 0 1 \n", + "802 0 0 \n", + "859 0 0 \n", + "\n", + " device.operatingSystem_Windows geoNetwork.region_Unknown \\\n", + "752 0 0 \n", + "753 0 0 \n", + "799 0 0 \n", + "802 1 0 \n", + "859 0 1 \n", + "\n", + " geoNetwork.region_West trafficSource.medium_organic \\\n", + "752 0 0 \n", + "753 0 1 \n", + "799 0 0 \n", + "802 1 0 \n", + "859 0 0 \n", + "\n", + " trafficSource.medium_referral trafficSource.medium_rest \\\n", + "752 0 0 \n", + "753 0 0 \n", + "799 1 0 \n", + "802 1 0 \n", + "859 1 0 \n", + "\n", + " quarter_2nd_quarter quarter_3rd_quarter quarter_4th_quarter \\\n", + "752 0 1 0 \n", + "753 0 1 0 \n", + "799 0 1 0 \n", + "802 0 1 0 \n", + "859 0 1 0 \n", + "\n", + " day_of_month_end day_of_month_middle \n", + "752 0 0 \n", + "753 0 0 \n", + "799 0 0 \n", + "802 0 0 \n", + "859 0 0 " + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_5.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BASE MODEL - Linear Regression\n" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error: 1.2553020337491887\n", + "Mean Absolute Error: 0.8631839361660864\n", + "Median Absolute Error: 0.6871135181393289\n", + "Cross-validated scores: [0.85842117 0.81551634 0.85596021 0.87236524 0.85582436]\n", + "Average score: 0.8516174625464815\n", + "Intercept: 17.81686178043045\n", + "Coefficients: [ 0.30593111 -0.2029752 -0.084798 -0.0948675 -0.04434118 -0.12262109\n", + " -0.10740589 -0.22252321 -0.16723227 -0.05742755 0.00887766 -0.01104059\n", + " -0.03370372 -0.00427124 0.00217226]\n", + "Features: Index(['totals.pageviews', 'totals.newVisits', 'device.browser_Rest',\n", + " 'device.operatingSystem_Mobile', 'device.operatingSystem_Windows',\n", + " 'geoNetwork.region_Unknown', 'geoNetwork.region_West',\n", + " 'trafficSource.medium_organic', 'trafficSource.medium_referral',\n", + " 'trafficSource.medium_rest', 'quarter_2nd_quarter',\n", + " 'quarter_3rd_quarter', 'quarter_4th_quarter', 'day_of_month_end',\n", + " 'day_of_month_middle'],\n", + " dtype='object')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Split the data into features and target\n", + "X = df_5.drop(columns='totals.transactionRevenue')\n", + "y = df_5['totals.transactionRevenue']\n", + "\n", + "# Split the data into training and testing sets \n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "\n", + "# Initialize a standard scaler\n", + "scaler = StandardScaler()\n", + "\n", + "# Fit the scaler\n", + "scaler.fit(X_train)\n", + "\n", + "# Scale the train and test data\n", + "X_train = scaler.transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "# regression model\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error, r2_score,mean_absolute_error, median_absolute_error\n", + "\n", + "# Initialize the model\n", + "model = LinearRegression()\n", + "\n", + "# Fit the model\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Median Absolute Error\n", + "dae = median_absolute_error(y_test, y_pred)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "\n", + "\n", + "\n", + "\n", + "print(\"Mean Squared Error:\", mse)\n", + "print(\"Mean Absolute Error:\", mae)\n", + "print(\"Median Absolute Error:\", dae)\n", + "\n", + "\n", + "\n", + "#cross validation\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "# Perform 5-fold cross validation\n", + "scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_absolute_error')\n", + "\n", + "# Print the cross-validated scores\n", + "print('Cross-validated scores:', -scores)\n", + "\n", + "# Print the average score\n", + "print('Average score:', -scores.mean())\n", + "\n", + "# intercept and coefficients\n", + "print(\"Intercept:\", model.intercept_)\n", + "print(\"Coefficients:\", model.coef_)\n", + "print(\"Features:\", X.columns)\n", + "\n", + "# plot the coefficients\n", + "plt.figure(figsize=(12,6))\n", + "plt.barh(X.columns, model.coef_)\n", + "plt.xlabel('Features', fontsize=12)\n", + "plt.ylabel('Coefficients', fontsize=12)\n", + "plt.xticks(rotation=90)\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "# Deviation Plot between Predict and Test in different colors\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(y_test.shape[0]), np.sort(y_test), color='r', label='Test')\n", + "plt.scatter(range(y_pred.shape[0]), np.sort(y_pred), color='g', label='Predict')\n", + "\n", + "plt.xlabel('index', fontsize=12)\n", + "plt.ylabel('TransactionRevenue', fontsize=12)\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "def lc(x):\n", + " return np.expm1(x)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random Forest Regression\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error rfr: 1.4767865786291972\n", + "Mean Absolute Error rfr: 0.9416846212787142\n", + "Median Absolute Error rfr: 0.7785681531636808\n", + "Cross-validated scores: [0.92595225 0.90835246 0.93342571 0.94780889 0.94403123]\n", + "Average score: 0.9319141078620603\n" + ] + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# other regression model\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "# Initialize the model\n", + "model = RandomForestRegressor(n_estimators=100, random_state=42)\n", + "\n", + "# Fit the model\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "dae_rfr = median_absolute_error(y_test, y_pred)\n", + "mse_rfr = mean_squared_error(y_test, y_pred)\n", + "mae_rfr = mean_absolute_error(y_test, y_pred)\n", + "\n", + "\n", + "\n", + "\n", + "print(\"Mean Squared Error rfr:\",mse_rfr)\n", + "print(\"Mean Absolute Error rfr:\", mae_rfr)\n", + "print(\"Median Absolute Error rfr:\", dae_rfr)\n", + "\n", + "\n", + "# Perform 5-fold cross validation\n", + "scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_absolute_error')\n", + "\n", + "# Print the cross-validated scores\n", + "print('Cross-validated scores:', -scores)\n", + "\n", + "# Print the average score\n", + "print('Average score:', -scores.mean())\n", + "\n", + "\n", + "#plot the feature importance\n", + "plt.figure(figsize=(12,6))\n", + "plt.barh(X.columns, model.feature_importances_)\n", + "plt.xlabel('Features', fontsize=12)\n", + "plt.ylabel('Importance', fontsize=12)\n", + "plt.xticks(rotation=90)\n", + "plt.show()\n", + "\n", + "# Deviation Plot between Predict and Test in different colors\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(y_test.shape[0]), np.sort(y_test), color='r', label='Test')\n", + "plt.scatter(range(y_pred.shape[0]), np.sort(y_pred), color='g', label='Predict')\n", + "\n", + "plt.xlabel('index', fontsize=12)\n", + "plt.ylabel('TransactionRevenue', fontsize=12)\n", + "plt.legend()\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lasso and Ridge Regression \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error lasso: 1.3129285103044421\n", + "Mean Absolute Error lasso: 0.8822896325408653\n", + "Median Absolute Error lasso: 0.7113929687488447\n", + "Cross-validated scores lasso : [0.88022993 0.84531319 0.88936473 0.91074682 0.90121395]\n", + "Average score lasso: 0.8853737257136396\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# lasso regression and ridge regression\n", + "\n", + "from sklearn.linear_model import Lasso, Ridge\n", + "\n", + "# Initialize the model\n", + "model = Lasso(alpha=0.1)\n", + "\n", + "# Fit the model\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "dae = median_absolute_error(y_test, y_pred)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "\n", + "print(\"Mean Squared Error lasso:\", mse)\n", + "print(\"Mean Absolute Error lasso:\", mae)\n", + "print(\"Median Absolute Error lasso:\", dae)\n", + "\n", + "# Perform 5-fold cross validation\n", + "scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_absolute_error')\n", + "\n", + "# Print the cross-validated scores\n", + "print('Cross-validated scores lasso :', -scores)\n", + "\n", + "# Print the average score\n", + "print('Average score lasso:', -scores.mean())\n", + "\n", + "#plot the coefficients\n", + "plt.figure(figsize=(12,6))\n", + "plt.barh(X.columns, model.coef_)\n", + "plt.xlabel('Features', fontsize=12)\n", + "plt.ylabel('Coefficients', fontsize=12)\n", + "plt.xticks(rotation=90)\n", + "plt.show()\n", + "\n", + "\n", + "# Deviation Plot between Predict and Test in different colors\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(y_test.shape[0]), np.sort(y_test), color='r', label='Test')\n", + "plt.scatter(range(y_pred.shape[0]), np.sort(y_pred), color='g', label='Predict')\n", + "\n", + "plt.xlabel('index', fontsize=12)\n", + "plt.ylabel('TransactionRevenue', fontsize=12)\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error ridge: 1.255301840905268\n", + "Mean Absolute Error ridge: 0.863183686584458\n", + "Median Absolute Error ridge: 0.6871210618560948\n", + "Cross-validated scores ridge : [0.85841856 0.81551492 0.85595857 0.8723664 0.8558236 ]\n", + "Average score ridge: 0.8516164129760136\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Initialize the model\n", + "model = Ridge(alpha=0.1)\n", + "\n", + "# Fit the model\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "dae = median_absolute_error(y_test, y_pred)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "\n", + "print(\"Mean Squared Error ridge:\", mse)\n", + "print(\"Mean Absolute Error ridge:\", mae)\n", + "print(\"Median Absolute Error ridge:\", dae)\n", + "\n", + "# Perform 5-fold cross validation\n", + "scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_absolute_error')\n", + "\n", + "# Print the cross-validated scores\n", + "print('Cross-validated scores ridge :', -scores)\n", + "\n", + "# Print the average score\n", + "print('Average score ridge:', -scores.mean())\n", + "\n", + "#plot the coefficients\n", + "plt.figure(figsize=(12,6))\n", + "plt.barh(X.columns, model.coef_)\n", + "plt.xlabel('Features', fontsize=12)\n", + "plt.ylabel('Coefficients', fontsize=12)\n", + "plt.xticks(rotation=90)\n", + "plt.show()\n", + "\n", + "# Deviation Plot between Predict and Test in different colors\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(y_test.shape[0]), np.sort(y_test), color='r', label='Test')\n", + "plt.scatter(range(y_pred.shape[0]), np.sort(y_pred), color='g', label='Predict')\n", + "\n", + "plt.xlabel('index', fontsize=12)\n", + "plt.ylabel('TransactionRevenue', fontsize=12)\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## XGBoost" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error xgb: 1.359722321269476\n", + "Mean Absolute Error xgb: 0.901661119257582\n", + "Median Absolute Error xgb: 0.7384854120054509\n" + ] + }, + { + "data": { + "image/png": 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lZ7s0zxozZow2bNigzz77rNrzxcXFuuGGG1RaWqrZs2fXeJ0pU6bo0Ucf9VaZAAAAAABfSU+X5s9XxtOjNK7LQe2K+/1UUngTzTpTSreuOo/wmznmY8eO1cKFC7VixQq1bdu2yvni4mJdd911+uWXX7R06VI1adKkxmtV12OenJzMHHMAAAAACEAZmzI05K0hMlU5vhpyDnGff918pZ/pX/E8oOaYm6apMWPGKCMjQ0uXLq01lP/0009asmRJraFckiIjIxUbG1vpAQAAAAAIPI5Sh8a9PUrV9SmXBfXxi8fLUerwdWkeY3kwv+OOOzRnzhzNnTtXMTExysnJUU5OjgoKCiRJJSUlGjJkiNasWaPXX39dDoejvE1RUZHF1QMAAAAAvClrzhPaVXxQqmH9N1OmdubtVNaOLN8W5kGWB/MXXnhBubm5SktLU0JCQvnjzTfflCTt2rVL77zzjnbt2qVzzz23UpuVK1daXD0AAAAAwGscDmX/Z5ZLTbMPB+5e5pYv/nayKe4pKSknbQMAAAAACEJZWUrY8atLTRNiErxcjPdY3mMOAAAAAEC1du9W6nYpKVcyauivNUwpOayxUlsH7l7mBHMAAAAAgH/av192U5q1+PjzE8J5WVifGTFQdlvg7mVOMAcAAAAA+Kdmzcp/bHxUVRaAa3xUmv+WlJ54mW/r8jDL55gDAAAAAFCtLVuUcaY05LoqneWSKR1scPznVq18XJhn0WMOAAAAAPA/Doccz8zUuL7HQ/mJ26UZzkPjr7LLccnFvq/PgwjmAAAAAAD/k5mprJjftCtONe9hbkg7GzmUtTuwt9ImmAMAAAAA/E9mprIbudY0kPcwlwjmAAAAAAA/lZDvYrsA3sNcIpgDAAAAAPxRWppre5hHNAvoPcwlgjkAAAAAwB+lpcneuEn5HuYnhvPyPcyvnh3Qe5hLBHMAAAAAgD+y26V//Uvpm5x7lbfKq3w6KU+a3+ZupZ89xJr6PIh9zAEAAAAAfi19kzTwBymrjZTdyDn3PHW7ZJ9/kdWleYRhmmYNo/WDR15enuLi4pSbm6vY2FirywEAAAAAnIzDIcXHS/m1rADXpIm0d6+zd93PuJNDGcoOAAAAAPA/f/tb7aFckg4elDIzfVKONxHMAQAAAAD+xeGQZsxwrS3BHAAAAAAAD8vKkg4ftroKnyGYAwAAAAD8S3a2623T0rxWhq8QzAEAAAAA/iUhwbV2sbFBEczZLg0AAAAA4F9SU50rrh88WH7IYVSzXdp//uOXK7K7i2AOAAAAAPA/hw6V/5hxpjSur7Qr7vfTSXnSrDOldN9X5nEMZQcAAAAA+JcbbnCuzC5nKB9ynbTrhK3Ad8dIQ+Zfq4xNGRYU6FkEcwAAAACA/ygqkubPl+Qcvj6ur2RKklG5mWlIMqXxi8fLUerwdZUeRTAHAAAAAPiPZ58t/zGrzfHh60b1TU1D2pm3U1k7snxTm5cQzAEAAAAA/uOtt8p/zG7k2kuyD7uxvZofIpgDAAAAAPyDwyGtXVv+NCHftZclxLi4vZqfIpgDAAAAAPzD0qXli75Jzi3RknIlw6y+uSFDybHJSm2d6qMCvYNgDgAAAADwD6+9Vump3ZRmLXb+fGI4L5t2PrPvTNltgb2XOcEcAAAAAOAfPv64yqH0TdKElTq+NPvvbLLprovvUvqZgb+TOcEcAAAAAGC9ggJp794qhwddL824WDJPSK8OleqplU+xjzkAAAAAAB4xeHCVQ3f1lhadUfvLRr07in3MAQAAAACoF4dD+uijSoeKbNI/LpZzMnkN+5hL0sGCg8rclunN6ryOYA4AAAAAsNZ111U59GxXuZxYCeYAAAAAANTVxIlSRtV54p+1tqAWixDMAQAAAADWuOce6emnqz3VqMj1y6SlpHmmHosQzAEAAAAAvldUJD31VI2nb9rg2mViI2IJ5gAAAAAAuG327FpPLz5Vzr3LzVqb6aUBL8lus3usLCuEWV0AAAAAACAEPflkjaeKbNKsshXZT6J5o+aeq8ki9JgDAAAAAHxr4kRp//4aT8/uKpW6mFazD2d7qCjrEMwBAAAAAL5TVFTjgm9lfjrF9cslxCTUsyDrEcwBAAAAAL7z5z+ftInhwhB2SWoU0UiprVPrWZD1COYAAAAAAN9wOKTXXjtpswt3uHa5W869JeAXfpMI5gAAAAAAX7nhBpeaJea7drmB7QfWoxj/QTAHAAAAAHhfUZE0f75rbV0cyh4sCOYAAAAAAO/r29flpvsautjuyL46FuNfCOYAAAAAAO+aN09atszl5glq5Fq7IFiRXSKYAwAAAAC86a9/lYYOdeslqX9/S0mxSTJqGNNuyFBybHJQrMguEcwBAAAAAN5QUCBFR0vPPuve68LCZO/dR0PPGSpTZo3NZvadGRQrsksEcwAAAACApw0aJDVoIB075v5rb7pJGT8u0lMrn6qxyV0X36X0M9PrXp+fIZgDAAAAADxn0CBp0aI6v9wx+3mNendUrW1e/vplOUoddb6HvyGYAwAAAAA8Iz+/XqFc48crc89KHSw4WGuzgwUHlbkts+738TMEcwAAAABA/b31lhQTU/fXn3qq9PTTLgfuYArmYVYXAAAAAAAIcFdfLb37bt1f37SptGWLJOnjLR+79JJgGspOMAcAAAAA1E1+vtSqlZSXV/drNGok7d8vSSoqKdLqPatdetmhwkN1v6efYSg7AAAAAMB1+fnSgAGSzeYcul6fUJ6SIh0+XP702dWub61W0x7ngYhgDgAAAAA4OYdD+sMfnGH8vfcks+Y9xl0yZ460dWulQ1k7slx++elNTq/f/f0IwRwAAAAAUDOHQ7r/fiksTPrpp/pfLy5OKimRhg+vcupI0RGXL3P7BbfXvxY/QTAHAAAAAFTvjTecgXzKFM9czzCkgwclu73a0xckXuDSZbq26qqIsAjP1OQHWPwNAAAAAPC7/Hxp6FDp/ffrP1z9RPPn1xjKJSkyLNKlyzzZ60lPVeQXCOYAAAAAAGcgT0qScnO9c/0FC6T09BpPO0odevbLky/+1iS6idJS0jxYmPUI5gAAAAAQ6s4/X1q3zjvXbtFC2r271p5yScrclqlfj/160suN6TpGdlvt1wo0BHMAAAAACFVFRc59xIuLvXP9zp2ltWtdapq5LdOldo5SRz0K8k+WL/42ZcoUdenSRTExMWrevLkGDRqkzZs3V2pjmqYeeeQRJSYmKjo6Wmlpafruu+8sqhgAAAAAAlhBgfTnP0uRkc6Ht0L5hAkuh3JJKjVLPdoukFgezJcvX6477rhDq1at0ieffKKSkhL16dNHR478vkz+9OnTNWPGDD333HP66quv1LJlS11++eU6XGEjegAAAABALRwO6aKLpAYNpP/8x9lb7mmRkdK0aVJhoTRjhlsv/XzH5y61i4+Kr0Nh/s3yoeyLFy+u9PyVV15R8+bNtXbtWvXo0UOmaWrmzJmaPHmy0o8vFPDf//5XLVq00Ny5c/WXv/zFirIBAAAAIDAUFEgDBkiffuq9e1x1lTRvnnNYfB0UlRRp+Y7lLrU9dOxQne7hzyzvMT9R7vEVABs3bixJ2rp1q3JyctSnT5/yNpGRkbr00ku1cuVKS2oEAAAAgIDQv7+zh9xbofyqq5xbqr33Xp1DuSQ9u/rkq7GXsRl+F2PrzfIe84pM09TEiRPVvXt3nXPOOZKknJwcSVKLFi0qtW3RooW2b99e7XUKCwtVWFhY/jwvL89LFQMAAACAn4qP997WZ61bSz/8IEVHe+Ryb373psttg22rNMnPeszHjBmjDRs26I033qhyzjCMSs9N06xyrMyUKVMUFxdX/khOTvZKvQAAAADgV4qKpKlTJcPwTig3DOeQ9e3bPRbKHaUOfZ39tUttw2xhBHNvGjt2rN555x0tW7ZMSUlJ5cdbtmwp6fee8zL79u2r0oteZtKkScrNzS1/7Ny503uFAwAAAIDVioqkHj2ci69NmuT56597rvTBB84V3K+/3qOXztyWqRKzxKW2A/4wIOj2MJf8YCi7aZoaO3as3n77bWVmZqpt27aVzrdt21YtW7bUJ598ovPOO0+SVFRUpOXLl2vatGnVXjMyMlKRkZFerx0AAAAALJWfL3XqJP3yi3euf/hwveaOu+L+T+93ue0dXe7wYiXWsbzH/I477tCcOXM0d+5cxcTEKCcnRzk5OSooKJDkHMI+fvx4Pfnkk3r77bf17bff6uabb1aDBg00bNgwi6sHAAAAAB8rKJBGjZLCw6WYGO+E8jZtnIu6eTmUF5UUafWe1S61DdZh7JIf9Ji/8MILkqS0tLRKx1955RXdfPPNkqR77rlHBQUFuv322/Xbb7/pwgsv1Mcff6yYmBgfVwsAAAAAFnE4pEsukb780rv3mTNHGj7cu/c47oo5V7jc9rwW5wXlMHZJMkzTNK0uwtvy8vIUFxen3NxcxcbGWl0OAAAAALjnrbc8Pre7kpQUafZsqU8fye6b8FtUUqTIJ1yfgvyPPv/QxG4TvViRZ7mTQy3vMQcAAAAAVMPhkD780Nl77a0toC+6SPrsM5+F8YpGvTvKrfZjuozxUiXWs3yOOQAAAADgOIdDeu89Zw92WJg0YIB3Qnnv3tLRo9IXX1gSyh2lDv3fhv9zuf2QM4coIizCixVZix5zAAAAALBafr6UliatXevd+0RFOe9lQRivaMmWJTLl+qzqeUPmebEa69FjDgAAAABWKCqSpk6VIiKcq6t7O5R37uxc0d3iUC5Jf3nvLy63HXzG4KBd9K0MPeYAAAAA4EtFRc6h5FlZvrlfSoq0caPXtz5zVVFJkbbnbXe5fbDuXV4RPeYAAAAA4AsFBVLHjlJkpG9CeffuUmGhtHWr34RySXrmy2dcbhtljwravcsrIpgDAAAAgLeULeYWFyc1aODsufam8HDn8PjCQmf4j/C/BdP+8/V/XG778sCXg34Yu8RQdgAAAADwjrfekoYOlUpLvX+vzp2l5cv9qme8Oo5ShzYf3OxS20bhjTS0w1AvV+QfCOYAAAAA4AlFRdLMmdIrr0ibN0um66uO11mLFtLu3X6xoJsrlm5d6vJq7A+nPezlavwHwRwAAAAA6qOgQLrwQu8PUz/RuHHOPwQEkJGLRrrc9q9d/+rFSvxLveaYFxQUaPfu3SopKfFUPQAAAAAQGAoKpDZtfDN3vEx09O9zyAMslL/57ZvadXiXS21bNWqliDD/mx/vLXUK5suWLVO3bt0UExOjNm3aaMOGDZKkO+64QxkZGR4tEAAAAAD8SlGRdNZZzkC+Y4f37xcZKY0aJR096nzce69fLupWE0epQ+/98J5uWHCDy6+5NOVSL1bkf9wO5kuXLlWfPn107Ngx3XXXXSqtsJBB06ZN9eqrr3qyPgAAAADwD0VFUlqaMyhv2uTde9nt0q23OoP4sWPSiy86e8sDzOvfvK6Iv0VowJsD3HrdiE4jvFSRf3J7jvlDDz2kK6+8UosWLVJJSYmmT59efq5Tp0565ZVXPFogAAAAAFjK4ZCuu07yxejglBTnsHg/X139ZBylDiU/nazs/Gy3X2szbLrs1Mu8UJX/crvH/Ouvv9Zf/vIXSZJhGJXONWvWTPv27fNMZQAAAABgJYdDuv9+KSzM+6F8+HDnvPGtWwM6lBeVFOnGjBsV9rewOoVySbqxw40hsXd5RW73mIeFham4uLjac/v27VNMTEy9iwIAAAAAyxQUSAMGSJ9+6v173X+/9NhjAbPdWW0mLJ6gmV/OrPd1XhrwUv2LCTBu95h36dJFr732WrXn5s+fr27dutW7KAAAAADwGYdDeu896dxznQG5QQPvhvLwcOmDD6SSEumJJwI+lDtKHUr8R6JHQvm5Lc4NqdXYy7jdY37ffffpiiuu0ODBg/XHP/5RhmHoyy+/1Msvv6z58+dr2bJl3qgTAAAAADwrP9+5mNvatb67ZwDuPV6TopIijXxnpF7f+LrHrrly5EqPXSuQGKZpmu6+aM6cORo/frx+/fXX8mPx8fF69tlnNXz4cI8W6Al5eXmKi4tTbm6uYmNjrS4HAAAAgFXy86WhQ6X335fcj0Lus9mk5GRp9GhpwoSA2uasNnd+dKdmrJrh0Wte/YertWjoIo9e00ru5NA6BXNJKigo0MqVK7V37141bdpUl1xyiRo2bFingr2NYA4AAACEMIdD+vhj58rq+fm+uefw4dLLLwdNEK/ogn9doLXZnh1l0CWxi1bfutqj17SaOznU7aHsZaKjo3XZZaG1hD0AAACAAGLFUPUgWsztRI5Sh1r9o5X2Ht3r0evOTZ+roR2GevSagcbtYL5ixYqTtunRo0edigEAAACAOikbor5kiXTsmO/vP3iw9L//BWUgl6SMTRm65q1rPHa92IhYzbtmnvq06xNyW6NVx+1gnpaWVmX/8hM5HI46FwQAAAAAJ1VUJM2YIT3/vLRrl3V19OghffJJUA5ZL/PGxjc0LGOYR65ll12HJx1WdES0R64XLNwO5tWtun7gwAEtWrRIn3/+uZ5//nmPFAYAAAAAVRQVSb17S1lZ1tZx5pnS+vVBHcgLigrU/rn22nl4p0eud9VpV+m9G9/zyLWCTZ0Xf6vObbfdpqioKM30s+X/WfwNAAAACEAFBdLYsVJGhpSbK5WWWl2R1Lq19MMPUnTw9fgWFBVo7OKxWvDtAh0qPuSx67aOba0f7vgh5HrJ3cmhNk/eePDgwZo3b54nLwkAAAAglOTnSwMGOOdqN2gg/ec/0m+/WR/Ke/eWjh6Vtm8PulCefyxfLZ9qqQZTGug/X//Ho6F83jXztH3C9pAL5e6q86rs1fntt99UWFjoyUsCAAAACFZl25hNny6tXu0Mvv7CZpM6dZKeeELq0yfoFnVzlDr04Y8f6roF16mgpMDj1x98xmD979r/sbCbi9wO5jt27KhyrLCwUBs2bNCkSZN00UUXeaQwAAAAAEGk4mJte/ZY3wNeHbtdGjlSmjUr6HrFK3pj4xsanjFcpjw2q7lcYsNEbR2/VRFhwTv33hvcDuYpKSnVrspumqbat2+v5557ziOFAQAAAAhwZWH8iSecQ9T9kWFIV14pzZsnNWpkdTVeVVRSpFOfOVW7D+/2yvUHnD5A7wx7xyvXDnZuB/OXX365SjCPiopSSkqKunTpIpvNo9PWAQAAAAQKf9nCzBXNm0tbtoREGJ+xaoaeWP6E8ku898eRedfM0/XnXO+16wc7t4P5zTff7IUyAAAAAAScQAriUsgMVc8/lq+h84dq8ZbFKlGJV+8VZYtS/uR85pLXk0cXfwMAAAAQpBwO6cMPpcmTpe+/l0q8G/g8wmaTGjaUevQIiaHqktTlX120JnuNT+7VuUVnrb1trU/uFezqFMznzJmjuXPnavv27SooqLyCn2EY2rJli0eKAwAAAGCRir3hu3dLpucXCvO4qCjpppuCvke8orLV1Sd/OlkbDmzwyT2j7FHaf9d+NYoK/j90+IrbwXzatGmaNGmSzjrrLHXq1EmRkZHeqAsAAACALxQUSGPHSgsWSIcOWV1N3XTvLn36qRQROiuBF5UUaeQ7I/X6xtd9et85g+doeMfhPr1nKHA7mP/rX//SHXfcoWeffdYb9QAAAADwpqIiaeZM6ZVXpM2bA6Mn/EQREVJCgjR6tDRhQkgE8rJF3J5f9bx2HfHdfP5wW7jObna2nuz1pPq068Ncci9xO5jn5ORo8ODB3qgFAAAAgLfk50udOkm//GJ1Je4791zpySelPn2cC7iFiLJh6iMXjdT+Y/t9dt/YiFjNu2YeQdyH3A7m559/vrZs2aJevXp5ox4AAAAAnlRQILVuLR04YHUlrouJCakF2yoqC+NjPhyj7XnbfXrvJlFNtHPCTkVHhMb8fH/idjCfMWOGbrzxRnXu3Fnnn3++N2oCAAAAUBcFBdK4cdK770r79ztXUvd3YWFSfLw0eHBILdpWpmxrsyW/LNEx85hldcxNn6uhHYZadv9Q53Ywv+WWW3Tw4EF17dpVLVu2VJMmTSqdNwxD33zzjccKBAAAAFCLssXb/vvfwNjCrGlT6a67QmZu+IkKigo07qNxenfTu8opyLG6HA0+Y7D+d+3/GLJuMbeDeZMmTdS0aVNv1AIAAACgOmVbl82eLeXkOAN4oCzaZrdLHTqE5BxxybpF22oTEx6jyamTNaHbBEWEhd4fR/yR28E8MzPTC2UAAAAAqKSsJ/y115zBPFDYbFJyckitmC45e8LHLh6rjO8ylFuUK/P4f/xF46jGuveSezX+ovGEcT/kdjAHAAAA4GEOh/Thh9IDDzi3MDtm3VxjtzVoIKWkSCNGSOPHh0QQL1ugbfKnk/X9ge9VIv+eQnD3xXdr+uXTrS4DtahTMN+/f79mzJihzMxMHThwQAsXLtTZZ5+tF198UV27dtV5553n6ToBAACA4FHWG75ggXTokNXVuM5mkxo3DsmF2sp6xOdtnKcjJUesLqdWhgy1jm2t0ReMZrh6gHA7mG/dulWXXHKJcnNz1alTJ/3yyy8qLCyUJG3YsEGrVq3SK6+84vFCAQAAgIBT1hM+ebK0aZNUXGx1Ra4zDCkqSmrfPmTmh1ecD77nyB6VqtTqktzSO6W33hn6DtudBSC3g/k999yj+Ph4rVmzRs2bN1dEhaEq3bt318MPP+zRAgEAAAC/5nBIH38sTZ8urV4tHT1qdUXuC/IF2opKijTzy5l6Ze0r2vrbVhWq0OqSPMIuuzq06KAnez2pPu36sLJ6AHM7mH/66ad64YUXlJiYKMcJ+yImJCRoz549HisOAAAAsFwg7g3uCrtdeuKJgF+gLf9YvoYtGKbMbZnKL8n3qwXXvCHaHq2HL32YIepBxu1gfuzYMTVu3Ljac0eOHJHNZqt3UQAAAIBPVRxy/v33gbEfeH3MmSMNH251FbUqm9O94NsFyi12rnJuyAj64F2dSHukRnQaoZlXzGSYepByO5i3b99eS5Ys0eWXX17l3IoVK3TOOed4pDAAAADA48r2A3/+eWnPHqk0sOYQ11l0tF/PFc8/lq+h84dqyS9LdMyseUX6UAnlYUaY4qPiNfiMwZrVdxZhPAS4HcxvvfVWTZw4UYmJiRp+/K9sRUVFmj9/vmbPnq3nnnvO40UCAAAAbgvWIeiuSEmRZs/2mxAeaNuL+VqUPUqXtb1M866Zp0ZRjawuBxYwTNN0+89Oo0aN0r///W/ZbDaVlpbKZrPJNE3deuut+uc//+mNOuslLy9PcXFxys3NVWxsrNXlAAAAwBNCtff7RBavnu5qbzd+x6JtocGdHFqnYC5Jq1at0vvvv6+9e/eqadOm6t+/vy6++OI6FextBHMAAIAARfiurGlT6a67fLZgGz3dnhFhi1BCowT2FQ8x7uRQt4eyl7nooot00UUX1fXlAAAAwO/y86WhQ6UlS6Rj9LqW8/LccHq7PSvaHq32TdvTEw63uR3ML7jgAo0cOVJDhw7VKaec4o2aAAAAEMxCee53bWw2qXFjafBgadYsZyivIwK3bzSOaqwhZw1htXTUm9vB3G63a8yYMbrzzjs1aNAg3XLLLbr88stlGIY36gMAAEAgqK7H2zCkus2aDH42mxQZ6VZveKjt1+1vDBmKskfRIw6vqNMc882bN+vll1/WnDlzlJOTo8TERN18880aMWKE2rVr540664U55gAAAPVQUCCNHSstWCDl5jrDNqHbdWFhUnx8lZ7w2uZvh+p+3VYjfMOTfLL4mySVlpZq8eLFeuWVV/Tee++pqKhI3bt31/Lly+t6Sa8gmAMAAJxEWfjOyPg9fBO8XXc8fDsGD9SHY/tp8orHalwsjdBtDRZgg6/5LJhX9Pnnn2vo0KHavXu3HH42T4hgDgAAQlrZMPNPP3UGcHq7XVJkk2Z0lZ7vKmU3khx2ybCLSO1nbLKpYXhD9WjTg33A4Vd8siq7JB0+fFjz5s3TK6+8oi+//FJRUVEaOnRofS4JAACAunI4pA8/lCZPlr7/XiqpYWurEAjlDkP68DTpgZ7S5sZSYZhkGpJqWhbJlAzzeBvb8Uf1zeAjDCtHKKlTMF+6dKleeeUVZWRkqKCgQF27dtXs2bM1dOhQeqQBAAB8oeK870OHrK7GI8rC9OSe0vdNpZIwVQ7M1YXqms7bamhfC0K3dcKMMMVHxWvwGYM1q+8sVjhHyHE7mKekpGjnzp1q3ry5br/9do0cOVJnnnmmN2oDAAAIbUEy7zs/TBp6jbQkRTpWcVrviaG6hjB9st848P5FQo9NNtltdsVFxhG+gWq4HczPO+88Pfvss7ryyitlP8mWDgAAADhBbcPN/XTud4FdGnuFtOAsKTfSGaRr7cWWfg/dZcPCa2jnf78t3BVuhKtRZCNdknyJ3kh/gzneQB24HczffvttjxawYsUKPfXUU1q7dq2ys7P19ttva9CgQeXn8/Pzdd9992nhwoU6ePCgUlJS9Ne//lWjR4/2aB0AAABeU1QkzZgh/f3v0sGDNbfzQCivdjh4pXu4Eapr6cl2pVJCd+AzZCjCFqGmDZvqqtOv0swrZtLTDXhBneaYFxYW6tVXX1VmZqYOHDig2bNn6/TTT9eiRYvUoUMHnXrqqS5f68iRI+rUqZNuueUWXXPNNVXOT5gwQcuWLdOcOXOUkpKijz/+WLfffrsSExM1cODAupQPAADgea4uvOaCiquB72kkldrlWqCWXJpbTahGGXq7Af/gdjA/cOCAevbsqe+++04tW7bU3r17dfjwYUnSwoUL9dFHH2n27NkuX69fv37q169fjee/+OILjRgxQmlpaZKkUaNG6cUXX9SaNWsI5gAAwPcqBvBNm6Ti4kqnq51P7U4vdS1DvwnLcBWLqQGBxe1gfs899+jQoUNas2aNOnbsqIiI31fw6Nmzp6ZNm+bRArt376533nlHI0eOVGJiojIzM/Xjjz9q1qxZHr0PAAAIIQUF0rhx0rvvSvv3O8O2Ks+lPhQpZ0CuKVQPPP7w0AJmrrZBaDBkyDzhE8F+3UDwcjuYv/fee5o2bZo6d+4sx/H/ESuTlJSkXbt2eaw4SXrmmWd06623KikpSWFhYbLZbPr3v/+t7t271/iawsJCFRYWlj/Py8vzaE0AAMA/FRQVaOziscr4LkO5Rbkyj/9Hjmoat5T0Z1YFh+9VDN30bAOQ6hDM8/Ly1KZNm2rPFRcXq6Qe86mq88wzz2jVqlV655131KZNG61YsUK33367EhIS1Lt372pfM2XKFD366KMerQMAAHheUUmRZqyaoedXPa89R/aoVKXl5wwZMh01RF53966uZSMZQjU8iW3BANSF28G8bdu2+uKLL9SrV68q51avXq327dt7pDBJKigo0P3336+3335bV111lSSpY8eOWr9+vf7+97/XGMwnTZqkiRMnlj/Py8tTcnKyx+oCAAC/904v+HaBDhUfKj9e3RDcE5W3MVXLNlomgRo+Vd1nN8IWoYRGCRp9wWhN6DZBEWERNbwaAOrO7WA+fPhwTZs2Teecc055WDYMQ1999ZVmzZqlyZMne6y44uJiFRcXy2azVTput9tVWlpaw6ukyMhIRUZGeqwOAACCRW091NLJQ7Uroftk5yu1Ocnq4YBU988lw8QBBAq3g/m9996rzz//XIMHD9Ypp5wiSbriiit08OBB9e3bV+PGjXPrevn5+fr555/Ln2/dulXr169X48aN1bp1a1166aW6++67FR0drTZt2mj58uX6v//7P82YMcPd0gEACHiOUoc+/PFDTf50sr4/8L1K5JxC5kpgdoUnQjfgjhM/u4RpAKHIME3T7f+FNU1Tb775pt5//33t3btXTZs2Vf/+/XXDDTdU6d0+mczMTPXs2bPK8REjRujVV19VTk6OJk2apI8//li//vqr2rRpo1GjRmnChAkyDNf+zJ6Xl6e4uDjl5uYqNjbWrfoAAPCEmgK15JleasAflX12WU0cQChyJ4fWKZjXxDRNvf7667rxxhs9dUmPIJgDAOqrYrDedGCTilXs3lxqwI+4+9k1ZCjKHqX2TdvryV5Pqk+7PrLbalkAAADgVg51eyh7Td588009+uij2rx5s98FcwAAyoL1A0sf0OYDm1VoFtY7VLs1lxrwAFcCNauCA0DgcTmYT506VS+++KL27t2r9u3ba8qUKerbt69Wrlyp22+/XRs3blTz5s313HPPebNeAEAIqbjqd26xc09qX/ZSE6rhSe5+dlkNHABCh0vB/Pnnn9f999+vuLg4dejQQTt37tSgQYP07LPP6o477lB4eLgeeugh3XXXXWrYsKG3awYABAh6qRHwSuXcM720mj3Tdfy5rfLnlvnUAAB3uRTMX375ZXXv3l3vv/++YmJi5HA4NHr0aN12221KSUnRRx99pHbt2nm7VgCAj9W0T3V16KWGXzoerGtj2H9vwlxqAIAVXArmmzdv1pw5cxQTEyPJuY/4Aw88oH//+9/629/+RigHAD91sj2ry3giVBOo4RGmfu+lNmvopVbN5w1JUQ6p/X7pyaVSn62S3bBLDRpIPXpI8+ZJjejBBgD4F5eC+dGjR5WYmFjpWKtWrSRJp59+uuerAgC41FtNLzX8ysmGfZc5IVRHlEoJh6XRa6QJq5zPJUlhYVJUlHT66dITT0h9+kh2eq8BAMHH5cXfatozPCzMYwu7A0DQqthznX0kWw456KWGfyjroT7+szu91FV6p3+R7O5+JMPDpbPPlp58UppB8AYAhCaXU/Wdd96p+Pj48udl25+PHz9ecXFx5ccNw9CiRYs8VyEA+KH8Y/kaOn+olvyyRMfMY1XOuxK6CdXwiOPB2tVeahlSuFlDD7W3GYazB7x9e2cQpwccAABJLgbz1q1ba+fOndq5c2el423atNGOHTsqHaupZx0A/Fn+sXwNWzBMmdsylV+ST082vK8ec6nDTCn+mDR4kzRrsRTt8FnVNYuIkBISpNGjpQkTnM8BAIBLXArm27Zt83IZAFA/rq4e7qk52UDFYO3qXGq/DNSuiolh8TQAALyECeIA/ErZvteTP52s7w98rxKVSGKRM3hBxbnVNZwPiJ5qT7PbnfO+W7Sg9xsAAB+pVzDfv3+/CgoKqhxv3bp1fS4LIAidbNuukwVvAjUk/d5D7UYvdUUhEaxdERYmxcdLgwdLs2ZJ0dFWVwQAQEhzO5gfPnxYEyZM0BtvvKFjx6oueCRJDkeo/n86QGg62UJoriB4hwg3e6k9sup3qDAMyazmH4cQDgCA33M7mI8fP15z587Vn/70J3Xs2FGRkZHeqAuAxU7Ww40QZFZ4VHOOXmoPI1ADABAy3A7m77//vqZOnapx48Z5ox4AXubp1ccR4ByqNVTbJDUslnpsk+bNlxqV+L7EoMUq5gAA4Di3g/mxY8fUoUMHb9QCoA5q6tlm9fHQcLL32SabosKilBLbRiO2NND4135SxK95PqwQCg93rmJ+ySXSG2+wojkAAKjC7WB+5ZVXKisrS7169fJGPQBOUHGV8k0HNqlYxS6FbkJ54LPLroYRDXV+wvm695J71fu03rLb7L83cDikDz+UJk+Wvv9eKqmuO7tU0lFJm3xUdZCpad52mbAwKSpKOv106YknpD59nKuaAwAAuMHtYP7AAw9oyJAhiomJ0YABA9SkSZMqbRo3buyR4oBQ4ih16OOfP9b0z6Zr9e7VOlp6tMa2hG7/V90fTyJsEUpolKDRF4zWhG4TFBHmwtDlsvA95J5awjc8hr26AQCABQzTrK0roCqbzeZ8oVHTPjX+typ7Xl6e4uLilJubq9jYWKvLQYhiMbXgYciQzbCpQVgD9WjTQ/OumadGUR4IcQUF0tix0oIFUm5u7T21qD+GmAMAAC9yJ4e63WP+0EMP1RrKgVBE6A4cNU0DsMmmhuENPRu0q5OfLw0bJmVmOn8mfHuHYUg2m9SgAT3gAADA77ndYx6I6DGHJxQUFWjs4rFa8O0CHSo+ZHU5Iedk8+pPOh/b0/LzpaFDpSVLpGPV7N1+srnJ8AybTWrYkPANAAD8jld7zCs6duyYfvvtN51yyimKioqqz6UAS9Hjbb2KwTvMCFN8VLwGnzFYs/rOUnSExfs3FxVJM2ZIs2dLOTlScfHJX0Mo9xz28wYAAEGuTsF85cqVuvfee7Vq1SqVlpbKZrPp4osv1tSpU9WtWzdP1wh4XMUgvvvIbhZT8zLj+ObY4bZw9xc/86WyAP7889KePVIpf6DxGXq+AQBACHM7mK9atUq9evVSfHy8Ro0apcTERO3evVsZGRnq1auXMjMzdeGFF3qjVsAlFUN39pFsOeRgT28v8tpCaJ5Q3XBzhph7jytbi9HzDQAAUIXbc8z79OmjQ4cOadmyZWrYsGH58SNHjqhnz5465ZRT9NFHH3m80PpgjnnwcWdrMdSNTTZFhUUpJT5FIzqN0PiLxvtfD/eJKu7rvWGD1dUEB8Nw7tPdvr305JPs0w0AAOAir84xX7VqlV5++eVKoVySGjZsqLvvvlt/+tOf3L0kUCPmfted5auPe1rF0M1+3p5hGM4h4wwfBwAAsJTbwdzhcCgyMrLac1FRUX63hzkCQ/6xfA2dP1RLflmiY2Y1K1yjVhG2CP+eu+2uinO9s7OdoRx1ZxjOR2QkPd8AAAB+yO1g3qlTJ73wwgsaMGBAlXMvvviiOnXq5JHCEJzKthzL+C5DuUW5Mo//B7Xzu1XKvaGsR3zkSGn/fqurCQyGIUVESE2bSlddJc2cybxtAACAAOR2ML/vvvs0aNAgnXfeebrxxhuVkJCg7OxszZ07V+vXr9fChQu9UCYCDUPQ6yYkAvjJ9v9GVeHhUlwci6YBAAAEKbcXf5OkuXPn6p577tGePXvKj7Vq1UrTp0/X0KFDPVqgJ7D4m/cxFP3kDBmKskepfdP2erLXk+rTro/sthAYSlxxWPquXVZX499YaA0AACBouJND6xTMJck0TW3evFkHDx5UkyZN1L59exmGUaeCvY1g7nllQ9IXfLtAh4oPWV2O5UI2dJcpKJDGjpUWLJAOHbK6Gv9Q09Zh7NcNAAAQEry6KnsZwzB0xhln1PXlCEAFRQUa99E4/Xf9f1VUWmR1OT4XpjBFhUfp9Can64meTwR/+GbIee1Y0RwAAAAe4lIwX7FihTp37qxGjRppxYoVJ23fo0ePehcG64Xa8HS7YVf7Ju0DZ89ubykqkk47jWHnJ4qIkBISpNGjpQkTnM8BAAAAD3BpKLvNZtOqVavUtWtX2Wy2Goesm6YpwzD8bss0hrK7riyMv7/l/aBfLT1gtxhzdT/vmoZSu9sm2NT2OzPHGwAAAB7i8aHsy5Yt01lnnSVJWrp0qd/OJYf7Kq6evutI8PSQBmzork1+vtSzp7RmjWvtXQncoRDK7Xapb1+GmwMAAMBv1Xnxt0BCj3llZWH8ieVPKL8k3+py6izotxar2DO+YYPV1QQGm80ZxNlaDAAAABbz6uJvvXr10uzZs6td+O3HH3/UbbfdpqVLl7p7WXhZ2cJtr294XUdLjlpdjsvssivcHq4WjVpo9PlB1Pt9orItxWbPlnJypOJiqyvyT+znDQAAgCDkdjDPzMxUXl5etecOHz6s5cuX17soeE5RSZHOffFcbTqwyepSamWTTQ3DG6pHmx6ad808NYry8yHHNa1Yzrxuz4qOlh5+mMXWAAAAENTqvF1adbKzs9WgQQNPXhJ1lH8sX53+2Um/5P5idSmVBPx+3wUFUuvW0oED1Z9nXnfdMQwdAAAAIcqlYL5o0SItWrSo/Pnf/vY3NWvWrFKbgoICZWZm6rzzzvNshXDb+S+er3U566wuQw3CGiglPsW/tx/Lz5eGDZMyM50/E5p9IzzcueI5C7IBAAAArgXz77//Xv/73/8kSYZhaOnSpbLZbJXaREZGqkOHDpo1a5bnq0StHKUOffzzx5r+2XRl7sy0rI4oe5Qua3tZYAxFl6QuXVxf4Rx1Z7M5tyBLSZFGjJDGj2dYOgAAAFCB26uyV9zTPFAE66rsBUUFGjBvgD7d+qllNaTEpWj2lbP9b0h6QYE0dqyUkSHl5jp7wukN9w7DcD4iI9n/GwAAADjOq6uyl5aW1rkweEb+sXy1e7ad9h7d6/N7R9uj/Xt+eEGBdMYZ0o4dVlcSXMaNk2bOtLoKAAAAICi5HcxXrVqlHTt26Lrrrqty7q233lKbNm104YUXeqQ4VNXlX120Jtt3w6/tsqtvu76BMTy9f3/p/fetrsJ/1GeF+IgIKSFBGj2aFdEBAAAAL3M7mN9///265JJLqg3m33//vV566SV98sknHikOlbWb1U5bDm3x+n2i7FG6qeNNmtV3lqIjAmBV7Px86ZRTpJISqyvxrbAwaeRIZ082q5cDAAAAAct28iaVbdiwQRdddFG15y688EJ988039S4KVfWf09+roTwmPEZTe01V4eRCFTxQoH9d/S9rQ3lBgfTnPzsDd9kc5poeMTGhE8rtdumqq6TDh6XiYunFFwnlAAAAQIBzu8f8yJEjCgur/mU2m02HDx+ud1Go7K6P79L7Wzw/RLtpdFPd1e0uTeg2wT+2MitbsO2//w2doH0yYWFSfDz7egMAAABBzO1g3rZtWy1btkxXXHFFlXPLli1TmzZtPFIYnIpKivSPL/7h0WsOPmOw/nft/6xfuK0siC9YIB06ZG0tnsK8bgAAAABucjuY33DDDXriiSfUvn173XLLLeXHX331Vc2cOVOTJk3yaIGh7vLXLvfYtab2muo/veODBkmLFlldhWfMmSMNH251FQAAAAAClNv7mBcVFalv377KzMxUdHS0EhMTtWfPHh07dkxpaWn68MMPFeFnPX2Buo95UUmRIp+IrPd1zmt+ntaNXueBijwkUEM5+3UDAAAAcJFX9zGPiIjQJ598orlz52rx4sXav3+/unbtqn79+mno0KGyE1I85tnVz9b5tX67zVlBgX+H8rvvlqZPt7oKAAAAACHE7R7zQBSoPeYJf09QzpEct17TO6W33hn6jv9uc9amjbRjh9VV/I553QAAAAC8wKs95vCNuz6+y61Q3jWhq1b+eaX1C7rVpKBAatHCuc2Xr91/v/TYYww5BwAAAOCX6hTMV6xYoWeeeUabNm1SQUFBpXOGYWjLFu/ttx0K3F2JPT4qXl+O+tKLFbmgqEiaMUN6/nkpO1tyOFxbodyb2rWTfviBQA4AAADAr9ncfcFnn32myy67TLm5udq0aZPOOOMMtWrVSjt27FBYWJh69OjhjTpDyuw1s91qP//a+V6qxAUFBVLHjs4F0SZNknbtcoZyybpQHhvr7Jn/6SdCOQAAAAC/53Ywf/jhh3XLLbdo8eLFkqTHH39cWVlZWrdunfLz85Wenu7xIkPNq+tfdblthC1CaSlpXqulWvn50oABztDboIG0caNv73+i8HDplFOk/v2dgTw3V2rkRwveAQAAAEAt3A7m3377rQYPHizDMCRJjuO9ox07dtSDDz6oxx57zLMVhpiikiJ9s/cbl9sP7TDUN/PKHQ7pvfecQTwmxvlzaan379u5szNsm2bNj6Ii6ddfpXffJZADAAAACDhuB/OjR4+qUaNGstlsioyM1IEDB8rPnXHGGfr+++89WmCoOe/F89xq/6/+//JSJRVkZEjR0c5e8hPWFPCKTp2kDz6QSkqktWsJ2wAAAACCmtvBvHXr1tq7d68k6ayzztL7779ffm758uVq0qSJ56oLMQVFBfr+gOt/2OiZ0lMRYV7e3isjQ7rmGqm42Lv3KXP11dL69VK/fswPBwAAABAS3F6VPS0tTZmZmRoyZIhuvfVW3X777dq0aZMiIyP18ccf68477/RGnSFh8JuD3Wq/ePhiL1VynMPhDOW+csEF0qJFvrsfAAAAAPgBt4P5o48+ql9//VWSdNttt+no0aN6/fXXZRiGHnjgAU2ePNnjRYYCR6lDS7cudbn9pW0u9V5vednWZ5Mmeef61enf3zlHHAAAAABCjNtD2Zs2bao//OEP5c8nTpyotWvXas2aNXrkkUcUHh7u1vVWrFihAQMGKDExUYZhaOHChVXabNq0SVdffbXi4uIUExOjiy66SDt27HC3dL+WtSNLxabrw8U/vvFjzxZQUCCNGiU1bPj71me+cuedhHIAAAAAIcvtYF6dnTt3avHixTp48KDbrz1y5Ig6deqk5557rtrzW7ZsUffu3XXGGWcoMzNT33zzjR588EFFRUXVt2y/sjtvt8ttrznjGs/2lg8c6Fxt/aWXpKNHPXfdkxk+XCoslP7+d9/dEwAAAAD8jNtD2R944AEdOXJETz/9tCRpyZIlGjBggAoLCxUfH6+srCydffbZLl+vX79+6tevX43nJ0+erCuvvFLTp08vP3bqqae6W7bf2390v0vtDBl689o3PXfjrl2lr77y3PVqYhhSVJTUvr305JNSnz4s7gYAAAAAqkOP+YIFC3TWWWeVP3/ggQfUsWNHLVy4UCkpKXr88cc9Vlxpaanef/99/eEPf9AVV1yh5s2b68ILL6x2uHuga9agmUvt/tr1r/Xft7yoSJo6VTrlFO+E8rKe8Ip7jZeWOnvjv/6aFdcBAAAAoAK3e8x3796tdu3aSZIOHjyor776Sh988IGuuOIKHTt2zKOrsu/bt0/5+fmaOnWqHn/8cU2bNk2LFy9Wenq6li1bpksvvbTa1xUWFqqwsLD8eV5ensdq8pZWsa1cajfozEH1u9E990hPPVW/a5zIZpM6dqQnHAAAAADqwO1gbpqmSktLJUmff/657Ha7evToIUlKSEjQgQMHPFZc2X0GDhyoCRMmSJLOPfdcrVy5Uv/85z9rDOZTpkzRo48+6rE6fGFv/t6TtrHJpouTLq77TbwRyseNk2bO9Ow1AQAAACCEuD2U/bTTTtN7770nSZo3b566du2q6OhoSVJ2drZOOeUUjxXXtGlThYWFVRo6L0lnnnlmrauyT5o0Sbm5ueWPnTt3eqwmb3CUOjTmgzEnbVeqUmXtyKrbTYqKPB/KJ04klAMAAABAPbkdzP/yl79o1qxZatKkid58803deuut5ec+//zzKiG6PiIiItSlSxdt3ry50vEff/xRbdq0qfF1kZGRio2NrfTwZ1k7snSgwLWRBpnbMut2kz/9qW6vq8mECdI//uHZawIAAABACHJ7KPvo0aN1yimnaOXKleratatuvPHG8nMFBQW6+eab3bpefn6+fv755/LnW7du1fr169W4cWO1bt1ad999t66//nr16NFDPXv21OLFi/Xuu+8qMzPT3dL9ljtbpbklP18aOlRavFgqKfHcde++W6qwSj4AAAAAoO4M0zRNKwvIzMxUz549qxwfMWKEXn31VUnSyy+/rClTpmjXrl1q3769Hn30UQ0cONDle+Tl5SkuLk65ubl+2Xs+c9VMTfhogkttl9y0RJedetnJG3p6G7SYGGnyZGdPeYQH91AHAAAAgCDkTg51u8fc09LS0nSyvw2MHDlSI0eO9FFFvtckuolL7RpFNFJaStrJG3oylD/+uLOHnDAOAAAAAF7h9hzz4uJiPf744zrrrLPUsGFD2e32So+wMMuzfsA5WHDQpXbXnnXtyfcwz8/3XCi/+25nLzmhHAAAAAC8xu0UPWnSJD399NPq16+fBg0apMjISG/UFVKaNWjmUrvL2rowhP3cc+tXTBnmkQMAAACAT7gdzN966y099NBDevjhh71RT0ja8tsWl9q1im1Ve4OiImmLa9eqVuvW0u23M48cAAAAAHzI7WD+22+/qUePHt6oJSQ5Sh16cc2LJ21nN+y6OOni2hs9+2zdC+nfX3r33bq/HgAAAABQJ27PMe/Ro4fWr1/vhVJCU9aOLO3J33PSdg7ToZW7Vtbe6K236lZEixaEcgAAAACwiNvB/JlnntF//vMfZWRkqKioyBs1hZTsw9meaZuRIa1e7X4BTZtKOTnuvw4AAAAA4BFuD2U/99xzVVxcrGuvvVaGYahBgwaVzhuGodzcXI8VGOwSYhLq39bhkO64w/2bM3wdAAAAACzndjC/5pprZBiGN2oJSRcnXSybbCpVaa3tkmKTlNo6tfqTWVnu9XqnpUkffCBFR7v+GgAAAACAV7gdzF999VUvlBG6Vu5aedJQLkm3dr615j3Mp01z76YPP0woBwAAAAA/4fYcc3iWq3PMT298evUnCgqkxYtdv2F0tJRaQ887AAAAAMDn3O4xL/Ptt99q06ZNKigoqHLuj3/8Y72KCiWuzjGvtl1GhnTtte7d8KWXJHsNPe8AAAAAAJ8zTNM03XnB0aNHdfXVV2vp0qUyDENlL68479zhcHi2ynrKy8tTXFyccnNzFRsba3U5lThKHUqZlaLdebtlqupbYchQUmySto7bWnkoe0aGdM017t0sNlZiYT4AAAAA8Dp3cqjbQ9n/9re/adu2bVq+fLlM01RGRoY++eQTpaen6/TTT9e6devqXHgostvsmtV3liRnCK+o7PnMvjMrh/K6rsLOtmgAAAAA4HfcDuaLFi3Svffeq4svvliS1Lp1a1122WX63//+p86dO+uFF17weJHBLv3MdN118V2yGZXfDpth010X36X0M9Mrv8DdVdgl6ayzWPANAAAAAPyQ28F827ZtOuOMM2S322UYho4ePVp+bvjw4Vq4cKEn6wsJGZsy9PeVf5fDrDwFwGE69PeVf1fGpozKL9i92/2b9OpVjwoBAAAAAN7idjCPj4/XkSNHJEnNmzfXTz/9VH6uuLi4/Bxc4yh1aNzicdXOLy8zfvF4OUorhPb9+92/0Wmn1aE6AAAAAIC3uR3MO3TooB9//FGS1LNnTz355JP67LPPtHr1aj322GPq1KmTx4sMZlk7srQrb1eN502Z2pm3U1k7sn4/eMop7t3Ebpduv72OFQIAAAAAvMnt7dL+9Kc/lfeSP/HEE+revbsuvfRSSc7e9A8++MCzFQY5V/cxr9Tu88/du8nEiVJEhHuvAQAAAAD4hNvB/Lrrriv/uW3btvrxxx/Lt067+OKL1bhxY48WGOyaN2zuXrsuXaQ1a1y/wd13S9On16EyAAAAAIAvuDWUvaCgQMOGDdNnn31Wfqxhw4YaMGCA+vfvTyj3tnbt3AvlTz1FKAcAAAAAP+dWMI+OjtaiRYtUWlrqrXpCzr4j+1xr93GGtGWL6xe226W//rWOVQEAAAAAfMXtxd/OPfdcffvtt96oJSQlxCS41m7GS+5duG1b5pUDAAAAQABwO5hPnTpV06dP1/Lly71RT8hJbZ2qpNgkGTKqPW/IUHJkM6X+XOzehaOjPVAdAAAAAMDbXArmK1asUH5+viTp9ttvV35+vnr16qWmTZuqQ4cO6tixY/mD7dLcY7fZNavvLEmqEs7Lns9sPFz2mrc5rx77lgMAAABAQHBpVfaePXvqiy++UNeuXdWkSRM1bdrU23WFlPQz0zX/uvkat3hcpT3Nk2KTNLPvTKUPvt/9i772mgcrBAAAAAB4i0vB3DR/767NzMz0Vi0hLf3MdA1sP1BZO7KUfThbCTEJSm2dKvvRAmnzNe5drEsXqVEj7xQKAAAAAPAot/cxh/fYbXalpaRVPnjTTe5dpEsXafVqj9UEAAAAAPAulxd/M4zqFyeDl1XYM/6kevUilAMAAABAgHG5x7xnz56y2U6e4w3DUG5ubr2KwnFFRdKBA663P/NM79UCAAAAAPAKl4N5WlqamjVr5s1acKLZs91r/9RT3qkDAAAAAOA1Lgfzhx56SF27dvVmLTjRRx+53nbgQPYuBwAAAIAA5PIcc/iYwyG5ugL+WWdJCxd6sxoAAAAAgJcQzP1VZqZ07JhrbW+5xaulAAAAAAC8h2Dur9zZL377dq+VAQAAAADwLpfmmJeWlnq7DpzInX/z007zXh0AAAAAAK+ix9xfNWniWjvDkG6/3bu1AAAAAAC8hmDur1q0cK3dlVdKERHerQUAAAAA4DUEc3/VqpVr7e66y7t1AAAAAAC8imDur55++uRtkpOl1FTv1wIAAAAA8BqCuT8qKJDeeefk7aZNk+x279cDAAAAAPAagrk/mjjRtXYrVni3DgAAAACA1xHM/dGaNZ5tBwAAAADwWwRzfxQX51q7+HivlgEAAAAA8D6Cub+55x7p009da+vqkHcAAAAAgN8imPuTe+6RnnrKtbZRUVKfPt6tBwAAAADgdQRzf1FU5Hool6R//5sV2QEAAAAgCBDM/cXs2e61nz/fO3UAAAAAAHyKYO4vtmzxbnsAAAAAgF8imPuLlBT32p92mlfKAAAAAAD4FsHcX3To4F77117zTh0AAAAAAJ8imPuLgwddb9uli9SokfdqAQAAAAD4DMHcXyQkuNbujDOk1au9WwsAAAAAwGcI5v5i//6Tt4mLk7791vu1AAAAAAB8hmDuDxwOacKEk7cLC/N+LQAAAAAAnyKY+4OsLGn37pO3O3jQ2RYAAAAAEDQI5v4gO9s7bQEAAAAAfo+x0f6gaVM5DCmrjZTdSErIl1K3S3azmrauLhIHAAAAAAgIBHM/kLHudY0bL+2K+/1YUq40a7GUvqlCw/h4KTXVx9UBAAAAALyJoewWy9iUoSEF/9Wu2MrHd8dKQ66TMs6scLBbN8lu92l9AAAAAADvIphbyFHq0LjF42QakozK58zjz8f3lRxl5/r08WV5AAAAAAAfIJhbKGtHlnbl7arxvGlIO+Occ89lt0u33+674gAAAAAAPmF5MF+xYoUGDBigxMREGYahhQsX1tj2L3/5iwzD0MyZM31WnzdlH3ZthfXsRpImTpQiIrxbEAAAAADA5ywP5keOHFGnTp303HPP1dpu4cKF+vLLL5WYmOijyrwvIca1FdYTOl0iTZ/u5WoAAAAAAFawfFX2fv36qV+/frW22b17t8aMGaOPPvpIV111lY8q877U1qlKik3S7rzdMlV1bzTDlJLypNQ3PpfSM6T0dAuqBAAAAAB4k+U95idTWlqqm266SXfffbfOPvtsl15TWFiovLy8Sg9/ZLfZNavvLEnOEF5R2fOZiyW7aUjjx0sOh28LBAAAAAB4nd8H82nTpiksLEx//etfXX7NlClTFBcXV/5ITk72YoX1k35muuaf9YhanfC3g6Q8af5bx/cxN01p504pK8uSGgEAAAAA3mP5UPbarF27VrNmzdK6detkGMbJX3DcpEmTNHHixPLneXl5/h3OS07XwJnO1dezG0kJ+VLqdsl+4uj2bNcWiwMAAAAABA6/DuZZWVnat2+fWrduXX7M4XDozjvv1MyZM7Vt27ZqXxcZGanIyEgfVekBCQmym1LatpO3AwAAAAAEF78O5jfddJN69+5d6dgVV1yhm266SbfccotFVXlBaqrUpIl08GD15w1DSkpytgMAAAAABBXLg3l+fr5+/vnn8udbt27V+vXr1bhxY7Vu3VpNmjSp1D48PFwtW7ZU+/btfV2q9yxaVHMol5xzzGfOlOx2n5UEAAAAAPANy4P5mjVr1LNnz/LnZXPDR4wYoVdffdWiqnzI4ZDGjau9TZMm0sCBvqkHAAAAAOBTlgfztLQ0mWbVPbxrUtO88oCVlSXt2lV7m4MHne3S0nxSEgAAAADAd/x+u7Sg5+pK66zIDgAAAABBiWBuNVdXWmdFdgAAAAAISgRzq6WmOldcr2mfdsOQkpNZkR0AAAAAghTB3Gp2uzRrlvPnE8N52XNWZAcAAACAoEUw9wfp6dL8+VKrVpWPJyU5j6enW1MXAAAAAMDrLF+VHcelpzu3RMvKci70lpDgHL5OTzkAAAAABDWCuT+x29kSDQAAAABCDEPZAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALhVldACpwOKSsLCk7W0pIkFJTJbvd6qoAAAAAAF5EMPcXGRnSuHHSrl2/H0tKkmbNktLTrasLAAAAAOBVDGX3BxkZ0pAhlUO5JO3e7TyekWFNXQAAAAAAryOYW83hcPaUm2bVc2XHxo93tgMAAAAABB2CudWysqr2lFdkmtLOnc52AAAAAICgQzC3Wna2Z9sBAAAAAAIKwdxqCQmebQcAAAAACCgEc6ulpjpXXzeM6s8bhpSc7GwHAAAAAAg6BHOr2e3OLdGkquG87PnMmexnDgAAAABBimDuD9LTpfnzpVatKh9PSnIeZx9zAAAAAAhaYVYXgOPS06WBA52rr2dnO+eUp6bSUw4AAAAAQY5g7k/sdiktzeoqAAAAAAA+xFB2AAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCYVYXgAocDikrS8rOlhISpNRUyW63uioAAAAAgBcRzP1FRoY0bpy0a9fvx5KSpFmzpPR06+oCAAAAAHgVQ9n9QUaGNGRI5VAuSbt3O49nZFhTFwAAAADA6wjmVnM4nD3lpln1XNmx8eOd7QAAAAAAQYdgbrWsrKo95RWZprRzp7MdAAAAACDoEMytlp3t2XYAAAAAgIBCMLdaQoJn2wEAAAAAAgrB3Gqpqc7V1w2j+vOGISUnO9sBAAAAAIKO5cF8xYoVGjBggBITE2UYhhYuXFh+rri4WPfee686dOighg0bKjExUX/84x+1Z88e6wr2NLvduSWaVDWclz2fOZP9zAEAAAAgSFkezI8cOaJOnTrpueeeq3Lu6NGjWrdunR588EGtW7dOGRkZ+vHHH3X11VdbUKkXpadLd90l2U54O2w253H2MQcAAACAoGWYZnX7dFnDMAy9/fbbGjRoUI1tvvrqK3Xt2lXbt29X69atXbpuXl6e4uLilJubq9jYWA9V60Fl+5hX91YYhjR/PuEcAAAAAAKIOznU8h5zd+Xm5sowDMXHx9fYprCwUHl5eZUefqu2fczLsI85AAAAAAStgArmx44d03333adhw4bV+heHKVOmKC4urvyRnJzswyrdxD7mAAAAABDSAiaYFxcX64YbblBpaalmz55da9tJkyYpNze3/LFz504fVVkH7GMOAAAAACEtzOoCXFFcXKzrrrtOW7du1dKlS086Pj8yMlKRkZE+qq6e2MccAAAAAEKa3/eYl4Xyn376SUuWLFGTJk2sLsmz2MccAAAAAEKa5T3m+fn5+vnnn8ufb926VevXr1fjxo2VmJioIUOGaN26dXrvvffkcDiUk5MjSWrcuLEiIiKsKttzyvYxHzLEGcIrLgLHPuYAAAAAEPQs3y4tMzNTPXv2rHJ8xIgReuSRR9S2bdtqX7ds2TKlpaW5dA+/3y5Ncm6ZNm5c5YXgkpOdoZyt0gAAAAAgoLiTQy0P5r4QEMFccm6JlpXlXOgtIcE5fJ2ecgAAAAAIOO7kUMuHsqMCu11ycRQAAAAAACA4+P3ibwAAAAAABDOCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgoTCrC8BxDoeUlSVlZ0sJCVJqqmS3W10VAAAAAMDLCOb+ICNDGjdO2rXr92NJSdKsWVJ6unV1AQAAAAC8jqHsVsvIkIYMqRzKJWn3bufxjAxr6gIAAAAA+ATB3EoOh7On3DSrnis7Nn68sx0AAAAAICgRzK2UlVW1p7wi05R27nS2AwAAAAAEJYK5lbKzPdsOAAAAABBwCOZWSkjwbDsAAAAAQMAhmFspNdW5+rphVH/eMKTkZGc7AAAAAEBQIphbyW53bokmVQ3nZc9nzmQ/cwAAAAAIYgRzq6WnS/PnS61aVT6elOQ8zj7mAAAAABDUwqwuAHKG74EDnauvZ2c755SnptJTDgAAAAAhgGDuL+x2KS3N6ioAAAAAAD7GUHYAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACwUZnUBvmCapiQpLy/P4koAAAAAAKGgLH+W5dHahEQwP3z4sCQpOTnZ4koAAAAAAKHk8OHDiouLq7WNYboS3wNcaWmp9uzZo5iYGBmGYXU5NcrLy1NycrJ27typ2NhYq8uBBfgMgM9AaOP9B58B8BkIbbz/wcU0TR0+fFiJiYmy2WqfRR4SPeY2m01JSUlWl+Gy2NhY/g8xxPEZAJ+B0Mb7Dz4D4DMQ2nj/g8fJesrLsPgbAAAAAAAWIpgDAAAAAGAhgrkfiYyM1MMPP6zIyEirS4FF+AyAz0Bo4/0HnwHwGQhtvP+hKyQWfwMAAAAAwF/RYw4AAAAAgIUI5gAAAAAAWIhgDgAAAACAhQjmAAAAAABYiGDuR2bPnq22bdsqKipK559/vrKysqwuCR7wyCOPyDCMSo+WLVuWnzdNU4888ogSExMVHR2ttLQ0fffdd5WuUVhYqLFjx6pp06Zq2LChrr76au3atcvXvwpcsGLFCg0YMECJiYkyDEMLFy6sdN5T7/dvv/2mm266SXFxcYqLi9NNN92kQ4cOefm3gytO9hm4+eabq3wnXHTRRZXa8BkIXFOmTFGXLl0UExOj5s2ba9CgQdq8eXOlNnwPBDdXPgN8DwS3F154QR07dlRsbKxiY2PVrVs3ffjhh+Xn+Q5AdQjmfuLNN9/U+PHjNXnyZH399ddKTU1Vv379tGPHDqtLgwecffbZys7OLn9s3Lix/Nz06dM1Y8YMPffcc/rqq6/UsmVLXX755Tp8+HB5m/Hjx+vtt9/WvHnz9Nlnnyk/P1/9+/eXw+Gw4tdBLY4cOaJOnTrpueeeq/a8p97vYcOGaf369Vq8eLEWL16s9evX66abbvL674eTO9lnQJL69u1b6Tvhgw8+qHSez0DgWr58ue644w6tWrVKn3zyiUpKStSnTx8dOXKkvA3fA8HNlc+AxPdAMEtKStLUqVO1Zs0arVmzRr169dLAgQPLwzffAaiWCb/QtWtX87bbbqt07IwzzjDvu+8+iyqCpzz88MNmp06dqj1XWlpqtmzZ0pw6dWr5sWPHjplxcXHmP//5T9M0TfPQoUNmeHi4OW/evPI2u3fvNm02m7l48WKv1o76kWS+/fbb5c899X5///33piRz1apV5W2++OILU5L5ww8/ePm3gjtO/AyYpmmOGDHCHDhwYI2v4TMQXPbt22dKMpcvX26aJt8DoejEz4Bp8j0Qik455RTz3//+N98BqBE95n6gqKhIa9euVZ8+fSod79Onj1auXGlRVfCkn376SYmJiWrbtq1uuOEG/fLLL5KkrVu3Kicnp9J7HxkZqUsvvbT8vV+7dq2Ki4srtUlMTNQ555zD5yPAeOr9/uKLLxQXF6cLL7ywvM1FF12kuLg4PhMBIjMzU82bN9cf/vAH3Xrrrdq3b1/5OT4DwSU3N1eS1LhxY0l8D4SiEz8DZfgeCA0Oh0Pz5s3TkSNH1K1bN74DUCOCuR84cOCAHA6HWrRoUel4ixYtlJOTY1FV8JQLL7xQ//d//6ePPvpIL730knJycnTxxRfr4MGD5e9vbe99Tk6OIiIidMopp9TYBoHBU+93Tk6OmjdvXuX6zZs35zMRAPr166fXX39dS5cu1T/+8Q999dVX6tWrlwoLCyXxGQgmpmlq4sSJ6t69u8455xxJfA+Emuo+AxLfA6Fg48aNatSokSIjI3Xbbbfp7bff1llnncV3AGoUZnUB+J1hGJWem6ZZ5RgCT79+/cp/7tChg7p166bTTjtN//3vf8sXeqnLe8/nI3B54v2urj2ficBw/fXXl/98zjnn6IILLlCbNm30/vvvKz09vcbX8RkIPGPGjNGGDRv02WefVTnH90BoqOkzwPdA8Gvfvr3Wr1+vQ4cOacGCBRoxYoSWL19efp7vAJyIHnM/0LRpU9nt9ip/3dq3b1+Vv6Yh8DVs2FAdOnTQTz/9VL46e23vfcuWLVVUVKTffvutxjYIDJ56v1u2bKm9e/dWuf7+/fv5TASghIQEtWnTRj/99JMkPgPBYuzYsXrnnXe0bNkyJSUllR/neyB01PQZqA7fA8EnIiJC7dq10wUXXKApU6aoU6dOmjVrFt8BqBHB3A9ERETo/PPP1yeffFLp+CeffKKLL77YoqrgLYWFhdq0aZMSEhLUtm1btWzZstJ7X1RUpOXLl5e/9+eff77Cw8MrtcnOzta3337L5yPAeOr97tatm3Jzc7V69eryNl9++aVyc3P5TASggwcPaufOnUpISJDEZyDQmaapMWPGKCMjQ0uXLlXbtm0rned7IPid7DNQHb4Hgp9pmiosLOQ7ADXz6VJzqNG8efPM8PBw8z//+Y/5/fffm+PHjzcbNmxobtu2zerSUE933nmnmZmZaf7yyy/mqlWrzP79+5sxMTHl7+3UqVPNuLg4MyMjw9y4caM5dOhQMyEhwczLyyu/xm233WYmJSWZS5YsMdetW2f26tXL7NSpk1lSUmLVr4UaHD582Pz666/Nr7/+2pRkzpgxw/z666/N7du3m6bpufe7b9++ZseOHc0vvvjC/OKLL8wOHTqY/fv39/nvi6pq+wwcPnzYvPPOO82VK1eaW7duNZctW2Z269bNbNWqFZ+BIDF69GgzLi7OzMzMNLOzs8sfR48eLW/D90BwO9lngO+B4Ddp0iRzxYoV5tatW80NGzaY999/v2mz2cyPP/7YNE2+A1A9grkfef755802bdqYERERZufOnSttq4HAdf3115sJCQlmeHi4mZiYaKanp5vfffdd+fnS0lLz4YcfNlu2bGlGRkaaPXr0MDdu3FjpGgUFBeaYMWPMxo0bm9HR0Wb//v3NHTt2+PpXgQuWLVtmSqryGDFihGmannu/Dx48aA4fPtyMiYkxY2JizOHDh5u//fabj35L1Ka2z8DRo0fNPn36mM2aNTPDw8PN1q1bmyNGjKjy/vIZCFzVvfeSzFdeeaW8Dd8Dwe1knwG+B4LfyJEjy/9/+mbNmpmXXXZZeSg3Tb4DUD3DNE3Td/3zAAAAAACgIuaYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AABB5NVXX5VhGNq2bZtHrpeZmSnDMJSZmemR6wEAgKoI5gAABJGrrrpKX3zxhRISEqwuBQAAuCjM6gIAAIDnNGvWTM2aNbO6DAAA4AZ6zAEACCInDmVPS0vTOeeco6+++kqpqalq0KCBTj31VE2dOlWlpaWVXvvDDz+ob9++atCggZo2barbbrtNhw8frvY+S5Ys0WWXXabY2Fg1aNBAl1xyiT799NPy8z/99JNiY2N17bXXVnrd0qVLZbfb9eCDD3r2FwcAIIARzAEACHI5OTkaPny4brzxRr3zzjvq16+fJk2apDlz5pS32bt3ry699FJ9++23mj17tl577TXl5+drzJgxVa43Z84c9enTR7Gxsfrvf/+rt956S40bN9YVV1xRHs5PP/10vfTSS5o/f76eeeaZ8jqGDRum1NRUPfLIIz753QEACAQMZQcAIMgdPHhQH3zwgbp27SpJ6t27tzIzMzV37lz98Y9/lCQ9/fTT2r9/v77++mt16tRJktSvXz/16dNHO3bsKL/W0aNHNW7cOPXv319vv/12+fErr7xSnTt31v33368vv/xSknT99ddr+fLluvvuu9W1a1dNnjxZpmnqjTfekN1u99WvDwCA36PHHACAINeyZcvyUF6mY8eO2r59e/nzZcuW6eyzzy4P5WWGDRtW6fnKlSv166+/asSIESopKSl/lJaWqm/fvvrqq6905MiR8vZPP/20zj77bPXs2VOZmZmaM2cOC9MBAHACeswBAAhyTZo0qXIsMjJSBQUF5c8PHjyotm3bVmnXsmXLSs/37t0rSRoyZEiN9/v111/VsGHD8vsMGzZMd999tzp37qzLL7+8Tr8DAADBjGAOAADUpEkT5eTkVDl+4rGmTZtKkp599llddNFF1V6rRYsW5T9/++23euihh9SlSxd99dVXmjFjhiZOnOjBygEACHwEcwAAoJ49e2r69On65ptvKg1nnzt3bqV2l1xyieLj4/X9999XuzBcRUeOHNG1116rlJQULVu2TPfdd5/uu+8+XXLJJbrwwgu98nsAABCICOYAAEDjx4/Xyy+/rKuuukqPP/64WrRooddff10//PBDpXaNGjXSs88+qxEjRujXX3/VkCFD1Lx5c+3fv1/ffPON9u/frxdeeEGSdNttt2nHjh1avXq1GjZsqH/84x/64osvdMMNN+jrr79WfHy8Bb8pAAD+h8XfAACAWrZsqeXLl+uss87S6NGjdeONNyoqKkrPPfdclbY33nijli1bpvz8fP3lL39R7969NW7cOK1bt06XXXaZJOnf//635syZo+eff15nn322JCkiIkJvvvmmfv31V91yyy0+/f0AAPBnhmmaptVFAAAAAAAQqugxBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALPT/EdLGJ2n72QoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#xgb regression\n", + "import xgboost as xgb\n", + "\n", + "# Initialize the model\n", + "model = xgb.XGBRegressor()\n", + "\n", + "# Fit the model\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "dae = median_absolute_error(y_test, y_pred)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "\n", + "print(\"Mean Squared Error xgb:\", mse)\n", + "print(\"Mean Absolute Error xgb:\", mae)\n", + "print(\"Median Absolute Error xgb:\", dae)\n", + "\n", + "\n", + "\n", + "# Deviation Plot between Predict and Test in different colors\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(y_test.shape[0]), np.sort(y_test), color='r', label='Test')\n", + "plt.scatter(range(y_pred.shape[0]), np.sort(y_pred), color='g', label='Predict')\n", + "\n", + "plt.xlabel('index', fontsize=12)\n", + "plt.ylabel('TransactionRevenue', fontsize=12)\n", + "plt.legend()\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SVR" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error svr: 1.2639191609659166\n", + "Mean Absolute Error svr: 0.8559963823783391\n", + "Median Absolute Error svr: 0.6711483938793492\n", + "Cross-validated scores svr : [0.84857646 0.80814385 0.84928007 0.8733592 0.85135905]\n", + "Average score svr: 0.8461437269566936\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# SVR \n", + "from sklearn.svm import SVR\n", + "\n", + "# Initialize the model\n", + "model = SVR(kernel='linear')\n", + "\n", + "# Fit the model\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "dae = median_absolute_error(y_test, y_pred)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "\n", + "print(\"Mean Squared Error svr:\", mse)\n", + "print(\"Mean Absolute Error svr:\", mae)\n", + "print(\"Median Absolute Error svr:\", dae)\n", + "\n", + "# Perform 5-fold cross validation\n", + "scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_absolute_error')\n", + "\n", + "# Print the cross-validated scores\n", + "print('Cross-validated scores svr :', -scores)\n", + "\n", + "# Print the average score\n", + "print('Average score svr:', -scores.mean())\n", + "\n", + "\n", + "# Deviation Plot between Predict and Test in different colors\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(y_test.shape[0]), np.sort(y_test), color='r', label='Test')\n", + "plt.scatter(range(y_pred.shape[0]), np.sort(y_pred), color='g', label='Predict')\n", + "\n", + "plt.xlabel('index', fontsize=12)\n", + "plt.ylabel('TransactionRevenue', fontsize=12)\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GradientBoosting Regressor" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error gbr: 1.2197352952624738\n", + "Mean Absolute Error gbr: 0.852128842190661\n", + "Median Absolute Error gbr: 0.6811395760995076\n", + "Cross-validated scores gbr : [0.84044903 0.80441982 0.84466965 0.86525018 0.84382516]\n", + "Average score gbr: 0.8397227680683059\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Gradient Boosting Regressor\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "# Initialize the model\n", + "model = GradientBoostingRegressor()\n", + "\n", + "# Fit the model\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "dae = median_absolute_error(y_test, y_pred)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "\n", + "print(\"Mean Squared Error gbr:\", mse)\n", + "print(\"Mean Absolute Error gbr:\", mae)\n", + "print(\"Median Absolute Error gbr:\", dae)\n", + "\n", + "# Perform 5-fold cross validation\n", + "scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_absolute_error')\n", + "\n", + "# Print the cross-validated scores\n", + "print('Cross-validated scores gbr :', -scores)\n", + "\n", + "# Print the average score\n", + "print('Average score gbr:', -scores.mean())\n", + "\n", + "#plot the feature importance\n", + "plt.figure(figsize=(12,6))\n", + "plt.barh(X.columns, model.feature_importances_)\n", + "plt.xlabel('Features', fontsize=12)\n", + "plt.ylabel('Importance', fontsize=12)\n", + "plt.xticks(rotation=90)\n", + "plt.show()\n", + "\n", + "# Deviation Plot between Predict and Test in different colors\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(y_test.shape[0]), np.sort(y_test), color='r', label='Test')\n", + "plt.scatter(range(y_pred.shape[0]), np.sort(y_pred), color='g', label='Predict')\n", + "\n", + "plt.xlabel('index', fontsize=12)\n", + "plt.ylabel('TransactionRevenue', fontsize=12)\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AdaBoostRegressor\n" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error adb: 1.3661555221913546\n", + "Mean Absolute Error adb: 0.9157086970158043\n", + "Median Absolute Error adb: 0.7813159158948864\n", + "Cross-validated scores adb: [0.89651134 0.84117506 0.89028526 0.90588058 0.90281921]\n", + "Average score adb: 0.8873342901131329\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.ensemble import AdaBoostRegressor\n", + "\n", + "\n", + "# Initialize the model\n", + "model = AdaBoostRegressor()\n", + "\n", + "# Fit the model on the training data\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions on the test data\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "dae = median_absolute_error(y_test, y_pred)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "\n", + "print(\"Mean Squared Error adb:\", mse)\n", + "print(\"Mean Absolute Error adb:\", mae)\n", + "print(\"Median Absolute Error adb:\", dae)\n", + "\n", + "# Perform 5-fold cross validation\n", + "scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_absolute_error')\n", + "\n", + "# Print the cross-validated scores (negated to get positive values)\n", + "print('Cross-validated scores adb:', -scores)\n", + "\n", + "# Print the average score\n", + "print('Average score adb:', -scores.mean())\n", + "\n", + "# Plot the feature importance\n", + "plt.figure(figsize=(12,6))\n", + "# If your AdaBoostRegressor uses DecisionTreeRegressor as the base estimator (which is default), \n", + "# you can access feature importances. Otherwise, this might not be available.\n", + "if hasattr(model, 'feature_importances_'):\n", + " plt.barh(X.columns, model.feature_importances_)\n", + " plt.xlabel('Importance', fontsize=12)\n", + " plt.ylabel('Features', fontsize=12)\n", + " plt.show()\n", + "else:\n", + " print('Feature importances are not available for the base estimator.')\n", + "\n", + "# Deviation Plot between Predict and Test in different colors\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(len(y_test)), np.sort(y_test), color='r', label='Test')\n", + "plt.scatter(range(len(y_pred)), np.sort(y_pred), color='g', label='Predict')\n", + "plt.xlabel('index', fontsize=12)\n", + "plt.ylabel('TransactionRevenue', fontsize=12)\n", + "plt.title('Sorted Test vs Predicted Values')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Decision Tree\n" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error (Decision Tree): 2.335621991931603\n", + "Mean Absolute Error (Decision Tree): 1.1754001839122272\n", + "Median Absolute Error (Decision Tree): 0.9379422904511081\n", + "Cross-validated scores (Decision Tree): [1.17243189 1.15729719 1.17649475 1.18016045 1.20238543]\n", + "Average score (Decision Tree): 1.1777539425022518\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error\n", + "from sklearn.model_selection import cross_val_score\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Initialize the model\n", + "dt_model = DecisionTreeRegressor()\n", + "\n", + "# Fit the model\n", + "dt_model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred_dt = dt_model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "mse_dt = mean_squared_error(y_test, y_pred_dt)\n", + "mae_dt = mean_absolute_error(y_test, y_pred_dt)\n", + "dae_dt = median_absolute_error(y_test, y_pred_dt)\n", + "\n", + "print(\"Mean Squared Error (Decision Tree):\", mse_dt)\n", + "print(\"Mean Absolute Error (Decision Tree):\", mae_dt)\n", + "print(\"Median Absolute Error (Decision Tree):\", dae_dt)\n", + "\n", + "# Perform 5-fold cross validation\n", + "scores_dt = cross_val_score(dt_model, X, y, cv=5, scoring='neg_mean_absolute_error')\n", + "\n", + "# Print the cross-validated scores (as positive values)\n", + "print('Cross-validated scores (Decision Tree):', -scores_dt)\n", + "\n", + "# Print the average score\n", + "print('Average score (Decision Tree):', -scores_dt.mean())\n", + "\n", + "# Plot the feature importance\n", + "plt.figure(figsize=(12,6))\n", + "plt.barh(X.columns, dt_model.feature_importances_)\n", + "plt.xlabel('Feature Importance', fontsize=12)\n", + "plt.ylabel('Features', fontsize=12)\n", + "plt.title('Feature Importance for Decision Tree Regressor')\n", + "plt.show()\n", + "\n", + "# Deviation Plot between Predict and Test in different colors\n", + "plt.figure(figsize=(12,6))\n", + "plt.scatter(range(len(y_test)), np.sort(y_test), color='r', label='Test', alpha=0.5)\n", + "plt.scatter(range(len(y_pred_dt)), np.sort(y_pred_dt), color='g', label='Predict', alpha=0.5)\n", + "\n", + "plt.xlabel('Index', fontsize=12)\n", + "plt.ylabel('TransactionRevenue', fontsize=12)\n", + "plt.title('Actual vs Predicted (Decision Tree)')\n", + "plt.legend()\n", + "plt.show()\n" + ] } ], "metadata": { diff --git a/regression/Untitled-1.ipynb b/regression/Untitled-1.ipynb index 709d82c..3d0f213 100644 --- a/regression/Untitled-1.ipynb +++ b/regression/Untitled-1.ipynb @@ -9,8 +9,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.11.4" } }, "nbformat": 4,